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27th ACM Conference on Computer-Supported Cooperative Work and Social computing, CSCW Companion 2024

作者： Das, Sanchari Kim, DongInn Abbott, Jacob Jean Camp, L. Ritchie School of Engineering and Computer Science University of Denver DenverCO United States Luddy School of Informatics Computing and Engineering Indiana University Bloomington BloomingtonIN United States

ISBN: (纸本)9798400711145

Ensuring user-centered phishing detection is a significant challenge due to the difficulty in distinguishing threats. To address this, we propose a personalized tool - Holistic User-Centered Identification of Threats at the Edge (HUCITE). Our system utilizes probabilistic logic to provide real-time risk estimates for local machines and employs simple cartoons as a user interface. Our approach focuses on identifying anomalies in a single machine and user browsing history for the generation of a personalized green list and global threat information for a shared red list. This approach enables blocking and identification of phishing websites, potentially malicious scripts, unfamiliar domains, and previously unencountered certificate authorities. By creating a zone of safety for a specific user and identifying departures from that zone, our approach addresses the limitations of traditional anomaly detection techniques. We present the underlying architecture and approach to local risk identification, reporting on an in-lab experiment involving 45 participants to test the effectiveness of our system in various stress conditions affecting users' phishing perception. © 2024 Owner/Author.

关键词： Phishing

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DualFraud: Dual-Target Fraud Detection and Explanation in Supply Chain Finance Across Heterogeneous Graphs 28th

DualFraud: Dual-Target Fraud Detection and Explanation in ...

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28th International Conference on Database Systems for Advanced Applications, DASFAA 2023

作者： Wu, Bin Chao, Kuo-Ming Li, Yinsheng School of Computer Science Fudan University Shanghai China Department of Computing and Informatics Bournemouth University Bournemouth United Kingdom

ISBN: (纸本)9783031306778

In supply chain finance, detecting fraudulent enterprises and transactions is crucial to minimize financial loss. Enterprises and transactions have heterogeneous information and fraud labels, thus, leveraging such information well can simultaneously improve fraud detection performance in enterprise and transaction domains. This paper describes our newly proposed multitask learning framework, DualFraud, which detects fraudulent enterprises and transactions with explainability based on heterogeneous graphs in supply chain finance. The main contributions of this work are the proposed framework that can facilitate these two domains to share and enhance learning and modelling capabilities to improve fraud projection. The explainer component is attached to generate rich and meaningful explanations for risk controllers across enterprise and transaction graphs. Experiments on datasets prove the effectiveness of fraud detection and explainability in both enterprises and transactions. The proposed DualFraud outperforms the other methods in the selected criteria. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Supply chains

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EMPATHIC: Emulating Human-Like Multimodal Personality Architecture Through Thoughtful Human-AI Conversation 14

EMPATHIC: Emulating Human-Like Multimodal Personality Archit...

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14th International Conference on Cloud computing, Data science and Engineering, Confluence 2024

作者： Varshini Devi, I. Oviya, I.R. Raja, Kalpana Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Chennai India School of Medicine Yale University Section for Biomedical Informatics and Data Science New HavenCT United States

ISBN: (纸本)9798350344837

In the evolving landscape of human-computer interaction, this paper introduces an innovative framework poised to revolutionize chatbot systems. Our framework, meticulously designed for emotionally aware multimodal chatbots, seamlessly integrates real-time face and emotion recognition with natural language processing. Currently in the developmental phase, the framework exhibits a distinctive proficiency in executing a diverse array of human instructions. These instructions span tasks such as generating detailed captions, object counting, and responding to general queries. Leveraging parameter-efficient fine-tuning from OpenFlamingo, augmented by the inclusion of the Low-rank Adapter (LoRA), our framework strategically prioritizes versatility and operational efficiency. The core of our approach lies in the establishment of instruction templates that intricately merge both vision and language data. This strategic integration forms the backbone of our multi-modality instruction tuning methodology, enhancing the adaptability of the chatbot. Recognizing the pivotal role of high-quality training data, we emphasize its significance in shaping the dialogue performance of the chatbot. As the framework progresses through its developmental stages, this research lays a robust foundation for the creation of a versatile conversational agent. The envisioned applications of this agent range from enhancing customer service experiences to providing valuable support in mental health scenarios. © 2024 IEEE.

