Reviewerly/exHarmony huggingface dataset:
OnDev-LCT: On-Device Lightweight Convolutional Transformers towards federated learning

Authors:
raw_author_name: Chu Myaet Thwal
raw_affiliation_string: Department of Computer Science and Engineering, Kyung Hee University, Yongin-si, Gyeonggi-do 17104, South Korea
raw_author_name: Minh N.H. Nguyen
raw_affiliation_string: Vietnam - Korea University of Information and Communication Technology, Danang, Viet Nam
raw_author_name: Ye Lin Tun
raw_affiliation_string: Department of Computer Science and Engineering, Kyung Hee University, Yongin-si, Gyeonggi-do 17104, South Korea
raw_author_name: Seong Tae Kim
raw_affiliation_string: Department of Computer Science and Engineering, Kyung Hee University, Yongin-si, Gyeonggi-do 17104, South Korea
raw_author_name: My T. Thai
raw_affiliation_string: Department of Computer and Information Science and Engineering, University of Florida, Gainesville, Florida 32611, USA
raw_author_name: Choong Seon Hong
raw_affiliation_string: Department of Computer Science and Engineering, Kyung Hee University, Yongin-si, Gyeonggi-do 17104, South Korea

Abstract: Federated learning (FL) has emerged as a promising approach to collaboratively train machine learning models across multiple edge devices while preserving privacy. The success of FL hinges on the efficiency of participating models and their ability to handle the unique challenges of distributed learning. While several variants of Vision Transformer (ViT) have shown great potential as alternatives to modern convolutional neural networks (CNNs) for centralized training, the unprecedented size and higher computational demands hinder their deployment on resource-constrained edge devices, challenging their widespread application in FL. Since client devices in FL typically have limited computing resources and communication bandwidth, models intended for such devices must strike a balance between model size, computational efficiency, and the ability to adapt to the diverse and non-IID data distributions encountered in FL. To address these challenges, we propose OnDev-LCT: Lightweight Convolutional Transformers for On-Device vision tasks with limited training data and resources. Our models incorporate image-specific inductive biases through the LCT tokenizer by leveraging efficient depthwise separable convolutions in residual linear bottleneck blocks to extract local features, while the multi-head self-attention (MHSA) mechanism in the LCT encoder implicitly facilitates capturing global representations of images. Extensive experiments on benchmark image datasets indicate that our models outperform existing lightweight vision models while having fewer parameters and lower computational demands, making them suitable for FL scenarios with data heterogeneity and communication bottlenecks.
An intelligent correlation learning system for person Re-identification

Authors:
raw_author_name: Samee Ullah Khan
raw_affiliation_string: Sejong University, Seoul, 143-747, South Korea
raw_author_name: Noman Khan
raw_affiliation_string: Sejong University, Seoul, 143-747, South Korea
raw_author_name: Tanveer Hussain
raw_affiliation_string: Department of Computer Science, Edgehill University, UK
raw_author_name: Sung Wook Baik
raw_affiliation_string: Sejong University, Seoul, 143-747, South Korea

Abstract: Person re-identification (PRe-id) aims to retrieve a target person's images captured across multiple/single non-overlapping cameras. To this end, significant techniques have been implemented that extract handcrafted, deep, part-based, and ensemble features to get more refined patterns for matching. But due to the limited focus on the multi-grained, view-consistent, and semantic correlation among different views, these approaches show low performance. Therefore, we present an attention-based multi-view correlation learning framework named (ACLS), which enables to learn multi-grained spatiotemporal features from individuals. The ACLS is mainly composed of three key steps: First, multi-view correlated visual features of pedestrians are extracted using a correlation vision transformer (CVIT) and a pyramid dilated network (PDN), followed by the person attention mechanism. Next, we employ convolutional long short-term memory (ConvLSTM) to extract spatiotemporal information from pedestrian images captured in different time frames. Finally, a deep fusion strategy is employed, which intelligently integrates features for the final matching task. Extensive evaluations are conducted over three famous datasets: Market-1501, DukeMCMT-reID, and CUHK03, results show tremendous ranking performance including 93.7%, 90.4%, and 85.7%. Thus, concluded remarks that our learning mechanism beats the current state-of-the-art (SOTA) methods.
Large language models (LLMs) in the evaluation of emergency radiology reports: performance of ChatGPT-4, Perplexity, and Bard

