Open Access. Powered by Scholars. Published by Universities.®

Computer Sciences Commons™

Open Access. Powered by Scholars. Published by Universities.®

2023

Discipline
Institution
Keyword
Publication
Publication Type
File Type

Articles 1471 - 1500 of 3503

Full-Text Articles in Computer Sciences

Semantic-Based Neural Network Repair, Richard Schumi, Jun Sun Jul 2023

Semantic-Based Neural Network Repair, Richard Schumi, Jun Sun

Research Collection School Of Computing and Information Systems

Recently, neural networks have spread into numerous fields including many safety-critical systems. Neural networks are built (and trained) by programming in frameworks such as TensorFlow and PyTorch. Developers apply a rich set of pre-defined layers to manually program neural networks or to automatically generate them (e.g., through AutoML). Composing neural networks with different layers is error-prone due to the non-trivial constraints that must be satisfied in order to use those layers. In this work, we propose an approach to automatically repair erroneous neural networks. The challenge is in identifying a minimal modification to the network so that it becomes valid. …

Go to article

Seed Selection For Testing Deep Neural Networks, Yuhan Zhi, Xiaofei Xie, Chao Shen, Jun Sun, Xiaoyu Zhang, Xiaohong Guan Jul 2023

Seed Selection For Testing Deep Neural Networks, Yuhan Zhi, Xiaofei Xie, Chao Shen, Jun Sun, Xiaoyu Zhang, Xiaohong Guan

Research Collection School Of Computing and Information Systems

Deep learning (DL) has been applied in many applications. Meanwhile, the quality of DL systems is becoming a big concern. To evaluate the quality of DL systems, a number of DL testing techniques have been proposed. To generate test cases, a set of initial seed inputs are required. Existing testing techniques usually construct seed corpus by randomly selecting inputs from training or test dataset. Till now, there is no study on how initial seed inputs affect the performance of DL testing and how to construct an optimal one. To fill this gap, we conduct the first systematic study to evaluate …

Go to article

Proactive Conversational Agents In The Post-Chatgpt World, Lizi Liao, Grace Hui Yang, Chirag Shah Jul 2023

Proactive Conversational Agents In The Post-Chatgpt World, Lizi Liao, Grace Hui Yang, Chirag Shah

Research Collection School Of Computing and Information Systems

ChatGPT and similar large language model (LLM) based conversational agents have brought shock waves to the research world. Although astonished by their human-like performance, we find they share a significant weakness with many other existing conversational agents in that they all take a passive approach in responding to user queries. This limits their capacity to understand the users and the task better and to offer recommendations based on a broader context than a given conversation. Proactiveness is still missing in these agents, including their ability to initiate a conversation, shift topics, or offer recommendations that take into account a more …

Go to article

Towards Omni-Generalizable Neural Methods For Vehicle Routing Problems, Jianan Zhou, Yaoxin Wu, Wen Song, Zhiguang Cao, Jie Zhang Jul 2023

Towards Omni-Generalizable Neural Methods For Vehicle Routing Problems, Jianan Zhou, Yaoxin Wu, Wen Song, Zhiguang Cao, Jie Zhang

Research Collection School Of Computing and Information Systems

Learning heuristics for vehicle routing problems (VRPs) has gained much attention due to the less reliance on hand-crafted rules. However, existing methods are typically trained and tested on the same task with a fixed size and distribution (of nodes), and hence suffer from limited generalization performance. This paper studies a challenging yet realistic setting, which considers generalization across both size and distribution in VRPs. We propose a generic meta-learning framework, which enables effective training of an initialized model with the capability of fast adaptation to new tasks during inference. We further develop a simple yet efficient approximation method to reduce …

Go to article

Forward/Backward And Content Private Dsse For Spatial Keyword Queries, Xiangyu Wang, Jianfeng Ma, Ximeng Liu, Yinbin Miao, Yang Liu, Robert H. Deng Jul 2023

Forward/Backward And Content Private Dsse For Spatial Keyword Queries, Xiangyu Wang, Jianfeng Ma, Ximeng Liu, Yinbin Miao, Yang Liu, Robert H. Deng

