Reviewerly/exHarmony huggingface dataset:
Characterization of the Quality of Experience and Immersion of Point Cloud Videos in Augmented Reality Through a Subjective Study

Point cloud streaming has recently attracted research attention as it has the potential to provide six degrees of freedom movement, which is essential for truly immersive media. The transmission of point clouds requires high-bandwidth connections, and adaptive streaming is a promising solution to cope with fluctuating bandwidth conditions. Thus, understanding the impact of different factors in adaptive streaming on the Quality of Experience (QoE) becomes fundamental. Point clouds have been evaluated in Virtual Reality (VR), where viewers are completely immersed in a virtual environment. Augmented Reality (AR) is a novel technology and has recently become popular, yet quality evaluations of point clouds in AR environments are still limited to static images. In this paper, we perform a subjective study of four impact factors on the QoE of point cloud video sequences in AR conditions, including encoding parameters (quantization parameters, QPs), quality switches, viewing distance, and content characteristics. The experimental results show that these factors significantly impact the QoE. The QoE decreases if the sequence is encoded at high QPs and/or switches to lower quality and/or is viewed at a shorter distance, and vice versa. Additionally, the results indicate that the end user is not able to distinguish the quality differences between two quality levels at a specific (high) viewing distance. An intermediate-quality point cloud encoded at geometry QP (G-QP) 24 and texture QP (T-QP) 32 and viewed at 2.5m can have a QoE (i.e., score 6.5 out of 10) comparable to a high-quality point cloud encoded at 16 and 22 for G-QP and T-QP, respectively, and viewed at a distance of 5 m. Regarding content characteristics, objects with lower contrast can yield better quality scores. Participants’ responses reveal that the visual quality of point clouds has not yet reached an immersion level as desired. The average QoE of the highest visual quality is less than 8 out of 10. There is also a good correlation between objective metrics (e.g., color Peak Signal-to-Noise Ratio (PSNR) and geometry PSNR) and the QoE score. Especially the Pearson correlation coefficients of color PSNR is 0.84. Finally, we found that machine learning models are able to accurately predict the QoE of point clouds in AR environments. The subjective test results and questionnaire responses are available on Github: https://github.com/minhkstn/QoE-and-Immersion-of-Dynamic-Point-Cloud.
ConvNeXtPose: A Fast Accurate Method for 3D Human Pose Estimation and Its AR Fitness Application in Mobile Devices

In general, 3D human-pose estimation requires high-performance computing resources. Existing methods working on mobile devices trade off accuracy in return for increased efficiency, often making the estimation accuracy far from sufficient for developing serious applications. In this paper, we present a mobile 3D human-pose estimation model, achieving real-time performances with a well-designed balance between efficiency and accuracy. As the backbone, our model leverages the cutting-edge ConvNeXt architecture, renowned for its feature extraction capabilities. We enhance its performance through strategic architectural modifications and incorporation of depthwise separable convolutions in the upsampling module. The experiments made with the Human3.6M dataset show that the accuracy delivered by our model is comparable to that of the state-of-the-art models, consuming significantly fewer computational resources. To showcase the practicality of our model, we present a prototype of an AR fitness application. Built upon our 3D human pose estimation model, it helps trainees recreate trainers’ poses from reference images. The effectiveness of the application is validated via experiments and evaluations. The source code can be found at: https://github.com/medialab-ku/ConvNeXtPose.
Enhancing Accuracy of Face Recognition in Occluded Scenarios With Occlusion-Aware Module-Based Network

Face recognition technology despite its extensive application across various domains. However, occlusion factors like masks and glasses are significantly impeded by current face recognition models, resulting in limitations to their practical usage. We present Occlusion-Aware Module Network called OAM-Net, designed to enhance the accuracy of occluded face recognition. OAM-Net comprises two sub-networks: an occlusion-aware sub-network and a key-region-aware sub-network. The occlusion-aware sub-network incorporates an attention module to adaptively modify the weights of convolutional kernels for optimizing the processing of occluded face images. Meanwhile, the key-region-aware sub-network integrates a Spatial Attention Residual Block (SARB) for precise identification and localization of key facial regions. The network’s generalization performance and accuracy are further enhanced by implementing a meta-learning-based strategy to boost the network’s generalization performance and accuracy. Experimental results affirm OAM-Net’s superior performance of OAM-Net over other state-of-the-art methods in occluded face recognition, underlining its significant potential for practical application.
Blockchain-Integrated Security for Real-Time Patient Monitoring in the Internet of Medical Things Using Federated Learning

