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Adaptive Clustering-Based Collusion Detection in Crowdsourcing 1

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19th International Conference on Advanced Intelligent Computing Technology and Applications (ICIC)

作者： Xu, Ruoyu Li, Gaoxiang Jin, Wei Chen, Austin Sheng, Victor S. Texas Tech Univ Comp Sci Dept Lubbock TX 79409 USA Univ North Texas Comp Sci & Engn Dept Denton TX 76203 USA Lubbock High Sch 2004 19th St Lubbock TX USA

ISBN: (数字)9789819947522

ISBN: (纸本)9789819947515;9789819947522

Crowdsourcing is a popular approach for crowd workers collaborating to have tasks done. However, some workers communicate with each other and share answers during the crowdsourcing process. This is referred to as "collusion". Copying from others and submitting repeated answers are detrimental to the quality of the tasks. Existing studies on collusion detection focus on ground truth problems (e.g., labeling tasks) and require a fixed threshold to be set in advance. In this paper, we aim to detect collusion behavior of workers in an adaptive way, and propose an Adaptive Clustering Based Collusion Detection approach (ACCD) for a broad range of task types and data types solved via crowdsourcing (e.g., continuous rating with or without distributions). Extensive experiments on both real-world and synthetic datasets show the superiority of ACCD over state-of-the-art approaches.

关键词： Crowdsourcing Collusion detection Colluders Collusion

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Hierarchical Graph Contrastive Learning

Hierarchical Graph Contrastive Learning

引用

European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD)

作者： Yan, Hao Wang, Senzhang Yin, Jun Li, Chaozhuo Zhu, Junxing Wang, Jianxin Cent South Univ Changsha Peoples R China Beihang Univ Beijing Peoples R China Natl Univ Def Technol Changsha Peoples R China

ISBN: (纸本)9783031434143;9783031434150

Unsupervised graph representation learning with GNNs is critically important due to the difficulty of obtaining graph labels in many real applications. Graph contrastive learning (GCL), a recently popular method for unsupervised learning on graphs, has achieved great success on many tasks. However, existing graph-level GCL models generally focus on comparing the graph-level representation or node-level representation. The hierarchical structure property, which is ubiquitous in many real world graphs such as social networks and molecular graphs, is largely ignored. To bridge this gap, this paper proposes a novel hierarchical graph contrastive learning model named HIGCL. HIGCL uses a multi-layered architecture and contains two contrastive objectives, inner-contrasting and hierarchical-contrasting. The former conducts inner-scale contrastive learning to learn the flat structural features in each layer, while the latter focuses on performing cross-scale contrastive learning to capture the hierarchical features across layers. Extensive experiments are conducted on graph-level tasks to show the effectiveness of the proposed method.

关键词： Graph Contrastive Learning Graph Neural Network Unsupervised Learning

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Description Quivers for Compact Representation of Concept Lattices and Ensembles of Decision Trees 17th

Description Quivers for Compact Representation of Concept La...

引用

17th International Conference on Formal Concept Analysis

作者： Dudyrev, Egor Kuznetsov, Sergei O. Napoli, Amedeo HSE Univ Moscow Russia Univ Lorraine CNRS INRIA LORIA F-54000 Nancy France

ISBN: (纸本)9783031359484;9783031359491

In this paper we introduce and study description quivers as compact representations of concept lattices and respective ensembles of decision trees. Formally, description quivers are directed multigraphs where vertices represent concept intents and (multiple) edges represent generators of intents. We study some properties of description quivers and shed light on their use for describing state-of-the-art symbolic machine learning models based on decision trees. We also argue that a concept lattice can be considered as a cornerstone in constructing an efficient machine learning model. We show that the proposed description quivers allow us to fuse decision trees just as we can sum linear regressions, while proposing a way to select the most important rules in decision models, just as we can select the most important coefficients in regressions.

关键词： Formal Concept Analysis Supervised Machine Learning Explainable artificial intelligence

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Topic-Selective Graph Network for Topic-Focused Summarization 1

引用

27th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD)

作者： Shi, Zesheng Zhou, Yucheng Nankai Univ Tianjin Peoples R China Univ Technol Sydney Ultimo Australia

ISBN: (数字)9783031333835

ISBN: (纸本)9783031333828;9783031333835

Due to the success of the pre-trained language model (PLM), existing PLM-based summarization models show their powerful generative capability. However, these models are trained on general-purpose summarization datasets, leading to generated summaries failing to satisfy the needs of different readers. To generate summaries with topics, many efforts have been made on topic-focused summarization. However, these works generate a summary only guided by a prompt comprising topic words. Despite their success, these methods still ignore the disturbance of sentences with non-relevant topics and only conduct cross-interaction between tokens by attention module. To address this issue, we propose a topic-arc recognition objective and topic-selective graph network. First, the topic-arc recognition objective is used to model training, which endows the capability to discriminate topics for the model. Moreover, the topic-selective graph network can conduct topic-guided cross-interaction on sentences based on the results of topic-arc recognition. In the experiments, we conduct extensive evaluations on NEWTS and COVIDET datasets. Results show that our methods achieve state-of-the-art performance.