Research Collection School Of Computing and Information Systems

Spatial keyword queries are attractive techniques that have been widely deployed in real-life applications in recent years, such as social networks and location-based services. However, existing solutions neither support dynamic update nor satisfy the privacy requirements in real applications. In this article, we investigate the problem of Dynamic Searchable Symmetric Encryption (DSSE) for spatial keyword queries. First, we formulate the definition of DSSE for spatial keyword queries (namely, DSSESKQ) and extend the DSSE leakage functions to capture the leakages in DSSESKQ. Then, we present a practical DSSESKQ construction based on geometric prefix encoding inverted-index and encrypted bitmap. Rigorous security analysis …

Go to article

Adaptive Split-Fusion Transformer, Zixuan Su, Jingjing Chen, Lei Pang, Chong-Wah Ngo, Yu-Gang Jiang Jul 2023

Adaptive Split-Fusion Transformer, Zixuan Su, Jingjing Chen, Lei Pang, Chong-Wah Ngo, Yu-Gang Jiang

Research Collection School Of Computing and Information Systems

Neural networks for visual content understanding have recently evolved from convolutional ones to transformers. The prior (CNN) relies on small-windowed kernels to capture the regional clues, demonstrating solid local expressiveness. On the contrary, the latter (transformer) establishes long-range global connections between localities for holistic learning. Inspired by this complementary nature, there is a growing interest in designing hybrid models which utilize both techniques. Current hybrids merely replace convolutions as simple approximations of linear projection or juxtapose a convolution branch with attention without considering the importance of local/global modeling. To tackle this, we propose a new hybrid named Adaptive Split-Fusion Transformer …

Go to article

Take A Break In The Middle: Investigating Subgoals Towards Hierarhical Script Generation, Xinze Li, Yixin Cao, Muhao Chen, Aixin Sun Jul 2023

Take A Break In The Middle: Investigating Subgoals Towards Hierarhical Script Generation, Xinze Li, Yixin Cao, Muhao Chen, Aixin Sun

Research Collection School Of Computing and Information Systems

Goal-oriented Script Generation is a new task of generating a list of steps that can fulfill the given goal. In this paper, we propose to extend the task from the perspective of cognitive theory. Instead of a simple flat structure, the steps are typically organized hierarchically — Human often decompose a complex task into subgoals, where each subgoal can be further decomposed into steps. To establish the benchmark, we contribute a new dataset, propose several baseline methods, and set up evaluation metrics. Both automatic and human evaluation verify the high-quality of dataset, as well as the effectiveness of incorporating subgoals …

Go to article

Cheer: Centrality-Aware High-Order Event Reasoning Network For Document-Level Event Causality Identification, Meiqi Chen, Yixin Cao, Yan Zhang, Zhiwei Liu Jul 2023

Cheer: Centrality-Aware High-Order Event Reasoning Network For Document-Level Event Causality Identification, Meiqi Chen, Yixin Cao, Yan Zhang, Zhiwei Liu

Research Collection School Of Computing and Information Systems

Document-level Event Causality Identification (DECI) aims to recognize causal relations between events within a document. Recent studies focus on building a document-level graph for cross-sentence reasoning, but ignore important causal structures — there are one or two “central” events that prevail throughout the document, with most other events serving as either their cause or consequence. In this paper, we manually annotate central events for a systematical investigation and propose a novel DECI model, CHEER, which performs high-order reasoning while considering event centrality. First, we summarize a general GNN-based DECI model and provide a unified view for better understanding. Second, we …

Go to article

Modularized Zero-Shot Vqa With Pre-Trained Models, Rui Cao, Jing Jiang Jul 2023

Modularized Zero-Shot Vqa With Pre-Trained Models, Rui Cao, Jing Jiang

Research Collection School Of Computing and Information Systems

Large-scale pre-trained models (PTMs) show great zero-shot capabilities. In this paper, we study how to leverage them for zero-shot visual question answering (VQA).Our approach is motivated by a few observations. First, VQA questions often require multiple steps of reasoning, which is still a capability that most PTMs lack. Second, different steps in VQA reasoning chains require different skills such as object detection and relational reasoning, but a single PTM may not possess all these skills. Third, recent work on zero-shot VQA does not explicitly consider multi-step reasoning chains, which makes them less interpretable compared with a decomposition-based approach. We propose …

