Reviewerly/exHarmony huggingface dataset:
A Dynamic Adaptive and Resource-Allocated Selection Method Based on TOPSIS and VIKOR in Federated Learning

Authors:
raw_author_name: Lin Li
raw_affiliation_string: School of Software Engineering, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China
raw_author_name: Wei Shi
raw_affiliation_string: School of Software Engineering, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China
raw_author_name: Shuyu Chen
raw_affiliation_string: School of Big Data and Software Engineering, Chongqing University, Chongqing, 400044, China
raw_author_name: Jun Liu
raw_affiliation_string: School of Software Engineering, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China
raw_author_name: Jiangping Huang
raw_affiliation_string: School of Software Engineering, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China

Abstract: Abstract Federated learning (FL) is a decentralized and privacy-preserving machine learning technique that protects data privacy by learning models locally and not sharing datasets. However, due to limited computing resources on devices and highly heterogeneous data in practical situations, the training efficiency and resource utilization of federated learning is low. In order to resolve these challenges, we introduce a blockchain-assisted dynamic adaptive and personalized federated learning framework (TV-FedAvg) in the presence of restricted computing power resources and data heterogeneity. After each round of local training, we utilize an improved scoring model based on VIKOR and TOPSIS to comprehensively score the devices. The scores are then utilized to choose devices for participation in global aggregation and to carry out model aggregation through blockchain consensus. Furthermore, resources are reallocated for the next round to enhance resource efficiency, model fairness, and performance. Finally, we demonstrate through experimentation that TV-FedAvg outperforms other models such as pFedMe, FedAvg, Per-FedAvg, and TOPSIS in terms of both efficiency and performance.
Intelligent visually lossless compression of dental images

Authors:
raw_author_name: Liudmyla Kryvenko
raw_affiliation_string: Department of Pediatric Dentistry and Implantology, Ukraine
raw_author_name: Olha Krylova
raw_affiliation_string: Department of Therapeutic Dentistry, Ukraine
raw_author_name: Vladimir Lukin
raw_affiliation_string: Department of Information and Communication Technologies, Ukraine
raw_author_name: Sergii Kryvenko
raw_affiliation_string: Department of Information and Communication Technologies, Ukraine

Abstract: Background: Tendencies to increase the mean size of dental images and the number of images acquired daily makes necessary their compression for efficient storage and transferring via communication lines in telemedicine and other applications. To be a proper solution, lossy compression techniques have to provide a visually lossless option (mode) where a desired quality (invisibility of introduced distortions for preserving diagnostically valuable information) is ensured quickly and reliably simultaneously with a rather large compression ratio. Objective: Within such an approach, our goal is to give answers to several practical questions such as what encoder to use, how to set its parameter that controls compression, how to verify that we have reached our ultimate goal, what are additional advantages and drawbacks of a given coder, and so on. Methods: We analyze the performance characteristics of several encoders mainly based on discrete cosine transform for a set of 512 × 512 pixel fragments of larger size dental images produced by Morita and Dentsply Sirona imaging systems. To control the visual quality of compressed images and the invisibility of introduced distortions, we have used modern visual quality metrics and distortion invisibility thresholds established for them in previous experiments. Besides, we have also studied the so-called just noticeable distortions (JND) concept, namely, the approach based on the first JND point when the difference between an image subject to compression and its compressed version starts to appear. Results: The rate-distortion dependences and coder setting parameters obtained for the considered approaches are compared. The values of the parameters that control compression (PCC) have been determined. The ranges of the provided values of compression ratio have been estimated and compared. It is shown that the provided CR values vary from about 20 to almost 70 for modern coders and almost noise-free images that is significantly better than for JPEG. For images with visible noise, the minimal and maximal values of produced CR are smaller than for the almost noise-free images. We also present the results of the verification of compressed image quality by specialists (professional dentists). Conclusion: It is shown that it is possible and easy to carry out visually lossless compression of dental images using the proposed approaches with providing quite high compression ratios without loss of data diagnostic value.
How Depth of Field in Screen Images Affects Egocentric Distance Perception

