03207nam 2200589 450 991079857040332120230808194033.090-04-32351-110.1163/9789004323513(CKB)3710000000744226(PQKBManifestationID)16499882(PQKBWorkID)15031948(PQKB)20553881(MiAaPQ)EBC4750824(nllekb)BRILL9789004323513(EXLCZ)99371000000074422620170904h20162016 uy 0engurcnu||||||||txtccrThe poverty of work selling servant, slave and temporary labor on the free market /by David Van ArsdaleLeiden, [The Netherlands] ;Boston, [Massachusetts] :Brill,2016.©20161 online resource (227 pages) illustrations, tablesStudies in Critical Social Sciences,1573-4234 ;Volume 90Bibliographic Level Mode of Issuance: Monograph90-04-32337-6 Includes bibliographical references and index.Preliminary Material -- A Perfect Marriage: Flexible Employment Standards and the Staffing Industry -- Inside Employment Agency Labor: Participant Observation Experiences -- Exchange Alley: The Origins of Employment Agencies -- From Slave Agency to Temporary Help: The Historical Development of Employment Agencies -- The Poverty of Work: Shifting from Jobs that Solved Poverty to Jobs that Make It -- Preventing the Reproduction of Deprived Employment Statuses among Temporary Laborers -- Appendix -- Bibliography -- Index.In The Poverty of Work , Van Arsdale goes inside the world of temping and discovers a type of work dreadfully insecure yet growing rapidly. Furthermore, through a comprehensive historiography, he illustrates how employment agencies moved from England to North America during the colonial period, where they sold workers into many deprived employment statuses, including indentured servitude and slavery. Van Arsdale contends that had the history of employment agencies been better understood, they would have likely been abolished with slavery, or at the very least, more tightly controlled by government. Today, left largely unregulated, employment agencies are powerful corporations generating astonishing revenue by selling flexible, on-demand temporary workers. Unfortunately, this labor is trapping millions in a cycle of unemployment, despair, and poverty.Studies in critical social sciences ;Volume 90.Employment agenciesUnemployedTemporary employmentPrecarious employmentSlave laborEmployment agencies.Unemployed.Temporary employment.Precarious employment.Slave labor.331.12/8Van Arsdale David G.1575124MiAaPQMiAaPQMiAaPQBOOK9910798570403321The poverty of work3851850UNINA12672nam 22007815 450 991052257370332120251113200927.03-030-95405-610.1007/978-3-030-95405-5(MiAaPQ)EBC6877811(Au-PeEL)EBL6877811(CKB)21047946300041(PPN)264961730(OCoLC)1295275029(DE-He213)978-3-030-95405-5(EXLCZ)992104794630004120220131d2022 u| 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierAdvanced Data Mining and Applications 17th International Conference, ADMA 2021, Sydney, NSW, Australia, February 2–4, 2022, Proceedings, Part I /edited by Bohan Li, Lin Yue, Jing Jiang, Weitong Chen, Xue Li, Guodong Long, Fei Fang, Han Yu1st ed. 2022.Cham :Springer International Publishing :Imprint: Springer,2022.1 online resource (449 pages)Lecture Notes in Artificial Intelligence,2945-9141 ;13087Print version: Li, Bohan Advanced Data Mining and Applications Cham : Springer International Publishing AG,c2022 9783030954048 Intro -- Preface -- Organization -- Contents - Part I -- Contents - Part II -- Healthcare -- Deep Learning Based Cardiac Phase Detection Using Echocardiography Imaging -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Phase I: Model Training -- 3.2 Phase II: Model Testing -- 4 Experiments -- 4.1 Datasets -- 4.2 Competing Approaches -- 4.3 Parameter Configuration -- 4.4 Hardware and Software Configuration -- 5 Evaluation -- 5.1 Evaluation Metrics -- 5.2 Comparison Among Competing Approaches -- 5.3 Effect of Image Preprocessing -- 5.4 Evaluation of Parameter Sensitivity -- 5.5 Performance Analysis of the Custom Loss Function -- 5.6 Evidence of Generalization of DeepPhase -- 6 Conclusion -- References -- An Empirical Study on Human Flying Imagery Using EEG -- 1 Introduction -- 2 Method -- 2.1 Experiment and EEG Recording -- 2.2 Classification and Feature Analysis -- 3 Results -- 3.1 Overall Classification Results -- 3.2 Frequency Band Specific Classification Results -- 3.3 Time Window Specific Classification Results -- 3.4 Time-frequency Specific Classification Results -- 3.5 EEG Activity Patterns in Most Significant Time-frequency Bin -- 4 Discussion -- 5 Conclusion and Future Work -- References -- Network Graph Analysis of Hospital and Health Services Functional Structures -- 1 Introduction -- 2 Subjects and Methods -- 3 Network Graphs -- 4 Results -- 5 Discussion -- 6 Conclusion and Future Work -- References -- Feature Selection in Gene Expression Profile Employing Relevancy and Redundancy Measures and Binary Whale Optimization Algorithm (BWOA) -- 1 Introduction -- 1.1 Objective and Contributions -- 2 Related Work -- 3 Proposed Methodology -- 3.1 Feature Scaling -- 3.2 Phase 1 Feature Selection: Relevance Analysis -- 3.3 Phase 2 Feature Selection: Redundancy Analysis -- 3.4 Phase 3 Feature Selection: Meta-heuristic Optimization.3.5 Binary Whale Optimization Algorithm (BWOA) -- 3.6 BWOA for Gene Selection -- 3.7 Classification -- 4 Datasets and Baselines -- 4.1 Gene Expression Datasets -- 4.2 Performance Metrics -- 4.3 Baseline Methods -- 5 Results and Discussion -- 6 Summary and Conclusions -- References -- Hand Bone Age Estimation Using Deep Convolutional Neural Networks -- 1 Introduction -- 2 Background and Related Works -- 3 Materials and Methods -- 3.1 Normalization -- 3.2 Hand Detection -- 3.3 Vision Pipeline -- 3.4 Proposed Bone Age Prediction Model -- 3.5 Dataset -- 4 Experimental Results and Discussion -- 5 Conclusion -- References -- An Interpretable Machine Learning Approach for Predicting Hospital Length of Stay and Readmission -- 1 Introduction -- 2 Methods -- 3 Results and Discussion -- 4 Conclusion -- References -- STCT: Spatial-Temporal Conv-Transformer Network for Cardiac Arrhythmias Recognition -- 1 Introduction -- 2 Related Works -- 2.1 Diagnosis of Cardiac Arrhythmias -- 2.2 Deep Learning-Based Cardiac Arrhythmias Diagnose -- 3 Methodology -- 3.1 Data Segmentation -- 3.2 the Proposed Model -- 4 Experiment -- 4.1 Datasets and Model Implementation -- 4.2 Comparison Model -- 4.3 Experimental Results -- 5 Conclusion -- References -- Education -- Augmenting Personalized Question Recommendation with Hierarchical Information for Online Test Platform -- 1 Introduction -- 2 Preliminary -- 2.1 Definitions and Problem Statement -- 2.2 Framework Overview -- 3 Method -- 3.1 Incorporating the Student and Question Hierarchical Information -- 3.2 Student Performance Predicting -- 3.3 The Framework APQR -- 4 Experiments -- 4.1 Online Test Dataset -- 4.2 Evaluation Metric -- 4.3 Baseline Algorithms -- 4.4 Overall Performance -- 4.5 Parameter Analysis -- 5 Related Work -- 5.1 Recommender System -- 5.2 Student Performance Modeling -- 6 Conclusion -- References.Smart Online Exam