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34th ACM Web Conference, WWW 2025

作者： Lin, Xixun Cao, Yanan Sun, Nan Zou, Lixin Zhou, Chuan Zhang, Peng Zhang, Shuai Zhang, Ge Wu, Jia Institute of Information Engineering Chinese Academy of Sciences Beijing China Institute of Information Engineering Chinese Academy of Sciences School of Cyber Security University of Chinese Academy of Sciences Beijing China School of Cyber Science and Engineering Wuhan University Wuhan China Academy of Mathematics and Systems Science CAS School of Cyber Security University of Chinese Academy of Sciences Beijing China Cyberspace Institute of Advanced Technology Guangzhou University Guangzhou China Academy of Mathematics and Systems Science CAS Beijing China School of Computer Science and Technology Donghua University Shanghai China Department of Computing Macquarie University Sydney Australia

ISBN: (纸本)9798400712746

Graph-level out-of-distribution (OOD) detection, which attempts to identify OOD graphs originated from an unknown distribution, is a vital building block for safety-critical applications in Web and society. Current approaches concentrate on how to learn better graph representations, but fail to provide any statistically guarantee on detection results, therefore impeding their deployments in the scenario where detection errors would result in serious consequences. To overcome this critical issue, we propose the Conformal Graph-level Out-of-distribution Detection (CGOD), extending the theory of conformal prediction to graph-level OOD detection with a rigorous control over the false positive rate. In CGOD, we develop a new aggregated non-conformity score function based on the proposed adaptive data augmentation. Through the guidance from two designed metrics, i.e., score consistency and representation diversity, our augmentation strategy can generate multiple non-conformity scores, and aggregating these generated non-conformity scores together is robust to the misleading information. Meanwhile, our score function can perceive the subsequent process of conformal inference, enabling the aggregated non-conformity score to be adaptive to different input graphs and deriving a more accurate score estimation. We conduct experiments on multiple real-world datasets with different empirical settings. Extensive results and model analyses demonstrate the superior performance of our approach over several competitive baselines. © 2025 Copyright held by the owner/author(s).

关键词： Network theory (graphs)

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Transformer-Based Indonesian Language Model for Emotion Classification and Sentiment Analysis

Transformer-Based Indonesian Language Model for Emotion Clas...

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Information Technology and Computing (ICITCOM), International Conference on

作者： Hendri Ahmadian Taufik Fuadi Abidin Hammam Riza Kahlil Muchtar Graduate School of Mathematics and Applied Sciences Universitas Syiah Kuala Banda Aceh Indonesia Department of Informatics Faculty of Mathematics and Natural Sciences Universitas Syiah Kuala Banda Aceh Indonesia Research Center for Artificial Intelligence and Cyber Security National Research and Innovation Agency Jakarta Indonesia Department of Electrical and Computer Engineering Faculty of Engineering Universitas Syiah Kuala Banda Aceh Indonesia

The rapid development of social networks has made much user-generated data accessible for public evaluation. These data can be used for multiple purposes, such as textual analysis of comments and reviews. This article employs a variant of the bidirectional encoder representations from Transformer (BERT) model designed explicitly for Bahasa Indonesia (called IndoBERT model) to enhance the performance of the Indonesian natural language understanding benchmark in tasks, such as sentiment analysis and emotion classification. The two tasks were tested using a hybrid method, combining the IndoBERT model's last hidden layer summation with a neural network model. The performance of the resulting model was assessed using the F1-score metric. The experimental results show that the proposed model attains an accuracy of 0.92 and 0.76 for sentiment analysis and emotion classification, respectively.

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Deep Learning Approach for Complex Activity Recognition using Heterogeneous Sensors from Wearable Device

Deep Learning Approach for Complex Activity Recognition usin...