关键词： Emotion Recognition

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Efficient Power Control for UAV Based on Trajectory and Game Theory

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Computers, Materials & Continua 2023年第3期74卷 5589-5606页

作者： Fadhil Mukhlif Ashraf Osman Ibrahim Norafida Ithnin Roobaea Alroobaea Majed Alsafyani Information Assurance and Security Research Group(IASRG) School of ComputingFaculty of EngineeringUniversiti Teknologi MalaysiaJohorMalaysia Faculty of Computing and Informatics University Malaysia SabahKota KinabaluMalaysia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaifSaudi Arabia

Due to the fact that network space is becoming more limited,the implementation of ultra-dense networks(UDNs)has the potential to enhance not only network coverage but also network *** Aerial Vehicle(UAV)communications have recently garnered a lot of attention due to the fact that they are extremely versatile and may be applied to a wide variety of contexts and purposes.A cognitive UAV is proposed as a solution for the Internet of Things ground terminal’s wireless nodes in this *** the IoT system,the UAV is utilised not only to determine how the resources should be distributed but also to provide power to the wireless *** quality of service(QoS)offered by the cognitive node was interpreted as a price-based utility function,which was demonstrated in the form of a non-cooperative game theory in order to maximise customers’net utility *** energyefficient non-cooperative game theory power allocation with pricing strategy abbreviated as(EE-NGPAP)is implemented in this study with two trajectories Spiral and Sigmoidal in order to facilitate effective power management in Internet of Things(IoT)wireless *** has also been demonstrated,theoretically and by the use of simulations,that the Nash equilibrium does exist and that it is one of a *** proposed energy harvesting approach was shown,through simulations,to significantly reduce the typical amount of power thatwas *** is taken into consideration to agree with the objective of 5G *** order to converge to Nash Equilibrium(NE),the method that is advised only needs roughly 4 iterations,which makes it easier to utilise in the real world,where things aren’t always the same.

关键词： UAV spiral&sigmoid trajectory drones IoT game theory energy efficiency 6G

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Retrieval and validation of long-term aerosol optical depth from AVHRR data over China

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International Journal of Digital Earth 2022年第1期15卷 1817-1832页

作者： Chunlin Jin Yong Xue Xingxing Jiang Shuhui Wu Yuxin Sun School of Environment and Spatial Informatics China University of Mining and TechnologyXuzhouPeople’s Republic of China School of Computing and Engineering College of Science and EngineeringUniversity of DerbyDerbyUK

Advanced Very High Resolution Radiometer(AVHRR)onboard National Oceanic and Atmospheric Administration(NOAA)satellites can provide over 40 years of global remote sensing observations,which can be used to retrieve long-term aerosol optical depth(AOD).This is of great significance to the study of global climate *** this paper,we proposed an algorithm to jointly calculate AOD and land surface properties from AVHRR *** assumptions that AOD doesn’t vary in adjacent space and earth surface property doesn’t vary in two days,the algorithm considered non-Lambertian surface reflection based on the shape of bidirectional reflectance distribution function(BRDF shape)and obtained AOD and surface property by optimal estimation(OE)*** algorithm has been applied to NOAA-7,9,11,14,16,18,and 19 satellites and AVHRR-retrieved AOD with 5×10 km over China(15°–60°N,70°–140°E)has been obtained from 1982 to *** of AVHRR-retrieved AOD against AErosol RObotic NETwork(AERONET)(in and around China)and China Aerosol Remote Sensing Network(CARSNET)AOD show good consistency with 62.62%points within the uncertainty ofΔτ=±(0.05+0.25τ)and root-mean-square error(RMSE)of *** comparison of the monthly mean AOD of multiple AOD datasets in the‘Beijing’,‘Dalanzadgad’,‘NCU_Taiwan’and‘Kanpur’stations shows that the results of the algorithm are *** yearly averaged AOD data also has similar agreements with MERRA-2(The Modern-Era Retrospective analysis for Research and Applications,Version 2)and AVHRRDB data(AVHRR‘Deep Blue’aerosol data set).The multi-year mean correlation coefficient is 0.70 and 0.61 and the percentages within the uncertainty are 80.01%and 67.29%compared with MERRA-2 AOD and AVHRRDB AOD respectively.