Proctoring Assist for Cheating Detection -- 1 Intruduction -- 2 Related Work -- 3 Proposed Technique Highlight -- 3.1 Problem Statement -- 3.2 High Level Architecture -- 4 Proposed Work Details -- 4.1 Exam Recording -- 4.2 Video Characteristic Analysis -- 4.3 Videos Transformed to Feature Vector -- 4.4 Data Uniforming by Video Length Equalizing -- 4.5 Training -- 5 Experiments -- 5.1 Dataset -- 5.2 Competing Approaches -- 5.3 Parameters, Hardware and Software -- 5.4 Evaluation -- 6 Conclusion -- References -- Design and Development of Real-Time Barrage System for College Class -- 1 Introduction -- 2 System Design -- 2.1 System Framework and Function Design -- 2.2 System Flow Design -- 3 Analysis of Sensitive Word Filtering Algorithm -- 4 System Realization -- 4.1 PC Function Realization -- 4.2 Mobile Function Realization -- 4.3 Server-Side Function Realization -- 5 Conclusion -- References -- Recommendation for Higher Education Candidates: A Case Study on Engineering Programs -- 1 Introduction -- 2 Related Work -- 3 ESTHER -- 3.1 Students Profiler -- 3.2 Programs Recommender -- 3.3 System Dependencies and Limitations -- 4 Case Study -- 4.1 Students Profiler -- 4.2 Programs Recommender -- 4.3 ESTHER Overview -- 5 Conclusions -- References -- Web Application -- UQ-AAS21: A Comprehensive Dataset of Amazon Alexa Skills -- 1 Introduction -- 2 Background and Related Work -- 2.1 Background -- 2.2 Related Work -- 3 The UQ-AAS21 Dataset -- 3.1 Data Scraping -- 3.2 Data Processing -- 3.3 Dataset Features -- 4 Preliminary Studies Based on UQ-AAS21 Datasets -- 4.1 Demographic Study -- 4.2 Analysis of Privacy Policy and Term of Use Document -- 5 Potential Usage of UQ-AAS21 -- 6 Conclusion -- References -- Are Rumors Always False?: Understanding Rumors Across Domains, Queries, and Ratings -- 1 Introduction -- 2 Background.2.1 Detecting Rumors on the Web -- 2.2 Actions Against Detected Rumors -- 2.3 Research Gap -- 3 Research Questions -- 4 Methodology -- 4.1 Data Collection -- 5 Empirical Analyses and Findings -- 5.1 What Are the Rumors About? -- 5.2 Where Do the Rumors Come From? -- 5.3 Who Contribute to Rumors? -- 5.4 When Are the Rumors Reported? -- 5.5 How Do Rumors Propagate? -- 6 Discussion -- 6.1 Key Findings -- 6.2 Implications for Public Trust and Explainable Rumor Detection -- 6.3 Limitations and Future Work -- 7 Conclusion -- References -- A Green Pipeline for Out-of-Domain Public Sentiment Analysis -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Problem Definition -- 3.2 Pre-trained Transformer Encoder -- 3.3 Pipeline Sentiment Analysis Model -- 4 Experiments -- 4.1 Experimental Setups -- 4.2 Sentiment Analysis Evaluation -- 4.3 Performance of Sub-models -- 4.4 Analysis and Case Study -- 5 Conclusion -- References -- Profiling Fake News: Learning the Semantics and Characterisation of Misinformation -- 1 Introduction -- 2 Experimental Dataset -- 2.1 Data Pre-processing -- 3 Proposed Solution Approach -- 3.1 Features Extraction and Selection -- 3.2 Classification Models -- 4 Experimental Results -- 5 Conclusion and Future Work -- References -- Mining Social Networks for Dissemination of Fake News Using Continuous Opinion-Based Hybrid Model -- 1 Introduction -- 2 Proposed Model -- 2.1 Initialization -- 2.2 Propagation -- 3 Results and Discussions -- 4 Conclusion -- References -- Predicting Network Threat Events Using HMM Ensembles -- 1 Introduction -- 2 Background and Related Work -- 3 Ensemble of Hidden Markov