Go to article

Is Web3 Better Than Web2 For Investors? Evidence From Domain Name Auctions, Ping Fan Ke, Yi Meng Lau, Daniel Varghese Hanley Jul 2023

Is Web3 Better Than Web2 For Investors? Evidence From Domain Name Auctions, Ping Fan Ke, Yi Meng Lau, Daniel Varghese Hanley

Research Collection School Of Computing and Information Systems

Blockchain-based assets are commonly believed to attract new investors. To investigate this claim, we compared investor preferences for Web2 and Web3 domain name auctions by analyzing daily auction patterns in Namecheap and OpenSea. Our results indicate that Web3 platforms may attract extreme investors with low or high values as a niche market. We found a significantly higher number of bids per auction, higher average bid prices, and greater price spreads on OpenSea, but a significantly lower number of unique bidders per auction. Our findings highlight the importance of considering the auction platform's characteristics and asset context when evaluating bid patterns …

Go to article

Balancing Privacy And Flexibility Of Cloud-Based Personal Health Records Sharing System, Yudi Zhang, Fuchun Guo, Willy Susilo, Guomin Yang Jul 2023

Balancing Privacy And Flexibility Of Cloud-Based Personal Health Records Sharing System, Yudi Zhang, Fuchun Guo, Willy Susilo, Guomin Yang

Research Collection School Of Computing and Information Systems

The Internet of Things and cloud services have been widely adopted in many applications, and personal health records (PHR) can provide tailored medical care. The PHR data is usually stored on cloud servers for sharing. Weighted attribute-based encryption (ABE) is a practical and flexible technique to protect PHR data. Under a weighted ABE policy, the data user's attributes will be “scored”, if and only if the score reaches the threshold value, he/she can access the data. However, while this approach offers a flexible access policy, the data owners have difficulty controlling their privacy, especially sharing PHR data in collaborative e-health …

Go to article

Reducing Spatial Labeling Redundancy For Active Semi-Supervised Crowd Counting, Yongtuo Liu, Sucheng Ren, Liangyu Chai, Hanjie Wu, Dan Xu, Jing Qin, Shengfeng He Jul 2023

Reducing Spatial Labeling Redundancy For Active Semi-Supervised Crowd Counting, Yongtuo Liu, Sucheng Ren, Liangyu Chai, Hanjie Wu, Dan Xu, Jing Qin, Shengfeng He

Research Collection School Of Computing and Information Systems

Labeling is onerous for crowd counting as it should annotate each individual in crowd images. Recently, several methods have been proposed for semi-supervised crowd counting to reduce the labeling efforts. Given a limited labeling budget, they typically select a few crowd images and densely label all individuals in each of them. Despite the promising results, we argue the None-or-All labeling strategy is suboptimal as the densely labeled individuals in each crowd image usually appear similar while the massive unlabeled crowd images may contain entirely diverse individuals. To this end, we propose to break the labeling chain of previous methods and …

Go to article

Fine-Grained Domain Adaptive Crowd Counting Via Point-Derived Segmentation, Yongtuo Liu, Dan Xu, Sucheng Ren, Hanjie Wu, Hongmin Cai, Shengfeng He Jul 2023

Fine-Grained Domain Adaptive Crowd Counting Via Point-Derived Segmentation, Yongtuo Liu, Dan Xu, Sucheng Ren, Hanjie Wu, Hongmin Cai, Shengfeng He

Research Collection School Of Computing and Information Systems

Due to domain shift, a large performance drop is usually observed when a trained crowd counting model is deployed in the wild. While existing domain-adaptive crowd counting methods achieve promising results, they typically regard each crowd image as a whole and reduce domain discrepancies in a holistic manner, thus limiting further improvement of domain adaptation performance. To this end, we propose to untangle domain-invariant crowd and domain-specific background from crowd images and design a fine-grained domain adaption method for crowd counting. Specifically, to disentangle crowd from background, we propose to learn crowd segmentation from point-level crowd counting annotations in a …

