00425nas a2200157   4500000000100000000000100001000000100002008004100003100001400044700002400058700002100082700001600103700001700119245012400136490000700260       2002                            d1 aA. Badrah1 aTadeusz Czachórski1 aJoanna Domańska1 aJ. Fourneau1 aF. Quessette00aPerformance evaluation of multistage interconnection networks with blocking - discrete and continuous time Markov model0 v1400847nas a2200181   4500000000100000000000100001008004100002260005000043100001900093700002100112700001400133700002200147700001400169700001300183245004800196300001200244520040900256       2014                            d  bSpringer International Publishing Switzerland1 aAdam Domański1 aJoanna Domańska1 aS. Chmiel1 aAndrzej Kwiecień1 aPiotr Gaj1 aP. Stera00aJavaScript Frameworks and Ajax Applications  a57–683 aJava Script frameworks are useful in the development of interactive web pages. Ajax technology supports a dialog with the WWW server. This article compares performance and functionality of the Ajax libraries for major Web browsers. The size of the libraries, their load time and execution time is compared. The article also evaluates the Document Object Model(DOM) support provided by selected libraries.00362nam a2200145   4500000000100000000000100001008004100002260005100043100002000094700001900114700002100133700001800154245002200172020002200194       2007                            d  bWyższa Szkoła Biznesu w Dąbrowie Górniczej1 aAndrzej Grzywak1 aAdam Domański1 aJoanna Domańska1 aM. Rostański00aSieci komputerowe  a978-83-88936-34-000437nas a2200169   4500000000100000000000100001008004100002260001800043100001400061700001400075700002400089700002100113700002000134700002500154700002400179245006400203       2004                            d  bWNTaWarszawa1 aB. Bylina1 aJ. Bylina1 aTadeusz Czachórski1 aJoanna Domańska1 aStefan Węgrzyn1 aBolesław Pochopień1 aTadeusz Czachórski00aWspółczesne problemy sieci komputerowych Nowe technologie00458nas a2200157   4500000000100000000000100001000000100002008004100003100001400044700001400058700002100072700002400093245016200117300001400279490000700293       2004                            d1 aB. Bylina1 aJ. Bylina1 aJoanna Domańska1 aTadeusz Czachórski00aThe use of vectorized and parallelized solutions to Markovian models in performance evaluation of communication networks: a case of leaky buckets with tokens  a153–1700 v1600343nas a2200133   4500000000100000000000100001008004100002100002400043700002100067700002400088700001600112245006700128300001400195       2004                            d1 aTadeusz Czachórski1 aJoanna Domańska1 aTadeusz Czachórski1 aN. Pekergin00aMarkovian models for long-range dependent traffic, new results  a213–22800333nas a2200133   4500000000100000000000100001000000100002008004100003100002400044700002100068245008900089300001400178490000700192       2002                            d1 aTadeusz Czachórski1 aJoanna Domańska00aWpływ samopodobnej natury ruchu na długości kolejek w przełącznikach sieciowych  a201–2120 v2300298nas a2200133   4500000000100000000000100001000000100002008004100003100002400044700002100068245005400089300001400143490000700157       2001                            d1 aTadeusz Czachórski1 aJoanna Domańska00aMarkovian models for long-range dependent traffic  a297–3080 v1300449nas a2200145   4500000000100000000000100001008004100002260005300043100002400096700002100120700001900141700002000160700002000180245010300200       2005                            d  bWydawnictwa Komunikacji i ŁącznościaWarszawa1 aTadeusz Czachórski1 aJoanna Domańska1 aAdam Domański1 aAndrzej Grzywak1 aPiotr Pikiewicz00aNowe Technologie w Komputerowych Systemach Zarządzania. Internet w Społeczeństwie Informacyjnym00441nas a2200157   4500000000100000000000100001008004100002260005300043100002400096700002100120700001900141700002000160700002500180700002400205245005400229       2005                            d  bWydawnictwa Komunikacji i ŁącznościaWarszawa1 aTadeusz Czachórski1 aJoanna Domańska1 aAdam Domański1 aStefan Węgrzyn1 aBolesław Pochopień1 aTadeusz Czachórski00aWysokowydajne sieci komputerowe. Nowe Technologie00359nas a2200121   4500000000100000000000100001008004100002260002600043100002400069700002100093700002200114245010100136       2006                            d  bPolitecnico de Torino1 aTadeusz Czachórski1 aJoanna Domańska1 aKrzysztof Grochla00aDynamics of TCP/IP flows in presence of various AQM algorithms, a fluid flow approximation model00338nas a2200121   4500000000100000000000100001008004100002260004200043100002400085700002100109700002200130245006400152       2006                            d  cVIbTechnical University of Catalonia1 aTadeusz Czachórski1 aJoanna Domańska1 aKrzysztof Grochla00aTCP/IP dynamics in wireless environment, a fluid-flow model00636nas a2200133   4500000000100000000000100001008004100002100002400043700002100067700002100088245006900109490000700178520031700185       2001                            d1 aTadeusz Czachórski1 aJoanna Domańska1 aArkadiusz Sochan00aSamopodobny charakter natężenia ruchu w sieciach komputerowych0 v223 aArtykuł przedstawia analizę natężenia ruchu pakietów w sieci Ethernet oraz ruchu ramek MPEG-2 w sieci ATM pod kątem występowania cechy zwanej samopodobieństwem. Zamieszczono krótką charakterystykę procesów samopodobnych oraz wnioski wypływające z samopodobnej natury ruchu w sieciach komputerowych.00324nas a2200133   4500000000100000000000100001008004100002260001800043100002400061700001900085700002100104700002000125245004500145       2004                            d  bWNTaWarszawa1 aTadeusz Czachórski1 aAdam Domański1 aJoanna Domańska1 aAndrzej Grzywak00aInternet w spoleczenstwie informatycznym00416nas a2200157   4500000000100000000000100001008004100002260001800043100002400061700001900085700002100104700002000125700002500145700002400170245006400194       2004                            d  bWNTaWarszawa1 aTadeusz Czachórski1 aAdam Domański1 aJoanna Domańska1 aStefan Węgrzyn1 aBolesław Pochopień1 aTadeusz Czachórski00aWspółczesne problemy sieci komputerowych Nowe technologie00322nas a2200121   4500000000100000000000100001008004100002100001600043700001900059700002100078245007700099022002400176       2010                            d1 aD. Augustyn1 aAdam Domański1 aJoanna Domańska00aA choice of optimal packet dropping function for active queue management  aISBN 3-642-13860-8.00377nam a2200109   4500000000100000000000100001008004100002260011800043100002100161245006300182020002200245       2014                            d  bInstytut Informatyki Teoretycznej i Stosowanej Polskiej Akademii NaukaPracownia Komputerowa Jacka Skalmierskiego1 aJoanna Domańska00aMarkowowskie modele natężenia przesyłów internetowych.  a978-83-62652-69-301416nas a2200181   4500000000100000000000100001000000100002008004100003100002100044700001900065700001600084700001700100245007400117300001400191490000700205520100800212022001401220       2014                            d1 aJoanna Domańska1 aAdam Domański1 aD. Augustyn1 aJerzy Klamka00aA red modified weighted moving average for soft real-time application  a697–7070 v243 aThe popularity of TCP/IP has resulted in an increase in usage of best-effort networks for real-time communication. Much effort has been spent to ensure quality of service for soft real-time traffic over IP networks. The Internet Engineering Task Force has proposed some architecture components, such as Active Queue Management (AQM). The paper investigates the influence of the weighted moving average on packet waiting time reduction for an AQM mechanism: the RED algorithm. The proposed method for computing the average queue length is based on a difference equation (a recursive equation). Depending on a particular optimality criterion, proper parameters of the modified weighted moving average funtion can be chosen. This change will allow reducing the number of violations of timing constraints and better use of this mechanism for soft real-time transmissions. The optimization problem is solved through simulation performed in OMNeT++ and later verified experimentally on a Linux implementation.  a1641-876X00325nas a2200133   4500000000100000000000100001000000100002008004100003100002100044700002400065245008100089300001400170490000700184       2003                            d1 aJoanna Domańska1 aTadeusz Czachórski00aWpływ samopodobnej natury ruchu na zachowanie mechanizmu cieknącego wiadra  a199–2120 v2400309nas a2200133   4500000000100000000000100001000000100002008004100003100002100044700002400065245006700089300001200156490000700168       2003                            d1 aJoanna Domańska1 aTadeusz Czachórski00aModel formowania pakietów w węźle brzegowym sieci optycznej  a51–610 v2401274nas a2200157   4500000000100000000000100001000000100002008004100003100002100044700001600065700001900081245012300100490000700223520087200230022001401102       2012                            d1 aJoanna Domańska1 aD. Augustyn1 aAdam Domański00aThe choice of optimal 3-rd order polynomial packet dropping function for NLRED in the presence of self-similar traffic0 v603 aAlgorithms of queue management in IP routers determine which packet should be deleted when necessary. The article investigates the influence of the self-similarity on the optimal packet rejection probability function in a special case of NLRED queues. This paper describes another approach to the non-linear packet dropping function. We propose to use the solutions based on the polynomials with degree equals to 3. The process of obtaining the optimal dropping packets function has been presented. Our researches were carried out using the Discrete Event Simulator OMNET++. The AQM model was early verified using the discrete-time Markov chain. The obtained results show that the traffic characteristic has the great impact on the network node behavior, but self-similarity of network traffic has no influence on the choosing of the optimal dropping packet function.  