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arXiv 2024年

作者： Gupta, Kishu Saxena, Deepika Gupta, Rishabh Singh, Ashutosh Kumar Department of Computer Science & Engineering National Sun Yat-sen University Kaohsiung80424 Taiwan Department of Computer Science & Engineering University of Aizu Fukushima Aizuwakamatsu Japan Department of Computer Science The University of Economics and Human Sciences Warsaw01043 Poland Department of Computer Applications SRM Institute of Science & Technology Delhi-NCR Campus Modinagar Uttar Pradesh Ghaziabad201204 India Department of Computer Science & Engineering Indian Institute of Information Technology Madhya Pradesh Bhopal462003 India

With the vigorous development of cloud computing, most organizations have shifted their data and applications to the cloud environment for storage, computation, and sharing purposes. During storage and data sharing across the participating entities, a malicious agent may gain access to outsourced data from the cloud environment. A malicious agent is an entity that deliberately breaches the data. This information accessed might be misused or revealed to unauthorized parties. Therefore, data protection and prediction of malicious agents have become a demanding task that needs to be addressed appropriately. To deal with this crucial and challenging issue, this paper presents a Malicious Agent Identification-based Data Security (MAIDS) Model which utilizes XGBoost machine learning classification algorithm for securing data allocation and communication among different participating entities in the cloud system. The proposed model explores and computes intended multiple security parameters associated with online data communication or transactions. Correspondingly, a security-focused knowledge database is produced for developing the XGBoost Classifier-based Malicious Agent Prediction (XC-MAP) unit. Unlike the existing approaches, which only identify malicious agents after data leaks, MAIDS proactively identifies malicious agents by examining their eligibility for respective data access. In this way, the model provides a comprehensive solution to safeguard crucial data from both intentional and non-intentional breaches, by granting data to authorized agents only by evaluating the agent’s behavior and predicting the malicious agent before granting data. The performance of the proposed model is thoroughly evaluated by accomplishing extensive experiments, and the results signify that the MAIDS model predicts the malicious agents with high accuracy, precision, recall, and F1-scores up to 95.55%, 95.30%, 95.50%, and 95.20%, respectively. This enormously enhances the system’s sec

关键词： Information leakage

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WaterHCI: Water in human-computer interaction

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Foundations and Trends in human-computer interaction 2024年第4期18卷 338-412页

作者： Vega, Maria Montoya Smith, Ian Clashing, Christal Patibanda, Rakesh Ananthanarayan, Swamy Pell, Sarah Jane Mueller, Florian Exertion Games Lab Department of Human-Centred Computing Monash University Australia Faculty of Computer Science University of New Brunswick Canada Department of Human-Centred Computing Monash University Australia

Over recent years, there has been an increase in the coming together of interactive technology and water, leading to the emergence of WaterHCI, a distinct subfield of human-computer interaction (HCI). However, there is little work that aims to paint a comprehensive picture of the work around WaterHCI experiences so far, limiting the opportunity to identify directions for future research. This monograph aims to address this through an articulation of prior WaterHCI works structured using two frameworks that aim to offer a better understanding of the design of aquatic experiences through four key user experiences across six different degrees of contact with water. This articulation allows us to highlight underexplored areas that could guide WaterHCI researchers in identifying what to research next in order to bring the field forward as a whole. Ultimately, our work aims to help so that more people can profit from the many benefits that combining interactive technology and water affords. ©2024 M. M. Vega et al.

关键词： aqua aquatic interfaces Water

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Extremely Lightweight Skin Segmentation Networks to Improve Remote Photoplethysmography Measurement 14th

Extremely Lightweight Skin Segmentation Networks to Improve ...

