04937nam 2200709Ii 45000010011000000030004000110050017000150060024000320070015000560080041000710200030001120200025001420240028001670400030001950500026002250820015002511000029002662450162002952640056004573000056005133360021005693370026005903380032006164900102006485040057007505050023008075050121008305050235009515050228011865050195014145050266016095050081018755060065019565100019020215100023020405100011020635100011020745100024020855100039021095100031021485100019021795100026021985201108022245240256033325300029035885380036036175380047036535880046037006500024037466500026037706500025037966550022038217000028038437000028038717000031038997000025039307000029039557100032039847760034040168300102040508560075041521000000056NOW20210117190106.0m eo d cr cn |||m|||a190401s2020 maua ob 000 0 eng d a9781680837490qelectronic z9781680837483qprint7 a10.1561/10000000562doi aCaBNVSLcCaBNVSLdCaBNVSL 4aQA76.592b.B43 2020eb04a004.162231 aBhat, Ganapati,eauthor.10aSelf-powered wearable IoT devices for health and activity monitoring /cGanapati Bhat, Ujjwal Gupta, Yigit Tuncel, Fatih Karabacak, Sule Ozev, Umit Y. Ogras. 1a[Hanover, Massachusetts] :bNow Publishers,c2020. a1 PDF (pages 145-269) :billustrations (some color) atext2rdacontent aelectronic2isbdmedia aonline resource2rdacarrier1 aFoundations and trends in electronic design automation,,x1551-3947 ;vVol. 13: No. 3, pp 145-269 aIncludes bibliographical references (pages 236-269).0 a1. Introduction --8 a2. Challenges of wearable health and activity monitoring. 2.1. Adaptation challenges ; 2.2. Technology challenges --8 a3. Driver application areas of wearable devices. 3.1. Introduction ; 3.2. Mobile health applications ; 3.3. Human activity monitoring ; 3.4. Challenges for mobile health and activity monitoring ; 3.5. Human computer interaction --8 a4. Physically flexible design of wearable devices. 4.1. Introduction ; 4.2. SoP design challenges and methodologies ; 4.3. SoP reliability challenges and test methodologies ; 4.4. SoP prototypes for wearable applications --8 a5. Energy harvesting and management. 5.1. Introduction ; 5.2. Energy harvesting approaches ; 5.3. Power management unit ; 5.4. Flexible energy storage ; 5.5. Energy management techniques -- 8 a6. Security and privacy considerations of wearable devices. 6.1. Attack modes requiring physical access ; 6.2. Firmware attacks ; 6.3. Energy attacks ; 6.4. Network based attacks ; 6.5. Experimental demonstration of security weaknesses inwearable IoT devices --8 a7. Conclusions and future directions -- List of abbreviations -- References. aRestricted to subscribers or individual document purchasers.0 aGoogle Scholar0 aGoogle Book Search0 aINSPEC0 aScopus0 aACM Computing Guide0 aDBLP Computer Science Bibliography0 aZentralblatt MATH Database0 aAMS MathSciNet0 aACM Computing Reviews3 aWearable devices have the potential to transform multiple facets of human life, including healthcare, activity monitoring, and interaction with computers. However, a number of technical and adaptation challenges hinder the widespread and daily usage of wearable devices. Recent research efforts have focused on identifying these challenges and solving them such that the potential of wearable devices can be realized. This monograph starts with a survey of the recent literature on the challenges faced by wearable devices. Then, it discusses potential solutions to each of the challenges. We start with the primary application areas that provide value to the users of wearable devices. We then present recent work on the design of physically flexible and bendable devices that aim to improve user comfort. We also discuss state-of-the-art energy harvesting and security solutions that can improve user compliance of wearable devices. Overall, this monograph aims to serve as a comprehensive resource for challenges and solutions towards self-powered wearable devices for health and activity monitoring. aGanapati Bhat, Ujjwal Gupta, Yigit Tuncel, Fatih Karabacak, Sule Ozev and Umit Y. Ogras (2020), "Self-Powered Wearable IoT Devices for Health and Activity Monitoring", Foundations and Trends in Electronic Design Automation: Vol. 13: No. 3, pp 145-269. aAlso available in print. aMode of access: World Wide Web. aSystem requirements: Adobe Acrobat reader. aTitle from PDF (viewed on Jan. 16, 2021). 0aWearable computers. 0aFlexible electronics. 0aHealthxMeasurement. 