a0239-752800357nas a2200145   4500000000100000000000100001008004100002260002000043100002100063700001900084700001700103700002100120700002300141245004700164       2008                            d  bWydawnictwo WSB1 aJoanna Domańska1 aAdam Domański1 aJerzy Klamka1 aJoanna Domańska1 aAdrian Kapczyński00aAdvanced problems of Internet technologies00734nas a2200157   4500000000100000000000100001008004100002260003200043100002100075700001900096700002400115700001600139245005700155300001400212520035000226       2011                            d  bSpringeraBerlin Heidelberg1 aJoanna Domańska1 aAdam Domański1 aTadeusz Czachórski1 aS. Balandin00aInternet Traffic Source Vased on Hidden Markov Model  a395–4043 aThis article shows how to use Hidden Markov Models to generate self-similar traffic. The well-known Bellcore traces are used as a training sequence to learn HMM model parameters. Performance of trained model are tested on the remaining portions of the sequence. Then we can use the HMM trained with the Bellcore data as the traffic source model.01216nas a2200157   4500000000100000000000100001008004100002260005000043100002100093700001900114700002400133700002400157245006900181300001400250520079400264       2014                            d  bSpringer International Publishing Switzerland1 aJoanna Domańska1 aAdam Domański1 aTadeusz Czachórski1 aTadeusz Czachórski00aA few investigations of long-range dependence in network traffic  a137–1443 aThe paper presents measurements and analysis of a LAN long-range dependence traffic collected in IITiS PAN (The Institute of Theoretical and Applied Informatics of the Polish Academy of Sciences). Several methods of Hurst parameter estimation were used, the results obtained by the methods differ substantially. The analysis was made for the whole traffic and traffics generated by particular types of protocols. We seek for a dependence of Hurst parameter on a protocol type. Then, a MMPP(Markov-Modulated Poisson Process) model was applied to mimic the traces. It allows us to consider Markovian queueing models with long-range dependent and self-similar traffic, an important factor as we dispose an efficient software tool to solve numerically very large continuous-time Markov chains.00421nas a2200145   4500000000100000000000100001008004100002260005000043100002100093700001900114700002400133700001400157245009200171300001200263       2015                            d  bSpringer International Publishing Switzerland1 aJoanna Domańska1 aAdam Domański1 aTadeusz Czachórski1 aPiotr Gaj00aEstimating the intensity of long-range dependence in real and synthetic traffic traces.  a11–2200941nas a2200193   4500000000100000000000100001008004100002260001600043100002100059700001900080700002400099700002000123700002300143700002300166700001700189245007900206300001200285520045000297       2013                            d  c1bSpringer1 aJoanna Domańska1 aAdam Domański1 aTadeusz Czachórski1 aAlexander Dudin1 aValentina Klimenok1 aGennadiy Tsarenkov1 aSergey Dudin00aFluid Flow Analysis of RED Algorithm with Modified Weighted Moving Average  a50–583 aWe study with the use of fluid flow approximation the impact of a modified weighted moving average on the performance of RED mechanism. A model of TCP/UDP connection with RED implemented in an intermediate IP router is used, the weighted moving average is determined on the basis of a difference (recursive) equation. The fluid flow approximation technique is applied to model the interactions between the set of TCP/UDP flows and RED mechanism.00398nas a2200145   4500000000100000000000100001008004100002260005100043100002100094700001900115700002400134700002000158700001700178245005700195       2007                            d  bWyższa Szkoła Biznesu w Dąbrowie Górniczej1 aJoanna Domańska1 aAdam Domański1 aTadeusz Czachórski1 aAndrzej Grzywak1 aJerzy Klamka00aPodstawy Budowy i Oprogramowania Sieci Typu Internet00910nas a2200181   4500000000100000000000100001008004100002260005000043100002100093700001900114700002400133700002200157700001400179700001300193245007800206300001200284520043200296       2014                            d  bSpringer International Publishing Switzerland1 aJoanna Domańska1 aAdam Domański1 aTadeusz Czachórski1 aAndrzej Kwiecień1 aPiotr Gaj1 aP. Stera00aModeling Packet Traffic with the Use of Superpositions of Two-State MMPPs  a24–363 aThe aim of this paper is to use the superposition of two-state Markov Modulated Poisson Processes to replicate the statistical nature of internet traffic over several time scales. This paper characterizes of network traffic using Bellcore data and LAN traces collected in IITiS PAN. The fitting procedure for matching second-order self-similar properties of real data traces to that of two-state MMPP`s has also been described.00622nas a2200133   4500000000100000000000100001008004100002100002100043700001900064700002400083245003200107300001200139520033700151       2012                            d1 aJoanna Domańska1 aAdam Domański1 aTadeusz Czachórski00aA HMM network traffic model  a17–203 aThe article proposes a description of Internet traffic based on Hidden Markov Model. The model refers to packet level traffic and is validated with the use of real traffic collected in our laboratory. A part of the gathered traces is used as a training sequence to learn HMM model parameters an the rest serves as testing sequences.00327nas a2200145   4500000000100000000000100001000000100002008004100003260000700044100002100051700001900072700002400091245006000115490000600175       2008                            d  c031 aJoanna Domańska1 aAdam Domański1 aTadeusz Czachórski00aImplementation of modified AQM mechanisms in IP routers0 v400336nas a2200145   4500000000100000000000100001008004100002100002100043700001900064700002400083245004000107300001200147490000900159022002200168       2007                            d1 aJoanna Domańska1 aAdam Domański1 aTadeusz Czachórski00aThe Drop-From-Front Strategy in AQM  a61–720 v4712  a978-3-540-74832-800306nas a2200121   4500000000100000000000100001008004100002100002100043700001900064700002400083245006300107300001400170       2005                            d1 aJoanna Domańska1 aAdam Domański1 aTadeusz Czachórski00aThe influence of traffic self-similarity on QoS Mechanisms  a300–30300353nas a2200145   4500000000100000000000100001000000100002008004100003100002100044700001900065700002400084245007800108300001400186490000700200       2004                            d1 aJoanna Domańska1 aAdam Domański1 aTadeusz Czachórski00aSelf-similarity of network traffic and the performances of QoS mechanisms  a337–3490 v1600902nas a2200181   4500000000100000000000100001000000100002008004100003100002100044700001900065700002400084700001700108245006700125300001400192490000700206520049300213022001400706       2014                            d1 aJoanna Domańska1 aAdam Domański1 aTadeusz Czachórski1 aJerzy Klamka00aFlluid flow approximation of time-limited TCP/UDP/XCP streams.  a217–2250 v623 aThis article presents the use of fluid flow approximation to model interactions between a set of TCP, UDP and XCP flows in the enveronment of IP routers using AQM (Active Queue Management) algorithms to control traffic congestion. In contrast to other works, independent UDP and TCP streams are considered and the model allows to start and end data transmissions in TCP, UDP and XCP strams at any time moment. It incorporates several Active Queue Management mechanisms: RED, NLRED, CHOKe.  a0239-752800924nas a2200181   4500000000100000000000100001008004100002260003200043100002100075700001900096700001600115700002200131700001400153700001300167245009500180300001200275520045500287       2011                            d  bSpringeraBerlin Heidelberg1 aJoanna Domańska1 aAdam Domański1 aD. Augustyn1 aAndrzej Kwiecień1 aPiotr Gaj1 aP. Stera00aThe Impact of the Modified Weighted Moving Average on the Performance of the RED Mechanism  a37–443 aAlgorithms of queue management in IP routers determine which packet should be deleted shen necessary. the article investigates the influence of weighted movind average on the performance of the RED mechanism. The proposed average queue length calculating method is based on a difference equation (a recursive equation). Depending on a particular criterion of optimization a proper parameters of modified weighted moving average function may be chosen00401nas a2200157   4500000000100000000000100001008004100002260004300043100002100086700001900107700001600126700001400142700001400156700001400170245005900184       2005                            d  bWydawnictwa Komunikacji i Łączności1 aJoanna Domańska1 aAdam Domański1 aP. Kasprzyk1 aM. Widera1 aA. Widera1 aPiotr Gaj00aSystemy czasu rzeczywistego Projektowanie i aplikacje.00498nas a2200193   4500000000100000000000100001008004100002260004300043100002100086700002200107700002400129700001400153700002000167700002100187700002400208700002500232245004100257490000600298       2006                            d  bWydawnictwo Komunikacji i Łączności1 aJoanna Domańska1 aKrzysztof Grochla1 aTadeusz Czachórski1 aT. Atmaca1 aStefan Węgrzyn1 aLech Znamirowski1 aTadeusz Czachórski1 aStanisław Kozielski00aNowe Technologie Sieci Komputerowych0 v100479nas a2200157   4500000000100000000000100001008004100002260002000043100002100063700002300084700002500107700002000132245006300152490000700215520009900222       2009                            d  bSpringer Verlag1 aJoanna Domańska1 aPrzemysław Głomb1 aPrzemysław Kowalski1 aSławomir Nowak00aModeling of Internet 3D Traffic Using Hidden Markov Models0 v643 a<p>Artykuł podejmuje temat modelowania złożoności przesyłu siatek progresywnych 3D.</p>
00293nas a2200133   4500000000100000000000100001008004100002100002100043700001600064700001400080700002400094245002700118300001400145       2003                            d1 aJoanna Domańska1 aI. Kotuliak1 aT. Atmaca1 aTadeusz Czachórski00aOptical packet filling  a419–42900340nas a2200133   4500000000100000000000100001008004100002260002000043100001900063700002100082700002000103700002300123245006000146       2008                            d  bWydawnictwo WSB1 aAdam Domański1 aJoanna Domańska1 aAndrzej Grzywak1 aAdrian Kapczyński00aZastosowanie Internetu w społeczeństwie informacyjnym00918nas a2200181   4500000000100000000000100001008004100002260003500043100001900078700002100097700002400118700002200142700001400164700001300178245011300191300001400304520041800318       2013                            d  c6bSpringeraBerlin Heidelberg1 aAdam Domański1 aJoanna Domańska1 aTadeusz Czachórski1 aAndrzej Kwiecień1 aPiotr Gaj1 aP. Stera00aComparison of CHOKe and gCHOKe active queues managemant algirithms with the use of fluid flow approximation.  a363–3713 aIn the article we examine a model of TCP connection with Active Queue Managemanet in an intermediate IP router. We model a system where CHOKe or gCHOKe are the AQM policy. We use the fluid flow approximation technique to model the interactions between the set of TCP/UDP flows and two variants of the CHOKe algoithms. The obtained results confirm the superiority of these algorithms over a standard RED algorithm.00787nas a2200193   4500000000100000000000100001008004100002260003200043100001900075700002100094700002400115700002200139700001400161700001300175245002200188490000800210520035300218020002200571       2012                            d  bSpringeraBerlin Heidelberg1 aAdam Domański1 aJoanna Domańska1 aTadeusz Czachórski1 aAndrzej Kwiecień1 aPiotr Gaj1 aP. Stera00aComputer Networks0 v2913 aIn the article we study a model of TCP/UDP connection with Active Queue Management in an intermediate IP router. We use the fluid flow approximation technique to model the interactions between the set of TCP/UDP flows and AQM. We model a system where RED or NLRED are the AQM policy.The obtained results confirm the advantage of our NLRED algorithm  a978-3-642-31216-800819nas a2200169   4500000000100000000000100001008004100002260008800043100001900131700002100150700002400171700002000195700001800215245008300233520031100316020002200627       2013                            d  bAcademy od Business in Dabrowa Gorniczaa41-300 Dąbrowa Górnicza Cieplaka Str. 1c1 aAdam Domański1 aJoanna Domańska1 aTadeusz Czachórski1 aPiotr Pikiewicz1 aM. Rostański00aInternet in the Information Society Computer Systems Architecture and Security3 aIn the article we study a model of the TCP connection with Active Queue Management in the intermediate bottleneck IP router. The fluid flow approximation technique is used to model the interactions between the set of TCP flows and AQM algorithms. The numerical part of the model is run in CUDA environment.  