Reviewerly/exHarmony huggingface dataset:
Learned video compression with channel-wise autoregressive entropy model

Although learned image compression methods have achieved competitive rate-distortion performances, learned video compression remains challenging. The current mainstream learned video compression frameworks usually improve the motion prediction module to reduce the redundancy in video sequences. Although these methods can achieve a great compression ratio, they often ignore the improvement in the entropy model, which does not fully exploit the temporal and spatial characteristics in the video. Meanwhile, these methods always suffer from the error propagation problem. To solve those problems, we propose a learned video compression framework with channel-wise autoregressive entropy model. Our framework captures spatial–temporal dependencies through a powerful entropy model to reduce the redundancy in video sequences. In particular, we do not directly compress the frames in the pixel domain, with no need for the motion prediction module, avoiding the error propagation problem. To better utilize the temporal contexts of the previous frame, we propose the window temporal prior module. Experiments show that our proposed video compression framework achieves promising compression effects in terms of peak signal-to-noise ratio and multiscale structural similarity.
Enhancing digital service communication strategies

Purpose Based on stimulus-organism-response theory, this study aims to explore how digital service communication strategies affected customers’ e-service agent use intentions through perceived warmth and competence. Design/methodology/approach A 2 × 2 scenario-based experiment was conducted to test the proposed relationships. Findings Findings indicated when a high-authority conversation party was engaged in digital service communications, customers showed higher e-service agent use intentions through perceived warmth and competence in an emoji-presence conversation style. Originality/value To the best of the authors’ knowledge, this study is the first to explore the effects of digital service communication strategies on customers’ internal and behavioral responses.
Digitalization of Classrooms: A Comparative Study on Teachers’ Perceptions about the Use of Digital Teaching Materials in Early Childhood and Primary Education

The incorporation of digital didactic materials into the teaching and learning process of students, both in Early Childhood and Primary Education, inevitably entails changes. Some of them concern teacher training, others the interactions and roles of the members of the educational community, and others the innovation, a priori, of the educational process itself. In this context, characterized by the digitization of classrooms, questions arise that this research aims to answer: What vision do teachers have of the incorporation of these media? In what learning situations are they used and for what purposes? Are there differences between the use of digital resources in the infant and primary school stages? This article aims to analyze and compare the perceptions of teachers in two different contexts: the Autonomous Community of the Canary Islands and the Valencian Community. By means of a qualitative study, the focus is on knowing the teachers’ assessment of the usefulness of these resources and on identifying the uses given to them in classroom practices. The results show how, despite the widespread use of digital materials in both stages, some teachers are rethinking their use with students at an early age.
End-to-End Joint Multi-Object Detection and Tracking for Intelligent Transportation Systems

Abstract Environment perception is one of the most critical technology of intelligent transportation systems (ITS). Motion interaction between multiple vehicles in ITS makes it important to perform multi-object tracking (MOT). However, most existing MOT algorithms follow the tracking-by-detection framework, which separates detection and tracking into two independent segments and limit the global efficiency. Recently, a few algorithms have combined feature extraction into one network; however, the tracking portion continues to rely on data association, and requires complex post-processing for life cycle management. Those methods do not combine detection and tracking efficiently. This paper presents a novel network to realize joint multi-object detection and tracking in an end-to-end manner for ITS, named as global correlation network (GCNet). Unlike most object detection methods, GCNet introduces a global correlation layer for regression of absolute size and coordinates of bounding boxes, instead of offsetting predictions. The pipeline of detection and tracking in GCNet is conceptually simple, and does not require complicated tracking strategies such as non-maximum suppression and data association. GCNet was evaluated on a multi-vehicle tracking dataset, UA-DETRAC, demonstrating promising performance compared to state-of-the-art detectors and trackers.
Personalized Text-to-Image Model Enhancement Strategies: SOD Preprocessing and CNN Local Feature Integration

Recent advancements in text-to-image models have been substantial, generating new images based on personalized datasets. However, even within a single category, such as furniture, where the structures vary and the patterns are not uniform, the ability of the generated images to preserve the detailed information of the input images remains unsatisfactory. This study introduces a novel method to enhance the quality of the results produced by text-image models. The method utilizes mask preprocessing with an image pyramid-based salient object detection model, incorporates visual information into input prompts using concept image embeddings and a CNN local feature extractor, and includes a filtering process based on similarity measures. When using this approach, we observed both visual and quantitative improvements in CLIP text alignment and DINO metrics, suggesting that the generated images more closely follow the text prompts and more accurately reflect the input image’s details. The significance of this research lies in addressing one of the prevailing challenges in the field of personalized image generation: enhancing the capability to consistently and accurately represent the detailed characteristics of input images in the output. This method enables more realistic visualizations through textual prompts enhanced with visual information, additional local features, and unnecessary area removal using a SOD mask; it can also be beneficial in fields that prioritize the accuracy of visual data.
Man as a “Work of Art”: The Religious–Dialogical Dimension of Education as a Path of Education for Beauty/Goodness