0aElectronic books.1 aGupta, Ujjwal,eauthor.1 aTuncel, Yigit,eauthor.1 aKarabacak, Fatih,eauthor.1 aOzev, Sule,eauthor.1 aOgras, Umit Y.,eauthor.2 aNow Publishers,epublisher.08iPrint version:z9781680837483 0aFoundations and trends in electronic design automation,;vVol. 13: No. 3, pp 145-269.x1551-3947 483Abstract with links to full textuhttp://dx.doi.org/10.1561/100000005604334nam 2200637Ii 45000010011000000030004000110050017000150060024000320070015000560080041000710200030001120200026001420240028001680400030001960500027002260820014002531000038002672450116003052640056004213000056004773360021005333370026005543380032005804900092006125040067007045050252007715050194010235050321012175050174015385050107017125060065018195100019018845100023019035100011019265100011019375100024019485100039019725100031020115100019020425100026020615200863020875240199029505300029031495380036031785380047032145880046032616500039033076500022033466550022033687000032033907000040034227100032034627760034034948300093035288560075036213300000002NOW20210117190106.0m eo d cr cn |||m|||a190401s2020 maua ob 001 0 eng d a9781680837858qelectronic z9781680837841q(print)7 a10.1561/33000000022doi aCaBNVSLcCaBNVSLdCaBNVSL 4aQA76.9.A25bF45 2020eb04a005.82231 aFeigenbaum, Joan,d1958-eauthor.10aAccountability in computing :bconcepts and mechanisms /cJoan Feigenbaum, Aaron D. Jaggard, Rebecca N. Wright. 1a[Hanover, Massachusetts] :bNow Publishers,c2020. a1 PDF (pages 247-399) :billustrations (some color) atext2rdacontent aelectronic2isbdmedia aonline resource2rdacarrier1 aFoundations and trends in privacy and security,x2474-1566 ;vVol. 2: No. 4, pp 247-399 aIncludes bibliographical references (pages 373-392) and index.0 a1. Introduction: The problem of "“Accountability". 1.1. Motivation ; 1.2. Why "accountability" is hard to pin down ; 1.3. Remarks on vocabulary ; 1.4. "Accountability" implicates many areas of computer science ; 1.5. Overview of contributions --8 a2. Perspectives, definitions, and concepts across disciplines. 2.1. Time, information, and action ; 2.2. Definitions of "accountability" ; 2.3. Accountability-related concepts and terms -- 8 a3. Accountability mechanisms and domains across disciplines. 3.1. Evidence without focus on external parties ; 3.2. Evidence to present to external parties ; 3.3. Judgment or blame ; 3.4. Punishment ; 3.5. Summary of systems and mechanisms in computer science ; 3.6. Accountability mechanisms in other disciplines --8 a4. Reasoning about accountability. 4.1. Tools for reasoning about accountability ; 4.2. Proofs about evidence in protocols ; 4.3. Accountability as a subject of study --8 a5. Conclusions. 5.1. Summary of key ideas ; 5.2. Key papers ; 5.3. Future work -- References -- Index. aRestricted to subscribers or individual document purchasers.0 aGoogle Scholar0 aGoogle Book Search0 aINSPEC0 aScopus0 aACM Computing Guide0 aDBLP Computer Science Bibliography0 aZentralblatt MATH Database0 aAMS MathSciNet0 aACM Computing Reviews3 aAccountability is a widely studied but amorphous concept, used to mean different things across different disciplines and domains of application. Here, we survey work on accountability in computer science and other disciplines. We motivate our survey with a study of the myriad ways in which the term “accountability” has been used across disciplines and the concepts that play key roles in defining it. This leads us to identify a temporal spectrum onto which we may place different notions of accountability to facilitate their comparison. We then survey accountability mechanisms for different application domains in computer science and place them on our spectrum. Building on this broader survey, we review frameworks and languages for studying accountability in computer science. Finally, we offer conclusions, open questions, and future directions. aJoan Feigenbaum, Aaron D. Jaggard and Rebecca N. Wright (2020), "Accountability in Computing: Concepts and Mechanisms", Foundations and Trends in Privacy and Security: Vol. 2: No. 4, pp 247-399. aAlso available in print. aMode of access: World Wide Web. aSystem requirements: Adobe Acrobat reader. aTitle from PDF (viewed on Jan. 12, 2021). 0aData encryption (Computer science) 0aComputer science. 0aElectronic books.1 aJaggard, Aaron D.,eauthor.1 aWright, Rebecca N.,d1967-eauthor.2 aNow Publishers,epublisher.08iPrint version:z9781680837841 0aFoundations and trends in privacy and security ;vVol. 2: No. 4, pp 247-399.x2474-1566 483Abstract with links to full textuhttp://dx.doi.org/10.1561/330000000205055nam 2200697Ii 45000010011000000030004000110050017000150060024000320070015000560080041000710200030001120200025001420240028001670400030001950500028002250820016002531000035002692450147003042640056004513000056005073360021005633370026005843380032006104900094006425040057007365050266007935050159010595050158012185050214013765050179015905050091017695050061018605050101019215060065020225100019020875100023021065100011021295100011021405100024021515100039021755100031022145100019022455100026022645201229022905240234035195300029037535380036037825380047038185880046038656500033039116500035039446500029039796550022040087000031040307000030040617000031040917100032041227760034041548300094041888560075042821500000078NOW20210117190106.0m eo d cr cn |||m|||a190401s2020 maua ob 000 0 eng d a9781680837391qelectronic z9781680837384qprint7 a10.1561/15000000782doi aCaBNVSLcCaBNVSLdCaBNVSL 4aQA76.9.D343bZ37 2020eb04a006.3122231 aZarrinkalam, Fattane,eauthor.10aExtracting, mining and predicting users' interests from social media /cFattane Zarrinkalam, Stefano Faralli, Guangyuan Piao, Ebrahim Bagheri. 