a978-83-62897-40-700445nas a2200169   4500000000100000000000100001008004100002260004300043100001900086700002100105700002400126700002500150700002200175700002000197700001700217245004100234       2006                            d  bWydawnictwa Komunikacji i Łączności1 aAdam Domański1 aJoanna Domańska1 aTadeusz Czachórski1 aBolesław Pochopień1 aAndrzej Kwiecień1 aAndrzej Grzywak1 aJerzy Klamka00aNowe Technologie Sieci Komputerowych00432nas a2200169   4500000000100000000000100001008004100002260004300043100001900086700002100105700002400126700002000150700002100170700002400191245004100215490000600256       2006                            d  bWydawnictwa Komunikacji i Łączności1 aAdam Domański1 aJoanna Domańska1 aTadeusz Czachórski1 aStefan Węgrzyn1 aLech Znamirowski1 aTadeusz Czachórski00aNowe Technologie Sieci Komputerowych0 v100721nas a2200169   4500000000100000000000100001000000100002008004100003100001900044700002100063700002400084245008800108300001300196490000700209520032100216022001400537       2013                            d1 aAdam Domański1 aJoanna Domańska1 aTadeusz Czachórski00aGCU-Accelerated fluid flow approximation of the Active Queues Management algorithms  a93–1040 v253 aIn the article we study a model of TCP connection with Active Queue Management in an intermediate IP router. We use the fluid flow approximation technique to model the interactions between the set of TCP flows and AQM algorithms. Computations for fluid flow approximation model are performed in the CUDA environment.  a1896-533400286nas a2200109   4500000000100000000000100001008004100002100001900043700002100062700002400083245006900107       2008                            d1 aAdam Domański1 aJoanna Domańska1 aTadeusz Czachórski00aThe impact of self-similarity on traffic shaping in wireless LAN00307nas a2200121   4500000000100000000000100001008004100002260006000043100001900103700002100122700002400143245001800167       2005                            d  bOficyna Wydawnicza Politechniki WrocławskiejaWrocław1 aAdam Domański1 aJoanna Domańska1 aTadeusz Czachórski00aInternet 200500287nas a2200121   4500000000100000000000100001008004100002100001900043700002100062700002400083700001800107245004000125       2008                            d1 aAdam Domański1 aJoanna Domańska1 aTadeusz Czachórski1 aF. Kłębczyk00aSome aspects of QoS in wireless LAN00324nas a2200133   4500000000100000000000100001008004100002260004300043100001900086700002100105700002400126700001800150245002200168       2008                            d  bWydawnictwa Komunikacji i Łączności1 aAdam Domański1 aJoanna Domańska1 aTadeusz Czachórski1 aF. Kłębczyk00aSieci Komputerowe00913nas a2200169   4500000000100000000000100001008004100002260009000043100001900133700002100152700001700173700002000190700001800210245008300228520041000311020002200721       2013                            d  bAcademy of Business in Dabrowa GorniczaaCielplaka Str. 1c, 41-300 Dąbrowa Górnicza1 aAdam Domański1 aJoanna Domańska1 aJerzy Klamka1 aPiotr Pikiewicz1 aM. Rostański00aInternet in the Information Society Computer Systems Architecture and Security3 aIn this article we study a model of network transmissions with Active Queue Management in an intermadiate IP router. We use the OMNET++ discrete event simulator to model the different variants of the CHOKe algorithms. We model a system where CHOKe, xCHOKe and gCHOKe are the AQM policy. The obtained results show the behaviour of these algorithms and confirm their advantage over a standard RED algorithm.  a978-83-62897-40-700797nas a2200169   4500000000100000000000100001000000100002008004100003100001900044700002100063700001700084245005300101300001200154490000700166520044000173022001400613       2013                            d1 aAdam Domański1 aJoanna Domańska1 aJerzy Klamka00aAnalysis of CHOKe-Family Active Queue Management  a49–660 v253 aIn the article we study a model of network transmissions with Active Queue Management in an intermediate IP router. We use the OMNET++ discrete event simulator to model the varies variants of the CHOKe algorithms. We model a system where CHOKe, xCHOKe and gCHOKe are the AQM policy. The obtained results shows the behaviour of these algorithms. The paper presents also the implementation of AQM mechanisms in the router based on Linux.  a1896-533400354nas a2200133   4500000000100000000000100001008004100002100001900043700002100062700002000083700002400103245007000127022002300197       2010                            d1 aAdam Domański1 aJoanna Domańska1 aSławomir Nowak1 aTadeusz Czachórski00aA contribution to the fair scheduling for the TCP and UDP streams  aISBN 3-642-13860-800611nas a2200253   4500000000100000000000100001000000100002008004100003100001500044700002300059700002700082700001900109700001400128700001400142700001500156700002100171700001700192700001600209700002000225245007500245300001400320490000900334022001400343       2013                            d1 aE. Helwich1 aJ. Wójkowska-Mach1 aM. Borszewska-Kornacka1 aJ. Gadzinowski1 aA. Kordek1 aD. Pawlik1 aJ. Szczapa1 aJoanna Domańska1 aJerzy Klamka1 aP.B. Heczko1 aE. Gułczyńska00aEpidemiologia zakażeń noworodków z bardzo małą masą urodzeniową  a224–2310 vXVII  a1428-345X00890nas a2200253   4500000000100000000000100001000000100002008004100003100002300044700001900067700002700086700002100113700001900134700001900153700001500172700001400187700001400201700001500215700001600230245016200246300001100408490000600419520021100425       2014                            d1 aJ. Wójkowska-Mach1 aA. Różańska1 aM. Borszewska-Kornacka1 aJoanna Domańska1 aJ. Gadzinowski1 aE. Gulczyńska1 aE. Helwich1 aA. Kordek1 aD. Pawlik1 aJ. Szczapa1 aP.B. Heczko00aNecrotising Enterocolitis in Preterm Infants: Epidemiology and Antibiotic Consumption in the Polish Neonatology Network Neonatal Intensive Care Units in 2009  ae928650 v93 aThe aim of this study was to describe the epidemiology of necrotising enterocolitis (NEC), antibiotic consumption and the usefulness of microbiological tests in very low birth weight (VLBW) Polish newborns.01496nas a2200265   4500000000100000000000100001008004100002100002300043700001900066700001900085700002700104700002100131700001500152700001500167700001400182700001400196700001900210700001500229700001400244245013900258300001400397490000700411520079800418022001401216       2014                            d1 aJ. Wójkowska-Mach1 aE. Gulczyńska1 aM. Nowiczewski1 aM. Borszewska-Kornacka1 aJoanna Domańska1 aA. Merritt1 aE. Helwich1 aA. Kordek1 aD. Pawlik1 aJ. Gadzinowski1 aJ. Szczapa1 aP Adamski00aLate-onset bloodstream infections of Very-Low-Birth-Weight infants: data from the Polish Neonatology Surveillance Network in 2009-2011  a339–3460 v143 aBackground: Late-Onset Bloodstream infections (LO-BSI) continue to be one of the most important complications associated with hospitalization of infants born with very low birth weight )VLBW). Ahe aims of this stydy were to assess the epidemiology of LO-BSI together with the risk factors and the distribution of causative pathogens at six Polish neonatal intensive care units that participated in the Polish Neonatology Surveillance Network from January 1, 2009 to December 31, 2011. Conclusions: Independent risk factor for LO-BSI in VLBV infants are: low gestational age and requirement for surgery. The incidence rates of LO-BSI especially CVC-BSI were higher in the Polish NICUs surveillance than those of other national networks, similar to the central- and peripheral utilization ratio.  a1471-233400650nas a2200265   4500000000100000000000100001000000100002008004100003260000600044100002300050700002700073700002100100700001900121700002000140700001500160700001400175700001400189700001500203700001700218700001400235245010000249300001400349490000700363022001400370       2012                            d  c71 aJ. Wójkowska-Mach1 aM. Borszewska-Kornacka1 aJoanna Domańska1 aJ. Gadzinowski1 aE. Gułczyńska1 aE. Helwich1 aA. Kordek1 aD. Pawlik1 aJ. Szczapa1 aJerzy Klamka1 aP. Heczko00aEarly-onset Infections of Very-low-birth-weight Infants in Polish Neonatal Intensive Care Units  a691–6950 v31  a0891-366800851nas a2200169   4500000000100000000000100001008004100002260003200043100001500075700001900090700002100109700002300130245005700153490000800210520044100218020002200659       2011                            d  bSpringeraBerlin Heidelberg1 aM. Hubczyk1 aAdam Domański1 aJoanna Domańska1 aAdrian Kapczyński00aInternet - Technical developments and Applications 20 v1183 aIn this paper we have examined Local and Remote File Inclusion vulnerabilities in details. It is proven that these security flaws may lead to variety of problems including generation of big load to the server as well as disclosure of files which should not be accessible by the clients. These attacks are not so popular as SQL injection or Cross-Site Scripting but still they are subject of serious threat because of their consequences.  a978-3-642-25354-600302nas a2200121   4500000000100000000000100001008004100002260004300043100002000086700002100106245004700127490000600174       2007                            d  bWydawnictwa Komunikacji i Łączności1 aSławomir Nowak1 aJoanna Domańska00aSieci Komputerowe Aplikacje i zastosowania0 v200308nas a2200121   4500000000100000000000100001008004100002100002000043700002100063700001900084245006900103022001400172       2009                            d1 aSławomir Nowak1 aJoanna Domańska1 aAdam Domański00aSimulation models of fair scheduling for the TCP and UDP streams  a1896-533400417nam a2200169   4500000000100000000000100001008004100002260000800043100002000051700002100071700002200092700002000114700002100134700002200155245004800177020002200225       2009                            d  bWSB1 aSławomir Nowak1 aJoanna Domańska1 aKrzysztof Grochla1 aSławomir Nowak1 aJoanna Domańska1 aKrzysztof Grochla00aSymulator zdarzeń dyskretnych OMNeT++v.3.3  a978-83-88936-70-801032nas a2200193   4500000000100000000000100001000000100002008004100003260000700044100002000051700002100071700001800092700002300110245008500133300001400218490000700232520058500239022001400824       2010                            d  c111 aSławomir Nowak1 aJoanna Domańska1 aMateusz Nowak1 aPrzemysław Głomb00aProgressive 3D meshes transmission: traffic generating and simulation evaluation  a247–2590 v223 aThe paper presents issues related to the progressive transmission of 3D meshes in clientserver architecture. A tool generating network traffic for different 3D scenes and scenarios is presented. The objective was to evaluate the transmission dynamics and possible impact of 3D traffic on the amount of data available in VR browsing client, which relates to browsing QoE (Quality of Experience). Simulation studies were carried out and HMM model of traffic generated by 3D server was worked out, planned for use in modeling works on future internet protocols, servicing 3D traffic.  