Go to article

Beyond "Protected" And "Private": An Empirical Security Analysis Of Custom Function Modifiers In Smart Contracts, Yuzhou Fang, Daoyuan Wu, Xiao Yi, Shuai Wang, Yufan Chen, Mengjie Chen, Yang Liu, Lingxiao Jiang Jul 2023

Beyond "Protected" And "Private": An Empirical Security Analysis Of Custom Function Modifiers In Smart Contracts, Yuzhou Fang, Daoyuan Wu, Xiao Yi, Shuai Wang, Yufan Chen, Mengjie Chen, Yang Liu, Lingxiao Jiang

Research Collection School Of Computing and Information Systems

A smart contract is a piece of application-layer code running on blockchain ledgers and it provides programmatic logic via transaction-based execution of pre-defined functions. Smart contract functions are by default invokable by any party. To safeguard them, the mainstream smart contract language, i.e., Solidity of the popular Ethereum blockchain, proposed a unique language-level keyword called “modifier,” which allows developers to define custom function access control policies beyond the traditional “protected” and “private” modifiers in classic programming languages.In this paper, we aim to conduct a large-scale security analysis of the modifiers used in real-world Ethereum smart contracts. To achieve this, we …

Go to article

Augmenting Fake Content Detection In Online Platforms: A Domain Adaptive Transfer Learning Via Adversarial Training Approach, Ka Chung Ng, Ping Fan Ke, Mike K. P. So, Kar Yan Tam Jul 2023

Augmenting Fake Content Detection In Online Platforms: A Domain Adaptive Transfer Learning Via Adversarial Training Approach, Ka Chung Ng, Ping Fan Ke, Mike K. P. So, Kar Yan Tam

Research Collection School Of Computing and Information Systems

Online platforms are experimenting with interventions such as content screening to moderate the effects of fake, biased, and incensing content. Yet, online platforms face an operational challenge in implementing machine learning algorithms for managing online content due to the labeling problem, where labeled data used for model training are limited and costly to obtain. To address this issue, we propose a domain adaptive transfer learning via adversarial training approach to augment fake content detection with collective human intelligence. We first start with a source domain dataset containing deceptive and trustworthy general news constructed from a large collection of labeled news …

Go to article

Duplicate Bug Report Detection: How Far Are We?, Ting Zhang, Donggyun Han, Venkatesh Vinayakarao, Ivana Clairine Irsan, Bowen Xu, Thung Ferdian, David Lo, Lingxiao Jiang Jul 2023

Duplicate Bug Report Detection: How Far Are We?, Ting Zhang, Donggyun Han, Venkatesh Vinayakarao, Ivana Clairine Irsan, Bowen Xu, Thung Ferdian, David Lo, Lingxiao Jiang

Research Collection School Of Computing and Information Systems

Many Duplicate Bug Report Detection (DBRD) techniques have been proposed in the research literature. The industry uses some other techniques. Unfortunately, there is insufficient comparison among them, and it is unclear how far we have been. This work fills this gap by comparing the aforementioned techniques. To compare them, we first need a benchmark that can estimate how a tool would perform if applied in a realistic setting today. Thus, we first investigated potential biases that affect the fair comparison of the accuracy of DBRD techniques. Our experiments suggest that data age and issue tracking system choice cause a significant …

Go to article

The Impact Of A Continuous Integration Service On The Delivery Time Of Merged Pull Requests, João Helis Bernardo, Daniel Alencar Da Costa, Uirá Kulesza, Christoph Treude Jul 2023

The Impact Of A Continuous Integration Service On The Delivery Time Of Merged Pull Requests, João Helis Bernardo, Daniel Alencar Da Costa, Uirá Kulesza, Christoph Treude

Research Collection School Of Computing and Information Systems

Continuous Integration (CI) is a software development practice that builds and tests software frequently (e.g., at every push). One main motivator to adopt CI is the potential to deliver software functionalities more quickly than not using CI. However, there is little empirical evidence to support that CI helps projects deliver software functionalities more quickly. Through the analysis of 162,653 pull requests (PRs) of 87 GitHub projects, we empirically study whether adopting a CI service (TRAVISCI) can quicken the time to deliver merged PRs. We complement our quantitative study by analyzing 450 survey responses from participants of 73 software projects. Our …