1a[Hanover, Massachusetts] :bNow Publishers,c2020. a1 PDF (pages 445-617) :billustrations (some color) atext2rdacontent aelectronic2isbdmedia aonline resource2rdacarrier1 aFoundations and trends in information retrieval,x1554-0677 ;vVol. 14: No. 5, pp 445-617 aIncludes bibliographical references (pages 587-617).8 a2. Foundations of social user interest modeling. 2.1. Information sources ; 2.2. User interest representation unit ; 2.3. Temporal user interest modeling ; 2.4. Semantics-enabled user interest profile representation ; 2.5. Cross-system user interest modeling --8 a3. User interest modeling approaches. 3.1. Explicit user interest detection ; 3.2. Implicit user interest mining ; 3.3. Future user interest prediction --8 a4. Evaluation of user interest models. 4.1. Evaluation methodologies ; 4.2. Benchmark datasets ; 4.3. Evaluation metrics ; 4.4. Summary and discussion --8 a5. Applications of user interest models. 5.1. Applications on social media platforms ; 5.2. Third-party applications ; 5.3. Other applications ; 5.4. Integration of social media and third-party applications --8 a6. Open challenges and future directions. 6.1. Semantics ; 6.2. Cross-system models ; 6.3. Dynamicity ; 6.4. Comprehensiveness ; 6.5. Explainability ; 6.6. Reproducibility --8 a7. Conclusion and discussion. 7.1. Conclusion ; 7.2. Discussion -- Acknowledgements --8 aAppendices. A.1. Glossary ; A.2. Acronyms -- References.0 a1. Introduction. 1.1. Definitions ; 1.2. Related review papers ; 1.3. Related research areas -- aRestricted to subscribers or individual document purchasers.0 aGoogle Scholar0 aGoogle Book Search0 aINSPEC0 aScopus0 aACM Computing Guide0 aDBLP Computer Science Bibliography0 aZentralblatt MATH Database0 aAMS MathSciNet0 aACM Computing Reviews3 aThe abundance of user generated content on social media provides the opportunity to build models that are able to accurately and effectively extract, mine and predict users' interests with the hopes of enabling more effective user engagement, better quality delivery of appropriate services and higher user satisfaction. While traditional methods for building user profiles relied on AI-based preference elicitation techniques that could have been considered to be intrusive and undesirable by the users, more recent advances are focused on a non-intrusive yet accurate way of determining users' interests and preferences. In this monograph, we will cover five important subjects related to the mining of user interests from social media: (1) the foundations of social user interest modeling, such as information sources, various types of representation models and temporal features, (2) techniques that have been adopted or proposed for mining user interests, (3) different evaluation methodologies and benchmark datasets, (4) different applications that have been taking advantage of user interest mining from social media platforms, and (5) existing challenges, open research questions and opportunities for further work. aFattane Zarrinkalam, Stefano Faralli, Guangyuan Piao and Ebrahim Bagheri (2020), "Extracting, Mining and Predicting Users' Interests from Social Media", Foundations and Trends in Information Retrieval: Vol. 14: No. 5, pp 445-617. aAlso available in print. aMode of access: World Wide Web. aSystem requirements: Adobe Acrobat reader. aTitle from PDF (viewed on Jan. 13, 2021). 0aData miningxSocial aspects. 0aSocial mediaxData processing. 0aArtificial intelligence. 0aElectronic books.1 aFaralli, Stefano,eauthor.1 aPiao, Guangyuan,eauthor.1 aBagheri, Ebrahim,eauthor.2 aNow Publishers,epublisher.08iPrint version:z9781680837384 0aFoundations and trends in information retrieval ;vVol. 14: No. 5, pp 445-617.x1554-0677483Abstract with links to full textuhttp://dx.doi.org/10.1561/150000007807442nam 2200769Ii 45000010011000000030004000110050017000150060024000320070015000560080041000710200030001120200025001420240028001670400030001950500023002250820014002481000037002622450193002992460023004922640056005153000056005713360021006273370026006483380032006744900091007065040057007975050023008545050054008775050756009315050278016875050033019655050120019985050294021185050247024125050033026595060065026925100019027575100023027765100011027995100011028105100024028215100039028455100031028845100019029155100026029345202737029605240270056975300029059675380036059965380047060325880046060796500018061256500035061436500021061786500027061996550022062267000036062487000032062847000033063167000028063497000026063777000036064037100032064397760034064718300092065058560075065972200000078NOW20210117190106.0m eo d cr cn |||m|||a190401s2020 maua ob 000 0 eng d a9781680839814qelectronic z9781680839821qprint7 a10.1561/22000000782doi aCaBNVSLcCaBNVSLdCaBNVSL 4aQA166b.S73 2020eb04a511.52231 aStankovicc, Ljubiasa,eauthor.10aData analyticcs on graphs.nPart II,pSignals on graphs /cLjubiasa Stankovic, Danilo Mandic, Miloas Dakovic, Miloas Brajovic, Bruno Scalzo, Shengxi Li and Anthony G. Constantinides.30aSignals on graphs 1a[Hanover, Massachusetts] :bNow Publishers,c2020. a1 PDF (pages 158-331) :billustrations (some color) atext2rdacontent aelectronic2isbdmedia aonline resource2rdacarrier1 aFoundations and trends in machine learning,x1935-8245 ;vVol. 13: No. 2-3, pp 158-331 aIncludes bibliographical references (pages 323-331).0 a1. Introduction --8 a2. Problem statement: an illustrative example -- 8 a3. Signals and systems on graphs. 