关键词： Text Summarization Topic Model Graph Neural Network

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Secure Forward Aggregation for Vertical Federated Neural Networks 1

引用

1st International Workshop on Trustworthy Federated Learning (FL)

作者： Cai, Shuowei Chai, Di Yang, Liu Zhang, Junxue Jin, Yilun Wang, Leye Guo, Kun Chen, Kai Hong Kong Univ Sci & Technol iSING Lab Kowloon Hong Kong Peoples R China Clustar Nanshan Peoples R China Peking Univ Beijing Peoples R China Fuzhou Univ Fuzhou Peoples R China

ISBN: (数字)9783031289965

ISBN: (纸本)9783031289958;9783031289965

Vertical federated learning (VFL) is attracting much attention because it enables cross-silo data cooperation in a privacy-preserving manner. While most research works in VFL focus on linear and tree models, deep models (e.g., neural networks) are not well studied in VFL. In this work, we focus on SplitNN, a well-known neural network framework in VFL, and identify a trade-off between data security and model performance in SplitNN. Briefly, SplitNN trains the model by exchanging gradients and transformed data. On the one hand, SplitNN suffers from the loss of model performance since multiply parties jointly train the model using transformed data instead of raw data, and a large amount of low-level feature information is discarded. On the other hand, a naive solution of increasing the model performance through aggregating at lower layers in SplitNN (i.e., the data is less transformed and more low-level feature is preserved) makes raw data vulnerable to inference attacks. To mitigate the above trade-off, we propose a new neural network protocol in VFL called Security Forward Aggregation (SFA). It changes the way of aggregating the transformed data and adopts removable masks to protect the raw data. Experiment results show that networks with SFA achieve both data security and high model performance.

关键词： Vertical federated learning Split Neural network Secure Forward aggregation

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Principal Components Analysis Based Imputation for Logistic Regression 36th

Principal Components Analysis Based Imputation for Logistic ...

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36th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems (IEA/AIE)

作者： Nguyen, Thuong H. T. Bao Le Phuc Nguyen Nguyen, Linh G. H. Tran Thu Thu Nguyen Nguyen, Binh T. AISIA Res Lab Ho Chi Minh City Vietnam Univ Sci Ho Chi Minh City Vietnam Vietnam Natl Univ Ho Chi Minh City Vietnam Simula Metropolitan Oslo Norway

ISBN: (纸本)9783031368189;9783031368196

The field of AI and machine learning is constantly evolving, and as the size of data continues to grow, so does the need for accurate and efficient methods of data processing. However, the data is not always perfect, and missing data is becoming common and occurs more frequently. Therefore, imputation techniques, aside from precision, needed to be scalable. For that reason, we examine the performance of Principle Components Analysis Imputation (PCAI) [9], an imputation speeding up framework, for logistic regression. Note that the coefficients of a logistic regression model are usually used for interpretation. Therefore, in addition to examining the improvement in the speed of PCAI, we examine how the coefficients of fitted logistic regression models change when using this imputation speeding-up mechanism. To demonstrate the efficiency of the mentioned method, the model's performance is compared against frequently used imputation methods on three popular datasets: Fashion MNIST, Gene, and Parkinson. And achieves lower time and better accuracy in most experiments.

关键词： Missing data Data imputation Logistic Regression Principle Components Analysis

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Conversation-Driven Refinement of Knowledge Graphs: True Active Learning with Humans in the Chatbot Application Loop 4th

Conversation-Driven Refinement of Knowledge Graphs: True Act...

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4th International Conference on artificial intelligence in HCI (AI-HCI) Held as Part of the 25th International Conference on Human-Computer Interaction (HCII)

作者： Buhl, Dominik Szafarski, Daniel Welz, Laslo Lanquillon, Carsten Heilbronn Univ Appl Sci D-74076 Heilbronn Germany

ISBN: (纸本)9783031358937;9783031358944

The value of knowledge-grounded cognitive agents is often limited by a lack of high-quality knowledge. Although advances in natural language processing have substantially improved knowledge-extraction capabilities, the demand for different types of knowledge fragments and the potential for error in extraction processes has created a next generation of the knowledge acquisition bottleneck. Instead of waiting for a perfect knowledge base, we propose a design for an agent that is aware of these issues and that actively seeks feedback from users in conversations to improve its knowledge and extraction processes. This approach allows for imperfection and incompleteness, and for the agent to improve over time. Any feedback provided by the users in this conversational application loop is used to not only refine the underlying knowledge graph, but also to improve the knowledge extraction processes. Eventually, the agent's knowledge and the quality of its answers rises while talking to its users.