Aiming to establish a dialogue with modern man, who lives in a culture marked by great challenges, church documents put a great emphasis on the via pulchritudinis. The via pulchritudinis, i.e., the way of beauty, is considered to be a privileged way of achieving dialogue. Awareness of the importance of beauty, in close connection with goodness and truth, is increasingly present in the theological and the religious–pedagogical thought. One of the specific questions related to education for beauty refers to the education of a person who is called to transform his or her life into a “work of art”. This paper reflects on only one specific aspect of the issue of shaping human beauty/goodness, that is, people’s “humanity”, in connection with the challenges of increasingly complex coexistence in diversity. Globalization and migration trends, primarily by complicating life together/coexistence, pose certain challenges to man as an individual, as well as to the entire society, especially the challenge that is coexistence of different cultures and religions. Since these problems also penetrate school classrooms, education systems are expected to provide appropriate responses. The paper consists of two parts. In the theoretical part, two specific fundamental issues related to the education of a person’s “humanity” in the atmosphere of “conflict culture” are considered: the issue of comprehensive education following the ancient connection of beauty and goodness, as well as the issue of education for coexistence. The second part presents the results of the research which was carried out in the Republic of Croatia and which aimed to examine the presence of curriculum content that enables the acquisition of religious–dialogical competence, using the method of text analysis. In more exact terms, faced with the challenges that arise from a society burdened with misunderstanding, violence and hostility, often linked with a religious affiliation and worldviews, we ask ourselves the following question: to what extent is the contemporary education system in the Republic of Croatia, at the level of its curricula, open to the comprehensive development of students, especially in terms of the development of the religious–dialogical dimension, necessary for living a good and beautiful life in the modern multicultural and multireligious world? The results reveal a different, mostly insufficient, openness of the curriculum to the religious dimension of intercultural education. That is one of the reasons why the education system does not seem to be open to the development of the knowledge, attitudes and skills necessary for a harmonious and “beautiful” coexistence in modern society.
Improving Throughput of Mobile Sensors via Certificateless Signature Supporting Batch Verification

Mobile sensors enjoy the advantages of easy installation and low consumption, which have been widely adopted in many information systems. In those systems where data are generated rapidly, the throughput of the sensors is one of the most fundamental factors that determine the system functionality. For example, to guarantee data integrity, digital signature techniques can be applied. In many practical scenarios, such as the smart grid system, data are generated rapidly and, hence, the signature together with the data must also be transmitted and verified in time. This requires the mobile sensors to support a high-throughput data processing ability. In this setting, how to achieve efficient signature schemes supporting batch verification must be considered. Many signatures, such as the original national cryptographic standard, namely, the SM2 algorithm, do not support batch verification and are in a public-key infrastructure setting. In this paper, we propose a SM2-based certificateless signature scheme with batch verification, which is suitable for the aforementioned environment. The scheme extends the Chinese cryptographic standard SM2 algorithm to the certificateless setting and multiple signatures can be verified simultaneously. Another advantage of this scheme is that its signing phase does not involve any pairing operation. The verification phase only requires a constant pairing operation, which is not related to the number of signatures to be verified. The construction is generic and can be instantiated using any traditional signature scheme.
Multimodal Emotion Recognition in Conversation Based on Hypergraphs

In recent years, sentiment analysis in conversation has garnered increasing attention due to its widespread applications in areas such as social media analytics, sentiment mining, and electronic healthcare. Existing research primarily focuses on sequence learning and graph-based approaches, yet they overlook the high-order interactions between different modalities and the long-term dependencies within each modality. To address these problems, this paper proposes a novel hypergraph-based method for multimodal emotion recognition in conversation (MER-HGraph). MER-HGraph extracts features from three modalities: acoustic, text, and visual. It treats each modality utterance in a conversation as a node and constructs intra-modal hypergraphs (Intra-HGraph) and inter-modal hypergraphs (Inter-HGraph) using hyperedges. The hypergraphs are then updated using hypergraph convolutional networks. Additionally, to mitigate noise in acoustic data and mitigate the impact of fixed time scales, we introduce a dynamic time window module to capture local-global information from acoustic signals. Extensive experiments on the IEMOCAP and MELD datasets demonstrate that MER-HGraph outperforms existing models in multimodal emotion recognition tasks, leveraging high-order information from multimodal data to enhance recognition capabilities.
Taxonomy and Survey of Current 3D Photorealistic Human Body Modelling and Reconstruction Techniques for Holographic-Type Communication