Models -- 3.1 Hidden Markov Model Structure -- 3.2 Event Sequence Clustering -- 3.3 Ensemble Creation and Prediction Methods -- 4 Data Set -- 5 Evaluation -- 6 Conclusion -- References -- On-device Application.Group Trip Planning Queries on Road Networks Using Geo-Tagged Textual Information -- 1 Introduction -- 2 Background and Problem Definition -- 3 Proposed Solution Methodologies -- 3.1 Brute Force Approach with Precomputed Distance -- 3.2 Group Nearest Neighbor (GNN) to Compute GTP Queries -- 3.3 Using R-trees to Compute GTP Queries -- 4 Experimental Evaluation -- 5 Conclusion and Future Direction -- References -- Deep Reinforcement Learning Based Iterative Participant Selection Method for Industrial IoT Big Data Mobile Crowdsourcing -- 1 Introduction -- 2 System Model and Deep Neural Network -- 2.1 System Model -- 2.2 Participant Selection Problem -- 3 System Framework and Deep Q-Network -- 3.1 System Framework -- 3.2 Deep Q-Network -- 4 Evaluation -- 4.1 Dataset and Experiment Setups -- 4.2 BaseLine Method -- 4.3 The Performance Evaluation and Comparison -- 5 Related Work -- 6 Conclusion -- References -- Know Your Limits: Machine Learning with Rejection for Vehicle Engineering -- 1 Introduction -- 2 Background -- 2.1 Vehicle Engineering: The Need for Usage Profiling -- 2.2 Related Work on Machine Learning with a Reject Option -- 3 Usage Profiling: Data Science Challenge -- 4 Our Approach for Vehicle Usage Profiling -- 4.1 Predictor h -- 4.2 Rejector r -- 4.3 Combined Model h' -- 5 Use-Case: Road-Roughness Analysis -- 5.1 Data Collection and Preprocessing -- 5.2 Experimental Methodology -- 5.3 Results -- 5.4 Discussion and Lessons Learned -- 6 Conclusion -- References -- Towards Generalizable Machinery Prognostics -- 1 Introduction -- 2 Background and Related Work -- 3 A Generic Approach to Incipient Failure Prediction -- 4 Learning the Prediction Horizon -- 5 Ablation Study -- 6 Results and Discussion -- 7 Conclusion -- 8 Future Work -- References -- A Trust Management-Based Route Planning Scheme in LBS Network -- 1 Introduction -- 2 Related Work.3 Framework of System.This book constitutes the proceedings of the 17th International Conference on Advanced Data Mining and Applications, ADMA 2021, held in Sydney, Australia in February 2022.* The 26 full papers presented together with 35 short papers were carefully reviewed and selected from 116 submissions. The papers were organized in topical sections in Part I, including: Healthcare, Education, Web Application and On-device application. * The conference was originally planned for December 2021, but was postponed to 2022. .Lecture Notes in Artificial Intelligence,2945-9141 ;13087Artificial intelligenceImage processingDigital techniquesComputer visionComputer engineeringComputer networksSocial sciencesData processingArtificial IntelligenceComputer Imaging, Vision, Pattern Recognition and GraphicsComputer Engineering and NetworksComputer Engineering and NetworksComputer Application in Social and Behavioral SciencesArtificial intelligence.Image processingDigital techniques.Computer vision.Computer engineering.Computer networks.Social sciencesData processing.Artificial Intelligence.Computer Imaging, Vision, Pattern Recognition and Graphics.Computer Engineering and Networks.Computer Engineering and Networks.Computer Application in Social and Behavioral Sciences.006.312006.312Li BohanMiAaPQMiAaPQMiAaPQBOOK9910522573703321Advanced Data Mining and Applications2982700UNINA