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2021 Research, Invention, and Innovation Congress: Innovation Electricals and Electronics, RI2C 2021

作者： Hnoohom, Narit Jitpattanakul, Anuchit You, Ilsun Mekruksavanich, Sakorn Mahidol University Image Information and Intelligence Laboratory Department of Computer Engineering Faculty of Engineering Nakhon Pathom Thailand King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science Bangkok Thailand Soonchunhyang University Department of Information Security Engineering Asan31538 Korea Republic of University of Phayao School of Information and Communication Technology Department of Computer Engineering Phayao Thailand

ISBN: (纸本)9781665403009

The classification of simple and complex sequences of operations is made easier according to the use of heterogeneous sensors from a wearable device. Sensor-based human activity recognition (HAR) is being used in smartphone platforms for elderly healthcare monitoring, fall detection, and inappropriate behavior prevention, such as smoking habit, unhealthy eating, and lack of exercise. Common machine learning and deep learning techniques have recently been presented to tackle the HAR issue, with a focus on everyday activities, particularly general human activities including moving, sitting, and standing. However, there is an intriguing and challenging HAR research subjects involving more complicated psychological activities in various environments, including smoking, eating, and drinking. The use of heterogeneous sensor data to enhance recognition performance over sensor-based deep learning networks is considered in this work. We demonstrate that using a combination of two inertial measurement units outperforms employing either an accelerometer or a gyroscope by utilizing four deep learning classifiers to recognize complex human activity (CHA). Furthermore, we describe the impact of five window sizes (5s - 40s) on a publicly accessible benchmark dataset and how increasing window size effects to the classification performance of CHA deep learning networks. © 2021 IEEE.

关键词： Deep learning

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Identity-Based Authentication for On-Demand Charging of Electric Vehicles

arXiv

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arXiv 2022年

作者： Asokraj, Surudhi Bianchi, Tommaso Brighente, Alessandro Conti, Mauro Poovendran, Radha The Network Security Lab The Department of Electrical and Computer Engineering University of Washington SeattleWA98195-2500 United States The Department of Mathematics University of Padua Via Trieste 63 Padua35121 Italy

Dynamic wireless power transfer provides means for charging Electric Vehicles (EVs) while driving, avoiding stopping for charging and hence fostering their widespread adoption. Researchers devoted much effort over the last decade to provide a reliable infrastructure for potential users to improve comfort and time management. Due to the severe security and performance system requirements, the different scheme proposed in last years lack of a unified protocol involving the modern architecture model with merged authentication and billing processes. Furthermore, they require the continuous interaction of the trusted entity during the process, increasing the delay for the communication and reducing security due to the large number of message exchanges. In this paper, we propose a secure, computationally lightweight, unified protocol for fast authentication and billing that provides on-demand dynamic charging to comprehensively deal with all the computational and security constraints. The protocol employs an ID-based public encryption scheme to manage mutual authentication and pseudonyms to preserve the user’s identity across multiple charging processes. Compared to state-of-the-art authentication protocols, our proposal overcomes the problem of overwhelming interactions and provides public scheme security against the use of simple operations in wide open communications without impacting on performance. © 2022, CC BY-NC-ND.

关键词： Authentication

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A Generalized Outage Prediction Model for Various Types of Extreme Climate Events in Texas

A Generalized Outage Prediction Model for Various Types of E...

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IEEE International Conference on Big Data

作者： Jangjae Lee Sangkeun Matthew Lee Stephanie German Paal Supriya Chinthavali Zachry Department of Civil and Environmental Engineering Texas A&M University College Station TX USA Computer Science and Mathematics Division Oak Ridge National Laboratory Oak Ridge TN USA Geospatial Science and Human Security Division Oak Ridge National Laboratory Oak Ridge TN USA

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

This study proposes a generalized model for predicting power outages for various types of extreme weather events. To accomplish the objective of this research, diverse features (e.g., weather data, geographical features, socio-demographic data, and infrastructure information) were leveraged as independent features, while the target variable was the number of customers who had problems with their electricity at the county level. Using the time of occurrence of extreme weather as defined by the National Weather Service, the top ten influential events were selected using the impact index based on cumulative power outages due to each type of extreme weather event. Additionally, a generalized model was created to predict power outages using weather data from one hour before the outage, outage data from one hour prior, as well as socio-demographic, geographic, and infrastructure information and this model was evaluated. The model was developed in two ways: first, as an Ensemble model trained using individual extreme weather events, and second, as a Unified model using all types of extreme weather conditions. As a result of evaluating the model using mean directional accuracy (MDA), one of the evaluation metrics, the ensemble model showed an accuracy of over 0.4 for weather event types such as Winter Storms, Cold/Wind Chill, Frost/Freeze, and Ice Storms. Although this study focused on creating a model specific to Texas, it is possible to expand the data to develop a nationwide model.

关键词： Measurement Accuracy Storms Electricity Predictive models Data models Ice Power system reliability Indexes Optimization

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Speaker's Emotions Recognition Module Based on the GoogleLeNet Neural Network

Speaker's Emotions Recognition Module Based on the GoogleLeN...