Authors:
raw_author_name: Yunchen Pu
raw_affiliation_string: School of Art Design and Media, East China University of Science and Technology, Shanghai, China
raw_author_name: Dadi An
raw_affiliation_string: School of Art Design and Media, East China University of Science and Technology, Shanghai, China

Abstract: In virtual environments (VEs) presented on flat screens, accurate egocentric distance perception is crucial for enhancing immersion. This study investigated depth of field-induced blur, typically perceived as an ordinal depth cue, to understand its role as a quantitative depth cue in VEs. We employed verbal judgment and position adjustment methods to quantify observers’ egocentric distance perception across various depth of field settings (n = 30 participants). Our analysis also examined the interplay between blur and other depth cues. Results indicated that increased blur in VEs leads to a greater underestimation of distances. Moreover, blur operates as a relatively independent depth cue, showing minimal interaction with other cues. These findings address a gap in research on the independent role of depth of field as a quantitative depth cue in VEs. This contributes to the visual design of VEs and supports the enhancement of user experience in human–computer interactions.
No‐reference image quality assessment via a dual‐branch residual network

Authors:
raw_author_name: Chang Liu
raw_affiliation_string: College of Computer and Information Engineering (College of Artificial Intelligence) Nanjing Tech University Nanjing People's Republic of China

Abstract: Abstract The No‐Reference Image Quality Assessment (NR‐IQA) method can predict quality scores of distorted images without the reference image. However, due to the variability in both color and structure of images, existing NR‐IQA methods struggle to accurately predict quality scores of distorted images. Therefore, an NR‐IQA method based on a Dual‐Branch Residual Network (DBRIQA) for evaluating the quality scores of color‐distorted images is proposed. First, guided filtering is applied to the hue images in the HSV space to extract distortion information from the image's color. Then, due to significant differences in distorted images, enhancements are made to the traditional residual blocks, forming a feature extraction module that captures multi‐scale features from the image. In order to capture the global relationships in the distorted image, a Global‐Level Attention Block (GLAB) is introduced, facilitating the interaction of information among the extracted features. Experiments were conducted across four publicly available IQA datasets, including LIVE, CSIQ, TID2008, and TID2013. The experimental results demonstrate that the proposed method exhibits strong performance and generalization capabilities in predicting image quality compared to peer methods.
Fraud victimization across the lifespan: evidence on repeat victimization using perpetrator data

Authors:
raw_author_name: Marguerite DeLiema
raw_affiliation_string: School of Social Work, University of Minnesota, Saint Paul, Minnesota, USA
raw_author_name: Lynn Langton
raw_affiliation_string: Victimization and Resilience Program, RTI International, Washington, DC, USA
raw_author_name: Daniel Brannock
raw_affiliation_string: Research Data Scientist, Center for Data Science, RTI International, Washington, DC, USA
raw_author_name: Edward Preble
raw_affiliation_string: Data Science, RTI International, Washington, DC, USA

Abstract: Older adults are thought to be more susceptible to scams, yet understanding the relationship between chronological age and victimization is limited by underreporting. This study avoids underreporting bias by merging four longitudinal databases of Americans (N = 1.33 million) who paid money in response to mail scams over 20 years. We investigate the risk of repeat victimization and victimization by multiple scam types over the life course. Victims in their 70s and 80s are 9% more likely to experience another victimization incident than those in their 50s. Those age 18 to 29 are 24% less likely to experience another victimization incident. Relative to adults in their 50s, the odds of victimization by multiple scams are greater for those in their 60s and 70s, but lower for those 80 + . This study demonstrates the research potential in using scammers' data to understand patterns of victimization. Fraud prevention efforts should target older individuals who are at higher risk of repeat victimization and suffer greater losses as a result.
Practical high-dimensional quantum key distribution protocol over deployed multicore fiber