关键词： Long-term AOD AVHRR joint retrieval BRDF shape

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A Scalable and Lightweight Authentication Architecture for the Internet of Things (IoT) in Smart Home Applications 2

A Scalable and Lightweight Authentication Architecture for t...

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2nd Zimbabwe Conference on Information Communication and Technology, ZCICT 2023

作者： Gamundani, Attlee M. Phillips, Amelia Muyingi, Hippolyte N. Namibia University of Science and Technology Faculty of Computing & Informatics Cyber Security Department Windhoek Namibia University of the Cumberlands School of Computer and Information Sciences WilliamsburgKY United States Namibia University of Science and Technology Faculty of Computing & Informatics Windhoek Namibia

ISBN: (纸本)9798350318128

In the evolving landscape of smart home applications, ensuring robust and efficient authentication within the Internet of Things (IoT) environments is paramount. This paper presents a scalable, lightweight authentication architecture for IoT-driven smart homes. The proposed architecture, drawing inspiration from cloud computing mechanisms, introduces a trusted, smart home agent, integrating the Data-Information-Knowledge-Wisdom (DIKW) model to facilitate efficient and secure data exchange within smart homes. Our solution addresses the challenges of minimal computation and processing overheads, essential in resource-constrained IoT devices, and focuses on device-level authentication to reduce computational costs. A comparative analysis with existing models demonstrates the superiority of our architecture in terms of computational efficiency, making it an ideal candidate for scalable deployments. The architecture successfully navigates the complexities of ensuring secure communication among autonomous IoT devices, balancing the dual needs of computational efficiency and robust security, thereby promising resilience against common security threats in the smart home ecosystem. © 2023 IEEE.

关键词： Authentication

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On the Computation of Equilibria in Discrete First-Price Auctions 24

On the Computation of Equilibria in Discrete First-Price Auc...

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25th Conference on Economics and Computation, EC 2024

作者： Filos-Ratsikas, Aris Giannakopoulos, Yiannis Hollender, Alexandros Kokkalis, Charalampos University of Edinburgh School of Informatics Edinburgh United Kingdom University of Glasgow School of Computing Science Glasgow United Kingdom University of Oxford All Souls College Oxford United Kingdom

ISBN: (纸本)9798400707049

We study the computational complexity of computing Bayes-Nash equilibria in first-price auctions with discrete value distributions and discrete bidding space, under general subjective beliefs. It is known that such auctions do not always have pure equilibria. In this paper we prove that the problem of deciding their existence is NP-complete, even for approximate equilibria. On the other hand, it can be shown that mixed equilibria are guaranteed to exist;however, their computational complexity has not been studied before. We establish the PPAD-completeness of computing a mixed equilibrium and we complement this by an efficient algorithm for finding symmetric approximate equilibria in the special case of iid priors. En route to these results, we develop a computational equivalence framework between continuous and discrete first-price auctions, which can be of independent interest, and which allows us to transfer existing positive and negative results from one setting to the other. Finally, we show that correlated equilibria of the auction can be computed in polynomial time. © 2024 Copyright held by the owner/author(s).

关键词： Nash equilibrium

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Predicting Protein-Protein Interactions Using Self-Attention-Based Deep Neural Networks and FastText Embeddings 15

Predicting Protein-Protein Interactions Using Self-Attention...