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14th International Conference on Intelligent human computer interaction, IHCI 2022

作者： Lee, Kunyoung You, Hojoon Oh, Jaemu Lee, Eui Chul Department of Computer Science Graduate School Sangmyung University Seoul Korea Republic of Department of AI and Informatics Graduate School Sangmyung University Seoul Korea Republic of Department of Human-Centered Artificial Intelligence Sangmyung University Seoul Korea Republic of

ISBN: (纸本)9783031271984

Recently, remote photoplethysmography (rPPG) has been studied and developed not only in a controlled environment but also in a wild environment such as telemedicine and driver monitoring. Although photoplethysmography (PPG) can be measured through a contact sensor, pulse signal can be obtained by remote measuring minute color changes on the skin surface using a camera. This pulse signal is called rPPG and has an advantage of sensing cardiac activity without a contact sensor. The processing pipeline of rPPG can be simply defined as region of interest selection (ROI), pulse signal extraction, signal processing. During this process, in ROI selection, skin segmentation is performed because only the skin pixel region is related to the rPPG signal. We propose extremely lightweight skin segmentation network (ELSNet) for applying deep learning to skin segmentation to measure reliable signals. Our method improved the success rate within 5BPM of heart rate estimation by about 6%, and in the talking environment, an average performance improvement of 9.5% was confirmed. In addition, it was confirmed that MAPE was improved by an average of 20%. The ELSNet shows 167 FPS throughput on Intel i9 CPU. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Deep learning

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Autonomous Vision-based Robotic Grasping of Household Objects: A Practical Case Study

Autonomous Vision-based Robotic Grasping of Household Object...

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RSI/ISM International Conference on Robotics and Mechatronics (ICROM)

作者： Asal Roudbari Mahdi Dalvand Navid Pasiar Navid Asadi Mohammad Yousefi Mehdi Tale Masouleh Electrical Engineering Department Sharif University of Technology Tehran Iran Human & Robot Interaction Lab University of Tehran Tehran Iran Human & Robot Interaction Lab School of Electrical&Computer Engineering University of Tehran Tehran Iran

Autonomous robotic grasping has emerged as a valuable capability for automating everyday household tasks like table setting (bossing), cleaning, and meal preparation. Still, accurately detecting and manipulating diverse everyday objects in unstructured home environments remains a significant challenge. In this study, an integrated robotic system combining YOLOv8s-seg deep learning for object detection and segmentation with computer vision techniques to determine optimal grasping points is proposed. The accuracy of this model has also been compared with other models through their mAP0.5, mAP.05-.95, precision and recall. A custom dataset of over 1000 images containing common household objects was created, with manual annotation and augmentation to train the YOLOv8s-seg segmentation model. The approach processes input images to understand desired arrangements and identify target objects and their locations for grasping. The detected objects are localized using camera-to-robot calibration and aligned with estimated orientations from computer vision techniques. Extensive tests have proven that representative household objects in cluttered arrangements have a grasping success rate of over 90% using a 3DOF Delta Parallel robot with a 2-finger gripper guided entirely by visual perception. By overcoming real-world challenges like transparent and reflective materials, the perception-guided robots provide precise and efficient robotic grasping in unstructured home environments. This integrated approach combines robust perception and localization, providing promising evidence for reliable automation of repetitive household organization tasks 1 . 1 A video of real-time grasp performance is uploaded as supplementary material.

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Adversarial Examples Detection with Enhanced Image Difference Features based on Local Histogram Equalization

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IEEE Transactions on Dependable and Secure Computing 2025年

作者： Yin, Zhaoxia Zhu, Shaowei Su, Hang Peng, Jianteng Lyu, Wanli Luo, Bin East China Normal University School of Communication & Electronic Engineering Shanghai China Anhui University China Tsinghua University Department of Computer Science and Technology Beijing100084 China OPPO Intellisense and Interaction Research Department China

Deep Neural Networks (DNNs) have recently made significant strides in various fields;however, they are susceptible to adversarial examples-crafted inputs with imperceptible perturbations that can mislead these networks. Notably, even when adversaries lack access to the complete model parameters, they can still generate adversarial examples targeting a range of DNN-based task systems. Various defense mechanisms have been proposed, such as feature compression and gradient masking. Nevertheless, extensive research indicates that these methods often address only specific attacks, rendering them ineffective against novel and unknown attack strategies. Recent studies have highlighted the efficacy of identifying adversarial examples in the frequency domain;however, these approaches are limited to frequency-based analysis. In this study, we experimentally observe that adversarial examples possess significant characteristics in local regions. Specifically, adversarial perturbations exhibit localized randomness, whereas the high-frequency information in normal examples is both locally coherent and semantically relevant. This critical distinction enables effectively distinguishing adversarial examples from normal ones. To leverage this insight, we aim to enhance the high-frequency features of input examples to amplify their feature disparities. We propose an image enhancement method utilizing local histogram equalization. Our experimental results demonstrate that this method substantially improves detector performance without modifying the existing detection models. Furthermore, this technique can be seamlessly integrated with task models, effectively reducing deployment costs in practical applications. © 2004-2012 IEEE.