The Internet of Medical Things (IoMT) heralds a transformative era in healthcare, with the potential to revolutionize patient care, healthcare services, and medical research. As with all technological progressions, IoMT introduces a suite of complex challenges, predominantly centered on security. In particular, ensuring the integrity, confidentiality, and availability of health data in real-time communication stands paramount, given the sensitivity of the information and the ramifications of potential breaches or misuse. In light of these challenges, existing security frameworks, while commendable, exhibit limitations. Specifically, they often grapple with comprehensive anomaly detection, effective resistance to replay attacks, and robust protection against threats like man-in-the-middle attacks, eavesdropping, data tampering, and identity spoofing. The proposed framework integrates state-of-the-art encryption techniques, cutting-edge pattern recognition modules, and adaptive learning mechanisms. These components collaboratively ensure data integrity during transmission, provide robust resistance against conventional and novel attack vectors, and adapt to evolving threats through continuous learning. Moreover, the framework incorporates sophisticated checksum techniques and advanced behavioral analysis, further enhancing its protective capabilities. Our system demonstrated significant improvements in anomaly detection and attack resistance metrics, consistently outperforming benchmark solutions like MRMS and BACKM-EHA.
Efficient TFHE Bootstrapping in the Multiparty Setting

TFHE is a practical fully homomorphic encryption scheme (FHE) capable of computing any boolean gate or non-linear function. The scheme was originally designed to work for the single key setting. To implement realistic application scenarios, it is necessary to extend it to handle multiple users. In this paper, we introduce a new approach to generate TFHE bootstrapping keys for (predefined) multiple users. Hence, a fixed number of users can enjoy the same level of efficiency as in the single key setting, keeping their individual input privacy. Our construction relies on a novel algorithm called homomorphic indicator, which can be of independent interest. We provide a detailed analysis of the noise growth and a set of secure parameters suitable to be used in practice. Moreover, we compare the complexity of our technique with other state-of-the-art constructions and show which method performs better depending on the parameter sets. We also provide a prototype implementation of our technique. To the best of our knowledge, this is the first implementation of TFHE in the multiparty setting.
Background-Aware Band Selection for Object Tracking in Hyperspectral Videos

Hyperspectral images contain many bands that can be used to obtain object material information for object tracking and remote sensing. Nevertheless, neighboring bands of hyperspectral images are often highly correlated, and a large number of bands increase the complexity of model learning. This issue is worsened by the shortage of labeled hyperspectral videos for fine-tuning pre-trained deep neural networks. To tackle these challenges, this paper introduces a novel background-aware band selection method to model spatial changes of an object and its corresponding local region, which is capable of selecting discriminative bands for object representation while reducing computational complexity. Specifically, the object and local region of each band is compared with other bands to obtain their dissimilarity scores. Guided by these scores, the top three bands are selected and form a three-channel image. This image is then fed into an object tracker. Experimental results demonstrate the efficiency and effectiveness of the proposed method on a benchmark hyperspectral object tracking dataset.
HeMoFi4Q: Morse Communication Based on Wi-Fi and Head Motion for Quadriplegia With Environmental Robustness

Quadriplegics face a communication obstacle as their physical abilities are restricted, leaving them unable to speak or use their limbs, with only upper neck being mobile. So, we propose a recognition system and a new communication language utilizing Morse code and head movements, to break this barrier. We aim to overcome the limitations of camera-based and wearable-sensor methods, including occlusion, privacy concerns, and user-inconvenience. The goal is to passively detect quadriplegics’ head movements and map them to its corresponding character. The dataset including all 26 alphabet letters, was gathered in various settings, including single-user and multi-human environments, with multiple locations for each setting. For evaluation, 2% samples are randomly selected from the unseen environment to be used with seen environment as a training dataset. Based on the results, our system demonstrates practical feasibility for real-world implementation, with accuracy rates of 94% and 80% achieved in single-user and multi-human environments, respectively.
From Pre-Training to Meta-Learning: A Journey in Low-Resource-Language Representation Learning