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2022 International Conference on Smart Information Systems and Technologies, SIST 2022

作者： Tereikovskyi, Ihor Tereikovska, Liudmyla Kryvoruchko, Olena Tereikovskyi, Oleh Tyshchenko, Dmytro Franchuk, Tamara National Technical University Of Ukraine Igor Sikorsky Kyiv Polytechnic Institute Dept. Of System Programming And Specialized Computer Systems Kyiv Ukraine Kyiv National University Of Construction And Architecture Dept. Of Information Technology For Design And Applied Mathematics Kyiv Ukraine Kyiv National University Of Trade And Economics Dept. Of Software Engineering And Cyber Security Kyiv Ukraine National Technical University Of Ukraine Igor Sikorsky Kyiv Polytechnic Institute Dept. Of Department Of Computer Engineering Kyiv Ukraine

ISBN: (数字)9781665467902

ISBN: (纸本)9781665467902

The article is devoted to the development of means for recognition of the emotions of the speaker, based on the neural network analysis of fixed fragments of the voice signal. The possibility of improving recognition tools through the use of a convolutional neural network of the GoogLeNet type is shown. The expediency of training the network on the examples of the TESS database is determined. A procedure for representing a voice signal in the form of a square grayscale image has been developed. Based on the use of the specified database, the modification of the GoogLeNet network was carried out. The architecture and software implementation of the recognition module have been developed. With the help of computer experiments, it has been established that after 150 epochs of training on a fairly limited training sample, the modified GoogLeNet makes it possible to achieve an accuracy of recognition of the speaker's emotions of approximately 0.97 on test examples. The obtained accuracy is comparable to the accuracy of the best modern systems of this purpose and confirms the promise of using neural network models like GoogLeNet for emotion recognition based on voice signal analysis. The need for further research aimed at reducing the resource intensity of the neural network model is shown. © 2022 IEEE.

关键词： Emotion Recognition

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'Permanent' algorithm for the meeting shedule problem 16

'Permanent' algorithm for the meeting shedule problem

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16th IEEE International Conference on computer Science and Information Technologies, CSIT 2021

作者： Turbal, Yuriy Babych, Sergii Kunanets, Nataliia Melnyk, Leonid Pasichnyk, Volodymyr National University of Water and Environmental Engineering Department of Computer Science and Applied Mathematics Rivne Ukraine College of National University of Life and Environmental Sciences of Ukraine Software Engineering Rivne Ukraine National University 'Lviv Polytechnic' Department of Information Systems and Networks Lviv Ukraine National University of Water and Environmental Engineering Department of Finance and Economic Security Rivne Ukraine

ISBN: (纸本)9781665442572

An algorithm for solving the problem of scheduling meetings in the process of working on a project is proposed in the paper. The problem of project managers meetings with working groups is considered under certain restrictions., the possibility of a simultaneous meeting of the project manager with one or several project groups is considered. The proposed procedure for finding the permanent allows to build all possible combinations of the meeting schedule at a given time. The specificity of the proposed approach is the ability to take into account any number of additional conditions. © 2021 IEEE.

关键词： Managers

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On the Privacy of Mental Health Apps: An Empirical Investigation and its Implications for Apps Development

arXiv

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arXiv 2022年

作者： Iwaya, Leonardo Horn Ali Babar, M. Rashid, Awais Wijayarathna, Chamila Centre for Research on Engineering Software Technologies The University of Adelaide AdelaideSA5005 Australia Australia Department of Mathematics and Computer Science Karlstad University Karlstad Sweden Bristol Cyber Security Group Department of Computer Science University of Bristol United Kingdom