3.1. Adjacency matrix and graph signal shift ; 3.2. Systems based on graph shifted signals ; 3.3. Graph fourier transform (GFT), adjacency matrix based definition ; 3.4. System on a graph in the GFT domain ; 3.5. Graph signal filtering in the spectral domain of theadjacency matrix ; 3.6. Graph fourier transform based on the laplacian ; 3.7. Ordering and filtering in the laplacian spectral domain ; 3.8. Systems on a graph defined using the graph laplacian ; 3.9. Convolution of signals on a graph ; 3.10. The z-transform of a signal on a graph ; 3.11. Shift operator in the spectral domain ; 3.12. Parseval's theorem on a graph ; 3.13. Optimal denoising ; 3.14. Summary of shift operators for systems on a graph --8 a4. Subsampling, compressed sensing, and reconstruction. 4.1. Subsampling of bandlimited graph signals ; 4.2. Subsampling of sparse graph signals ; 4.3. Measurements as linear combinations of samples ; 4.4. Aggregate sampling ; 4.5. Random sampling with optimal strategy -- 8 a5. Filter bank on a graph --8 a6. Time-varying signals on graphs. 6.1. Diffusion on graph and low pass filtering ; 6.2. Taubin's a-B algorithm -- 8 a7. Random graph signal processing. 7.1. Review of WSS and related properties for random signals in standard time domain ; 7.2. Adjacency matrix based definition of GWSS ; 7.3. Wiener filter on a graph ; 7.4. Spectral domain shift based definition of GWSS ; 7.5. Isometric shift operator --8 a8. Vertex-frequency representations. 8.1. Localized graph Fourier transform (LGFT) ; 8.2. Inversion of the LGFT ; 8.3. Uncertainty principle for graph signals ; 8.4. Graph spectrogram and frames ; 8.5. Vertex-frequency energy distributions --8 a9. Conclusion -- References. aRestricted to subscribers or individual document purchasers.0 aGoogle Scholar0 aGoogle Book Search0 aINSPEC0 aScopus0 aACM Computing Guide0 aDBLP Computer Science Bibliography0 aZentralblatt MATH Database0 aAMS MathSciNet0 aACM Computing Reviews3 aThe area of Data Analytics on graphs deals with information processing of data acquired on irregular but structured graph domains. The focus of Part I of this monograph has been on both the fundamental and higher-order graph properties, graph topologies, and spectral representations of graphs. Part I also establishes rigorous frameworks for vertex clustering and graph segmentation, and illustrates the power of graphs in various data association tasks. Part II embarks on these concepts to address the algorithmic and practical issues related to data/signal processing on graphs, with the focus on the analysis and estimation of both deterministic and random data on graphs. The fundamental ideas related to graph signals are introduced through a simple and intuitive, yet general enough case study of multisensor temperature field estimation. The concept of systems on graph is defined using graph signal shift operators, which generalize the corresponding principles from traditional learning systems. At the core of the spectral domain representation of graph signals and systems is the Graph Fourier Transform (GFT), defined based on the eigendecomposition of both the adjacency matrix and the graph Laplacian. Spectral domain representations are then used as the basis to introduce graph signal filtering concepts and address their design, including Chebyshev series polynomial approximation. Ideas related to the sampling of graph signals, and in particular the challenging topic of data dimensionality reduction through graph subsampling, are presented and further linked with compressive sensing. The principles of time-varying signals on graphs and basic definitions related to random graph signals are next reviewed. Localized graph signal analysis in the joint vertex-spectral domain is referred to as the vertex-frequency analysis, since it can be considered as an extension of classical time-frequency analysis to the graph serving as signal domain. Important aspects of the local graph Fourier transform (LGFT) are covered, together with its various forms including the graph spectral and vertex domain windows and the inversion conditions and relations. A link between the LGFT with a varying spectral window and the spectral graph wavelet transform (SGWT) is also established. Realizations of the LGFT and SGWT using polynomial (Chebyshev) approximations of the spectral functions are further considered and supported by examples. Finally, energy versions of the vertex-frequency representations are introduced, along with their relations with classical timefrequency analysis, including a vertex-frequency distribution that can satisfy the marginal properties. The material is supported by illustrative examples. aLjubiasa Stankovic, Danilo Mandic, Miloas Dakovic, Miloas Brajovic, Bruno Scalzo, Shengxi Li and Anthony G. Constantinides (2020), "Data Analytics on Graphs Part II: Signals on Graphs", Foundations and Trends in Machine Learning: Vol. 13: No. 2-3, pp 158-331. aAlso available in print. aMode of access: World Wide Web. aSystem requirements: Adobe Acrobat reader. aTitle from PDF (viewed on Jan. 13, 2021). 