Reviewerly/exHarmony huggingface dataset:
Toward 6G-Based Metaverse: Supporting Highly-Dynamic Deterministic Multi-User Extended Reality Services

Metaverse is the concept of a fully immersive and universal virtual space for multiuser interaction, collaboration, and socializing; forming the next evolution of the Internet. Metaverse depends on the convergence of multiple broad technologies that enable eXtended Reality (XR), which is an umbrella term for technologies that lie on the reality-virtuality continuum, namely Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR). Compared to streaming volumetric content to a single user, the XR applications that involve multiple users who simultaneously watch the same volumetric content (e.g., VR-based online training/ education and multi-user online gaming) are much more attractive. Multi-user XR/Metaverse puts additional demand on the underlying networks, making it more difficult to offer high-quality immersive material in real-time. To cope with this, in this article, we present a comprehensive system and component design for immersive and seamless multi-user XR experiences. To satisfy the Quality of Experience/Quality of Service (QoE/ QoS) requirements and especially stream synchronization requirement in XR collaboration scenarios, we propose an AI-powered deterministic multi-user extended reality resource orchestrator (PRECISENESS) that aims to solve the multi-user XR service provisioning problem. Finally, we demonstrate the performance of our proposed solution in a single-site multi-user XR use case. The obtained results demonstrate that our solution can deliver high-quality immersive XR services.
Analysis of pseudorandom number generation processes in EP CRYSTALS-Dilithium

The paper provides an analysis of pseudorandom number generation processes in the Crystals-Dilithium post-quantum electronic signature scheme, a finalist in the NIST PQC post-quantum cryptography competition. The main focus is on the pseudo-random number generator based on the AES block cipher in counter mode. A formal model was built for this pseudo-random number generator that meets the requirements of the latest version of the AIS 31 standard, containing requirements for secure pseudorandom number generators. A pseudo-random number generator based on the AES block cipher in counter mode is shown to satisfy the requirements of functional class DRG.3, provided that the initial value for the generator is obtained from a truly random number source (either a physically truly random or a non-physical truly random source) or another generator of pseudo-random numbers having a security class not lower than the DRG class.3. In addition, the use of shake128/256 for the generation of pseudorandom sequences in Crystals-Dilithium will be analyzed. Based on the results of the analysis, recommendations are given regarding the compilation parameters depending on the conditions of use. Namely, it is concluded that an AES-based generator is more vulnerable to side-channel attacks, from which it follows that it is not recommended to use the DILITHIUM_USE_AES flag unless there are additional guarantees to protect the machine from side-channel attacks. If the operating system's cryptographic API is trusted, it is recommended to use the DILITHIUM_RANDOMIZED_SIGNING compilation flag. In systems with low trust in the cryptographic API of the operating system, it is possible to use a variant with deterministic signature generation.
Electronic information resources: definition and classification

The article provides a review of normative documents and scientific works devoted to the concept of "electronic information resource" as an important type of information resources, caused by the influence of information technologies. The classification of electronic information resources is given, summarizing the most common features and categories and also taking into account the correspondence between the features of the classification of information resources and electronic information resources.
Dinamika Kepribadian Tokoh dalam Novel Konspirasi Alam Semesta Karya Fiersa Besari

Humans are created with two types of inherent personalities, namely positive and negative personalities. The attitude or personality that exists in each individual can change due to events that have occurred in the past, and differences in personality possessed by individuals can also cause conflicts that lead to anxiety. This is what is called the dynamics of personality which can also be contained in the novel which contains the author's way of expressing an event through his fictional characters. This study aims to describe the personality dynamics that occur in the characters in the novel Conspiracy of the Universe by Fiersa Besari. This type of research includes qualitative descriptive research with a literary psychology approach. The data in this study are in the form of whole sentences or quotes that reflect the personality or behavior of the characters who are included in the personality dynamics which are divided into instincts and anxiety in Fiersa Besari's novel Conspiracy of the Universe by Fiersa Besari. The data collection technique uses reading techniques and note-taking techniques. Data analysis used techniques with three steps, namely data reduction, data presentation, and drawing conclusions or verification. The results of this study reveal that the dynamics of personality found in the characters Juang Astrajingga and Ana Tidae are instincts and anxiety which include 6 data on life instincts in the form of sex instincts, thirst, creative activities and instinctive derivatives. Furthermore, 2 data on death instinct in the form of aggressive verbal and non-verbal activities, 6 data on realistic anxiety, 1 data on neurotic anxiety, and 1 data on moral anxiety. Keywords: novel, character, personality dynamics, literary psychology, psychoanalysis
Differences in communicative network activity among students of different areas of professional training