An increasing number of mental health services are offered through mobile systems, a paradigm called mHealth. Although there is an unprecedented growth in the adoption of mHealth systems, partly due to the COVID-19 pandemic, concerns about data privacy risks due to security breaches are also increasing. Whilst some studies have analyzed mHealth apps from different angles, including security, there is relatively little evidence for data privacy issues that may exist in mHealth apps used for mental health services, whose recipients can be particularly vulnerable. This paper reports an empirical study aimed at systematically identifying and understanding data privacy incorporated in mental health apps. We analyzed 27 top-ranked mental health apps from Google Play Store. Our methodology enabled us to perform an in-depth privacy analysis of the apps, covering static and dynamic analysis, data sharing behaviour, server-side tests, privacy impact assessment requests, and privacy policy evaluation. Furthermore, we mapped the findings to the LINDDUN threat taxonomy, describing how threats manifest on the studied apps. The findings reveal important data privacy issues such as unnecessary permissions, insecure cryptography implementations, and leaks of personal data and credentials in logs and web requests. There is also a high risk of user profiling as the apps' development do not provide foolproof mechanisms against linkability, detectability and identifiability. Data sharing among third parties and advertisers in the current apps' ecosystem aggravates this situation. Based on the empirical findings of this study, we provide recommendations to be considered by different stakeholders of mHealth apps in general and apps developers in particular. We conclude that while developers ought to be more knowledgeable in considering and addressing privacy issues, users and health professionals can also play a role by demanding privacy-friendly apps. Copyright © 2022, The Authors. All r

关键词： Data privacy

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Divertible Searchable Symmetric Encryption for Secure Cloud Storage

Divertible Searchable Symmetric Encryption for Secure Cloud ...

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GLOBECOM 2022 - 2022 IEEE Global Communications Conference

作者： Xi Zhang Cheng Huang Ye Su Jing Qin Xuemin Shen School of Mathematics Shandong University Jinan China Department of Electrical and Computer Engineering University of Waterloo Waterloo Canada State Key Laboratory of Information Security Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781665435413

Searchable Symmetric Encryption (SSE) is a promising method for users to store data in remote clouds securely and search them using keywords over an encrypted index. In this paper, we explore a new function named “keyword diverting” and propose a variant of SSE named Divertible Searchable Symmetric Encryption (DivSSE). Specifically, the index in DivSSE is encoded into an inverted, compressed, and encrypted format, by using the super-increasing sequence, symmetric homomorphic encryption (SHE), and a secure hash function. According to the homomorphic properties of SHE, users can construct a unique keyword diverting token, which can be utilized to update the encrypted index by obliviously merging data identifiers corresponding to different keywords without searching in advance and thus achieve keyword diverting. Moreover, based on function secret sharing, DivSSE can protect users' search patterns and reduce communication costs with the assistance of two independent clouds. Detailed security proof demonstrates that DivSSE can achieve parallel privacy, forward privacy, and backward privacy. Extensive performance evaluation also shows that DivSSE is efficient in terms of computational and communication overheads.

关键词： Performance evaluation Cloud computing Privacy Data privacy Costs Heuristic algorithms Merging

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Real-Time GPS Spoofing Detection in Consumer Drones through Multi-Sensor Data Fusion and TimesNet

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IEEE Transactions on Consumer Electronics 2025年

作者： Wang, Jialiang Nie, Liuyang Gu, Zhaojun Wang, Jingyu Tan, Rui Kumari, Saru Civil Aviation University of China College of Computer Science and Technology Tianjin300300 China Civil Aviation University of China Information Security Evaluation Center Tianjin300300 China Civil Aviation University of China College of Safety Science and Engineering Tianjin300300 China Chaudhary Charan Singh University Department of Mathematics Uttar Pradesh Meerut India Lovely Professional University School Chemical Engineering and Physical Sciences Punjab Jalandhar144411 India

To address the threat of GPS spoofing attacks on consumer drones, this paper proposes a real-time detection method based on multi-sensor data fusion and TimesNet. By integrating data from multi-source heterogeneous sensors such as the Inertial Measurement Unit (IMU), magnetometer, and barometer, a spatiotemporal feature cross-validation mechanism is constructed, overcoming the limitations of traditional single-sensor detection. Innovatively, the TimesNet deep temporal network is introduced, leveraging key frequency-domain period extraction and multi-scale feature reorganization techniques to achieve the second-level real-time detection response. A dynamic adversarial GPS spoofing sample generation algorithm is proposed to simulate multi-dimensional position offset attacks in complex battlefield environments, constructing an adversarial sample library covering eight typical flight trajectories. Experimental results demonstrate that the proposed method achieves an average detection accuracy of 99.57% and an F1-score of 99.30% across complex trajectories such as straight lines, curves, and spirals, with a detection response time controlled within 0.35-0.70 seconds. Compared to existing visual matching and single-sensor solutions, this method requires no additional hardware and can be deployed on embedded platforms with only 1.2M parameters, providing a lightweight solution for drone navigation security. © 1975-2011 IEEE.

关键词： Drones

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