a1896-533400318nas a2200121   4500000000100000000000100001008004100002100002000043700001900063700002100082700002400103245006900127       2010                            d1 aSławomir Nowak1 aAdam Domański1 aJoanna Domańska1 aTadeusz Czachórski00aSimulation models of fair scheduling for the TCP and UDP streams01091nas a2200193   4500000000100000000000100001008004100002260003200043100001400075700001900089700002100108700002200129700001400151700001300165245002200178490000800200520066700208020002200875       2012                            d  bSpringeraBerlin Heidelberg1 aP. Pawlas1 aAdam Domański1 aJoanna Domańska1 aAndrzej Kwiecień1 aPiotr Gaj1 aP. Stera00aComputer Networks0 v2913 aThis article describes the universal web pages content parser-cross-platform application enhancing the process of data extraction from the web pages. In this implementation user friendly interface, possibility of significant automation and reusability of already created patterns had been the key elements. Moreover, the original approach to the issue of parsing the not well-formed HTML, stating the application`s core, is precisely presented. Universal web pages content parser shows that the simplified web scrapping utility may be available to masses and not well-formed HTML sources may feed useful tree-like data structures as well as the well-formed ones.  a978-3-642-31216-800860nas a2200157   4500000000100000000000100001008004100002260011200043100001600155700001900171700002100190700002400211245007000235300001400305520038300319       2010                            d  c1bInstitute of Theoretical and Applied Informatics of the Polish Academy of SciencesaGliwice, Bałtycka 51 aD. Augustyn1 aAdam Domański1 aJoanna Domańska1 aTadeusz Czachórski00aActive Queue Management with non-linear packets dropping function  a133–1423 aAbstract: Algorithms of queue management in IP routers determine which packet should be deleted when necessary. The article investigates the influence of packet rejection probability function on the performance, i.e. response time for in case of RED and nRED queues. In particular, the self-similar traffic is considered. The quantitative analysis based on simulations is shown.00430nas a2200157   4500000000100000000000100001008004100002260006700043100002400110700001900134700002100153700001600174245006200190300001200252490000800264       2016                            d  bSpringer International Publishing SwitzerlandaBrunów, Poland1 aTadeusz Czachórski1 aAdam Domański1 aJoanna Domańska1 aArtur Rataj00aA Study of IP Router Queues with the Use of Markov Models  a294-3050 v60800519nas a2200169   4500000000100000000000100001008004100002260004400043100001900087700002100106700002400127245010300151856005100254300001400305490000800319020002200327       2016                            d  bSpringer International PublishingaCham1 aAdam Domański1 aJoanna Domańska1 aTadeusz Czachórski00aThe Impact of the Degree of Self-Similarity on the NLREDwM Mechanism with Drop from Front Strategy  uhttp://dx.doi.org/10.1007/978-3-319-39207-3_17  a192–2030 v608  a978-3-319-39207-300516nas a2200193   4500000000100000000000100001008004100002260004400043100002400087700001900111700002100130700001900151700001600170245004100186856005100227300001400278490000800292020002200300       2016                            d  bSpringer International PublishingaCham1 aTadeusz Czachórski1 aAdam Domański1 aJoanna Domańska1 aMichele Pagano1 aArtur Rataj00aDelays in IP Routers, a Markov Model  uhttp://dx.doi.org/10.1007/978-3-319-47217-1_20  a185–1920 v659  a978-3-319-47217-100535nas a2200181   4500000000100000000000100001008004100002260004400043100001900087700002100106700001900127700002400146245008800170856005100258300001400309490000800323020002200331       2016                            d  bSpringer International PublishingaCham1 aAdam Domański1 aJoanna Domańska1 aMichele Pagano1 aTadeusz Czachórski00aThe Fluid Flow Approximation of the TCP Vegas and Reno Congestion Control Mechanism  uhttp://dx.doi.org/10.1007/978-3-319-47217-1_21  a193–2000 v659  a978-3-319-47217-102454nas a2200253   4500000000100000000000100001008004100002100002800043700002100071700003000092700002100122700002000143700002200163700001600185700002100201700001800222700001900240245010500259856007100364300001200435490000700447520173200454022001402186       2016                            d1 aJadwiga Wójkowska-Mach1 aT. Allen Merritt1 aMaria Borszewska-Kornacka1 aJoanna Domańska1 aEwa Gulczyńska1 aMarek Nowiczewski1 aEwa Helwich1 aAgnieszka Kordek1 aDorota Pawlik1 aPaweł Adamski00aDevice-associated pneumonia of very low birth weight infants in Polish Neonatal Intensive Care Units  uhttp://www.sciencedirect.com/science/article/pii/S1896112615000498  a90 - 950 v613 a<p>AbstractPurpose Late-Onset Pneumonia (LO-PNEU) is still the most important complication associated with the hospitalization of infants with very low birth weight (&lt;1501 g). The purpose of this paper is to summarize the results of an ongoing surveillance program defining LO-PNEU as associated or not associated with respiratory support in the \{NICU\} and distribution of causative pathogens from the Polish Neonatology Surveillance Network (PNSN). Materials and methods Surveillance of infections was conducted in the years 2009–2011 at six Polish NICUs. Results The incidence was 3.1/1000 \{NICU\} patient days (pds). The mean gestational age and birth weight among infants with LO-PNEU were significantly lower. The \{VAP\} incidence was of 18.2/1000 \{NICU\} pds for mechanically ventilated (MV) infants, while the rates for those receiving only \{CPAP\} were as low as 7.7/1000 \{NICU\} pds. \{MV\} significantly increased the risk of PNEU, but \{MV\} or \{CPAP\} for &lt;10 days did not increase the risk of LO-PNEU. Significantly associated with LO-PNEU was the use of central or peripheral venous catheters and total parenteral nutrition for longer periods. Microorganisms isolated in cases of LO-PNEU were Gram-positive cocci (53.5%) and Gram-negative rods, with predominating E. coli. Non fermentative bacilli were significantly more frequent in cases of \{VAP\} than in other cases. Conclusions Observed incidence rates associated with \{VAP\} and CPAP-PNEU, were higher than in other national surveillance systems and expressing the feasibility of lowering the risk of LO-PNEU and increasing patient safety. The incidence of pneumonia was found to be lower when using \{CPAP\} as compared to using MV.</p>
  a1896-112601382nas a2200193   4500000000100000000000100001008004100002260003700043100002100080700001700101700002400118700002200142700002300164245009800187300001200285490000800297520086300305020002001168       2018                            d  bSpringeraLondon, United Kingdom1 aJoanna Domańska1 aErol Gelenbe1 aTadeusz Czachórski1 aAnastasios Drosou1 aDimitrios Tzovaras00aResearch and innovation action for the security of the internet of things: The SerIoT project  a101-1180 v8213 a<p>The Internet of Things (IoT) was born in the mid 2010’s, when the threshold of connecting more objects than people to the Internet, was crossed. Thus, attacks and threats on the content and quality of service of the IoT platforms can have economic, energetic and physical security consequences that go way beyond the traditional Internet’s lack of security, and way beyond the threats posed by attacks to mobile telephony. Thus, this paper describes the H2020 project “Secure and Safe Internet of Things” (SerIoT) which will optimize the information security in IoT platforms and networks in a holistic, cross-layered manner (i.e. IoT platforms and devices, honeypots, SDN routers and operator’s controller) in order to offer a secure SerIoT platform that can be used to implement secure IoT platforms and networks anywhere and everywhere.</p>
  a978-331995188-100916nas a2200193   4500000000100000000000100001008004100002260001200043100002100055700001800076700002000094700002400114245005900138856006700197300001200264490000800276520041600284022002200700       2018                            d  c08/20181 aJoanna Domańska1 aMateusz Nowak1 aSławomir Nowak1 aTadeusz Czachórski00aEuropean Cybersecurity Research and the SerIoT Project  uhttps://link.springer.com/chapter/10.1007/978-3-030-00840-6_19  a166-1730 v9353 a<p>This paper briefly reviews some recent research in Cybersecurity in Europe funded by the European Commission in areas such as mobile telephony, networked health systems, the Internet of Things. We then outline the objectives of the SerIoT Project which started in 2018 to address the security needs of fields such as Smart Cities, Smart Transportation Systems, Supply Chains and Industrial Informatics.</p>
  a978-3-030-00839-000706nas a2200217   4500000000100000000000100001008004100002100002100043700002300064700003100087700002800118700003100146700001700177700002800194700002100222700001900243700002100262245018800283300001000471490000700481       2018                            d1 aAnna Różańska1 aAndrzej Jarynowski1 aKatarzyna Kopeć-Godlewska1 aJadwiga Wójkowska-Mach1 aAgnieszka Misiewska-Kaczur1 aMarzena Lech1 aMałgorzata Rozwadowska1 aMarlena Karwacka1 aJoanna Liberda1 aJoanna Domańska00aDoes surgical site infection after Caesarean section in Polish hospitals reflect high-quality patient care or poor postdischarge surveillance? Results from a 3-year multicenter study.  a20-250 v4600416nas a2200157   4500000000100000000000100001008004100002100001900043700002100062700002400083700001700107700001900124245009700143300001000240490000800250       2019                            d1 aAdam Domański1 aJoanna Domańska1 aTadeusz Czachórski1 aJerzy Klamka1 aJakub Szyguła00aThe AQM Dropping Packet Probability Function Based on Non-integer Order PI^αD^β Controller  a36-480 v49600432nas a2200169   4500000000100000000000100001008004100002100001900043700002100062700002400083700001700107700001800124700001900142245008100161300001200242490000800254       2018                            d1 aAdam Domański1 aJoanna Domańska1 aTadeusz Czachórski1 aJerzy Klamka1 aDariusz Marek1 aJakub Szyguła00aGPU Accelerated Non-integer Order PI^αD^β Controller Used as AQM Mechanism  a286-2990 v86000465nas a2200169   4500000000100000000000100001008004100002100001900043700002100062700002400083700001700107700001800124700001900142245011600161300001000277490000800287       2018                            d1 aAdam Domański1 aJoanna Domańska1 aTadeusz Czachórski1 aJerzy Klamka1 aDariusz Marek1 aJakub Szyguła00aThe Influence of the Traffic Self-similarity on the Choice of the Non-integer Order PI^α Controller Parameters  a76-830 v93500373nas a2200145   4500000000100000000000100001008004100002260002200043100001700065700002100082700002400103700002100127700002300148245005600171       2018                            d  bIEEEaRome, Italy1 aErol Gelenbe1 aJoanna Domańska1 aTadeusz Czachórski1 aAnastasis Drosou1 aDimitrios Tzovaras00aSecurity for Internet of Things: The SerIoT Project00380nas a2200145   4500000000100000000000100001008004100002100001900043700002100062700002400083700001700107245009100124300001200215490000700227       2016                            d1 aAdam Domański1 aJoanna Domańska1 aTadeusz Czachórski1 aJerzy Klamka00aThe use of a non-integer order PI controller with an active queue management mechanism  a777-7890 v2600384nas a2200145   4500000000100000000000100001008004100002100001900043700002100062700002400083700001700107245009400124300001200218490000800230       2017                            