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Community Evolution Tracking Based on Core Node Extension and Edge Variation Discerning 17th

Community Evolution Tracking Based on Core Node Extension a...

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17th CCF conference on computer supported cooperative work and social computing, ChineseCSCW 2022

作者： Zhuang, Qifeng Yu, Zhiyong Guo, Kun College of Computer and Data Science Fuzhou University Fuzhou350108 China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou350108 China Key Laboratory of Spatial Data Mining and Information Sharing Ministry of Education Fuzhou350108 China

ISBN: (纸本)9789819923557

Communities exist anywhere in various complex networks, and community evolution tracking is one of the most well-liked areas of inquiry in the study of dynamic complex networks. Community evolution tracking has many applications in daily life, such as predicting social network behaviors or analyzing the spread of infectious illnesses. However, the majority of existing evolution tracking algorithms obtain community detection results before matching the community in tracking evolution events, making it difficult to trace the whole evolution of communities because of community matching errors. In addition, the majority of evolution tracking algorithms do not adequately account for the potential scenarios in community evolution, resulting in erroneous detection of evolution events. In this research, a community evolution tracking algorithm based on edge variation discerning and core node extension is proposed. First, we detect communities based on the core node extension strategy, which avoids the problem of community matching errors. Second, we track community evolution based on the edge variation discerning strategy, which fully considers various situations that may occur during the community evolution process. According to the outputs of our experiments, our system can effectively track the evolution of communities in synthetic dynamic networks. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Complex networks

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Reimagining Wearable-Based Digital Contact Tracing: Insights from Kenya and Côte d'Ivoire 25

Reimagining Wearable-Based Digital Contact Tracing: Insights...

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2025 CHI conference on Human Factors in computing Systems, CHI 2025

作者： Salehzadeh Niksirat, Kavous Munyendo, Collins W. Batista Leal Neto, Onicio Katya, Muswagha Kouassi, Cyrille Ochieng, Kevin Georgina, Angoa Olayo, Bernard Barras, Jean-Philippe Cattuto, Ciro Aviv, Adam J. Troncoso, Carmela Epfl VD Lausanne Switzerland University of Lausanne VD Lausanne Switzerland Department of Computer Science The George Washington University WashingtonDC United States University of Arizona TucsonAZ United States EssentialTech Centre Epfl VD Lausanne Switzerland Centre Suisse de Recherches Scientifiques Abidjan Ivory Coast Center for Public Health and Development Nairobi Kenya Infineon Technologies Neubiberg Germany Isi Foundation Turin Italy

ISBN: (纸本)9798400713941

While digital contact tracing has been extensively studied in Western contexts, its relevance and application in Africa remain largely unexplored. This study focuses on Kenya and Côte d'Ivoire to uncover user perceptions and inform the design of culturally resonant contact tracing technologies. Utilizing a wearable proximity sensor as a technology probe, we conducted field studies with healthcare workers and community members in rural areas through interviews (N = 19) and participatory design workshops (N = 72). Our findings identify critical barriers to adoption, including low awareness, widespread misconceptions, and social stigma. The study emphasizes the need for culturally sensitive and discreet wearables and advocates for awareness campaigns over mandates to foster adoption. Our work addresses the unique needs of Kenyan and Ivorian populations, offering vital design recommendations and insights to guide designers and policymakers in enhancing digital contact tracing adoption across Africa. © 2025 Copyright held by the owner/author(s).

关键词： HCI4D

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System-aware dynamic partitioning for batch and streaming workloads 21

System-aware dynamic partitioning for batch and streaming wo...