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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Oviya, I.R. Shanmukha Sravya, N. Raja, Kalpana Amrita Vishwa Vidyapeetham Amrita School Of Computing Department of Computer Science Engineering Chennai601103 India Yale University Section for Biomedical Informatics and Data Science School of Medicine New HavenCT United States

ISBN: (纸本)9798350370249

Predicting Protein-protein interactions are central to understanding the intricate mechanisms governing cellular functions, as proteins seldom act in isolation. While current machine learning and deep learning models focus on encoding protein sequences based on residue composition or physicochemical properties, they often overlook the complex relationships between amino acid segments within lengthy sequences. To address these limitations, we propose a novel hybrid model that combines a self-attention to enhance Deep neural network feature extraction with an with FastText Protein embeddings for predicting PPI. Utilizing transfer learning via the unsupervised FastText embedding technique to derive statistical representations of protein sequences, offers advantages over traditional protein encoding techniques in the context of analysing k-Mer sequences. Our model's performance is assessed through 5-fold cross-validation on datasets of *** and Human-Bacillus Anthracis protein interactions, resulting in accuracies of 92.28% and 94.08%, respectively. Compared to existing machine learning models, our model demonstrates superior performance on both datasets. © 2024 IEEE.

关键词： Adversarial machine learning

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R2V-PPI: Enhancing Prediction of Protein-Protein Interactions Using Word2Vec Embeddings and Deep Neural Networks 4

R2V-PPI: Enhancing Prediction of Protein-Protein Interaction...

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4th International Conference on Advances in Electrical, computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Oviya, I.R. Sravya N, Shanmukha Raja, Kalpana Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science Engineering Chennai601103 India Yale University Section for Biomedical Informatics and Data Science School of Medicine New HavenCT United States

ISBN: (纸本)9798350343670

Proteins play a crucial role in living organisms, and understanding protein-protein interactions is vital for comprehending their functions and aiding drug discovery. In recent years, advanced deep learning models have been developed to predict protein-protein interactions from sequences. However, these large-scale approaches often suffer from deficiencies, resulting in both false positive and false negative predictions. Traditional encoding methods struggle to effectively represent the semantic meaning of individual residues in protein sequences and their impact on neighbouring residues. To overcome these deficiencies, we propose a novel method named R2V-PPI (Residue-to-Vector embeddings based Protein-Protein Interaction Framework) that effectively represents protein sequences and predicts protein-protein interactions. Inspired by Word2Vec, R2V-PPI learns informative representations of individual amino acid residues, capturing the context and relationships between them in raw protein sequences. These representations provide valuable input features for a downstream deep learning model. Our methodology offers a robust and versatile approach for predicting protein-protein interactions, especially when structural information is limited or unavailable. Evaluation on the S. cerevisiae dataset demonstrates that R2V-PPI outperforms existing state-of-the-art machine learning models. © 2024 IEEE.

关键词： Deep Learning Machine Learning Protein Feature Encoding Protein Protein interaction Protein sequence

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Advancing general robotic manipulation with multimodal foundation models: An embodied AI paradigm

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science China Technological sciences 2025年第5期68卷 294-296页

作者： Shifeng HUANG He WANG Xing ZHOU Wenkai CHEN Haibin YANG Jianwei ZHANG Department of Mechanical and Aerospace Engineering Hong Kong University of Science and Technology Foshan Institute of Intelligent Equipment Technology Foshan Huashu Robot Co. Ltd. Center on Frontiers of Computing Studies (CFCS) School of Computer SciencePeking University Beijing Academy of Artificial Intelligence Department of Informatics Universit?t Hamburg

Can current robotic technologies truly replicate the full scope and intricacies of human labour? In practice, the adoption of robots remains limited, especially in open, unstructured environments commonly encountered in everyday scenarios such as services, healthcare, agriculture,construction, and numerous other fields. From the perspective of general robotic manipulation, the challenges arise from three factors.

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