d1 aAdam Domański1 aJoanna Domańska1 aTadeusz Czachórski1 aJerzy Klamka00aSelf-similarity Traffic and AQM Mechanism Based on Non-integer Order Pi^αD^β Controller  a336-3500 v71800327nas a2200133   4500000000100000000000100001008004100002100002100043700001900064700002400083245006800107300001000175490000800185       2017                            d1 aJoanna Domańska1 aAdam Domański1 aTadeusz Czachórski00aHidden Markov Models in Long Range Dependence Traffic Modelling  a75-860 v70000439nas a2200157   4500000000100000000000100001008004100002260002000043100001800063700002000081700002100101700002400122245006400146856004900210020002200259       2019                            d  aAarhus, Denmark1 aMateusz Nowak1 aSławomir Nowak1 aJoanna Domańska1 aTadeusz Czachórski00aCognitive Packet Networks for the Secure Internet of Things  uhttps://ieeexplore.ieee.org/document/8766380  a978-1-7281-2171-000408nas a2200145   4500000000100000000000100001008004100002260003500043100001800078700002000096700002100116245005400137856004900191020002200240       2019                            d  c06/2019bIEEEaPrague, Czechia1 aMateusz Nowak1 aSławomir Nowak1 aJoanna Domańska00aCognitive Routing for Improvement of IoT Security  uhttps://ieeexplore.ieee.org/document/8822037  a978-1-7281-3236-502334nas a2200853   4500000000100000000000100001008004100002260002100043100001900064700002100083700002300104700002100127700002400148700002500172700002500197700002800222700002700250700002000277700002100297700002500318700001800343700002500361700002100386700002200407700001800429700001300447700001800460700001900478700002100497700001600518700002100534700001800555700002100573700001900594700001900613700002200632700001900654700001900673700001700692700002100709700002300730700001600753700001800769700002300787700002500810700002200835700001900857700002400876700002400900700001900924700001800943700001800961700002000979700002400999700002001023700002201043700002301065700001501088700001701103700002001120700001801140700002401158700002101182700002101203700002301224700001401247700002401261700002001285700001801305700002401323700001901347245009601366020001801462       2018                            d  bRiver Publishers1 aEnrico Ferrera1 aClaudio Pastrone1 aPaul-Emmanuel Brun1 aRemi De Besombes1 aKonstantinos Loupos1 aGerasimos Kouloumpis1 aPatrick O’Sullivan1 aAlexandros Papageorgiou1 aPanayiotis Katsoulakos1 aBill Karakostas1 aAntonis Mygiakis1 aChristina Stratigaki1 aBora Caglayan1 aBasile Starynkevitch1 aChristos Skoufis1 aStelios Christofi1 aNicolas Ferry1 aHui Song1 aArnor Solberg1 aPeter Matthews1 aAntonio Skarmeta1 aJosé Santa1 aMichail Beliatis1 aMirko Presser1 aJosiane Parreira1 aJuan Martínez1 aPayam Barnaghi1 aShirin Enshaeifar1 aThorben Iggena1 aMarten Fischer1 aRalf Tönjes1 aMartin Strohbach1 aAlessandro Sforzin1 aHien Truong1 aJohn Soldatos1 aSofoklis Efremidis1 aGeorgios Koutalieris1 aPanagiotis Gouvas1 aJuergen Neises1 aGeorge Hatzivasilis1 aIoannis Askoxylakis1 aVivek Kulkarni1 aArne Broering1 aDariusz Dober1 aKostas Ramantas1 aChristos Verikoukis1 aJoachim Posegga1 aDomenico Presenza1 aGeorge Spanoudakis1 aDanilo Pau1 aErol Gelenbe1 aSławomir Nowak1 aMateusz Nowak1 aTadeusz Czachórski1 aJoanna Domańska1 aAnastasis Drosou1 aDimitrios Tzovaras1 aTommi Elo1 aSanteri Paavolainen1 aDmitrij Lagutin1 aHelen Leligou1 aPanagiotis Trakadas1 aGeorge Polyzos00aIoT European Security and Privacy Projects: Integration, Architectures and Interoperability  a978877022007100430nas a2200157   4500000000100000000000100001008004100002100001900043700002100062700002400083700001700107700001800124700001900142245010200161490000900263       2019                            d1 aAdam Domański1 aJoanna Domańska1 aTadeusz Czachórski1 aJerzy Klamka1 aDariusz Marek1 aJakub Szyguła00aAQM Mechanism with the Dropping Packet Function Based on the Answer of Several  PIα  Controllers0 v103900385nas a2200145   4500000000100000000000100001008004100002100001900043700002100062700002400083700001700107700001900124700001800143245007800161       2020                            d1 aAdam Domański1 aJoanna Domańska1 aTadeusz Czachórski1 aJerzy Klamka1 aJakub Szyguła1 aDariusz Marek00aDiffusion Approximation Model of TCP NewReno Congestion Control Mechanism00389nas a2200157   4500000000100000000000100001008004100002260002100043100001900064700001900083700002100102700002400123700001800147700001700165245004900182       2020                            d  c23-26 March 20201 aJakub Szyguła1 aAdam Domański1 aJoanna Domańska1 aTadeusz Czachórski1 aDariusz Marek1 aJerzy Klamka00a AQM mechanism with neuron tuning parameters01711nas a2200181   4500000000100000000000100001008004100002260001400043100001700057700002100074700002000095700001800115700002000133245006300153856005800216490000800274520124700282       2020                            d  cJuly 20201 aErol Gelenbe1 aJoanna Domańska1 aPiotr Fröhlich1 aMateusz Nowak1 aSławomir Nowak00aSelf-Aware Networks that Optimize Security, QoS and Energy  uhttps://ieeexplore.ieee.org/abstract/document/91035250 v1083 aThe need to adaptively manage computer systems and networks so as to offer good Quality of Service (QoS) and Quality of Experience (QoE), with secure operation at relatively low levels of energy consumption is challenged by their sheer complexity and the wide variability of the workloads. A possible way forward is through self-awareness, whereby self-measurement and self-observation, together with on-line control mechanisms which operate adaptively to attain the required performance and QoE. We survey the premises for these ideas coming from Cognitive Science and Active Networks, and review recent work on self-aware computer systems and networks, including those that propose the use of Software Defined Networks as a means to implement these concepts. Then we provide some examples from the literature on self-aware systems to illustrate the performance gains that they can provide. Finally we detail an example system and its working algorithms to allow the reader to understand how such a system may be implemented. Measurements showing how it can react rapidly to changing network conditions regarding Quality of Service (QoS) and security are also presented. Some conclusions and suggestions for further work are also presented
00338nas a2200145   4500000000100000000000100001008004100002100001900043700002100062700002000083700001900103700002400122245003900146490000700185       2020                            d1 aAdam Domański1 aJoanna Domańska1 aKatarzyna Filus1 aJakub Szyguła1 aTadeusz Czachórski00aThe self-similar markovian sources0 v1000399nas a2200145   4500000000100000000000100001008004100002260003700043100001700080700002200097700002000119700002100139245004400160856004900204       2020                            d  c10/2020bIEEEXpressaNice France1 aErol Gelenbe1 aMiltiadis Siavvas1 aPaweł Boryszko1 aJoanna Domańska00aOptimum checkpoints for time and energy  uhttps://ieeexplore.ieee.org/document/928595900430nas a2200157   4500000000100000000000100001008004100002260000900043100002000052700001900072700002100091700001800112700001900130245011600149490000700265       2020                            d  c20201 aKatarzyna Filus1 aAdam Domański1 aJoanna Domańska1 aDariusz Marek1 aJakub Szyguła00aLong-Range Dependent Traffic Classification with Convolutional Neural Networks Based on Hurst Exponent Analysis0 v2200445nas a2200169   4500000000100000000000100001008004100002100001900043700001900062700002100081700002400102700001700126700001800143245004900161856005800210490000700268       2021                            d1 aJakub Szyguła1 aAdam Domański1 aJoanna Domańska1 aTadeusz Czachórski1 aJerzy Klamka1 aDariusz Marek00aThe IoT Gateway with Active Queue Management  uhttps://www.amcs.uz.zgora.pl/?action=paper&paper=15980 v3101417nas a2200205   4500000000100000000000100001008004100002260001200043100001800055700001900073700002100092700002400113700001700137700001900154245008700173856007100260490000800331520085800339022001401197       2021                            d  c01/20211 aDariusz Marek1 aAdam Domański1 aJoanna Domańska1 aTadeusz Czachórski1 aJerzy Klamka1 aJakub Szyguła00aCombined diffusion approximation - simulation model of AQM’s transient behaviour  uhttp://www.sciencedirect.com/science/article/pii/S01403664203199400 v1663 a<p>The article introduces an approach combining diffusion approximation and simulation ones. Furthermore, it describes how it can be used to evaluate active queue management (AQM) mechanisms. Based on the obtained queue distributions, the simulation part of the model decides on package losses and modifies the flow intensity sent by the transmitter. The diffusion is used to estimate queue distributions and the goal of the simulation part of the model is to represent the AQM mechanism. On the one hand, the use of the diffusion part considerably accelerates the performance of the whole model. On the other hand, the simulation increases the accuracy of the diffusion part. We apply the model to compare the performance of fractional order PIη controller used in AQM with the performance of RED, a well known active queue management mechanism.</p>
  a0140-366400540nas a2200181   4500000000100000000000100001008004100002260003800043100002000081700002200101700002100123700001700144245008700161856006600248300001200314490001000326022002200336       2021                            d  bSpringer International Publishing1 aKatarzyna Filus1 aMiltiadis Siavvas1 aJoanna Domańska1 aErol Gelenbe00aThe Random Neural Network as a Bonding Model for Software Vulnerability Prediction  uhttps://link.springer.com/chapter/10.1007/978-3-030-68110-4_7  a102-1160 v12527  a978-3-030-68110-401179nas a2200181   4500000000100000000000100001008004100002260003800043100002000081700002100101700001700122245007000139856006600209300001000275490001000285520068000295022002200975       2021                            d  bSpringer International Publishing1 aKatarzyna Filus1 aJoanna Domańska1 aErol Gelenbe00aRandom Neural Network for Lightweight Attack Detection in the IoT  uhttps://link.springer.com/chapter/10.1007/978-3-030-68110-4_5  a79-910 v125273 a<p>Cyber-attack detection has become a basic component of all information processing systems, and once an attack is detected it may be possible to block or mitigate its effects. This paper addresses the use of a learning recurrent Random Neural Network (RNN) to build a lightweight detector for certain types of Botnet attacks on IoT systems. Its low computational cost based on a small 12-neuron recurrent architecture makes it particularly attractive for edge devices. The RNN can be trained off-line using a fast simplified gradient descent algorithm, and we show that it can lead to high detection rates of the order of 96%, with false alarm rates of a few percent.</p>
