LEADER 03849nam 2200649 450 001 9910451978603321 005 20200520144314.0 010 $a1-4426-9608-7 024 7 $a10.3138/9781442696082 035 $a(CKB)2550000000100903 035 $a(OCoLC)794619881 035 $a(CaPaEBR)ebrary10560438 035 $a(SSID)ssj0000716973 035 $a(PQKBManifestationID)11479564 035 $a(PQKBTitleCode)TC0000716973 035 $a(PQKBWorkID)10739699 035 $a(PQKB)10356444 035 $a(CEL)438775 035 $a(CaBNVSL)slc00228883 035 $a(MiAaPQ)EBC3280030 035 $a(MiAaPQ)EBC4672867 035 $a(DE-B1597)479170 035 $a(OCoLC)987927120 035 $a(DE-B1597)9781442696082 035 $a(Au-PeEL)EBL4672867 035 $a(CaPaEBR)ebr11258518 035 $a(OCoLC)958559368 035 $a(EXLCZ)992550000000100903 100 $a20160915h20112011 uy 0 101 0 $aeng 135 $aurcn||||||a|| 181 $ctxt 182 $cc 183 $acr 200 10$aFor humanity's sake $ethe Bildungsroman in Russian culture /$fLina Steiner 210 1$aToronto, [Canada] ;$aBuffalo, [New York] ;$aLondon, [England] :$cUniversity of Toronto Press,$d2011. 210 4$d©2011 215 $a1 online resource (295 p.) 311 $a1-4426-4343-9 320 $aIncludes bibliographical references and index. 327 $tFrontmatter -- $tContents -- $tAcknowledgments -- $tIntroduction -- $tPART I. Culture ( Obrazovanie, Bildung ) and the Bildungsroman on Russian Soil -- $t1. Russian Literature from the National Awakening of the 1800s to the Rise of Pochvennichestvo in the 1850s -- $t2. Apollon Grigor'ev's Theory of Russian Culture -- $t3. Yurii Lotman's Idea of the 'Semiosphere ' -- $t4. The Semiospheric Novel and the Broadening of Cultural Self-Consciousness -- $tPart II. Nineteenth-Century Russian Novels of Emergence -- $t5. Pushkin's Quest for National Culture: The Captain's Daughter as a Russian Bildungsroman -- $t6. Educating Russia, Building Humanity: Tolstoy's War and Peace -- $t7. Dostoevsky on Individual Reform and National Reconciliation: The Adolescent -- $tConclusion -- $tAppendix -- $tNotes -- $tBibliography -- $tIndex 330 $aFor Humanity's Sake is the first study in English to trace the genealogy of the classic Russian novel, from Pushkin to Tolstoy to Dostoevsky. Lina Steiner demonstrates how these writers' shared concern for individual and national education played a major role in forging a Russian cultural identity.For Humanity's Sake highlights the role of the critic Apollon Grigor'ev, who was first to formulate the difference between West European and Russian conceptions of national education or Bildung - which he attributed to Russia's special sociopolitical conditions, geographic breadth, and cultural heterogeneity. Steiner also shows how Grigor'ev's cultural vision served as the catalyst for the creative explosion that produced Russia's most famous novels of the 1860s and 1870s.Positing the classic Russian novel as an inheritor of the Enlightenment's key values - including humanity, self-perfection, and cross-cultural communication - For Humanity's Sake offers a unique view of Russian intellectual history and literature. 606 $aRussian fiction$y19th century$xHistory and criticism 606 $aNational characteristics, Russian, in literature 608 $aElectronic books. 615 0$aRussian fiction$xHistory and criticism. 615 0$aNational characteristics, Russian, in literature. 676 $a891.73/309 700 $aSteiner$b Lina$f1973-$0890498 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910451978603321 996 $aFor humanity's sake$91989193 997 $aUNINA 999 $p$54.60$u07/24/2015$5Eng LEADER 04961nam 2200577 450 001 9910463986203321 005 20200520144314.0 010 $a3-8382-6114-3 035 $a(CKB)2670000000547943 035 $a(EBL)3029491 035 $a(SSID)ssj0001468907 035 $a(PQKBManifestationID)11857375 035 $a(PQKBTitleCode)TC0001468907 035 $a(PQKBWorkID)11525449 035 $a(PQKB)10418123 035 $a(MiAaPQ)EBC2056684 035 $a(MiAaPQ)EBC5782049 035 $a(Au-PeEL)EBL5782049 035 $a(OCoLC)880723462 035 $a(EXLCZ)992670000000547943 100 $a20190619d2012 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aEssays on algorithmic trading /$fMarkus Gsell 210 1$aStuttgart :$cIbidem Verlag,$d2012. 