Authors:
raw_author_name: E. Sala
raw_affiliation_string: ARC Advanced Radiology Center (ARC), Department of Oncological Radiotherapy, and Hematology, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy; Università Cattolica del Sacro Cuore, Facoltà di Medicina e Chirurgia, Rome, Italy

Abstract: Large language models (LLMs), especially those based on the Generative Pre-trained Transformer (GPT) architecture, have become widely popular and have been applied in various fields due to their ability to provide written responses to a diverse range of queries swiftly and accurately. LLMs have demonstrated a transformative and potentially revolutionary capacity in multiple medical subfields, including radiology.1
Secret sharing: A comprehensive survey, taxonomy and applications

Authors:
raw_author_name: Arup Kumar Chattopadhyay
raw_affiliation_string: Central Institute of Technology Kokrajhar, India
raw_author_name: Sanchita Saha
raw_affiliation_string: Central Institute of Technology Kokrajhar, India; Haldia Institute of Technology, India
raw_author_name: Amitava Nag
raw_affiliation_string: Central Institute of Technology Kokrajhar, India
raw_author_name: Sukumar Nandi
raw_affiliation_string: Indian Institute of Technology Guwahati, India

Abstract: The emergence of ubiquitous computing and different disruptive technologies caused magnificent development in information and communication technology. Likewise, cybercriminals are also carefully considering different newer ways of attacks. Protecting the confidentiality, integrity, and authentication of sensitive information is the day's major challenge. Secret sharing is a method that allows a trusted authority (the dealer) to distribute a secret or a number of secrets among some target participants with the intention that certain predetermined groups of participants can collaborate to recover the secret or secrets. Any other group formed by the participants cannot do so. Threshold secret sharing (TSS) is a particular form of secret sharing. It permits any group consisting of at least a specific number (called the threshold) of participants to reconstruct the secret or secrets. However, any group with fewer than the specified number of participants is forbidden to do so. It provides tolerance against single point of failure (SPOF), which has attracted a large number of researchers to contribute in this field. It has the potential to be implemented in numerous practical and secure applications. In this paper, we present a comprehensive survey of a variety of existing threshold secret sharing schemes. We have identified various aspects of developing secure and efficient secret sharing schemes. We have also highlighted some of the applications based on secret sharing. Finally, the open challenges and future research directions in the field of secret sharing are identified and discussed.
The role of perceived risk and information security on customers' acceptance of service robots in the hotel industry

Authors:
raw_author_name: Abraham Pizam
raw_affiliation_string: University of Central Florida, 9907 Universal Blvd, Orlando, FL 32832, USA
raw_author_name: Ahmet Bulent Ozturk
raw_affiliation_string: University of Central Florida, 9907 Universal Blvd, Orlando, FL 32832, USA
raw_author_name: Ahmet Hacikara
raw_affiliation_string: University of Central Florida, 9907 Universal Blvd, Orlando, FL 32832, USA
raw_author_name: Tingting Zhang
raw_affiliation_string: University of Central Florida, 9907 Universal Blvd, Orlando, FL 32832, USA
raw_author_name: Adela Balderas-Cejudo
raw_affiliation_string: Deusto Business School, University of Deusto (Spain)
raw_author_name: Dimitrios Buhalis
raw_affiliation_string: Bournemouth University, D202, Talbot Campus, Poole, Dorset BH12 5BB, UK
raw_author_name: Galia Fuchs
raw_affiliation_string: Ben-Gurion University of the Negev, Be'er Sheva, Israel, 8410501
raw_author_name: Tadayuki Hara
raw_affiliation_string: University of Central Florida, 9907 Universal Blvd, Orlando, FL 32832, USA
raw_author_name: Jessica Meira
raw_affiliation_string: University of Central Florida, 9907 Universal Blvd, Orlando, FL 32832, USA
raw_author_name: Raquel Garcia Mercedes Revilla
raw_affiliation_string: UDIMA, Universidad a Distancia de Madrid, Spain
raw_author_name: Deepa Sethi
raw_affiliation_string: Indian Institute of Management Kozhikode, IIMK Campus P. O., Kozhikode, Kerala, India
raw_author_name: Ye Shen
raw_affiliation_string: California Polytechnic State University, 1 Grand Avenue, San Luis Obispo, CA 93407, USA
raw_author_name: Olimpia State
raw_affiliation_string: Bucharest University of Economic Studies, 6, Piata Romana, Sector 1, Bucharest, Romania