  a978-3-030-68110-400460nas a2200169   4500000000100000000000100001008004100002100002000043700002000063700002100083700002200104700001700126245007700143856004500220490001100265022001400276       2021                            d1 aKatarzyna Filus1 aPaweł Boryszko1 aJoanna Domańska1 aMiltiadis Siavvas1 aErol Gelenbe00aEfficient Feature Selection for Static Analysis Vulnerability Prediction  uhttps://www.mdpi.com/1424-8220/21/4/11330 v21 (4)  a1424-822000416nas a2200157   4500000000100000000000100001008004100002100002000043700002000063700001900083700002100102245006300123856004600186490001200232022001400244       2021                            d1 aŁukasz Sobczak1 aKatarzyna Filus1 aAdam Domański1 aJoanna Domańska00aLIDAR Point Cloud generation for SLAM algorithm evaluation  uhttps://www.mdpi.com/1424-8220/21/10/33130 v21 (10)  a1424-822000431nas a2200169   4500000000100000000000100001008004100002260001200043100001800055700001900073700002100092700001900113700002400132700001700156245008100173490000700254       2021                            d  c05/20211 aDariusz Marek1 aAdam Domański1 aJoanna Domańska1 aJakub Szyguła1 aTadeusz Czachórski1 aJerzy Klamka00aDiffusion model of a non-integer order PIγ controller with TCP/UDP  streams0 v2300473nas a2200169   4500000000100000000000100001008004100002100001900043700001900062700002100081700001800102700002000120700001800140245008800158856004600246490001100292       2021                            d1 aJakub Szyguła1 aAdam Domański1 aJoanna Domańska1 aDariusz Marek1 aKatarzyna Filus1 aSzymon Mendla00aSupervised learning of Neural Networks for Active Queue Management in  the Internet  uhttps://www.mdpi.com/1424-8220/21/15/49790 v21(15)16164nas a2200253   45000000001000000000001000010080041000022600021000431000025000647000022000897000024001117000023001357000025001587000020001837000020002037000021002237000026002447000029002707000017002997000023003167000022003392450090003615201545900451       2022                            d  c03/2022aBelgium1 aCharalampos Marantos1 aMiltiadis Siavvas1 aDimitrios Tsoukalas1 aChristos Lamprakos1 aLazaros Papadopoulos1 aPaweł Boryszko1 aKatarzyna Filus1 aJoanna Domańska1 aApostolos Ampatzoglou1 aAlexander Chatzigeorgiou1 aErol Gelenbe1 aDionysios Kehagias1 aDimitrios Soudris00aSDK4ED: One-click platform for Energy-aware, Maintainable and Dependable Applications3 a<p><span style="font-family:sans-serif; font-size:14.944px">Developing</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">modern</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">secure</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">and</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">low-energy</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">applica</span><span style="font-family:sans-serif; font-size:14.944px">tions in a short time imposes new challenges and creates the need </span><span style="font-family:sans-serif; font-size:14.944px">of</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">designing</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">new</span><span style="font-family:sans-serif; font-size:14.944px">&nbsp;</span><span style="font-family:sans-serif; font-size:14.944px">software</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">tools</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">to</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">assist</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">developers</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">in</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">all</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">phases </span><span style="font-family:sans-serif; font-size:14.944px">of</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">application</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">development.</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">The</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">design</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">of</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">such</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">tools</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">cannot </span><span style="font-family:sans-serif; font-size:14.944px">be</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">considered</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">a</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">trivial</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">task,</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">as</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">they</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">should</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">be</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">able</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">to</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">provide</span> <span style="font-family:sans-serif; font-size:14.944px">optimization</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">of</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">multiple</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">quality</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">requirements.</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">In</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">this</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">paper,</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">we </span><span style="font-family:sans-serif; font-size:14.944px">introduce</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">the</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">SDK4ED</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">platform,</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">which</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">incorporates</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">advanced</span> <span style="font-family:sans-serif; font-size:14.944px">methods and tools for measuring and optimizing maintainability, </span><span style="font-family:sans-serif; font-size:14.944px">dependability</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">and</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">energy.</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">The</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">presented</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">solution</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">offers</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">a</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">com</span><span style="font-family:sans-serif; font-size:14.944px">plete</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">tool-flow</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">for</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">providing</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">indicators</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">and</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">optimization</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">meth</span><span style="font-family:sans-serif; font-size:14.944px">ods</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">with</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">emphasis</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">on</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">embedded</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">software.</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">Effective</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">forecasting</span> <span style="font-family:sans-serif; font-size:14.944px">models</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">and</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">decision-making</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">solutions</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">are</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">also</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">implemented</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">to </span><span style="font-family:sans-serif; font-size:14.944px">improve</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">the</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">quality</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">of</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">the</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">software,</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">respecting</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">the</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">constraints</span> <span style="font-family:sans-serif; font-size:14.944px">imposed</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">on</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">maintenance</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">standards,</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">energy</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">consumption</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">limits </span><span style="font-family:sans-serif; font-size:14.944px">and</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">security</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">vulnerabilities.</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">The</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">use</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">of</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">the</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">SDK4ED</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">platform</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">is</span> <span style="font-family:sans-serif; font-size:14.944px">demonstrated</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">in</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">a</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">healthcare</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">embedded</span><span style="font-family:sans-serif; font-size:14.944px"> </span><span style="font-family:sans-serif; font-size:14.944px">application.</span></p>