Go to article

Social Troubleshooting Workshops: Upskilling Students' Soft And Self-Reflection Skills, Sandra Schulz, Rita Garcia, Christoph Treude Jul 2023

Social Troubleshooting Workshops: Upskilling Students' Soft And Self-Reflection Skills, Sandra Schulz, Rita Garcia, Christoph Treude

Research Collection School Of Computing and Information Systems

This poster focuses on workshops to support students’ soft and selfreflection skills during collaborative learning. These workshops intend to help reduce anxiety during group work and to promote inclusive and equitable collaborative learning environments. Unfortunately, single-paced instructional approaches are typically applied in learning environments [3] and do not consider students’ needs when learning nor provide soft-skills guidance that encourages equal participation. The workshops offer educator and student support for equitable group work through upskilling students’ soft skills, such as leadership and communication, that promote better teamwork. By assisting students in developing and practising soft and self-reflection skills, they might have …

Go to article

Contrastive Video Question Answering Via Video Graph Transformer, Junbin Xiao Xiao, Pan Zhou, Angela Yao, Yicong Li, Richang Hong, Shuicheng Yan, Tat-Seng Chua Jul 2023

Contrastive Video Question Answering Via Video Graph Transformer, Junbin Xiao Xiao, Pan Zhou, Angela Yao, Yicong Li, Richang Hong, Shuicheng Yan, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

We propose to perform video question answering (VideoQA) in a Contrastive manner via a Video Graph Transformer model (CoVGT). CoVGT’s uniqueness and superiority are three-fold: 1) It proposes a dynamic graph transformer module which encodes video by explicitly capturing the visual objects, their relations and dynamics, for complex spatio-temporal reasoning. 2) It designs separate video and text transformers for contrastive learning between the video and text to perform QA, instead of multi-modal transformer for answer classification. Fine-grained video-text communication is done by additional cross-modal interaction modules. 3) It is optimized by the joint fully- and self-supervised contrastive objectives between the …

Go to article

Mdps As Distribution Transformers: Affine Invariant Synthesis For Safety Objectives, S. Akshay, Krishnendu Chatterjee, Tobias Meggendorfer, Dorde Zikelic Jul 2023

Mdps As Distribution Transformers: Affine Invariant Synthesis For Safety Objectives, S. Akshay, Krishnendu Chatterjee, Tobias Meggendorfer, Dorde Zikelic

Research Collection School Of Computing and Information Systems

Markov decision processes can be viewed as transformers of probability distributions. While this view is useful from a practical standpoint to reason about trajectories of distributions, basic reachability and safety problems are known to be computationally intractable (i.e., Skolem-hard) to solve in such models. Further, we show that even for simple examples of MDPs, strategies for safety objectives over distributions can require infinite memory and randomization.In light of this, we present a novel overapproximation approach to synthesize strategies in an MDP, such that a safety objective over the distributions is met. More precisely, we develop a new framework for template-based …

Go to article

Knowledge-Enhanced Mixed-Initiative Dialogue System For Emotional Support Conversations, Yang Deng, Wenxuan Zhang, Yifei Yuan, Wai Lam Jul 2023

Knowledge-Enhanced Mixed-Initiative Dialogue System For Emotional Support Conversations, Yang Deng, Wenxuan Zhang, Yifei Yuan, Wai Lam

Research Collection School Of Computing and Information Systems

Unlike empathetic dialogues, the system in emotional support conversations (ESC) is expected to not only convey empathy for comforting the help-seeker, but also proactively assist in exploring and addressing their problems during the conversation. In this work, we study the problem of mixed-initiative ESC where the user and system can both take the initiative in leading the conversation. Specifically, we conduct a novel analysis on mixed-initiative ESC systems with a tailor-designed schema that divides utterances into different types with speaker roles and initiative types. Four emotional support metrics are proposed to evaluate the mixed-initiative interactions. The analysis reveals the necessity …