Authors:
raw_author_name: Mujtaba Zahidy
raw_affiliation_string: Department of Electrical and Photonics Engineering, Technical University of Denmark, Ørsteds Pl., Kgs. Lyngby, 2800, Denmark
raw_author_name: Domenico Ribezzo
raw_affiliation_string: Department of Physical and Chemical Sciences, University of L'Aquila, L'Aquila, Italy
raw_author_name: Claudia De Lazzari
raw_affiliation_string: QTI S.r.l., Firenze, 50125, Italy
raw_author_name: Ilaria Vagniluca
raw_affiliation_string: QTI S.r.l., Firenze, 50125, Italy
raw_author_name: Nicola Biagi
raw_affiliation_string: QTI S.r.l., Firenze, 50125, Italy
raw_author_name: Ronny Müller
raw_affiliation_string: Department of Electrical and Photonics Engineering, Technical University of Denmark, Ørsteds Pl., Kgs. Lyngby, 2800, Denmark
raw_author_name: Tommaso Occhipinti
raw_affiliation_string: QTI S.r.l., Firenze, 50125, Italy
raw_author_name: Leif K. Oxenløwe
raw_affiliation_string: Department of Electrical and Photonics Engineering, Technical University of Denmark, Ørsteds Pl., Kgs. Lyngby, 2800, Denmark
raw_author_name: Michael Galili
raw_affiliation_string: Department of Electrical and Photonics Engineering, Technical University of Denmark, Ørsteds Pl., Kgs. Lyngby, 2800, Denmark
raw_author_name: Dajana Cassioli
raw_affiliation_string: Department of Information Engineering, Computer Science and Mathematics, University of L'Aquila, L'Aquila, Italy
raw_author_name: Antonio Mecozzi
raw_affiliation_string: Department of Physical and Chemical Sciences, University of L'Aquila, L'Aquila, Italy
raw_author_name: Cristian Antonelli
raw_affiliation_string: Department of Physical and Chemical Sciences, University of L'Aquila, L'Aquila, Italy
raw_author_name: Alessandro Zavatta
raw_affiliation_string: QTI S.r.l., Firenze, 50125, Italy
raw_author_name: Davide Bacco
raw_affiliation_string: QTI S.r.l., Firenze, 50125, Italy

Abstract: Quantum key distribution (QKD) is a secure communication scheme for sharing symmetric cryptographic keys based on the laws of quantum physics, and is considered a key player in the realm of cyber-security. A critical challenge for QKD systems comes from the fact that the ever-increasing rates at which digital data are transmitted require more and more performing sources of quantum keys, primarily in terms of secret key generation rate. High-dimensional QKD based on path encoding has been proposed as a candidate approach to address this challenge. However, while proof-of-principle demonstrations based on lab experiments have been reported in the literature, demonstrations in realistic environments are still missing. Here we report the generation of secret keys in a 4-dimensional hybrid time-path-encoded QKD system over a 52-km deployed multicore fiber link forming by looping back two cores of a 26-km 4-core optical fiber. Our results indicate that robust high-dimensional QKD can be implemented in a realistic environment by combining standard telecom equipment with emerging multicore fiber technology.
Occupying Another’s Digital Space: Privacy of Smartphone Users as a Situated Practice

Authors:
raw_author_name: Iuliia Avgustis
raw_affiliation_string: Research Unit “Human Computer Interaction and Human-Centered Development (INTERACT)”, Faculty of Information Technology and Electrical Engineering, University of Oulu, Oulu, Finland
raw_author_name: Netta Iivari
raw_affiliation_string: Research Unit “Human Computer Interaction and Human-Centered Development (INTERACT)”, Faculty of Information Technology and Electrical Engineering, University of Oulu, Oulu, Finland

Abstract: Abstract A smartphone’s screen is commonly regarded as a private space, and the action of looking at it is usually considered a violation of one’s privacy both by researchers and designers. However, our study demonstrates how participants in the interaction themselves negotiate moment by moment and achieve an understanding of someone’s screen space as public or private. In this paper, we analyze the interactional sequences of uninvited looks at another participant’s phone. Drawing on visual ethnography and ethnomethodologically informed multimodal interaction analysis, we video-recorded and analyzed everyday interactions between friends and acquaintances. Our findings show that looking at someone’s smartphone display is often performed and oriented to as a resource in interaction rather than an invasion of privacy. We therefore characterize the interactional functions of gazes and glances at another’s screen. We also discuss the research and design implications of approaching privacy as a situated practice.
A Personalized Federated Learning Method Based on Clustering and Knowledge Distillation