关键词： Frequency domain analysis

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Isoform Function Prediction Based on Heterogeneous Graph Attention Networks

Isoform Function Prediction Based on Heterogeneous Graph Att...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Guo, Kuo Li, Yifan Chen, Hao Shen, Hong-Bin Yang, Yang Shanghai Jiao Tong University Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Department of Computer Science and Engineering Shanghai200240 China Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Institute of Image Processing and Pattern Recognition Shanghai200240 China Carnegie Mellon University School of Computer Science Computational Biology Department PittsburghPA15213 United States

ISBN: (纸本)9798350337488

Isoforms refer to different mRNA molecules transcribed from the same gene, which can be translated into proteins with varying structures and functions. Predicting the functions of isoforms is an essential topic in bioinformatics as it can provide valuable insights into the intricate mechanisms of gene regulation and biological processes. Conventionally, gene function labels are standardized in Gene Ontology (GO) terms. However, traditional methods for predicting isoform function are largely limited by the absence of isoform-specific labels, sparse annotations, and the vast number of GO terms. To address these issues, we propose HANIso, a deep learning-based method for isoform function prediction. HANIso leverages a pretrained protein language model to extract features from protein sequences. It also integrates heterogeneous information, such as isoform sequence features, GO annotations, and isoform interaction data, using a Heterogeneous Graph Attention Network (HAN). This allows the model to learn the importance of different sources of information and their semantic relationships through the attention mechanism. Our method can predict function labels at both the gene level and isoform level. We conduct experiments on two species datasets, and the results demonstrate that our method outperforms existing methods on both AUROC and AUPRC. HANIso has the potential to overcome the limitations of traditional methods and provide a more accurate and comprehensive understanding of isoform function. © 2023 IEEE.

关键词： alternative splicing gene ontology heterogeneous graph attention network isoform function prediction protein language model

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Towards Automated Support for the Co-Evolution of Meta-Models and Grammars

arXiv

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arXiv 2023年

作者： Zhang, Weixing Department of Computer Science & Engineering Division of Interaction Design & Software Engineering Chalmers University of Technology University of Gothenburg Gothenburg Sweden

Blended modeling is an emerging paradigm involving seamless interaction between multiple notations for the same underlying modeling language. We focus on a model-driven engineering (MDE) approach based on meta-models to develop textual languages to improve the blended modeling capabilities of modeling tools. In this thesis, we propose an approach that can support the co-evolution of meta-models and grammars as language engineers develop textual languages in a meta-model-based MDE setting. Firstly, we comprehensively report on the challenges and limitations of modeling tools that support blended modeling, as well as opportunities to improve them. Second, we demonstrate how language engineers can extend Xtext’s generator capabilities according to their needs. Third, we propose a semi-automatic method to transform a language with a generated grammar into a Python-style language. Finally, we provide a solution (i.e., GrammarOptimizer) that can support rapid prototyping of languages in different styles and the co-evolution of meta-models and grammars of evolving languages. © 2023, CC BY-NC-ND.

关键词： Modeling languages

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Please don't answer out of context: Personalized Dialogue Generation Fusing Persona and Context

Please don't answer out of context: Personalized Dialogue Ge...