0aGraph theory. 0aGraph theoryxData processing. 0aGraphic methods. 0aQuantitative research. 0aElectronic books.1 aMandic, Danilo,d1985-eauthor.1 aDakovic, Miloas,eauthor.1 aBrajovic, Miloas,eauthor.1 aScalzo, Bruno,eauthor.1 aLi, Shengxi,eauthor.1 aConstantinides, A. G.,eauthor.2 aNow Publishers,epublisher.08iPrint version:z9781680839821 0aFoundations and trends in machine learning ;vVol. 13: No. 2-3, pp 158-331.x1935-8245 483Abstract with links to full textuhttp://dx.doi.org/10.1561/220000007803369nam 2200589Ii 4500001001300000003000400013005001700017006002400034007001500058008004100073020003000114020002500144024003000169040003000199050002300229082001600252100003000268245007800298264005600376300004000432336002100472337002600493338003200519490008200551504005500633505012500688505017100813505015000984506006501134520071001199524015301909530002902062538003602091538004702127588004602174650002602220650002602246650005202272650003302324650005002357650001902407653001902426653002502445653002302470653000902493655002202502700003002524710003202554776003402586830008202620856007702702110.00000018NOW20210117190106.0m eo d cr cn |||m|||a190401s2021 maua ob 000 0 eng d a9781680837551qelectronic z9781680837544qprint7 a10.1561/110.000000182doi aCaBNVSLcCaBNVSLdCaBNVSL 4aT49.5b.G67 2021eb04a338.9272231 aGores, Thorsten,eauthor.14aThe globalization of the Bayh-Dole Act /cThorsten Gores, Albert N. Link. 1a[Hanover, Massachusetts] :bNow Publishers,c2021. a1 PDF (pages 1-90) :billustrations atext2rdacontent aelectronic2isbdmedia aonline resource2rdacarrier1 aAnnals of science and technology policy,x2475-1812 ;vVol. 5: No. 1, pp 1-90 aIncludes bibliographical references (pages 80-90).0 a1. Introduction. 1.1. Setting the stage ; 1.2. A public sector entrepreneurship lens ; 1.3. Overview of the monograph --8 a2. University technology transfer policies across countries. 2.1. The diffusion of a policy idea ; 2.2. Country-by-country university technology transfer emulation --8 a3. Concluding remarks. 3.1. Summary of the monograph ; 3.2. A research agenda -- Acknowledgements -- Appendix -- About the authors -- References. aRestricted to subscribers or individual document purchasers.3 aThe Globalization of the Bayh-Dole Act examines an overlooked metric associated with the impact of the Bayh-Dole Act, namely its effect on influencing university-based technology transfer policies in other countries. To substantiate this thesis, Bayh-Dole like university technology transfer policies in 20 other countries are reviewed. In an effort toward an assessment of these Bayh-Dole like policies, the monograph explores in each country higher education expenditures on research and development (R&D) before and after the Bayh-Dole like policies were adopted. The authors conclude that, in terms of this metric, in some countries the Bayh-Dole like policies have been more effective than in others. aThorsten Gores and Albert N. Link (2021), "The Globalization of the Bayh-Dole Act", Annals of Science and Technology Policy: Vol. 5: No. 1, pp 1-90. aAlso available in print. aMode of access: World Wide Web. aSystem requirements: Adobe Acrobat reader. aTitle from PDF (viewed on Jan. 13, 2021). 0aTechnology and state. 0aTechnology transfer.. 0aTechnological innovationsxLaw and legislation. 0aPatent laws and legislation. 0aTechnological innovationsxGovernment policy. 0aGlobalization. aBayh-Dole Act. aTechnology transfer. aPolicy evaluation. aR&D. 0aElectronic books.1 aLink, Albert N.,eauthor.2 aNow Publishers,epublisher.08iPrint version:z9781680837544 0aAnnals of science and technology policy ;vVol. 5: No. 1, pp 1-90.x2475-1812483Abstract with links to full textuhttp://dx.doi.org/10.1561/110.0000001805929nam 2200673Ia 45000010011000000030004000110050017000150060024000320070015000560080041000710200031001120200026001430240028001690400030001970500023002270820016002501000027002662450250002932640056005433000024005993360021006233370026006443380032006704900080007025040055007825050159008375050200009965050221011965050115014175050224015325050110017565050066018665060065019325202138019975240323041355300029044585380036044875380047045235880046045706500014046166500029046306500029046596550022046887000032047107000028047427000032047707000030048027000032048327000021048647000028048857000024049137000029049377000037049667000031050037100032050347760034050668300080051008560075051802300000059NOW20210117190106.0m eo d cr cn |||m|||a210116s2021 mau ob 000 0 eng d a9781680837698 (electronic) z9781680837681 (print)7 a10.1561/23000000592doi aCaBNVSLcCaBNVSLdCaBNVSL 4aTJ211b.G37 2021eb04a629.8922231 aGarg, Sourav,eauthor.10aSemantics for robotic mapping, perception and interaction :ba survey /cSourav Garg, Niko Sunderhauf, Feras Dayoub, Douglas Morrison, Akansel Cosgun, Gustavo Carneiro, Qi Wu, Tat-Jun Chin, Ian Reid, Stephen Gould, Peter Corke, Michael Milford. 