Abstract: This study proposed and tested a theoretical framework that investigated the influences of perceived risk and information security on hotel customers’ intention to use service robots. In addition, the impacts of self-efficacy, innovativeness, and facilitating conditions on perceived risk and information security were examined. Structural equation modeling (SEM) was used to test the proposed model by utilizing data collected from eleven countries including the United States, United Kingdom, Turkey, Spain, Romania, Japan, Israel, India, Greece, Canada, and Brazil. The study results demonstrated that perceived risk had a negative impact on customers’ intention to use service robots while information security had a positive impact. In addition, the study results indicated that self-efficacy negatively influenced perceived risk, and positively influenced perceived information security; and innovativeness and facilitating condition positively influenced information security. The study findings offer several important contributions to the hospitality robotics technology adoption literature and present valuable implications for hospitality practitioners and service robot vendors.
Effects of resource orchestration, strategic information exchange capabilities, and digital orientation on innovation and performance of hotel supply chains

Authors:
raw_author_name: Matloub Hussain
raw_affiliation_string: College of Business Administration, University of Sharjah, Sharjah, United Arab Emirates
raw_author_name: Avraam Papastathopoulos
raw_affiliation_string: School of Management, Canadian University Dubai City walk - Al Wasl - Dubai

Abstract: Supply chain (SC) innovation has become a competitive source for hotels to enhance performance in the turbulent business environment. Drawing on the resource orchestration (RO) and information sharing (IS) theories, we propose an integrated theoretical framework delineating how strategic information exchange (SIE) and RO capabilities and digital orientation of a hotel foster innovation and enhance the performance of its SC. Based on PLS-SEM analysis of data collected from 281 hotels in the United Arab Emirates, our findings indicate that RO and SIE capabilities of hotels influence the performance of SCs, whereas SC innovation mediates the said relationships. However, contrary to the expectations, digital orientation did not moderate the relationships between RO and SIE capabilities and SC innovation of hotels.
SEAM: An integrated activation-coupled model of sentence processing and eye movements in reading

Authors:
raw_author_name: Maximilian M. Rabe
raw_affiliation_string: University of Potsdam, Department of Psychology, Karl-Liebknecht-Str. 24/25, 14482, Potsdam, Germany
raw_author_name: Dario Paape
raw_affiliation_string: University of Potsdam, Department of Linguistics, Karl-Liebknecht-Str. 24/25, 14482, Potsdam, Germany
raw_author_name: Daniela Mertzen
raw_affiliation_string: University of Potsdam, Department of Linguistics, Karl-Liebknecht-Str. 24/25, 14482, Potsdam, Germany
raw_author_name: Shravan Vasishth
raw_affiliation_string: University of Potsdam, Department of Linguistics, Karl-Liebknecht-Str. 24/25, 14482, Potsdam, Germany
raw_author_name: Ralf Engbert
raw_affiliation_string: University of Potsdam, Department of Psychology, Karl-Liebknecht-Str. 24/25, 14482, Potsdam, Germany