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14th IEEE/acm International conference on Utility and Cloud computing, UCC 2021

作者： Zvara, Zoltán Szabó, Péter G. N. Lóránt, Balázs Barnabás Benczúr, András A. Institute for Computer Science and Control Budapest Hungary University of Pécs Faculty of Engineering and Information Technology Pécs Hungary Eötvös University Budapest Doctoral School of Informatics Budapest Hungary

ISBN: (纸本)9781450385640

When processing data streams with highly skewed and nonstationary key distributions, we often observe overloaded partitions when the hash partitioning fails to balance data correctly. To avoid slow tasks that delay the completion of the whole stage of computation, it is necessary to apply adaptive, on-the-fly partitioning that continuously recomputes an optimal partitioner, given the observed key distribution. While such solutions exist for batch processing of static data sets and stateless stream processing, the task is difficult for long-running stateful streaming jobs where key distribution changes over time. Careful checkpointing and operator state migration is necessary to change the partitioning while the operation is running. Our key result is a lightweight on-the-fly Dynamic Repartitioning (DR) module for distributed data processing systems (DDPS), including Apache Spark and Flink, which improves the performance with negligible overhead. DR can adaptively repartition data during execution using our Key Isolator Partitioner (KIP). In our experiments with real workloads and power-law distributions, we reach a speedup of 1.5-6 for a variety of Spark and Flink jobs. © 2021 Owner/Author.

关键词： Batch data processing

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Horizontal Federated Traffic Speed Prediction Base on Secure Node Attribute Aggregation 17th

Horizontal Federated Traffic Speed Prediction Base on Secur...

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17th CCF conference on computer supported cooperative work and social computing, ChineseCSCW 2022

作者： Ye, Enjie Guo, Kun Guo, Wenzhong Chen, Dangrun Zhang, Zihan Li, Fuan Zheng, JiaChen Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350108 China College of Computer and Data Science/College of Software Fuzhou University Fuzhou350108 China Key Laboratory of Spatial Data Mining and Information Sharing Ministry of Education Fuzhou350108 China

ISBN: (纸本)9789819923847

Federated graph learning has been widely used in distributed graph machine learning tasks. The data distribution of existing graph-based federated Spatio-temporal prediction methods is mainly segmented by graph topology. However, in the real-world Spatio-temporal traffic speed prediction task, a location will have data from different devices belonging to different companies. A node may have multi-party information in the real-world distributed traffic speed prediction scenario. The difference in multi-party information leads to the information not being fully utilized. Moreover, the direct transmission of node embedding in the federated learning process may also risk privacy leaks. Using homomorphic encryption and other encryption methods will bring a high computational overhead. Therefore we propose a new distributed privacy-preserving traffic speed prediction algorithm, which uses secure node attribute aggregation strategy(SNAAS) to apply to the multi-party collaborative traffic speed prediction scenario when the graph topology structure is public. At the same time, secret sharing technology is used in SNAAS to protect the attribute matrix and reduce the overhead of secret computing. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Forecasting

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TransCasm: A Bilingual Corpus of Sarcastic Tweets 1

TransCasm: A Bilingual Corpus of Sarcastic Tweets

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1st workshop on Natural Language Processing for Political Sciences, PoliticalNLP 2022

作者： Simon, Desline Castilho, Sheila Lohar, Pintu Afli, Haithem ADAPT Centre Department of Computer Sciences Munster Technological University Cork Ireland ADAPT Centre School of Computing Dublin City University Dublin Ireland

ISBN: (纸本)9791095546887

Sarcasm is extensively used in User Generated Content (UGC) in order to express one's discontent, especially through blogs, forums, or social media such as Twitter. Several works have attempted to detect and analyse sarcasm in UGC. However, the lack of freely available corpora in this field makes the task even more difficult. In this work, we present "TransCasm"corpus, a parallel corpus of sarcastic tweets translated from English into French along with their non-sarcastic representations. To build the bilingual corpus of sarcasm, we select the "SIGN"corpus, a monolingual data set of sarcastic tweets and their non-sarcastic interpretations, created by (Peled and Reichart, 2017). We propose to define linguistic guidelines for developing "TransCasm"which is the first ever bilingual corpus of sarcastic tweets. In addition, we utilise "TransCasm"for building a binary sarcasm classifier in order to identify whether a tweet is sarcastic or not. Our experiment reveals that the sarcasm classifier achieves 61% accuracy on detecting sarcasm in tweets. "TransCasm"is now freely available online and is ready to be explored for further research. © 2022 European Language Resources Association (ELRA).