The article is devoted to the problem of communicative network activity among students of different areas of professional training. The study of the communicative activity of students, its impact on their life becomes necessary due to their high involvement in the virtual world. The main form of interaction on the Internet for students is communicative network activity. The purpose of the research is to study the differences in communicative network activity among students of different areas of professional training. The author used such research methods as: theoretical, empirical, methods of mathematical processing of psychological data: H-Kruskal-Wallis criterion, U-Mann-Whitney criterion. Computer data processing was carried out using the software packages "Excel" and "IBM SPSS Statistics 21". A set of the following methods is used: "Questionnaire of problematic use of social networks"; "Methodology for assessing involvement in the use of information and communication technologies". The empirical base of the study was 240 respondents, with an average age of 20.3 years. Students of the technical direction have an uncontrolled mental return to the virtual world, a tendency to use electronic technologies in a variety of ways; the cognitive absorption and the appearance of negative consequences of immersion in the Internet are slightly expressed in the scientific profile; the humanitarian direction lacks diversity in the use of electronic technologies, however, they have a high involvement in performing economic actions in the virtual world. It is important to note the existence of general trends that are independent of the professional direction of the training of students: the priority of online communication, communication in social networks, visiting the Internet in order to avoid feeling lonely. The results obtained during the study of the differences in the communicative network activity of students of different areas of professional training can be used to make practical recommendations for the formation and development of digital competence skills.
Design of a Cryptographic Algorithm in the Form of an API in Order to Secure Monetary Transactions in a Supermarket

Supermarkets and large-scale retail stores are usually subject to huge monetary transactions for certain customers’ purchases. The computerization of these systems is common in supermarkets but the security of these transactions remains a mystery. This article presents an algorithm as an API based on symmetric cryptography that can enable end-to-end encryption of a monetary transaction in a supermarket. This algorithm is the first part of the complete supermarket management system which will be presented in the following article. The Python language and the Flask framework allow us to develop the algorithm as an independent component. Tests have been performed and our algorithm uses 98.49% less memory and 10.18% time saving than the AES algorithm.
An Attention based Fusion of ResNet50 and InceptionV3 Model for Water Meter Digit Recognition

Digital water meter digit recognition from images of water meter readings is a challenging research problem. One key reason is that this might be a lack of publicly available datasets to develop such methods. Another reason is the digits suffer from poor quality. In this work, we develop a dataset, called MR-AMR-v1, which comprises 10 different digits (0 to 9) that are commonly found in electrical and electronic water meter readings. Additionally, we generate a synthetic benchmarking dataset to make the proposed model robust. We propose a weighted probability averaging ensemble-based water meter digit recognition method applied to snapshots of the Fourier transformed convolution block attention module (FCBAM) aided combined ResNet50-InceptionV3 architecture. This benchmarking method achieves an accuracy of 88% on test set images (benchmarking data). Our model also achieves a high accuracy of 97.73% on the MNIST dataset. We benchmark the result on this dataset using the proposed method after performing an exhaustive set of experiments.
Mastering the Cosmos: Leveraging Optimization Methods for Advances in Space Exploration

Exploration of space has always been a major undertaking for humanity, driven by our natural curiosity and desire to fathom the secrets of the cosmos. Space exploration has advanced significantly over the years, from manned expeditions to the moon to the deployment of sophisticated robotic probes to distant celestial planets. However, as we attempt to broaden our understanding and reach into the cosmos, new challenges emerge that demand novel ways. Optimization methods are critical in the allocation of resources for robotic space exploration, which is critical for the success of long-duration missions. The chapter focuses on the concepts and procedures of optimization for autonomous robot space exploration and examines how optimization algorithms can efficiently optimize resource utilization in the context of robotic space exploration. At the end of the chapter, a formal optimization technique designed specifically for space exploration missions is presented. The method involves a coordinated multi-robot exploration (CME) with a bio-inspired algorithm, i.e., Reptile algorithm. Proper simulation results have been added as a reference for readers. In addition, special emphasis is paid to the optimization framework’s adaptability to diverse exploration requirements.
Multi-View Hybrid Contrastive Learning for Bundle Recommendation