Abstract: Models of eye-movement control during reading, developed largely within psychology, usually focus on visual, attentional, lexical, and motor processes but neglect post-lexical language processing; by contrast, models of sentence comprehension processes, developed largely within psycholinguistics, generally focus only on post-lexical language processes. We present a model that combines these two research threads, by integrating eye-movement control and sentence processing. Developing such an integrated model is extremely challenging and computationally demanding, but such an integration is an important step toward complete mathematical models of natural language comprehension in reading. We combine the SWIFT model of eye-movement control (Seelig et al., 2023) with key components of the Lewis and Vasishth sentence processing model (Lewis and Vasishth, 2005). This integration becomes possible, for the first time, due in part to recent advances in successful parameter identification in dynamical models, which allows us to investigate profile log-likelihoods for individual model parameters. We present a fully implemented proof-of-concept model demonstrating how such an integrated model can be achieved; our approach includes Bayesian model inference with Markov Chain Monte Carlo (MCMC) sampling as a key computational tool. The integrated Sentence-Processing and Eye-Movement Activation-Coupled Model (SEAM) can successfully reproduce eye movement patterns that arise due to similarity-based interference in reading. To our knowledge, this is the first-ever integration of a complete process model of eye-movement control with linguistic dependency completion processes in sentence comprehension. In future work, this proof of concept model will need to be evaluated using a comprehensive set of benchmark data.
Resolution-independent generative models based on operator learning for physics-constrained Bayesian inverse problems

Authors:
raw_author_name: Xinchao Jiang
raw_affiliation_string: State Key Laboratory of Advanced Design and Manufacturing Technology for Vehicle, Hunan University, Changsha 410082, PR China
raw_author_name: Hu Wang
raw_affiliation_string: Beijing Institute of Technology Shenzhen Automotive Research Institute, Shenzhen, 518000, PR China; State Key Laboratory of Advanced Design and Manufacturing Technology for Vehicle, Hunan University, Changsha 410082, PR China

Abstract: The Bayesian inference approach is widely used to solve inverse problems due to its versatile and natural ability to handle ill-posedness. However, there are often challenges when dealing with situations involving continuous fields or parameters with large-resolution discrete representations. Furthermore, the prior distribution of the unknown parameters is also commonly difficult to determine. Therefore, in this study, an operator learning-based generative adversarial network (OL-GAN) is proposed and integrated into the Bayesian inference framework to address these issues. Compared with classical Bayesian approaches, the distinctive characteristic of the proposed method is that it learns the joint distributions of parameters and responses. By using the trained generative model to handle the prior in Bayes' rule, the posteriors of the unknown parameters can theoretically be approximated by any sampling algorithms (e.g., Markov chain Monte Carlo, MCMC) under the proposed framework. More importantly, efficient sampling can be implemented in a low-dimensional latent space shared by the components of the joint distribution. The latent space is typically a simple and easy-to-sample distribution (e.g., Gaussian, uniform), which significantly reduces the computational cost associated with the Bayesian inference while avoiding prior selection. Furthermore, the generator is resolution-independent due to the incorporation of operator learning. Predictions can thus be obtained at desired coordinates, and inversions can be performed even if the observation data are misaligned with the training data. Finally, the effectiveness of the proposed method is validated through several numerical experiments.
DeepSecDrive: An explainable deep learning framework for real-time detection of cyberattack in in-vehicle networks

Authors:
raw_author_name: Weiping Ding
raw_affiliation_string: Faculty of Data Science, City University of Macau, Macau; School of Information Science and Technology, Nantong University, Nantong 226019, China
raw_author_name: Ibrahim Alrashdi
raw_affiliation_string: Department of Computer Science, College of Computer and Information Sciences, Jouf University, Sakaka, Aljouf 72388, Saudi Arabia
raw_author_name: Hossam Hawash
raw_affiliation_string: Faculty of Computers and Informatics, Zagazig University, Zagazig, 44519 Ash Sharqia Governorate, Egypt
raw_author_name: Mohamed Abdel-Basset
raw_affiliation_string: Faculty of Computers and Informatics, Zagazig University, Zagazig, 44519 Ash Sharqia Governorate, Egypt

Abstract: Autonomous driving (AD) technologies are becoming increasingly popular, promising significant enhancements in road safety and efficiency. However, the susceptibility of autonomous vehicles to cyberattacks highlights the critical need for protecting the security and dependability of in-vehicular networks (IVNs). This study proposes a lightweight, efficient, and explainable deep learning framework, DeepSecDrive, for early detection of security threats against IVN. DeepSecDrive presents robust feature extractor units designed with deformable convolutions and a lightweight non-local network (LNLN). The former promotes dynamic change of receptive field to enhance extraction of complex features from the traffics of in-vehicle networks, while the latter enables learning contextual representations and non-local relationships in vehicular data. DeepSecDrive integrates Shapley Additive exPlanations to construct multi-level interpretability units, providing local and global explanations for model decisions in terms of feature contributions to the overall performance. Proof-of-concept experiments are conducted to evaluate the efficacy and durability of DeepSecDrive on real-world IVN attacks from Car-Hacking dataset, and the experimental results demonstrate the ability of our model to outperform state-of-the-art detection methods.
Recovering sign bits of DCT coefficients in digital images as an optimization problem