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International Joint Conference on Neural Networks (IJCNN)

作者： Fucheng Wang Yunfei Yin Faliang Huang Kaigui Wu College of Computer Science Chongqing University Chongqing China Guangxi Key Lab of Human-machine Interaction and Intelligent Decision Nanning Normal University Nanning China

In realistic conversations, “responses” are closely related to persona and context. However, current personalized dialogue generation methods only focus on the consistency of the persona of responses and yet ignore context coherence, as may produce low-quality responses. To address the issue, we propose a novel model, named PCF (Persona and Context Fusion), which builds two decoders for understanding personality consistency and context coherence respectively on a common encoder-decoder architecture. In this model, an inter-layer attention fusion mechanism is designed for the two decoders to effectively fuse persona and context, and a decoupled way of training is conducted on an additional large-scale non-dialogue inference dataset to enhance the consistent understanding ability of the two decoders. Furthermore, considering that generating responses may be tedious, the ScaleGrad loss function is applied to enhance the diversity of responses. Experimental results on two publicly available datasets show that the dialogues generated by our PCF are significantly higher in quality than strong baselines.

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NLP in human Rights Research: Extracting Knowledge Graphs About Police and Army Units and Their Commanders 16

NLP in Human Rights Research: Extracting Knowledge Graphs Ab...

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16th Linguistic Annotation Workshop, LAW 2022

作者： Bauer, Daniel Longley, Tom Ma, Yueen Wilson, Tony Computer Science Department Columbia University United States Security Force Monitor Human Rights Institute Columbia Law School United States

ISBN: (纸本)9782493814081

In this paper we explore the use of an NLP system to assist the work of Security Force Monitor (SFM). SFM creates data about the organizational structure, command personnel and operations of police, army and other security forces, which assists human rights researchers, journalists and litigators in their work to help identify and bring to account specific units and personnel alleged to have committed abuses of human rights and international criminal law. This paper presents an NLP system that extracts from English language news reports the names of security force units and the biographical details of their personnel, and infers the formal relationship between them. Published alongside this paper are the system's code and training dataset. We find that the experimental NLP system performs the task at a fair to good level. Its performance is sufficient to justify further development into a live workflow that will give insight into whether its performance translates into savings in time and resource that would make it an effective technical intervention. © 2022 European Language Resources Association (ELRA).

关键词： Information retrieval

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LabelEase: A Semi-Automatic Tool for Efficient and Accurate Trace Labeling in Microservices

LabelEase: A Semi-Automatic Tool for Efficient and Accurate ...

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International Symposium on Software Reliability Engineering (ISSRE)

作者： Shenglin Zhang Zeyu Che Zhongjie Pan Xiaohui Nie Yongqian Sun Lemeng Pan Dan Pei Nankai University Haihe Laboratory of Information Technology Application Innovation Computer Network Information Center Chinese Academy of Sciences Tianjin Key Laboratory of Software Experience and Human Computer Interaction Huawei Beijing National Research Center for Information Science and Technology Tsinghua University

ISBN: (数字)9798350353884

ISBN: (纸本)9798350353891

Trace data is crucial for system observability and maintainability within microservices architectures, and many operation algorithms depend heavily on trace data, including anomaly detection, root cause analysis, etc. However, the actual performance of these algorithms might be unsatisfactory due to the absence of high-quality labeled datasets for effective training and evaluation. Since billions of traces could be generated daily for large-scale microservices, labeling overhead is the main hurdle to obtaining high-quality trace *** this paper, we propose LabelEase, a novel semi-automatic trace labeling tool, which uses active learning techniques to achieve efficient and accurate trace labeling. For anomaly trace labeling, LabelEase clusters similar traces with a graph-based trace representation technique and selects a few representative traces for human labeling, avoiding labeling most of the traces. For root cause labeling, LabelEase aggregates the labeled anomalous traces and identifies the service’s failures for operators to label. Our systematic experiments on two large-scale datasets show that LabelEase achieves over 0.98 F 1 -score in anomaly trace labeling and 0.89 precision of failure detection in root cause labeling, LabelEase can reduce operators’ labeling overhead by more than 99.9%. To the best of our knowledge, we are the first to propose a semi-automatic trace labeling tool capable of achieving efficient and accurate trace labeling.

关键词： Training Location awareness Accuracy Costs Transfer learning Microservice architectures Manuals Data models Labeling Anomaly detection

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