1a[Hanover, Massachusetts] :bNow Publishers,c2021. a1 PDF (pages 1-224) atext2rdacontent aelectronic2isbdmedia aonline resource2rdacarrier1 aFoundations and trends in robotics,x1935-8261 ;vVol. 8: No. 1-2, pp 1-224 aIncludes bibliographical references (pages 80-90).0 a1. Introduction. 1.1. Past coverage including survey and review papers. 1.2. Summary and rationale for this survey ; 1.3. Taxonomy and survey structure --8 a2. Static and un-embodied scene understanding. 2.1. Image classification and retrieval ; 2.2. Object detection and recognition ; 2.3. Dense pixel-wise segmentation ; 2.4. Scene representations --8 a3. Dynamic environment understanding and mapping. 3.1. A brief history of maps and representations ; 3.2. Places and objects for semantic mapping ; 3.3. Semantic representations for SLAM and 3D scene understanding --8 a4. Interacting with humans and the world. 4.1. Perception of interaction ; 4.2. Perception for Interaction -- 8 a5. Improving task capability. 5.1. Semantics for localization and place recognition ; 5.2. Semantics to deal with challenging environmental conditions ; 5.3. Enabling semantics for robotics: additional considerations --8 a6. Practical aspects: applications and enhancers. 6.1. Applications ; 6.2. Critical upcoming enhancers --8 a7. Discussion and conclusion -- Acknowledgment -- References. aRestricted to subscribers or individual document purchasers.3 aFor robots to navigate and interact more richly with the world around them, they will likely require a deeper understanding of the world in which they operate. In robotics and related research fields, the study of understanding is often referred to as semantics, which dictates what does the world "mean" to a robot, and is strongly tied to the question of how to represent that meaning. With humans and robots increasingly operating in the same world, the prospects of human-robot interaction also bring semantics and ontology of natural language into the picture. Driven by need, as well as by enablers like increasing availability of training data and computational resources, semantics is a rapidly growing research area in robotics. The field has received significant attention in the research literature to date, but most reviews and surveys have focused on particular aspects of the topic: the technical research issues regarding its use in specific robotic topics like mapping or segmentation, or its relevance to one particular application domain like autonomous driving. A new treatment is therefore required, and is also timely because so much relevant research has occurred since many of the key surveys were published. This survey therefore provides an overarching snapshot of where semantics in robotics stands today. We establish a taxonomy for semantics research in or relevant to robotics, split into four broad categories of activity, in which semantics are extracted, used, or both. Within these broad categories we survey dozens of major topics including fundamentals from the computer vision field and key robotics research areas utilizing semantics, including mapping, navigation and interaction with the world. The survey also covers key practical considerations, including enablers like increased data availability and improved computational hardware, and major application areas where semantics is or is likely to play a key role. In creating this survey, we hope to provide researchers across academia and industry with a comprehensive reference that helps facilitate future research in this exciting field. aSourav Garg, Niko Sünderhauf, Feras Dayoub, Douglas Morrison, Akansel Cosgun, Gustavo Carneiro, Qi Wu, Tat-Jun Chin, Ian Reid, Stephen Gould, Peter Corke and Michael Milford (2020), "Semantics for Robotic Mapping, Perception and Interaction: a Survey", Foundations and Trends in Robotics: Vol. 8: No. 1–2, pp 1-224. aAlso available in print. aMode of access: World Wide Web. aSystem requirements: Adobe Acrobat reader. aTitle from PDF (viewed on Jan. 13, 2021). 0aRobotics. 0aRoboticsxHuman factors. 0aHuman-robot interaction. 