Authors:
raw_author_name: Ruiyuan Lin
raw_affiliation_string: Multimedia Communication Lab, Department of Electrical and Computer Engineering, University of Southern California, United States of America
raw_author_name: Sheng Liu
raw_affiliation_string: School of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, Hunan, China
raw_author_name: Jun Jiang
raw_affiliation_string: School of Software & Microelectronics, Electronic & Information Engineering, Peking University, China
raw_author_name: Shujun Li
raw_affiliation_string: Institute of Cyber Security for Society (iCSS) & School of Computing, University of Kent, UK
raw_author_name: Chengqing Li
raw_affiliation_string: School of Computer Science, Xiangtan University, Xiangtan 411105, Hunan China
raw_author_name: C.-C. Jay Kuo
raw_affiliation_string: Multimedia Communication Lab, Department of Electrical and Computer Engineering, University of Southern California, United States of America

Abstract: Recovering unknown, missing, damaged, distorted, or lost information in DCT coefficients is a common task in multiple applications of digital image processing, including image compression, selective image encryption, and image communication. This paper investigates the recovery of sign bits in DCT coefficients of digital images, by proposing two different approximation methods to solve a mixed integer linear programming (MILP) problem, which is NP-hard in general. One method is a relaxation of the MILP problem to a linear programming (LP) problem, and the other splits the original MILP problem into some smaller MILP problems and an LP problem. We considered how the proposed methods can be applied to JPEG-encoded images and conducted extensive experiments to validate their performances. The experimental results showed that the proposed methods outperformed other existing methods by a substantial margin, both according to objective quality metrics and our subjective evaluation.
Path planning of unmanned vehicles based on adaptive particle swarm optimization algorithm

Authors:
raw_author_name: Jiale Zhao
raw_affiliation_string: Innovation Platform for Academicians of Hainan Province, Hainan University, Haikou, 570228, China; School of Information and Communication Engineering, Hainan University, Haikou, 570228, China
raw_author_name: Chaoshuo Deng
raw_affiliation_string: School of Computer and Information Engineering, Tianjin Chengjian University, Tianjin 300384, China
raw_author_name: Huanhuan Yu
raw_affiliation_string: School of Computer Science and Technology, Hainan University, Haikou, 570228, China
raw_author_name: Deshun Li
raw_affiliation_string: School of Computer Science and Technology, Hainan University, Haikou, 570228, China

Abstract: Path planning technology is the basis of autonomous driving of unmanned vehicles. However, there are some problems in the traditional path planning technology. For example, high-quality global paths can't be generated quickly; Lacking of security verification ability; The performance of dynamic obstacle avoidance is poor. Therefore, this paper proposes a path planning method of unmanned vehicles based on adaptive particle swarm optimization algorithm (APSO). Firstly, a map simplification strategy (MSS) is proposed. The grid map is preprocessed by map simplification strategy to reduce the search space and time of path planning algorithm; Secondly, an APSO algorithm is proposed. The algorithm coordinates the search of particles through three adaptive factors and Levy flight strategy. Then, a security checking strategy is proposed. Security checking strategy can be used to verify the safety of global path; Finally, a dynamic obstacle avoidance strategy based on behavior is proposed. Vehicles can independently analyze the types of collision and adopt corresponding obstacle avoidance strategies. The simulation results show that MSS-APSO algorithm and APSO algorithm surpass original algorithms and comparison algorithms; MSS-APSO algorithm has strong applicability in real map environment; The obstacle avoidance strategy has great obstacle avoidance ability and real-time performance; The map simplification strategy can improve iterations of the algorithm and quality of the global path.