Language representation learning is a vital field in Natural Language Processing (NLP) that aims to capture the intricate semantics and contextual information of text. With the advent of deep learning and neural network architectures, representation learning has revolutionized the NLP landscape. However, the majority of research in this field has concentrated on resource-rich languages, putting Low-Resource Languages (LRL) at a disadvantage due to their limited linguistic resources and the absence of pre-trained models. This paper addresses the significance of language representation learning in a low-resource language, Greek, and its impact on various downstream tasks that heavily rely on semantically and contextually enriched language representations. Accurate classification requires an understanding of nuanced linguistic cues and contextual dependencies. Effective representations bridge the gap between raw text data and classification models, encoding semantic meaning, syntactic structures, and contextual information. By leveraging various representation learning techniques using Transformer-based Language Models (LM), such as domain-adaption and contrastive learning, we aim to enhance the performance of text classification in this LRL setting. We explore the challenges and opportunities in developing effective representations and propose a multi-stage LM pre-training and meta-learning approach to improve performance in classification downstream tasks. The proposed approach was evaluated on Greek expert-annotated texts from social media posts, news articles, press text clippings and internet articles such as blog posts and opinion pieces. The results show significant improvements in the classification effectiveness of each task in terms of micro-averaged F1-score in sentiment, irony, hate speech, emotion and three custom classification tasks.
PP-ST: An Indoor Mobile Robot Path Tracking Algorithm

Aiming at the problems that Pure Pursuit algorithm is too slow to approach the target path and take shortcuts at the corners under long forward-looking distance, and large heading jitter at the corners of the target path under short forward-looking distance, this paper proposes a path tracking algorithm based on Pure Pursuit and Stanley. First, a new nearest point selection strategy is proposed according to the need of algorithm fusion. Then, new algorithm coefficients are designed in order to adapt the algorithm to indoor environment. Finally, a new fusion method is proposed to solve the above problems. In order to verify the effectiveness of the proposed algorithm, it was tested in three different paths and compared with the improved Pure Pursuit algorithm proposed by two other related researchers in 2023. The experimental results show that the proposed fusion algorithm is 62.8% faster than the improved Pure Pursuit algorithm proposed by Steve Macenski et al. in approaching the target path in indoor environment, and the proposed fusion algorithm’s distance error is 40% less than that of the improved Pure Pursuit algorithm proposed by Steve Macenski et al. in indoor environment; the proposed fusion algorithm’s average heading angle error at the corners of the target path is 20.3% less than that of the improved Pure Pursuit algorithm proposed by Wu, Jiangdong et al. in indoor environment, and the proposed fusion algorithm’s maximum heading angle error is 30.9% less than that of the improved Pure Pursuit algorithm proposed by Wu, Jiangdong et al. in indoor environment. Overall, the fusion algorithm is more suitable than the other two algorithms for mobile robot path tracking in indoor environments.
Cyberhate Dissemination: A Systematic Literature Map

Cyberhate against a person or a group based on their apparent identity, such as ethnicity, religion or nationality, on social media platforms is quickly growing and spreading. This has negative effects on both the online-generated content quality and the users of these platforms. Fortunately, the interest of computer science researchers in finding ways to stop cyberhate spread on social media platforms has been increasing recently. However, and to the best knowledge of the author, no studies have yet provided an overview and categorization of the various forms of conducted research on this subject, despite the increased interest in the subject. The author attempts to address this gap by performing a systematic mapping of the literature to generate an inclusive view of the subject in the last ten years (2012-2022). As a result, 274 primary studies were identified that fulfilled the devised criteria for including and excluding articles related to the context of this study. Following that, a grouping of these primary studies into categories based on their research type, contribution type, and research focus was conducted. The findings showed that the majority of the studies focused on offering cyberhate detection solutions. The findings also show that evaluation and validation of cyberhate detection solution, employing digital intervention approaches for reducing cyberhate dissemination by users, and the prevention and management of cyberhate propagation are all areas where research is lacking. The goal of this study is to assist practitioners and domain researchers in identifying current research gaps and promising areas for future research.
Improving Skeleton-Based Action Recognition Using Part-Aware Graphs in a Multi-Stream Fusion Context

Skeleton-based human action recognition with Graph Convolutional Networks is an active research field that has gained increased popularity over the last few years. A challenge in skeleton-based action recognition is the design of a model in a way that captures fine-grained motions and the relations between the movements of different parts of the skeleton towards the recognition of specific actions. In this paper, the use of a set of part-aware graphs for the skeleton representation is proposed aiming to enhance discrimination between actions in the recognition task since each action put emphasis on specific parts of the skeleton. Extensive experimental work has been carried out in a consistent evaluation framework taking into account different combinations of part-aware graphs and feature representations leading to a configuration that achieves the optimal balance. Based upon two well-established datasets, namely NTU RGB+D and NTU RGB+D 120, we demonstrate that the proposed methodology compares favourably with the state-of-the-art. Code is publicly available at: <link _to _access _code _when _published>.
An Evaluation of Storage Alternatives for Service Interfaces Supporting a Decentralized AI Marketplace