0aElectronic books.1 aSunderhauf, Niko,eauthor.1 aDayoub, Feras,eauthor.1 aMorrison, Douglas,eauthor.1 aCosgun, Akansel,eauthor.1 aCarneiro, Gustavo,eauthor.1 aWu, Qi,eauthor.1 aChin, Tat-Jun,eauthor.1 aReid, Ian,eauthor.1 aGould, Stephen,eauthor.1 aCorke, Peter I.,d1959-eauthor.1 aMilford, Michael,eauthor.2 aNow Publishers,epublisher.08iPrint version:z9781680837681 0aFoundations and trends in robotics ;vVol. 8: No. 1-2, pp 1-224.x1935-8261483Abstract with links to full textuhttp://dx.doi.org/10.1561/230000005905384nam 2200661Ii 45000010011000000030004000110050017000150060024000320070015000560080041000710200030001120200025001420240028001670400030001950500024002250820015002491000024002642450106002882640056003943000026004503360021004763370026004973380032005234900095005555040057006505050216007075050209009235050198011325050119013305050161014495050220016105050177018305050145020075060065021525100019022175100023022365100011022595100011022705100024022815100039023055100031023445100019023755100026023945201587024205240194040075300029042015380036042305380047042665880049043136500049043626550022044117000029044337000024044627100032044867760034045188300095045528560075046473100000011NOW20210117190106.0m eo d cr cn |||m|||a190401s2020 mau ob 000 0 eng d a9781680837797qelectronic z9781680837780qprint7 a10.1561/31000000112doi aCaBNVSLcCaBNVSLdCaBNVSL 4aTK1005b.S66 2020eb04a621.312231 aSong, Yue,eauthor.10aNetwork-based analysis of rotor angle stability of power systems /cYue Song, David J. Hill, Tao Liu. 1a[Hanover, Massachusetts] :bNow Publishers,c2020. a1 PDF (pages 222-345) atext2rdacontent aelectronic2isbdmedia aonline resource2rdacarrier1 aFoundations and trends in electric energy systems,x2332-6565 ;vVol. 4: No. 3, pp 222-345 aIncludes bibliographical references (pages 331-345).0 a1. Introduction. 1.1. Concept of rotor angle stability ; 1.2. Traditional analysis methods for rotor angle stability ; 1.3. Motivation of network-based stability analysis ; 1.4. Organization of this monograph --8 a2. Notations and models. 2.1. Notations for matrices and graphs ; 2.2. Basics of power system components ; 2.3. Network reduced model of power systems ; 2.4. Structure preserving model of power systems --8 a3. Graph theory: Laplacian matrices of signed graphs. 3.1. Graph theory preliminaries ; 3.2. Matrix condition for Laplacian inertia ; 3.3. Laplacian inertia and extended effective resistance --8 a4. Graph theory: cutsets and cycles. 4.1. Concepts of cutset and cycle ; 4.2. Characterizing cutsets by cycles -- 8 a5. Small-sisturbance angle stability analysis. 5.1. Linking stability to graph Laplacian ; 5.2. Network-based stability criteria ; 5.3. Numerical example --8 a6. Graph cutsets and transient stability. 6.1. Basics of energy function and stability region ; 6.2. Linking transient dynamics to graph cutsets ; 6.3. An improved version of cutset index ; 6.4. Numerical example --8 a7. Effective resistance based TSC-OPF. 7.1. Linking effective resistance to transient dynamics ; 7.2. Formulation and convexification of TSC-OPF ; 7.3. Numerical example --8 a8. Conclusion and future directions. 8.1. Concluding remarks ; 8.2. Emerging topics and future directions -- Acknowledgements -- References. aRestricted to subscribers or individual document purchasers.0 aGoogle Scholar0 aGoogle Book Search0 aINSPEC0 aScopus0 aACM Computing Guide0 aDBLP Computer Science Bibliography0 aZentralblatt MATH Database0 aAMS MathSciNet0 aACM Computing Reviews3 aRotor angle stability refers to the ability of synchronous machines in a power system to remain in synchronism after a disturbance. It is one of the basic requirements for secure operation of electric power systems. Traditional analysis methods for rotor angle stability are oriented to node dynamics, especially the impact of generator modeling and parameters, while power network parameters are simply treated as some coefficients in the system dynamical models. Thanks to the progress on graph theory and network science, there is an emerging trend of investigating the connections between power network structures and system dynamic behaviors. This monograph surveys the network-based results on rotor angle stability in both early and recent years, where the role of power network structure is elaborated. It reveals that rotor angle dynamics essentially link to some graph quantities (e.g., Laplacian matrix, cutset, effective resistance) defined over the underlying power network structure. New theories for angle stability are developed using advanced graph theory tools tailored for power networks. These results provide novel solutions to some important problems that have not been well addressed in the traditional node-based studies, such as the impact of those lines with large angle differences on stability, cutset vulnerability assessment and convexification of stability constrained optimal power flow. The purpose of this monograph is to establish a networkbased paradigm that sheds new light on the mechanism of angle stability under small and large disturbances. aYue Song, David J. Hill and Tao Liu (2020), "Network-Based Analysis of Rotor Angle Stability of Power Systems", Foundations and Trends in Electric Energy Systems: Vol. 4: No. 3, pp 222-345. aAlso available in print. aMode of access: World Wide Web. aSystem requirements: Adobe Acrobat reader. aTitle from PDF (viewed on January 11, 2021). 0aElectric power systemsxMathematical models. 0aElectronic books.1 aHill, David J.,eauthor.1 aLiu, Tao,eauthor. 