215 $a1 online resource (226 p.) 300 $aDescription based upon print version of record. 311 $a3-8382-0114-0 320 $aIncludes bibliographical references at the end of each chapters. 327 $a""Table of Contents""; ""1 Introduction""; ""1.1 Motivation and objective of the thesis""; ""1.2 Structure of the thesis""; ""2 Research Context: Securities Trading""; ""2.1 Value chain""; ""2.2 The role of Algorithmic Trading""; ""2.2.1 Purposes and users""; ""2.2.2 Benchmarks and strategies""; ""2.2.3 Stages of development""; ""2.2.4 Implications for market operators""; ""3 Research Approach and Methodology""; ""3.1 Quantitative Survey""; ""3.2 Literature Review""; ""3.3 Simulation of Financial Markets""; ""3.4 Empirical Analysis""; ""4 Main Results"" 327 $a""4.1 Paper 1 : Investigating the adoption decision""""4.2 Paper 2 : Theoretically assessing structural behavioral differences""; ""4.3 Paper 3 : Assessing the impact on the market outcome by simulation""; ""4.4 Paper 4: Empirically assessing the impact on trading behavior""; ""5 Contribution to theory and practical implications""; ""5.1 Contribution to theory""; ""5.2 Practical implications""; ""6 Limitations and potential further research""; ""6.1 Limitations""; ""6.2 Future Research""; ""References"" 327 $a""Paper 1: Technological Innovations in Securities Trading: The Adoption of Algorithmic Trading """"1 Introduction""; ""2 Related work""; ""3 Methodology""; ""4 Research Model""; ""4.1 Usage""; ""4.2 Intention to use""; ""4.3 Performance Expectancy""; ""4.4 Effort Expectancy""; ""4.5 Task-Technology Fit""; ""4.6 Technology Expertise""; ""5 Results""; ""5.1 Quality criteria of the measurement model""; ""5.1.1 Reflective constructs""; ""5.1.2 Formative construct""; ""5.2 Quality criteria of the structural model""; ""6 Conclusion""; ""References"" 327 $a""Paper 2: Is Algorithmic Trading distinctively different?""""1 Introduction""; ""2 Stylized traders in the literature""; ""3 What are algorithmic trading models doing?""; ""4 Why algorithmic trading models are different""; ""4.1 Are algorithmic trading models informed traders?""; ""4.2 Are algorithmic trading models momentum traders?""; ""4.3 Are algorithmic trading models noise traders?""; ""4.4 What are algorithmic trading models after all?""; ""5 Conclusion""; ""References""; ""Paper 3: Assessing the impact of Algorithmic Trading on markets : A simulation approach "" 327 $a""1 Introduction""""2 Related work""; ""2.1 Algorithmic Trading""; ""2.2 Simulation of financial markets""; ""3 The simulation model""; ""3.1 Behavior of Traders""; ""3.2 Parameterizatio n""; ""4 Results obtained""; ""5 Conclusion and Outlook""; ""References""; ""Paper 4: The Behavior of Algorithmic Traders in Equity Markets - Empirical Evidence from Xetra ""; ""1 Introduction""; ""2 Related Work""; ""3 The Xetra Trading System""; ""3.1 Continuous Trading""; ""3.2 Call Auctions""; ""3.3 Trading Schedule""; ""3.4 Dataset and Methodology""; ""4 Results""; ""4.1 Continuous Trading"" 327 $a""4.2 Call Auctions"" 330 $aTechnological innovations are altering the traditional value chain in securities trading. Hitherto the order handling, i.e. the appropriate implementation of a general trading decision into particular orders, has been a core competence of brokers. Labeled as Algorithmic Trading, the automation of this task recently found its way both into the brokers' portfolio of service offerings as well as to their customers' trading desks. The software performing the order handling thereby constantly monitors the market(s) in real-time and further evaluates historical data to dynamically determine appropri 606 $aProgram trading (Securities) 608 $aElectronic books. 