00451nas a2200145   4500000000100000000000100001008004100002100002000043700002000063700002100083700001500104245012400119856005500243490000700298       2022                            d1 aKatarzyna Filus1 aSławomir Nowak1 aJoanna Domańska1 aJakub Duda00aCost-Effective Filtering of Unreliable Proximity Detection Results Based on BLE RSSI and IMU Readings Using Smartphones  uhttps://www.nature.com/articles/s41598-022-06201-y0 v1200461nas a2200181   4500000000100000000000100001008004100002100001800043700001900061700001900080700002100099700002000120700002100140245005300161856004400214490000700258022001400265       2022                            d1 aDariusz Marek1 aJakub Szyguła1 aAdam Domański1 aJoanna Domańska1 aKatarzyna Filus1 aMarta Szczygieł00aAdaptive Hurst-Sensitive Active Queue Management  uhttps://www.mdpi.com/1099-4300/24/3/4180 v24  a1099-430000340nas a2200121   4500000000100000000000100001008004100002260003500043100002000078700002100098245004100119856005800160       2022                            d  bIEEEaPadova, 18-23 July, 20221 aKatarzyna Filus1 aJoanna Domańska00aNAM: What Does a Neural Network See?  uhttps://ieeexplore.ieee.org/abstract/document/989244200511nas a2200181   4500000000100000000000100001008004100002100001800043700001900061700002100080700001900101700002400120700001700144700002000161245006400181856007700245490000700322       2022                            d1 aDariusz Marek1 aAdam Domański1 aJoanna Domańska1 aJakub Szyguła1 aTadeusz Czachórski1 aJerzy Klamka1 aKatarzyna Filus00aApproximation Models for the Evaluation of TCP/AQM Networks  uhttps://journals.pan.pl/dlibra/publication/141986/edition/123961/content0 v7000397nas a2200133   4500000000100000000000100001008004100002260001200043100002000055700002100075245007600096856008300172490000800255       2023                            d  c10/20221 aKatarzyna Filus1 aJoanna Domańska00aSoftware Vulnerabilities in TensorFlow-Based Deep Learning Applications  uhttps://www.sciencedirect.com/science/article/pii/S0167404822003406?via%3Dihub0 v12400454nas a2200145   4500000000100000000000100001008004100002100002000043700002000063700002100083700001900104245012300123856005500246490000700301       2022                            d1 aŁukasz Sobczak1 aKatarzyna Filus1 aJoanna Domańska1 aAdam Domański00aFinding the best hardware configuration for 2D SLAM in indoor environments via simulation based on Google Cartographer  uhttps://www.nature.com/articles/s41598-022-22938-y0 v1200409nas a2200145   4500000000100000000000100001008004100002100002000043700002000063700002100083700001900104245008700123856004600210490000700256       2022                            d1 aŁukasz Sobczak1 aKatarzyna Filus1 aJoanna Domańska1 aAdam Domański00aBuilding a real-time testing platform for unmanned ground vehicles with UDP Bridge  uhttps://www.mdpi.com/1424-8220/22/21/84930 v2200460nas a2200157   4500000000100000000000100001008004100002260003800043100002000081700002000101700002100121700001900142700001700161245007400178856005000252       2022                            d  bIEEEaOsaka, 17-20 December, 20221 aKatarzyna Filus1 aŁukasz Sobczak1 aJoanna Domańska1 aAdam Domański1 aRafał Cupek00aReal-time testing of vision-based systems for AGVs with ArUco markers  uhttps://ieeexplore.ieee.org/document/1002081700353nas a2200121   4500000000100000000000100001008004100002100002000043700002100063245006700084856007200151490000800223       2023                            d1 aKatarzyna Filus1 aJoanna Domańska00aGlobal Entropy Pooling Layer for Convolutional Neural Networks  uhttps://www.sciencedirect.com/science/article/pii/S09252312230073850 v55500302nas a2200109   4500000000100000000000100001008004100002260003100043100002000074700002100094245007700115       2023                            d  bIEEEaThessaloniki, Greece1 aKatarzyna Filus1 aJoanna Domańska00aRecycling of Generic ImageNet-trained Models for Smart-city Applications01730nas a2200145   4500000000100000000000100001008004100002260003600043100001800079700002000097700002100117245010300138856005000241520129300291       2023                            d  c01/2024bIEEEaSorrento, Italy 1 aNur Kelesoglu1 aKatarzyna Filus1 aJoanna Domańska00aHierAct: a Hierarchical Model for Human Activity Recognition in Game-Like Educational Applications  uhttps://ieeexplore.ieee.org/document/103869193 a<p>Constantly evolving landscape of modern education makes the integration of technology crucial to enable more interactive and immersive learning experiences. One of the key problems in this domain is Human Activity Recognition (HAR), which uses standard smartphone sensors to understand and recognize user movements. While HAR exhibits high potential in this area, its persistent challenge is to differentiating between similar activities, whose occurrences can often lead to misclassification and reduced quality of applications. To tackle this problem, we employ specialized classifiers and use them in a hierarchical manner instead of using monolith multi-class models. We propose HierAct - a Hierarchical model that solves the multi-class human activity recognition problem. We also release a new HAR dataset - EduAct - (https://github.com/iitis/HierAct-Dataset) focused on activities that could be used to create game-like educational applications.Our results show that the performance of the proposed model is substantially better than other state-of-the-art machine learning models. Our work shows the potential of hierarchical classifiers in HAR applications for education, by offering educators and students a more accurate, reliable, and engaging interactive experience.</p>
00389nas a2200121   4500000000100000000000100001008004100002260007200043100001900115700002000134700002100154245009200175       2023                            d  c01/2024bIEEE International Conference on Big DataaSorrento, Italy1 aMarzena Halama1 aKatarzyna Filus1 aJoanna Domańska00aRobust category recognition based on deep templates for educational mobile applications00264nas a2200109   4500000000100000000000100001008004100002260002000043100002000063700002100083245005000104       2023                            d  aSorrento, Italy1 aKatarzyna Filus1 aJoanna Domańska00aNetSat: Network Saturation Adversarial Attack00387nas a2200133   4500000000100000000000100001008004100002260003500043100002000078700002000098700001900118700002100137245009500158       2023                            d  c01/2024bIEEEaSorrento, Italy1 aŁukasz Sobczak1 aKatarzyna Filus1 aMarzena Halama1 aJoanna Domańska00aVisual examination of relations between known classes for deep neural network classifiers.00838nas a2200289   4500000000100000000000100001008004100002100002200043700002400065700002500089700002500114700002300139700001700162700002100179700002100200700002600221700002000247700002100267700001900288700002600307700002200333700002900355700001700384700002300401700002300424245010100447       2024                            d1 aMiltiadis Siavvas1 aDimitrios Tsoukalas1 aCharalambos Marantos1 aLazaros Papadopoulos1 aChristos Lamprakos1 aOliviu Matei1 aChristos Strydis1 aMuhammad Siddiqi1 aPhilippe Chrobocinski1 aKatarzyna Filus1 aJoanna Domańska1 aParis Avgeriou1 aApostolos Ampatzoglou1 aDimitrios Soudris1 aAlexander Chatzigeorgiou1 aErol Gelenbe1 aDionysios Kehagias1 aDimitrios Tzovaras00aSDK4ED: a platform for building energy efficient, dependable, and maintainable embedded software00393nas a2200133   4500000000100000000000100001008004100002260001200043100002000055700002100075245008300096856007200179490000800251       2024                            d  c12/20241 aKatarzyna Filus1 aJoanna Domańska00aExtracting coarse-grained classifiers from large Convolutional Neural Networks  uhttps://www.sciencedirect.com/science/article/pii/S09521976240153550 v13800328nas a2200109   4500000000100000000000100001008004100002100002000043700002100063700001700084245011700101       2024                            d1 aKatarzyna Filus1 aJoanna Domańska1 aJerzy Klamka00aEvaluating Convolutional Neural Networks via measuring similarity of their first-layer filters and Gabor filters00371nas a2200133   4500000000100000000000100001008004100002260001200043100002000055700002100075245006100096856007200157490000800229       2024                            d  c12/20241 aKatarzyna Filus1 aJoanna Domańska00aSimilarity-driven adversarial testing of neural networks  uhttps://www.sciencedirect.com/science/article/pii/S09507051240125530 v30500597nas a2200181   4500000000100000000000100001008004100002260001200043100001800055700002000073700002000093700002100113700002200134700001800156700003000174245008000204856013100284       2025                            d  c02/20251 aNur Kelesoglu1 aŁukasz Sobczak1 aPiotr Biernacki1 aJoanna Domańska1 aFrancisco Ferrão1 aShahid Mumtaz1 aFideline Tchuenbou-Magaia00aArtificial Intelligence for Electronic-based Systems in Safety Applications  uhttps://www.researchgate.net/publication/389204536_Artificial_Intelligence_for_Electronic-based_Systems_in_Safety_Applications00323nas a2200121   4500000000100000000000100001008004100002100002000043700002100063245009500084490000800179022001400187       2025                            d1 aKatarzyna Filus1 aJoanna Domańska00aWhat is the doggest dog? Examination of typicality perception in ImageNet-trained networks0 v188  a0893-608001474nas a2200157   4500000000100000000000100001008004100002260000800043100002000051700002000071700002100091245008000112856004400192300001400236520106600250       2025                            d  bACM1 aŁukasz Sobczak1 aPiotr Biernacki1 aJoanna Domańska00aVisual Encoding Method for Semantic Mapping with Federated Learning Concept  uhttps://doi.org/10.1145/3704413.3765513  a428 - 4353 a<p>We present a visual encoding method for semantic mapping in indoor environments, designed to minimize redundancy in image data and support federated learning across a fleet of service robots. Our pipeline combines 2D LiDAR-based segmentation with RGB image filtering based on geometric orientation, distance, visibility, and uniqueness. The result is a compact set of representative visual samples suitable for downstream semantic tasks such as object recognition or language grounding. We evaluate our method in a Gazebo simulation using a TurtleBot platform and compare it against a naive odometry-based sampling strategy. Our approach achieves up to 57.5\% reduction in collected images while preserving scene coverage. Additionally, we demonstrate how multiple robots can collaboratively improve the visual map in a federated setup, reducing collection time and enabling model generalization across diverse environments. The proposed method offers an efficient and scalable solution for semantic mapping under bandwidth and computation constraints.</p>
00323nas a2200109   4500000000100000000000100001008004100002100002000043700002100063245006300084856006600147       2025                            d1 aKatarzyna Filus1 aJoanna Domańska00aHow CNNs and ViTs perceive similarities between categories  uhttps://link.springer.com/chapter/10.1007/978-3-032-06078-5_201702nas a2200133   4500000000100000000000100001008004100002260004600043100001900089700001800108700002100126245011700147520130400264       2025                            d  c2025bIEEE BigData 2025aMacau SAR, China1 aMarzena Halama1 aKonrad Połys1 aJoanna Domańska00aSpeech-based analysis of aggression in social content: resistance of Large Language Models to signal degradation3 a<p>Artificial Intelligence (AI) systems, including robotic assistants and voice interfaces, are increasingly present in public spaces such as airports, train stations, hospitals, and government offices, offering new opportunities to enhance user safety and comfort. Detecting aggression and hate speech in audio data from such locations is critical to protecting users and creating safe spaces. Previous approaches in this area have mainly relied on classification using classical Machine Learning (ML) methods and Deep Learning (DL) techniques, primarily focused on pure text analysis. However, most systems do not take into account real-world challenges such as background noise, incomplete utterances, or transcription errors resulting from automatic speech recognition. In this work, we propose a comprehensive approach that exploits the potential of Large Language Models (LLMs) to classify aggressive content in degraded audio transcriptions. We create a realistic test environment by converting tagged social media posts with offensive content into recordings with simulated noise and compare the performance of the models under different data quality conditions. Our research provides practical guidance on how to build more resilient and accessible voice moderation systems using LLM.</p>