2 aNow Publishers,epublisher.08iPrint version:z9781680837780 0aFoundations and trends in electric energy systems ;vVol. 4: No. 3, pp 222-345.x2332-6565483Abstract with links to full textuhttp://dx.doi.org/10.1561/310000001105042nam 2200685Ii 45000010011000000030004000110050017000150060024000320070015000560080041000710200030001120200025001420240028001670400030001950500024002250820015002491000039002642450130003032640056004333000041004893360021005303370026005513380032005774900091006095040057007005050117007575050174008745050164010485050191012125050215014035050171016185050138017895050062019275060065019895100019020545100023020735100011020965100011021075100024021185100039021425100031021815100019022125100026022315201325022575240215035825300029037975380036038265380047038625880049039096500028039586500015039866500030040016530030040316550022040617000040040837100032041237760034041558300092041898560075042812600000022NOW20210117190106.0m eo d cr cn |||m|||a190401s2020 maua ob 000 0 eng d a9781680837452qelectronic z9781680837445qprint7 a10.1561/26000000222doi aCaBNVSLcCaBNVSLdCaBNVSL 4aQ325.6b.V36 2020eb04a006.312231 aVamvoudakis, Kyriakos G.,eauthor.10aSynchronous reinforcement learning-based control for cognitive autonomy /cKyriakos G. Vamvoudakis, Nick-Marios T. Kokolakis. 1a[Hanover, Massachusetts] :bNow Publishers,c2020. a1 PDF (pages 1-175) :billustrations atext2rdacontent aelectronic2isbdmedia aonline resource2rdacarrier1 aFoundations and trends in systems and control,x2325-6826 ;vVol. 8: No. 1-2, pp 1-175 aIncludes bibliographical references (pages 155-175).0 a1. Introduction. 1.1. A unified approach ; 1.2. RL and Cognitive Autonomy ; 1.3. Organization ; 1.4. Notation --8 a2. Optimal regulation. 2.1. Introduction and motivation ; 2.2. Bellman-based RL ; 2.3. RL based on an integral Bellman form ; 2.4. Saturating actuators and relaxed PE --8 a3. Game-theoretic learning. 3.1. Introduction and motivation ; 3.2. Zero-sum games ; 3.3. Non-zero-sum games ; 3.4. Stackelberg games ; 3.5. Graphical games --8 a4. Model-free RL with Q-learning. 4.1. Introduction and motivation ; 4.2. Q-learning for optimal regulation ; 4.3. Q-learning for Nash games ; 4.4. Q-learning for multi-agent systems -- 8 a5. Model-based and model-free intermittent RL. 5.1. Introduction and motivation ; 5.2. Optimal control of nonlinear systems ; 5.3. Optimal tracking control of nonlinear systems ; 5.4. Intermittent Q-learning --8 a6. Bounded rationality and non-equilibrium RL in games. 6.1. Introduction and motivation ; 6.2. Non-equilibrium dynamic games and RL ; 6.3. Games with adversaries -- 8 a7. Applications to autonomous vehicles. 7.1. Kinodynamic motion planning ; 7.2. Bounded rationality in adversarial target tracking --8 a8. Concluding remarks -- Acknowledgements -- References. aRestricted to subscribers or individual document purchasers.0 aGoogle Scholar0 aGoogle Book Search0 aINSPEC0 aScopus0 aACM Computing Guide0 aDBLP Computer Science Bibliography0 aZentralblatt MATH Database0 aAMS MathSciNet0 aACM Computing Reviews3 aThis monograph provides an exposition of recently developed reinforcement learning-based techniques for decision and control in human-engineered cognitive systems. The developed methods learn the solution to optimal control, zero-sum, non zero-sum, and graphical game problems completely online by using measured data along the system trajectories and have proved stability, optimality, and robustness. It is true that games have been shown to be important in robust control for disturbance rejection, and in coordinating activities among multiple agents in networked teams. We also consider cases with intermittent (an analogous to triggered control) instead of continuous learning and apply those techniques for optimal regulation and optimal tracking. We also introduce a bounded rational model to quantify the cognitive skills of a reinforcement learning agent. In order to do that, we leverage ideas from behavioral psychology to formulate differential games where the interacting learning agents have different intelligence skills, and we introduce an iterative method of optimal responses that determine the policy of an agent in adversarial environments. Finally, we present applications of reinforcement learning to motion planning and collaborative target tracking of bounded rational unmanned aerial vehicles. aKyriakos G. Vamvoudakis and Nick-Marios T. Kokolakis (2020), "Synchronous Reinforcement Learning-Based Control for Cognitive Autonomy", Foundations and Trends in Systems and Control: Vol. 8: No. 1-2, pp 1-175. aAlso available in print. aMode of access: World Wide Web. aSystem requirements: Adobe Acrobat reader. aTitle from PDF (viewed on January 11, 2021). 0aReinforcement learning. 0aCognition. 0aNonlinear control theory. aAdaptive control systems. 0aElectronic books.1 aKokolakis, Nick-Marios T.,eauthor.2 aNow Publishers,epublisher.08iPrint version:z9781680837445 0aFoundations and trends in systems and control ;vVol. 8: No. 1-2, pp 1-175.x2325-6826 483Abstract with links to full textuhttp://dx.doi.org/10.1561/2600000022