615 0$aProgram trading (Securities) 676 $a332.60285 700 $aGsell$b Markus$0974749 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910463986203321 996 $aEssays on algorithmic trading$92219587 997 $aUNINA LEADER 11872nam 22005413 450 001 996490360703316 005 20240110173616.0 010 $a3-031-16014-2 035 $a(MiAaPQ)EBC7098187 035 $a(Au-PeEL)EBL7098187 035 $a(CKB)24865886600041 035 $a(BIP)085277696 035 $a(PPN)264952936 035 $a(EXLCZ)9924865886600041 100 $a20220923d2022 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aComputational Collective Intelligence $e14th International Conference, ICCCI 2022, Hammamet, Tunisia, September 28-30, 2022, Proceedings 210 1$aCham :$cSpringer International Publishing AG,$d2022. 210 4$d©2022. 215 $a1 online resource (863 pages) 225 1 $aLecture Notes in Computer Science ;$vv.13501 311 08$aPrint version: Nguyen, Ngoc Thanh Computational Collective Intelligence Cham : Springer International Publishing AG,c2022 9783031160134 327 $aIntro -- Preface -- Organization -- Contents -- Collective Intelligence and Collective Decision-Making -- Inferring Event Causality in Films via Common Knowledge Corpora -- 1 Introduction -- 1.1 Background -- 1.2 Research Objectives and Contributions -- 2 Background and Related Work -- 2.1 Event Causality in Films -- 2.2 Computational Methods -- 3 Event Causality Inference System -- 4 Evaluation Experiments -- 5 Discussion -- References -- Cooperation Game on Communication Multigraph with Fuzzy Parameters -- 1 Introduction -- 2 Preliminaries -- 3 Concept of the FUZZY Value of the Sub-additive Cooperative Game on Communication Multigraph with Fuzzy Capacities -- 4 Concept of the FUZZY Value of the Sub-additive Cooperative Game on Communication Multigraph with Fuzzy Capacities and Fuzzy Goal -- 5 Conclusions and Future Works -- References -- Impact of Similarity Measure on the Quality of Communities Detected in Social Network by Hierarchical Clustering -- 1 Introduction -- 2 Organizational Social Networks -- 3 Community Detection Problem -- 4 Hierarchical Clustering Approach to the Community Detection -- 4.1 Hierarchical Clustering -- 4.2 Similarity Measures in Social Networks -- 4.3 Hierarchical Clustering Approach to the Community Detection in Organizational Social Network -- 5 Computational Experiment -- 6 Conclusions -- References -- An Approach to Modeling a Real-Time Updated Environment Based on Messages from Agents -- 1 Introduction -- 2 Related Work -- 3 Proposed Approach -- 3.1 Environment Model Assumptions -- 3.2 Environment Model -- 4 Case Study: AriaDNA Life System -- 4.1 Space and Dimensions -- 4.2 Entity Types -- 4.3 Messages -- 4.4 Interactions -- 5 Summary -- References -- Updating the Result Ontology Integration at the Concept Level in the Event of the Evolution of Their Components -- 1 Introduction -- 2 Related Works. 327 $a3 Basic Notions -- 4 Methods of Updating Integrated Ontologies -- 4.1 Updating Results of Ontology Integration After Concept Removal -- 4.2 Updating Results of Ontology Integration After Adding New Concepts -- 4.3 Updating Results of Ontology Integration After Concepts Modification -- 5 Experimental Evaluation -- 6 Summary -- References -- Integrating Machine Learning into Learner Profiling for Adaptive and Gamified Learning System -- 1 Introduction -- 2 Proposed Method -- 2.1 Functional Architecture -- 3 Experimentation and Results -- 3.1 Used Method -- 3.2 Results -- 4 Conclusion -- References -- Deep Learning Techniques -- A New Deep Learning Fusion Approach for Emotion Recognition Based on Face and Text -- 1 Introduction -- 2 Facial-Textual Emotion Recognition (FTxER) Architecture -- 3 Results and Discussion -- 4 Conclusion and Future Work -- References -- Cycle Route Signs Detection Using Deep Learning -- 1 Introduction -- 2 Problem Definition -- 2.1 Car License Plate Recognition and Reading -- 2.2 Real-time Traffic Sign