关键词： social networking (online)

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Globally Consistent Vertical Federated Graph Autoencoder for Privacy-Preserving Community Detection 17th

Globally Consistent Vertical Federated Graph Autoencoder for...

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17th CCF conference on computer supported cooperative work and social computing, ChineseCSCW 2022

作者： Fang, Yutong Huang, Qingqing Ye, Enjie Guo, Wenzhong Guo, Kun Chen, Xiaoqi Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350108 China College of Computer and Data Science/College of Software Fuzhou University Fuzhou350108 China Key Laboratory of Spatial Data Mining and Information Sharing Ministry of Education Fuzhou350108 China

ISBN: (纸本)9789819923557

Community detection is a trendy area in research on complex network analysis and has a wide range of real-world applications, like advertising. As people become increasingly concerned about privacy, protecting participants’ privacy in distributed community detection has become a new challenge. When applied to not identically and independently distributed (Non-IID) data, most federated graph algorithms suffer from the weight divergence problem caused by diversified training of local and global models, resulting in accuracy degradation. Furthermore, the privacy protection approaches based on anonymization, such as differential privacy (DP), and cryptography, such as homomorphic encryption (HE), incur accuracy loss and high time consumption, respectively. In this paper, we propose a globally consistent vertical federated graph autoencoder (GCVFGAE) algorithm, which builds a globally consistent model among the coordinator and all participants to solve the Non-IID graph data problem. As well, an attribute blinding strategy based on security aggregation is developed to protect the network privacy of each participant without losing accuracy. Both real-world and artificial networks’ experiments show that our algorithm reaches higher accuracy than the existing vertical federated graph neural networks (GNNs) and the simple distributed graph autoencoder without federated learning and detects communities identical to those found by the standard graph autoencoder (GAE). © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Population dynamics

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Multi-UAV cooperative Exploring for the Unknown Indoor Environment Based on Dynamic Target Tracking 17th

Multi-UAV Cooperative Exploring for the Unknown Indoor Env...

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17th EAI International conference on Collaborative computing: Networking, Applications, and worksharing, CollaborateCom 2021

作者： Li, Ning Tan, Jiefu Wu, Yunlong Xu, Jiachi Wang, Huan Wu, Wendi College of Computer National University of Defense Technology Hunan Changsha410073 China Beijing100071 China Tianjin300457 China

ISBN: (纸本)9783030926342

This paper proposes a method for collaborative exploration adopting multiple UAVs in an unknown GPS-denied indoor environment. The core of this method is to use the Tracking-D*Lite algorithm to track moving targets in unknown terrain, combined with the Wall-Around algorithm based on the Bug algorithm to navigate the UAV in the unknown indoor environment. The method adopts the advantages of the above two algorithms, where the UAV applies the Wall-Around algorithm to fly around the wall and utilizes the Tracking-D*Lite algorithm to achieve collaboration among UAVs. This method is simulated and visualized by using Gazebo, and the results show that it can effectively take the advantages of multiple UAVs to explore the unknown indoor environments. Moreover, the method can also draw the boundary-contour map of the entire environment at last. Once extended to the real world, this method can be applied to dangerous buildings after earthquakes, hazardous gas factories, underground mines, or other search and rescue scenarios. © 2021, ICST Institute for computer Sciences, social Informatics and Telecommunications Engineering.

关键词： Unmanned aerial vehicles (UAV)

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CPP-Based cooperative Defense Against DoS Attacks in Future Non-terrestrial Networks 15th

CPP-Based Cooperative Defense Against DoS Attacks in Future ...