Authors:
raw_author_name: Jianfei Zhang
raw_affiliation_string: School of Computer Science and Technology, Changchun University of Science and Technology, Changchun 130031, China

Abstract: Federated learning (FL) is a distributed machine learning paradigm under privacy preservation. However, data heterogeneity among clients leads to the shared global model obtained after training, which cannot fit the distribution of each client’s dataset, and the performance of the model degrades. To address this problem, we proposed a personalized federated learning method based on clustering and knowledge distillation, called pFedCK. In this algorithm, each client has an interactive model that participates in global training and a personalized model that is only trained locally. Both of the models perform knowledge distillation with each other through the feature representation of the middle layer and the soft prediction of the model. In addition, in order to make an interaction model only obtaining the model information from the client, which has similar data distribution and avoids the interference of other heterogeneous information, the server will cluster the clients according to the similarity of the amount of parameter variation uploaded by different interaction models during every training round. By clustering clients, interaction models with similar data distributions can cooperate with each other to better fit the local dataset distribution. Thereby, the performance of personalized model can be improved by obtaining more valuable information indirectly. Finally, we conduct simulation experiments on three benchmark datasets under different data heterogeneity scenarios. Compared to the single model algorithms, the accuracy of pFedCK improved by an average of 23.4% and 23.8% over FedAvg and FedProx, respectively; compared to typical personalization algorithms, the accuracy of pFedCK improved by an average of 0.8% and 1.3%, and a maximum of 1.0% and 2.9% over FedDistill and FML.
A Practical Approach to Automatic Earthquake Catalog Compilation in Local OBS Networks Using Deep-Learning and Network-Based Algorithms

Authors:
raw_author_name: Vera Schlindwein
raw_affiliation_string: 1Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven, Germany; 2University of Bremen, Bremen, Germany

Abstract: Abstract In land-based seismology, modern automatic earthquake detection and phase picking algorithms have already proven to outperform classic approaches, resulting in more complete catalogs when only taking a fraction of the time needed for classic methods. For marine-based seismology, similar advances have not been made yet. For ocean-bottom seismometer (OBS) data, additional challenges arise, such as a lower signal-to-noise ratio and fewer labeled data sets available for training deep-learning models. However, the performance of available deep-learning models has not yet been extensively tested on marine-based data sets. Here, we apply three different modern event detection and phase picking approaches to an ∼12 month local OBS data set and compare the resulting earthquake catalogs and location results. In addition, we evaluate their performance by comparing different subcatalogs of manually detected events and visually revised picks to their automatic counterparts. The results show that seismicity patterns from automatically compiled catalogs are comparable to a manually revised catalog after applying strict location quality control criteria. However, the number of such well-constrained events varies between the approaches and catalog completeness cannot be reliably determined. We find that PhaseNet is more suitable for local OBS networks compared with EQTransformer and propose a pick-independent event detection approach, such as Lassie, as the preferred choice for an initial event catalog compilation. Depending on the aim of the study, different schemes of manual repicking should be applied because the automatic picks are not yet reliable enough for developing a velocity model or interpreting small-scale seismicity patterns.
Intent Identification by Semantically Analyzing the Search Query