Detection Using CNN -- 3 Solution -- 3.1 YOLO -- 3.2 OCR -- 4 Implementation -- 4.1 Technologies Used -- 4.2 Data Training -- 4.3 Applications -- 5 Testing of Developed Application -- 6 Conclusion -- References -- Data Augmentation for Morphological Analysis of Histopathological Images Using Deep Learning -- 1 Introduction -- 2 Materials and Methods -- 2.1 Materials -- 2.2 Methods -- 2.3 Morphing Concept -- 3 Results -- 4 Discussion -- 5 Conclusions -- References -- An End-to-End Framework for Evaluating Explainable Deep Models: Application to Historical Document Image Segmentation -- 1 Introduction -- 2 Related Work -- 3 Proposed Framework -- 4 Experiments and Results -- 4.1 Experimental Corpus -- 4.2 Implementation Details -- 4.3 Results -- 5 Conclusions and Further Work -- References. 327 $aDeep Convolutional Neural Network for Arabic Speech Recognition -- Abstract -- 1 Introduction -- 2 Related Works -- 3 Proposed System -- 3.1 Convolutional Neural Network (CNN) -- 3.2 LSTM -- 4 Experimental Results and Discussion -- 4.1 Datasets and Input Features -- 4.2 Results and Discussion -- 5 Conclusion and Future Works -- References -- RingNet: Geometric Deep Representation Learning for 3D Multi-domain Protein Shape Retrieval -- 1 Introduction -- 2 Related Works -- 3 RingNet Neural Network -- 3.1 Descriptors Calculation -- 3.2 RingNet Layer -- 3.3 Fusion Layer -- 4 Experimental Results -- 4.1 Dataset and Metrics -- 4.2 3D Protein Shape Classification -- 4.3 3D Protein Shape Retrieval -- 5 Conclusion -- References -- Patch Selection for Melanoma Classification -- 1 Introduction -- 2 Materials and Methods -- 2.1 Dataset Description and Preprocessing -- 2.2 Entropy -- 2.3 Mean Exhaustive Minimum Distance (MEMD) Criterion -- 2.4 Network Architecture -- 3 Experimental Results -- 4 Conclusion -- References -- Natural Language Processing -- Multi-model Analysis of Language-Agnostic Sentiment Classification on MultiEmo Data -- 1 Introduction -- 2 Related Work -- 3 Experimental Setup -- 3.1 Pipeline -- 3.2 MultiEmo Dataset -- 3.3 Scenarios -- 4 Experimental Results -- 5 Conclusion and Future Work -- References -- Sentiment Analysis of Tunisian Users on Social Networks: Overcoming the Challenge of Multilingual Comments in the Tunisian Dialect -- Abstract -- 1 Introduction -- 2 Specificities of Tunisian Dialect Sentiment Analysis -- 2.1 The Use of Multilingual Vocabulary -- 2.2 The Phenomenon of Linguistic Code-switching -- 3 Background and Related Work -- 3.1 Arabic Dialects Sentiment Analysis -- 3.2 Tunisian Dialect Sentiment Analysis -- 3.3 Discussion -- 4 Proposed Methodology -- 4.1 Data Collection -- 4.2 Data Preprocessing -- 4.3 Model Training. 327 $a5 Experiments and Results -- 5.1 Baseline -- 5.2 Dataset -- 5.3 Experimental Results -- 5.4 Discussion -- 6 Conclusion -- Acknowledgment -- References -- Non-Contextual vs Contextual Word Embeddings in Multiword Expressions Detection -- 1 Introduction -- 2 Related Work -- 3 Dataset -- 3.1 Non-Contextual Dataset -- 3.2 Contextual Dataset -- 4 Methods for Multiword Expression Detection -- 5 Experiments -- 6 Results -- 7 Discussion -- 8 Conclusions and Future Work -- References -- Context-free Transformer-based Generative Lemmatiser for Polish -- 1 Introduction -- 2 Related Works -- 2.1 Contextual Lemmatisation -- 2.2 Context-free Lemmatisation -- 2.3 Lemmatisation for the Polish Language -- 3 Problem Description -- 4 Architecture -- 4.1 Transformer-based Lemmatiser -- 5 Experiments -- 6 Conclusions -- References -- French Object Clitics in the Interlanguage: A Linguistic Description and a Formal Analysis in the ACCG Framework -- 1 Introduction -- 2 Interlanguage -- 2.1 The Concept -- 2.2 French Object Clitics in the Interlanguage -- 3 Applicative Combinatory Categorical Grammar -- 4 Object Clitics by Means of ACCG -- 5 