Go to article

Towards Robust Personalized Dialogue Generation Via Order-Insensitive Representation Regularization, Liang Chen, Hongru Wang, Yang Deng, Wai-Chung Kwan, Zezhong Wang, Kam-Fai Wong Jul 2023

Towards Robust Personalized Dialogue Generation Via Order-Insensitive Representation Regularization, Liang Chen, Hongru Wang, Yang Deng, Wai-Chung Kwan, Zezhong Wang, Kam-Fai Wong

Research Collection School Of Computing and Information Systems

Generating persona consistent dialogue response is important for developing an intelligent conversational agent. Recent works typically fine-tune large-scale pre-trained models on this task by concatenating persona texts and dialogue history as a single input sequence to generate the target response. While simple and effective, our analysis shows that this popular practice is seriously affected by order sensitivity where different input orders of persona sentences significantly impact the quality and consistency of generated response, resulting in severe performance fluctuations (i.e., 29.4% on GPT2 and 83.2% on BART). To mitigate the order sensitivity problem, we propose a model-agnostic framework, ORder Insensitive Generation …

Go to article

Peerda: Data Augmentation Via Modeling Peer Relation For Span Identification Tasks, Weiwen Xu, Xin Li, Yang Deng, Wai Lam, Lidong Bing Jul 2023

Peerda: Data Augmentation Via Modeling Peer Relation For Span Identification Tasks, Weiwen Xu, Xin Li, Yang Deng, Wai Lam, Lidong Bing

Research Collection School Of Computing and Information Systems

Span identification aims at identifying specific text spans from text input and classifying them into pre-defined categories. Different from previous works that merely leverage the Subordinate (SUB) relation (i.e. if a span is an instance of a certain category) to train models, this paper for the first time explores the Peer (PR) relation, which indicates that two spans are instances of the same category and share similar features. Specifically, a novel Peer Data Augmentation (PeerDA) approach is proposed which employs span pairs with the PR relation as the augmentation data for training. PeerDA has two unique advantages: (1) There are …

Go to article

Product Question Answering In E-Commerce: A Survey, Yang Deng, Wenxuan Zhang, Qian Yu, Wai Lam Jul 2023

Product Question Answering In E-Commerce: A Survey, Yang Deng, Wenxuan Zhang, Qian Yu, Wai Lam

Research Collection School Of Computing and Information Systems

Product question answering (PQA), aiming to automatically provide instant responses to customer’s questions in E-Commerce platforms, has drawn increasing attention in recent years. Compared with typical QA problems, PQA exhibits unique challenges such as the subjectivity and reliability of user-generated contents in E-commerce platforms. Therefore, various problem settings and novel methods have been proposed to capture these special characteristics. In this paper, we aim to systematically review existing research efforts on PQA. Specifically, we categorize PQA studies into four problem settings in terms of the form of provided answers. We analyze the pros and cons, as well as present existing …

Go to article

Goal Awareness For Conversational Ai: Proactivity, Non-Collaborativity, And Beyond, Yang Deng, Wenqiang Lei, Minlie Huang, Tat-Seng Chua Jul 2023

Goal Awareness For Conversational Ai: Proactivity, Non-Collaborativity, And Beyond, Yang Deng, Wenqiang Lei, Minlie Huang, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Conversational systems are envisioned to provide social support or functional service to human users via natural language interactions. Conventional conversation researches mainly focus on the responseability of the system, such as dialogue context understanding and response generation, but overlooks the design of an essential property in intelligent conversations, i.e., goal awareness. The awareness of goals means the state of not only being responsive to the users but also aware of the target conversational goal and capable of leading the conversation towards the goal, which is a significant step towards higher-level intelligence and artificial consciousness. It can not only largely improve …

Go to article

A Review On The Effects Of Chanting And Solfeggio Frequencies On Well-Being, Xuyu Yang, Fiona Fui-Hoon Nah, Fen Lin Jul 2023

A Review On The Effects Of Chanting And Solfeggio Frequencies On Well-Being, Xuyu Yang, Fiona Fui-Hoon Nah, Fen Lin

Research Collection School Of Computing and Information Systems

This paper presents our literature review on how chanting and solfeggio frequencies affect brain activity, enhance well-being, and inform the design of sound therapy. We call for more scientific research to investigate and better understand the effects of chanting and solfeggio frequencies on well-being.