Bundle recommendation aims to provide users with convenient one-stop solutions by recommending bundles of related items that cater to their diverse needs. However, previous research has neglected the interaction between bundle and item views and relied on simplistic methods for predicting user-bundle relationships. To address this limitation, we propose Hybrid Contrastive Learning for Bundle Recommendation (HCLBR). Our approach integrates unsupervised and supervised contrastive learning to enrich user and bundle representations, promoting diversity. By leveraging interconnected views of user-item and user-bundle nodes, HCLBR enhances representation learning for robust recommendations. Evaluation on four public datasets demonstrates the superior performance of HCLBR over state-of-the-art baselines. Our findings highlight the significance of leveraging contrastive learning and interconnected views in bundle recommendation, providing valuable insights for marketing strategies and recommendation system design.
A Method for Enhancing the Quality of Compressed Videos Based on 2D Convolution and Aggregating Spatio-Temporal Information

Existing block-based video coding frameworks are often affected by the quantization step size and motion compensation accuracy, resulting in the loss of high-frequency information and compression artifacts. Especially in the case of limited coding resources, the blurring of content edges and obvious compression distortion will have a negative impact on the subjective quality of the video. Therefore, there is an urgent need to build a quality enhancement method to improve the compressed video quality at the receiving end under the same coding resources. This paper proposes a compression video quality enhancement method based on 2D convolution that aggregates spatial and temporal information. Based on the objective facts of analyzing the spatiotemporal correlation and video quality fluctuation, this method constructs a multi-frame input mechanism consisting of the current frame to be enhanced and its adjacent frames; furthermore, it efficiently extracts and integrates the temporal and spatial information features of the input video sequence by utilizing the excellent feature extraction and fusion capabilities of the encoder-decoder structure, achieving implicit alignment. On this basis, an attention mechanism is integrated to more accurately locate and extract key information in the video, thereby more accurately restoring the detail information in the video and improving the performance of the model. In public benchmark tests, our method achieved average ΔPSNR gains of 0.801 dB, 0.796 dB, 0.792 dB, and 0.714 dB on 18 video test sequences with QP = 22, 27, 32, and 37, respectively, outperforming other methods. Compared with the state-of-the-art algorithms, our method achieved speed improvements of 13.2%, 10.5%, and 6.2% for processing videos with resolutions of 832 × 480, 1080 × 720, and 1920 × 1080, respectively. The above results show that our method can improve the compressed video quality at the receiving end under the same coding resources and outperforms other methods in terms of performance.
Underwater Image Enhancement and Restoration Techniques: A Comprehensive Review, Challenges, and Future Trends

Underwater optical images serve as crucial carriers and representations of ocean information. They play a vital role in the field of marine exploration. However, the quality of images captured by underwater cameras often falls short of the expected standards due to the complex underwater environment. This limitation significantly hampers the application and advancement of intelligent underwater image processing systems. Consequently, underwater image enhancement and restoration have been attracting extensive research efforts. In this paper, we review the degradation mechanisms and imaging models of underwater images, and summarize the challenges associated with underwater image enhancement and restoration. Meanwhile, we provide a comprehensive overview of the research progress in underwater optical image enhancement and restoration, and introduces the publicly available underwater image datasets and commonly-used quality evaluation metrics. Through extensive and systematic experiments, the superiority and limitations of underwater image enhancement and restoration methods are further explored. Finally, this review discusses the existing issues in this field and prospects future research directions. It is hoped that this paper will provide valuable references for future studies and contribute to the advancement of research in this domain.
Semantic Guided Attention for Weakly Supervised Group Activity Recognition

The objective of group activity recognition is to identify behaviors performed by multiple individuals within a given scene. However, current weakly supervised approaches often rely on object detectors or use self-attention mechanisms. The former approach is susceptible to background clutter and entails high computational costs, while the latter method learns weights from the input video and assigns them to key targets which is not reliable enough to find the key person. To address these limitations, we present a novel weakly supervised framework. Our proposed framework eliminates the need for ground-truth bounding boxes or object detectors. Meanwhile, it incorporates the semantics of individual action labels to replace self-attention to guide the learning process, enabling the extraction of more sophisticated semantic features relevant to activity. This approach also explores the interactions to promote group activity classification. Experimental results demonstrate that our method achieves state-of-the-art performances on both volleyball and collective datasets.
Markov decision processes approximation with coupled dynamics via Markov deterministic control systems