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15th EAI International conference on Communications and Networking, ChinaCom 2020

作者： Cong, Zhaori Zhang, Zhilong Liu, Danpu Beijing Laboratory of Advanced Information Network Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing100876 China

ISBN: (纸本)9783030677190

In future non-terrestrial networks, satellites with sufficient computing resources are expected to serve as base stations or access points, which changes the structure of traditional satellite networks into a more flexible environment. For such satellite nodes, denial of service (DoS) attacks may become a potential secure threat that should be prevented. Existing researches mainly focus on the defense against DoS attacks on ground nodes and have no consideration of the attacks on future satellites. Moreover, the problem of reducing the access delay when a satellite is under DoS attack has not been addressed. In this paper, we study the DoS attack defense strategy in non-terrestrial networks. By adopting client puzzle protocol (CPP) and load balancing, we propose a cooperative defense strategy where multiple auxiliary nodes are used to help the attacked node to process the intensive attack requests. An access delay minimization problem to optimize the selection of auxiliary nodes, puzzle difficulty as well as traffic offload ratios is then formulated based on queuing theory and solved. Simulation results show that the proposed scheme not only improve the network’s anti-attack capability, but also achieves desirable performance in average access delay. © 2021, ICST Institute for computer Sciences, social Informatics and Telecommunications Engineering.

关键词： Denial-of-service attack

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A Study Based on P300 Component in Single-Trials for Discriminating Depression from Normal Controls 15th

A Study Based on P300 Component in Single-Trials for Discrim...

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15th CCF conference on computer supported cooperative work and social computing, Chinese CSCW 2020

作者： Zhang, Wei Gong, Tao Li, Jianxiu Li, Xiaowei Hu, Bin Gansu Provincial Key Laboratory of Wearable Computing School of Information Science and Engineering Lanzhou University Lanzhou China CAS Center for Excellence in Brain Science and Intelligence Technology Shanghai Institutes for Biological Sciences Chinese Academy of Sciences Beijing China Joint Research Center for Cognitive Neurosensor Technology of Lanzhou University and Institute of Semiconductors Chinese Academy of Sciences Beijing China Ministry of Education Lanzhou China

ISBN: (纸本)9789811625398

The investigation of attentional bias of depression based on P300 component has drawn interest within the last decades. Follow-up of previous research suggested the differential amplitudes between depression and normal controls (NCs) in response to various facial stimuli. In this paper, we used single-trials features in the occurrence of P300 to recognize depression from NCs. EEG activity was recorded from 24 patients and 29 NCs in a dot-probe task. We considered two traditionally used feature-extraction methods: ReliefF and principal component analysis (PCA). Then, the k-nearest neighbor (KNN), BFTree, C4.5, logistic regression and NaiveBayes were adopted in this study to make a performance comparison. The combination of NaiveBayes and PCA was applied to classify the P300 component evoked by sad-neutral pairs, which achieved higher classification accuracy than other classifiers. The classification accuracy was 98%. The classification results support that the P300 component of ERPs may reflect information processing of the specific response of depression to specific stimuli and may be applied as a physiologic index for aided diagnosis of depression in future research. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Classification (of information)

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Hybrid collaboration - Moving beyond purely co-located or remote collaboration 17

Hybrid collaboration - Moving beyond purely co-located or re...

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17th European conference on computer supported cooperative work, ECSCW 2019

作者： Neumayr, Thomas Saatçi, Banu Augstein, Mirjam Jetter, Hans-Christian Klokmose, Clemens Nylandsted Anderst-Kotsis, Gabriele Rintel, Sean University of Applied Sciences Upper Austria Austria Aarhus University Denmark Johannes Kepler University Linz Austria Microsoft Research Cambridge United Kingdom

New collaborative practices and technologies increasingly blur the traditional boundaries between co-located and remote collaboration. Using technologies such as connected interactive whiteboards and mobile devices, team meetings are increasingly partially distributed with co-located and remote members. Collaboration tools such as Slack also invite users to transcend the dichotomy of synchronous and asynchronous team work. In a first attempt to frame this new kind of collaborative practices, Neumayr et al. (2018) have formulated their framework of "Hybrid Collaboration" to enable the description and analysis of current hybrid collaboration practices. Still, there is a considerable knowledge gap in the field of hybrid collaboration although it is daily common practice. This one-day workshop aims at bringing together researchers and practitioners working on empirical research methodologies and currently existing practical use cases of hybrid collaboration while ultimately striving for a high level of usability and UX in the tools we develop in the realm of either co-located or remote collaboration settings. © 2019 held by Authors.

关键词： Interactive computer systems

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