Go to article

Authenticated Public Key Elliptic Curve Based On Deep Convolutional Neural Network For Cybersecurity Image Encryption Application, Esam A. A. Hagras, Saad Aldosary, Haitham Khaled, Tarek M. Hassan Jul 2023

Authenticated Public Key Elliptic Curve Based On Deep Convolutional Neural Network For Cybersecurity Image Encryption Application, Esam A. A. Hagras, Saad Aldosary, Haitham Khaled, Tarek M. Hassan

Research outputs 2022 to 2026

The demand for cybersecurity is growing to safeguard information flow and enhance data privacy. This essay suggests a novel authenticated public key elliptic curve based on a deep convolutional neural network (APK-EC-DCNN) for cybersecurity image encryption application. The public key elliptic curve discrete logarithmic problem (EC-DLP) is used for elliptic curve Diffie–Hellman key exchange (EC-DHKE) in order to generate a shared session key, which is used as the chaotic system’s beginning conditions and control parameters. In addition, the authenticity and confidentiality can be archived based on ECC to share the (Formula presented.) parameters between two parties by using the EC-DHKE …

Go to article

On Irs-Assisted Covert Communication With A Friendly Uav, Xiaobei Xu, Linzi Hu, Sha Wei, Yuwen Qian, Shihao Yan, Feng Shu, Jun Li Jul 2023

On Irs-Assisted Covert Communication With A Friendly Uav, Xiaobei Xu, Linzi Hu, Sha Wei, Yuwen Qian, Shihao Yan, Feng Shu, Jun Li

Research outputs 2022 to 2026

Driven by the rapidly growing demand for information security, covert wireless communication has become an essential technology and attracted tremendous attention. However, traditional wireless covert communication is continuously exposing the inherent limitations, creating challenges around deployment in environments with a large number of obstacles, such as cities with high-rise buildings. In this paper, we propose an intelligent reflecting surface (IRS)-assisted covert communication system (CCS) for communicating with a friendly unmanned aerial vehicle (UAV) in which the UAV generates artificial noise (AN) to interfere with monitoring. Furthermore, we model the power of AN emitted by the UAV using an uncertainty model, …

Go to article

Well-Conditioned T-Matrix Formulation For Scattering By A Dielectric Obstacle, Murat Enes Hati̇poğlu, Fati̇h Di̇kmen Jul 2023

Well-Conditioned T-Matrix Formulation For Scattering By A Dielectric Obstacle, Murat Enes Hati̇poğlu, Fati̇h Di̇kmen

Turkish Journal of Electrical Engineering and Computer Sciences

The classic formulation of the extended boundary condition method is revisited to inject the regularization operators for the unknown coefficients of the eigen-function expansions for the travelling and standing waves throughout the dielectric scatterer. It is shown that, using the new definitions, the existing algorithm of the scattering field calculation can be kept the same for its well-conditioned version. This is exemplified for scalar 2D problems for both TM and TE polarization under illumination of a line source. The condition numbers of the matrix operators in the new version of the algorithm are drastically reduced when the regularization interfaces are …

Go to article

Lightweight Deep Neural Network Models For Electromyography Signal Recognition For Prosthetic Control, Ahmet Mert Jul 2023

Lightweight Deep Neural Network Models For Electromyography Signal Recognition For Prosthetic Control, Ahmet Mert

Turkish Journal of Electrical Engineering and Computer Sciences

In this paper, lightweight deep learning methods are proposed to recognize multichannel electromyography (EMG) signals against varying contraction levels. The classical machine learning, and signal processing methods namely, linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), root mean square (RMS), and waveform length (WL) are adopted to convolutional neural network (CNN), and long short-term memory neural network (LSTM). Eight-channel recordings of nine amputees from a publicly available dataset are used for training and testing the proposed models considering prosthetic control strategies. Six class hand movements with three contraction levels are applied to WL and RMS-based feature extraction. After that, they …

Go to article

First Prev Next Last