关键词： Conversational AI Human-in-the-Loop AI Knowledge-Grounded Cognitive Assistants Knowledge Graph Refinement

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Improving Stroke Trace Classification Explainability Through Counterexamples 21st

Improving Stroke Trace Classification Explainability Through...

引用

21st International Conference on artificial intelligence in Medicine (AIME)

作者： Leonardi, Giorgio Montani, Stefania Striani, Manuel Univ Piemonte Orientale Lab Integrato Intelligenza Artificiale & Informat Azienda Osped Ss Antonio & Biagio & Cesare Arrigo Alessandria Italy

ISBN: (纸本)9783031343438;9783031343445

Deep learning process trace classification is proving powerful in several application domains, including medical ones;however, classification results are typically not explainable, an issue which is particularly relevant in medicine. In our recent work we tackled this problem, by proposing trace saliency maps, a novel tool able to highlight what trace activities are particularly significant for the classification task. A trace saliency map is built by generating artificial perturbations of the trace at hand that are classified in the same class as the original one, called examples. In this paper, we investigate the role of counterexamples (i.e., artificial perturbations that are classified in a different class with respect to the original trace) in refining trace saliency map information, thus improving explainability. We test the approach in the domain of stroke.

关键词： Deep learning

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Is It Possible to Measure Real-Time VR Motion Sickness with EDA Wavelet Features? 4th

Is It Possible to Measure Real-Time VR Motion Sickness with ...

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4th International Conference on artificial intelligence in HCI (AI-HCI) Held as Part of the 25th International Conference on Human-Computer Interaction (HCII)

作者： Seong, Sujin Park, Jaehyun Incheon Univ Dept Ind Management Engn Incheon South Korea

ISBN: (纸本)9783031358906;9783031358913

Virtual reality (VR) has emerged as a more commercial mainstream, and the number of users complaining of VR motion sickness is increasing rapidly. As a result, many methods, physiological indicators: electrogastrography (EGG), heart rate (HR), temperature, electrodermal activity (EDA), subjective measurement methods, simulator sickness questionnaire (SSQ), virtual reality symptom questionnaire (VRSQ), fast motion sickness score (FMS), have been studied to quantify VR motion sickness. Consequently, this study attempted to track motion sickness using EDA (sweat). The study group consisted of ten female and ten male participants who have access to VR (average age: 21.2 years;standard deviation: 2.07 years) with no physical or visual health problems. The VR environment was designed by wearing a head-mounted display (HMD) through a simple operation method. Subjects wore an EDA sensor (Empatica E4) in a VR environment and were asked to respond to SSQ, VRSQ, and FMS questionnaires. For all items of FMS and SSQ (nausea, oculomotor, disorientation, and total) and Total of VRSQ, the coefficient of determination was high in the order of 1(st) trial > 2nd trial > Rest. Overall, the motion sickness score was rated the lowest during the rest period. The coefficient of determination of EDA (sweat) in rest was low in all except Disorientation in VRSQ. And VR motion sickness may have different symptoms complaining to the user depending on the VR environment. Hence, this study suggests that the EDA (sweat) signal may be suitable depending on the user's immersion interval and the symptoms.

关键词： HMD VR motion sickness EDA VRSQ SSQ FMS

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Comparison of Geospatial Trajectory Clustering and Feature Trajectory Clustering for Public Transportation Trip Data 18th

Comparison of Geospatial Trajectory Clustering and Feature T...

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18th International Conference on Hybrid artificial intelligence Systems

作者： Cogollos Adrian, Hector Baruque Zanon, Bruno Porras Alfonso, Santiago Dolezel, Petr Univ Burgos Dept Digitalizac Grp Inteligencia Computac Aplicada GICAP Escuela Politecn Super Av Cantabria S-N Burgos 09006 Spain Univ Burgos Fac Ciencias Econ & Empresariales Dept Econ Aplicada Metaheurist GRINUBUMET Pza Infanta Dl Elena S-N Burgos 09001 Spain Univ Pardubice Fac Elect Engn & Informat Studentska 95 Pardubice 53210 Czech Republic

ISBN: (纸本)9783031407246;9783031407253

One of the techniques for the analysis of travel patterns on a public transport network is the clustering of the users movements, in order to identify movement patterns. This paper analyses and compares two different methodologies for public transport trajectory clustering: feature clustering and geospatial trajectory clustering. The results of clustering trip features, such as origin, destination, or distance, are compared against the clustering of travelled trajectories by their geospatial characteristics. Algorithms based on density and hierarchical clustering are compared for both methodologies. In geospatial clustering, different metrics to measure distances between trajectories are included in the comparison. Results are evaluated by analysing their quality through the silhouette coefficient and graphical representations of the clusters on the map. The results show that geospatial trajectory clustering offers better quality than feature trajectory clustering. Also, in the case of long and complete trajectories, density clustering using edit distance with real penalty distance outperforms other combinations.

关键词： HDBSCAN Agglomerative Clustering Dynamic Time Warping distance Edit distance with Real Penalty

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