Authors:
raw_author_name: Tangina Sultana
raw_affiliation_string: Department of Computer Science and Engineering, Global Campus, Kyung Hee University, Yongin-si 1732, Republic of Korea; Department of Electronics and Communication Engineering, Hajee Mohammad Danesh Science and Technology University, Dinajpur 5200, Bangladesh
raw_author_name: Ashis Kumar Mandal
raw_affiliation_string: Department of Computer Science and Engineering, Hajee Mohammad Danesh Science and Technology University, Dinajpur 5200, Bangladesh; Department of Computer Science, University of Saskatchewan, Saskatoon, SK S7N 5A2, Canada
raw_author_name: Hasi Saha
raw_affiliation_string: Department of Computer Science and Engineering, Hajee Mohammad Danesh Science and Technology University, Dinajpur 5200, Bangladesh
raw_author_name: Md. Delowar Hossain
raw_affiliation_string: Department of Computer Science and Engineering, Global Campus, Kyung Hee University, Yongin-si 1732, Republic of Korea; Department of Computer Science and Engineering, Hajee Mohammad Danesh Science and Technology University, Dinajpur 5200, Bangladesh

Abstract: Understanding and analyzing the search intent of a user semantically based on their input query has emerged as an intriguing challenge in recent years. It suffers from small-scale human-labeled training data that produce a very poor hypothesis of rare words. The majority of data portals employ keyword-driven search functionality to explore content within their repositories. However, the keyword-based search cannot identify the users’ search intent accurately. Integrating a query-understandable framework into keyword search engines has the potential to enhance their performance, bridging the gap in interpreting the user’s search intent more effectively. In this study, we have proposed a novel approach that focuses on spatial and temporal information, phrase detection, and semantic similarity recognition to detect the user’s intent from the search query. We have used the n-gram probabilistic language model for phrase detection. Furthermore, we propose a probability-aware gated mechanism for RoBERTa (Robustly Optimized Bidirectional Encoder Representations from Transformers Approach) embeddings to semantically detect the user’s intent. We analyze and compare the performance of the proposed scheme with the existing state-of-the-art schemes. Furthermore, a detailed case study has been conducted to validate the model’s proficiency in semantic analysis, emphasizing its adaptability and potential for real-world applications where nuanced intent understanding is crucial. The experimental result demonstrates that our proposed system can significantly improve the accuracy for detecting the users’ search intent as well as the quality of classification during search.
Scene text recognition with context-aware autonomous bidirectional iterative models

Authors:
raw_author_name: Xiaoqing Zhao
raw_affiliation_string: College of Computer Science and Technology, Xinjiang University, Urumqi, China
raw_author_name: Miaomiao Xu
raw_affiliation_string: College of Computer Science and Technology, Xinjiang University, Urumqi, China
raw_author_name: Yanbing Li
raw_affiliation_string: College of Computer Science and Technology, Xinjiang University, Urumqi, China
raw_author_name: Hao Huang
raw_affiliation_string: College of Computer Science and Technology, Xinjiang University, Urumqi, China
raw_author_name: Wushour Silamu
raw_affiliation_string: College of Computer Science and Technology, Xinjiang University, Urumqi, China

Abstract: This research focuses on Scene Text Recognition (STR), a crucial component in various applications of artificial intelligence such as image retrieval, office automation, and intelligent traffic systems. Recent studies have shown that semantic-aware approaches significantly improve the performance of STR tasks, with context-aware STR methods becoming mainstream. Among these, the fusion of visual and language models has shown remarkable effectiveness. We propose a novel method (PABINet) that incorporates three key components: a Visual-Language Decoder, a Language Model, and a Fusion Model. First, during training, the Visual-Language Decoder masks the original labels in the Transformer decoder using permutation masks, with each mask being unique. This enhances word memorization and learning through contextual semantic information, resulting in robust semantic knowledge. During the inference stage, the Visual-Language Decoder employs autonomous Autoregressive model (AR) inference to generate results. Subsequently, the Language Model scrutinizes and corrects the output of the Visual-Language Encoder using a cloze mask approach, achieving context-aware, autonomous, bidirectional inference. Finally, the Fusion Model concatenates and refines the outputs of both models through iterative layers.Experimental results demonstrate that our PABINet performs exceptionally well when handling various quality images. When trained with synthetic data, PABINet achieves a new STR benchmark (average accuracy of 92.41%), and when trained with real data, it establishes new state-of-the-art results (average accuracy of 96.28%).