Conclusion -- References -- Contradiction Detection Approach Based on Semantic Relations and Evidence of Uncertainty -- 1 Introduction -- 2 Related Work -- 3 Motivation and Proposed Model -- 3.1 Motivation -- 3.2 Description of the Approach -- 4 Semantic Construction of a Sentence -- 4.1 Pretreatment -- 4.2 Concept Extraction -- 4.3 Extraction of Uncertainty Expressions -- 4.4 Calculation of Degree of Uncertainty -- 4.5 Extraction of Binary Relations -- 5 Contradiction Assessment -- 5.1 Detection of Opposing Information -- 5.2 Detection of Contradiction -- 6 Experimental Evaluation -- 6.1 Test Environment -- 6.2 Experiments and Results -- 7 Conclusion -- References -- C-DESERT Score for Arabic Text Summary Evaluation -- Abstract. 327 $a1 Introduction -- 2 Related works -- 3 Proposed method -- 3.1 Document Embedding Model -- 3.2 Building the Doc2Vec Model -- 3.3 Features -- 3.4 Combination Scheme -- 4 Experiments -- 4.1 Data Sets -- 4.2 Result -- 5 Conclusion -- References -- Data Mining and Machine Learning -- Proficiency Level Classification of Foreign Language Learners Using Machine Learning Algorithms and Multilingual Models -- 1 Introduction -- 2 Related Works -- 3 Experiments -- 3.1 Datasets -- 3.2 Features -- 3.3 Methods -- 3.4 Tool Implementation -- 3.5 Experiment Scenarios -- 4 Results and Analysis -- 4.1 Experiment One -- 4.2 Experiment Two -- 4.3 Experiment Three -- 5 Conclusions -- References -- Simulation System for Producing Real World Dataset to Predict the Covid-19 Contamination Process? -- 1 Introduction -- 2 Contact Tracing -- 2.1 GPS Accuracy -- 2.2 Bluetooth LE Distance Measurement -- 2.3 WiFi -- 2.4 Zigbee -- 2.5 Comparing Technologies -- 3 Dataset Collection -- 3.1 Simulating Real World Scene -- 4 Processing Data -- 4.1 Graph Modeling -- 4.2 Retrieving Basic Statistics -- 4.3 Graph Data Science Algorithms -- 5 Conclusion -- References -- Design and Compression Study for Convolutional Neural Networks Based on Evolutionary Optimization for Thoracic X-Ray Image Classification -- 1 Introduction -- 2 Related Work -- 3 Proposed Method -- 3.1 CNN Design -- 3.2 CNN Compression -- 4 Experiments -- 4.1 Expirement Configuration and Setup -- 4.2 Results and Discussion -- 5 Conclusion -- References -- TF-MOPNAS: Training-free Multi-objective Pruning-Based Neural Architecture Search -- 1 Introduction -- 2 Background and Related Works -- 2.1 Progressive Search Space Shrinking and Architecture Selection -- 2.2 Training-free Metrics in Pruning-based NAS -- 3 Training-free Multi-objective Pruning-based Neural Architecture Search -- 4 Experiments and Results. 327 $a4.1 Results on NAS-Bench-101. 330 8 $aThis book constitutes the refereed proceedings of the 14th International Conference on Computational Collective Intelligence, ICCCI 2022, held in Hammamet, Tunisia, in September 2022.The 56 full papers and 10 short papers were carefully reviewed and selected from 420 submissions. The papers are grouped in topical sections on collective intelligence and collective decision-making; deep learning techniques; natural language processing; data minning and machine learning; knowledge engineering and semantic web; computer vision techniques; social networks and intelligent systems; cybersecurity and internet of things; cooperative strategies for decision making and optimization; computational intelligence for digital content understanding; applications for industry 4.0. 410 0$aLecture Notes in Computer Science 610 $aScience 676 $a006.3 700 $aNguyen$b Ngoc Thanh$c(Computer scientist)$0601234 701 $aManolopoulos$b Yannis$01258245 701 $aChbeir$b Richard$0951994 701 $aKozierkiewicz$b Adrianna$01258246 701 $aTrawi?ski$b Bogdan$01258247 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996490360703316 996 $aComputational Collective Intelligence$92915919 997 $aUNISA