Given the exploding interest in generative AI and the concern that a few companies like Microsoft will monopolize access to such models, we address this centralization risk in the context of a DApp that matches buyers and sellers of various AI services. A key question for a decentralized marketplace is where and how to store the metadata that specifies the services’ properties in human and machine-readable formats. Having one or a few actors controlling access to that data constitutes undesirable centralization. We explore data storage alternatives to ensure decentralization, equitable match-making, and efficiency. Classifying decentralized storage alternatives as simple peer-to-peer replication, replication governed by a permissionless consensus, and replication governed by a private consensus, we select an exemplar for each category: IPFS, Tendermint Cosmos and Hyperledger Fabric. We conduct experiments on performance and find that read and write speeds are fastest for IPFS, about two times slower for Tendermint and slowest for Hyperledger. Writing using IPFS and Tendermint takes significantly longer than reading, and finally, specifically with IPFS, write speeds strongly depend on configuration. Given these results and the properties of the storage technologies, we conclude that simple peer-to-peer storage is the best option for the proposed AI marketplace.
The socio-economic status of adult female perpetrators of criminal offences in the Republic of Serbia

The subject of this paper is the research of socioeconomic status of adult female perpetrators of criminal offences in the Republic of Serbia. For this purpose, the following characteristics of adult women perpetrators have been observed: age, marital status, education, employment status , occupation. Empirical research was conducted in which the structure of female perpetrators by age, by education, by marital status, by employment status and by occupation was calculated. The empirical research used data obtained from the Statistical Office of the Republic of Serbia for 2021. The structure was calculated for criminal offences that have the largest share in total crime, namely, for criminal offences against life and limb, criminal offences against marriage and family, criminal offences against property, criminal offences against the economy, criminal offences against human's health and criminal offences against public peace and order. A comparison was made with the adult male perpetrators of criminal offences and with the total population of the Republic Serbia for each of the observed characteristics. The results of the research showed that female perpetrators have a higher unemployment rate and a lower educational structure than male perpetrators and the total population of the Republic of Serbia. The unemployment rate is very high and amounts to 38.7%, and one third of the female perpetrators do not have more than a primary education. The results reveal poor socioeconomic status of adult female perpetrators, indicating the strong influence of socioeconomic status on crime of female perpetrators in the Republic of Serbia.
Generating Labeled Multiple Attribute Trajectory Data With Selective Partial Anonymization Based on Exceptional Conditional Generative Adversarial Network

Trajectory data generated in location-based service environments contain highly sensitive personal information, making them a prime target for privacy attacks. At the same time, however, valuable statistical information can be obtained from such private data. Optimizing this tradeoff between utility and privacy presents a challenge. This study introduces a novel method for partially anonymizing sensitive areas using a conditional generative adversarial network. The proposed method enables the learning of complex spatial, temporal, and categorical features of the selected sensitive area through the utilization of our condition label structure and loss function. In this study, we evaluate and analyze the contents by considering the spatial-temporal characteristics and dividing them into spatial usability and temporal usability. The experimental results demonstrate that the proposed method outperforms related models that employ generative adversarial networks. We achieved high scores in a majority of spatial evaluation items while also discussing the aspects that obtained relatively low scores.
Mining Periodic-Frequent Patterns in Irregular Dense Temporal Databases Using Set Complements

Periodic-frequent patterns are a vital class of regularities in a temporal database. Most previous studies followed the approach of finding these patterns by storing the temporal occurrence information of a pattern in a list. While this approach facilitates the existing algorithms to be practicable on sparse databases, it also makes them impracticable (or computationally expensive) on dense databases due to increased list sizes. A renowned concept in set theory is larger the set, the smaller its complement will be. Based on this conceptual fact, this paper explores the complements, redefines the periodic-frequent pattern and proposes an efficient depth-first search algorithm called PFPM-C, that finds all periodic-frequent patterns by storing only non-occurrence information of a pattern in a database. Experimental results on several databases demonstrate that our algorithm is efficient.
可視領域の異方性を考慮した移動ロボット群の広域持続被覆制御

A persistent coverage control problem in a complex geometry environment is considered for two-wheeled mobile robots with anisotropic sensor ranges. The persistent coverage control is one of the coverage control methods, which covers the region persistently along the gradient of the time-varying density function. In this paper, we first focus on the persistent coverage control for the two-wheeled mobile robots assuming that the sensing region is anisotropic, and derive a control law which coordinates the rotation and the forward motion. In order to achieve coverage control in the environments with complex geometry, we propose a region assignment planning by employing Monte Carlo tree search for the area allocation. The strength and limitation of the proposed method are discussed based on simulation and experimental results.