01037nas a2200133   4500000000100000000000100001008004100002100001900043700002100062700002000083700002700103245005100130520072200181       2025                            d1 aMarzena Halama1 aJoanna Domańska1 aAnna Gancarczyk1 aMirzokhid Abdirakhimov00aTworzenie bazy danych zeolitów z użyciem LLM3 a<p>Praca dotyczy opracowania zautomatyzowanego systemu tworzenia i zarządzania bazą danych zeolitów z wykorzystaniem dużych modeli językowych (LLM). Zaprojektowane rozwiązanie umożliwia ekstrakcję, ujednolicanie i strukturyzację informacji dotyczących syntezy, właściwości oraz zastosowań zeolitów na podstawie dokumentów tekstowych, takich jak publikacje naukowe i raporty badawcze. W procesie budowy bazy wykorzystano dane International Zeolite Association – Structure Commission oraz model LLM do identyfikacji kluczowych parametrów syntezy. Opracowana baza danych stanowi fundament pod przyszły system rekomendacyjny wspierający dobór odpowiednich zeolitów do konkretnych zastosowań.</p>
02683nas a2200133   4500000000100000000000100001008004100002100001800043700002000061700002100081245010200102856005500204520229000259       2026                            d1 aNur Kelesoglu1 aŁukasz Sobczak1 aJoanna Domańska00aWhen LLMs Speak ZigBee: Exploring Low-Latency and Reasoning Models for Network Traffic Generation  uhttps://www.nature.com/articles/s41598-026-37246-y3 a<p>The generation of realistic network traffic is a critical requirement for testing, simulation, and security evaluation in ZigBee-based IoT systems. In this study, we propose a novel framework that extracts sample ZigBee traffic packets and generates semantically meaningful and protocol-compliant synthetic traffic by using Large Language Models (LLMs) such as GPT-4.1 and GPT-5. Unlike traditional rule-based or statistical generators, our approach is data-driven and incorporates sample-based few-shot learning, prompt engineering, and a human-in-the-loop feedback mechanism. To evaluate the effectiveness of the proposed framework, we conduct two sets of experiments. The first focuses on generating unidirectional traffic to emulate typical device-to-hub communication, while the second extends this setup to bidirectional exchanges, capturing realistic request--response dynamics and interaction patterns. The realism of the generated traffic is assessed using a multi-dimensional evaluation framework that includes statistical similarity measures, such as Jensen--Shannon Divergence, as well as protocol compliance, semantic correctness, temporal consistency, and diversity metrics. In addition, we compare the performance of LLM-based generators with classical deep learning baselines, including recurrent neural networks (RNNs) and generative adversarial networks (GANs). We further analyze the computational cost and the impact of internal reasoning effort on traffic generation by systematically evaluating different GPT-5 reasoning configurations. Experimental results show that both GPT-4.1 and GPT-5 successfully learn the temporal and structural dependencies of ZigBee traffic and significantly outperform RNN and GAN baselines in terms of semantic correctness and long-duration generation. Across all experiments, GPT-4.1 consistently generates traffic that more closely resembles real ZigBee traffic while requiring substantially lower computational resources. These findings highlight that low-latency, non-reasoning LLMs can be particularly well suited for highly structured, protocol-constrained network traffic generation tasks, and demonstrate the potential of LLM-based approaches for realistic IoT traffic generation in research and security evaluation.</p>
00560nas a2200109   4500000000100000008004100001260026300042100001800305700002000323700002100343245008600364       2026                            d  b(This work was partly supported by the National Centre for Research and Development (NCBR) and co-funded by the European Union under the European Funds for ModernEconomy(FENG)Programme(SMARTKonsorcja),projectno.FENG.01.01-IP.01-A0GV/24-00,)aKraków, Poland 1 aNur Kelesoglu1 aŁukasz Sobczak1 aJoanna Domańska00aCPU Utilization Forecasting in Large-Scale Cloud Clusters Using Gradient Boosting01663nas a2200121   4500000000100000008004100001260007000042100001900112700001800131700002100149245008600170520128500256       2026                            d  b2nd International Conference PUT STEM Day 2026: Book of Abstracts1 aMarzena Halama1 aKonrad Połys1 aJoanna Domańska00aAI-Assisted Zeolite Design through Retrieval-Augmented Scientific Language Models3 a<p style="border-style:none;line-height:normal;margin-bottom:.0001pt;text-align:justify;"><span style="color:black;font-size:11.0pt;font-weight:normal;" lang="EN-US">Designing materials such as zeolites is a major challenge due to the wide range of possible structures and synthesis conditions. Furthermore, knowledge on this subject is widely scattered across numerous publications and experimental notes. Therefore, large language models (LLMs) represent a promising tool for building knowledge bases, supporting the analysis of scientific literature, although their responses may be incomplete or fallible. In addition, in the case of zeolites, the relationship between material properties and their topology is of crucial importance, which limits the effectiveness of approaches based solely on text. In this study, we investigate a Retrieval-Augmented Generation (RAG) approach extended with a multimodal component that combines textual and structural information.</span></p><p style="border-style:none;line-height:normal;margin-bottom:.0001pt;text-align:justify;">&nbsp;</p><p style="border-style:none;line-height:normal;margin-bottom:.0001pt;text-align:justify;">&nbsp;</p><p style="border-style:none;line-height:normal;margin-bottom:.0001pt;text-align:justify;">&nbsp;</p>00343nas a2200121   4500000000100000008004100001260002500042100001900067700001800086700002000104700002100124245007600145       2026                            d  bAGH University Press1 aMarzena Halama1 aKonrad Połys1 aSławomir Nowak1 aJoanna Domańska00aExternal Semantic Memory as a Cognitive Layer for Large Language Models01600nas a2200133   4500000000100000008004100001260004000042100002000082700001800102700002100120245007800141856005000219520119700269       2026                            d  c10.08.2026bIEEEaKitakyushu, Japan1 aŁukasz Sobczak1 aNur Kelesoglu1 aJoanna Domańska00aReliability-Aware LLM Reasoning: Handling Uncertainty in Robot Perception  uhttps://ieeexplore.ieee.org/document/117148513 a<p>Robots operating in human environments must often make decisions based on perceptual information that<br>is uncertain, incomplete, or ambiguous. This paper proposes a reliability-aware reasoning framework that enables large<br>language models (LLMs) to account for perceptual uncertainty when selecting actions in human-robot interaction scenarios.<br>The environment is represented as a structured scene composed of detected objects enriched with confidence estimates, attribute reliability, and spatial uncertainty information. Using this representation, the LLM evaluates candidate objects through a reliability scoring mechanism that integrates multiple sources of perceptual evidence and supports uncertainty-aware decision making.The proposed approach is evaluated using perception episodes with controlled levels of uncertainty and compared with a baseline LLM-based matching strategy that ignores perceptual<br>reliability. Experimental results show that incorporating uncertainty-aware reasoning substantially improves decision<br>robustness under medium and high uncertainty conditions while reducing safety-critical behaviors caused by overconfident<br>autonomous decisions.</p>01381nas a2200133   4500000000100000008004100001260004000042100001800082700002100100700002000121245009900141856005000240520095700290       2026                            d  c10.08.2026bIEEEaKitakyushu, Japan1 aNur Kelesoglu1 aJoanna Domańska1 aŁukasz Sobczak00aRisk-Aware Response Refinement for Safer LLM-Powered Socially Assistive Robots in Elderly Care  uhttps://ieeexplore.ieee.org/document/117147883 a<p>Safe and reliable human–robot interaction remains a challenge, especially for Large Language Model (LLM)-powered socially assistive robots in elderly care. This paper proposes a risk-aware response framework that explicitly models the safety implications of user queries. &nbsp;We introduce a Query Risk Assessment Module (QRAM), which computes a Query Risk Score (QRS) using structured semantic indicators to classify user inputs into different risk levels. Based on this, we develop two strategies: risk-aware response refinement and risk-aware response generation. To assess safety, we introduce the Risk-Aware Response Safety Score (RRSS), a metric that captures both the presence of risk-bearing language and the absence of necessary safety-critical guidance. Experimental results across multiple LLMs demonstrate that incorporating query-level risk awareness reduces response-level risk, with both strategies outperforming raw responses.</p>00420nas a2200121   4500000000100000008004100001260003000042100001800072700002000090700002100110245011600131856005100247       2026                            d  c10/05/2026aNaples, Italy1 aNur Kelesoglu1 aŁukasz Sobczak1 aJoanna Domańska00aFrom Plots to Words: Model-Aware Multimodal Explanations as a Foundation for Accessible, Non-Visual Interaction  uhttps://dl.acm.org/doi/10.1145/3776591.383386500536nas a2200157   4500000000100000008004100001100002000042700002200062700002000084700002300104700002000127700002100147700002100168245012200189856006700311       2026                            d1 aKatarzyna Filus1 aJorge Cruz-Duarte1 aMateusz Korpyś1 aAndrzej Kołodziej1 aAnna Gancarczyk1 aEl-Ghazali Talbi1 aJoanna Domańska00aFeature- and Similarity-Based Explainability of Experimental Materials Data via Black-Box Hyperparameter Optimization  uhttps://link.springer.com/chapter/10.1007/978-3-032-36226-1_1100338nas a2200109   4500000000100000008004100001260001600042100002000058700001800078700002100096245011100117       2026                            d  aBari, Italy1 aŁukasz Sobczak1 aNur Kelesoglu1 aJoanna Domańska00aKnowledge-Graph-Grounded Multimodal Explanations for Trustworthy Time-Series Forecasting in DevOps Systems00381nas a2200121   4500000000100000008004100001100002000042700002000062700001800082700002100100700003000121245010800151       2026                            d1 aKatarzyna Filus1 aObinna Nwadiuto1 aZlatka Stoeva1 aJoanna Domańska1 aFideline Tchuenbou-Magaia00aLightweight Time-Series Representation for Battery State of Health Estimation from Graphene Sensor Data00338nas a2200097   4500000000100000008004100001100001800042700002000060700002100080245013900101       2026                            d1 aNur Kelesoglu1 aŁukasz Sobczak1 aJoanna Domańska00aGrounding LLM-Generated Network Traffic in Semantics: A Knowledge-Graph Framework for Detecting and Explaining Protocol Hallucinations