Abstract This article presents an approximation of discrete Markov decision processes with small noise on Borel spaces with an infinite horizon and an expected total discounted cost by the corresponding deterministic Markov process. In both cases, the dynamics evolve through a system consisting of two coupled difference equations. It is assumed that the difference equations of the system are perturbed by a small noise. Under our assumptions, a bound for the stability index is given, and the optimal cost convergence rate is estimated using a small perturbation parameter. Moreover, the convergence of the optimal policy on compact subsets is verified. Finally, two examples are presented to illustrate the developed theory.
Double-Stream Network for Clothes-Changing Person Re-identification Based on Clothes Related Feature Suppression and Attention Mechanism

The standard person re-identification task has a basic assumption that pedestrians will not change their clothes. However, in a more realistic and challenging scenario for clothes-changing person re-identification, this assumption does not hold, resulting in the failure of most mainstream methods in this scenario. To this end, a double-stream network for clothes-changing person re-identification based on clothes related feature suppression and attention mechanism is proposed. Firstly, the network is composed of a clothes feature stream and a pedestrian feature stream. By using attention modules on the two streams, salient clothes features and pedestrian features are extracted respectively. Then, the clothes related feature suppression module is used in the pedestrian feature stream to force it to learn clothes unrelated features. Finally, the triplet loss and cross entropy loss are used to supervise the training of the network. The ablation experiment shows that each module effectively improves the performance of the model. A large number of experiments on the PRCC and VC-Clothes datasets have been conducted in order to assess the proposed model. The experimental results show that the accuracy of mAP and Rank-1 is improved compared with other representative methods.
Cross-modal Domain Adaptive Instance Segmentation in SAR Images via Instance-aware Adaptation

Synthetic Aperture Radar (SAR) images are valuable assets in remote sensing and earth observation due to the imaging ability in all-weather and all-time scenarios. However, acquiring and labeling SAR images can be challenging and expensive. Therefore, the domain adaptation (DA) from easily available optical image data to SAR image data is a significant motivation in SAR image interpretation. In this paper, a novel cross-modal DA approach from optical to SAR is introduced, namely CDA-SAR. It is pre-trained on easily obtained optical remote sensing images and achieves cross-modal instance segmentation in SAR images via unsupervised domain adaptation (UDA). The proposed UDA method contains sample-level and feature-level adaptation strategies to perform style-transferring and feature alignment in the latent space, respectively. The proposed domain adaptive approach is validated to be universal to various CNN-based instance segmentation models. Experimental results prove CDA-SAR achieves more than 280% performance improvement on HRSID dataset than that without DA. Furthermore, it shows robustness in complex background interference and multi-scale object distribution scenes in SAR images.
Research on Contrast Calculation Method for Color Image

Contrast is one of the commonly used indicators for evaluating image quality and is related to color information. However, the traditional contrast calculation method only reflects the clarity of the image by counting the distribution probability of the gray difference between adjacent pixels in the region, which lacks consideration of the chrominance information. We propose a contrast calculation method considering the perceptual color difference in color images. First, we obtain each pixel’s hue and saturation information from the HSV color space. And the luminance information of each pixel is obtained while retaining the color contrast information of the original image. Then, the contrast of a color image is obtained by counting the number of adjacent pixels whose color difference exceeds the threshold and calculating the sum of color differences. Experiment results show that our method is less affected by the uniform color patches than other methods. It can effectively reflect the relationship between the contrast values of different images and reduce the information loss of color contrast. In addition, for different color images, the magnitude of the contrast value obtained by our method is consistent with the trend of human visual judgment.
Real-Time Image Stitching with Transformers for Complex Traffic Environment

The existing traditional image stitching methods suffer from accumulating errors in the matching process, which severely limits the final stitching results. Additionally, they cannot find correct correspondences in complex scenes with poor texture, repetitive patterns, and lighting variations. To address these issues, we propose a real-time image stitching method based on the Transformer structure. The self-attention mechanism of the Transformer can dynamically adjust the receptive field based on input content, allowing for effective global information retrieval. Compared to traditional manual and CNN-based deep learning methods, our approach generates dense and high-quality matches even in complex environments. We reduce the complexity of the Transformer model and improve matching speed through knowledge distillation. We use the Kmeans algorithm to optimize point selection for feature point screening and compute the homography matrix value based on the matching result for pixel transformation. After aligning the overlapping regions of the images and averaging them, we apply mask weights obtained from Gaussian fusion of target image layers to achieve the final stitching result. Experimental results demonstrate that our method produces more accurate matches in complex scenes with weak textures compared to traditional and deep learning methods, while also having the shortest matching and stitching times and good stitching results.