The continuous evolution of video technologies is now primarily focused on enhancing 3D video paradigms and consistently improving their quality, realism, and level of immersion. Both the research community and the industry work towards improving 3D content representation, compression, and transmission. Their collective efforts culminate in the striving for real-time transfer of volumetric data between distant locations, laying the foundation for holographic-type communication (HTC). However, to truly enable a realistic holographic experience, the 3D representation of the HTC participants must accurately convey the real individuals’ appearance, emotions, and interactions by creating authentic and animatable 3D human models. In this regard, our paper aims to examine the most recent and widely acknowledged works in the realm of 3D human body modelling and reconstruction. In addition, we provide insights into the datasets and the 3D parametric body models utilized by the examined approaches, along with the employed evaluation metrics. Our contribution involves organizing the examined techniques, making comparisons based on various criteria, and creating a taxonomy rooted in the nature of the input data. Furthermore, we discuss the assessed approaches concerning different indicators and HTC.
Agricultural Equipment Rental System

Agriculture forms the backbone of Indian economy and there is always a need of supporting and improving it .As a part of which some of Indian NGO's are with an initiative of supporting the farmers by facilitating them with the modern agricultural equipment's on rental basis .Modern agricultural equipment's make farmers work more efficient and easy .As a part of which there are some organizations that are set up to help those farmers who are in need of such equipment's ,where the organization owns the equipment's and rent those on request of farmers at liable amounts. At present, farmers need to travel to a place to borrow all the essential needs, which is a tiresome and not a cost-effective work. So, a smart digital farming is listed as the highest-ranking technology opportunity in the latest Global Opportunity report in terms of its expected positive impact on society. This paper is on digitizing the process of renting the agricultural equipment’s by the farmers .We aim at developing an application that farmers can use to get their equipment’s on rent and also check the availability and renting .We also allow them to book the equipment’s in advance .It also helps us to get the track of equipment’s that are on rent .We also aim at developing analytic for the state heads to make better availability of equipment’s and to keep track of the equipment’s as well, which could help in providing better support for farmers
Comparative Analysis of Membership Inference Attacks in Federated and Centralized Learning

The vulnerability of machine learning models to membership inference attacks, which aim to determine whether a specific record belongs to the training dataset, is explored in this paper. Federated learning allows multiple parties to independently train a model without sharing or centralizing their data, offering privacy advantages. However, when private datasets are used in federated learning and model access is granted, the risk of membership inference attacks emerges, potentially compromising sensitive data. To address this, effective defenses in a federated learning environment must be developed without compromising the utility of the target model. This study empirically investigates and compares membership inference attack methodologies in both federated and centralized learning environments, utilizing diverse optimizers and assessing attacks with and without defenses on image and tabular datasets. The findings demonstrate that a combination of knowledge distillation and conventional mitigation techniques (such as Gaussian dropout, Gaussian noise, and activity regularization) significantly mitigates the risk of information leakage in both federated and centralized settings.
Heuristics and Rescheduling in Prioritised Multi-Robot Path Planning: A Literature Review

The benefits of multi-robot systems are substantial, bringing gains in efficiency, quality, and cost, and they are useful in a wide range of environments from warehouse automation, to agriculture and even extend in part to entertainment. In multi-robot system research, the main focus is on ensuring efficient coordination in the operation of the robots, both in task allocation and navigation. However, much of this research seldom strays from the theoretical bounds; there are many reasons for this, with the most-prominent and -impactful being resource limitations. This is especially true for research in areas such as multi-robot path planning (MRPP) and navigation coordination. This is a large issue in practice as many approaches are not designed with meaningful real-world implications in mind and are not scalable to large multi-robot systems. This survey aimed to look into the coordination and path-planning issues and challenges faced when working with multi-robot systems, especially those using a prioritised planning approach, and identify key areas that are not well-explored and the scope of applying existing MRPP approaches to real-world settings.
Utilizing Blockchain Technology and Fingerprint Authentication for an Electronic Voting System.

The advent of digital technology has brought about a significant transformation in people's lives. However, unlike various aspects of modern life that have embraced digitalization, the electoral system continues to rely heavily on conventional paper-based methods. This reliance poses challenges to the fundamental principles of security and transparency, particularly in traditional offline elections. Presently, general elections employ a centralized system managed by a single organization, leading to potential vulnerabilities within the system. The concentration of control over the database presents opportunities for manipulation. Leveraging blockchain technology offers a viable solution by introducing a decentralized system where the database is collectively owned by multiple users. Blockchain's effectiveness in decentralized systems, as evidenced by its successful application in Bitcoin, presents an opportunity to address the vulnerabilities of the traditional electoral system. This project seeks to implement a blockchain-based algorithm for recording voting results across all polling locations. Unlike Bitcoin's Proof of Work mechanism, this approach will employ a predetermined rotation system for each node within the blockchain structure, ensuring enhanced security and integrity in the electoral process
Advancing OCR Accuracy in Image-to-LaTeX Conversion—A Critical and Creative Exploration

This paper comprehensively assesses the application of active learning strategies to enhance natural language processing-based optical character recognition (OCR) models for image-to-LaTeX conversion. It addresses the existing limitations of OCR models and proposes innovative practices to strengthen their accuracy. Key components of this study include the augmentation of training data with LaTeX syntax constraints, the integration of active learning strategies, and the employment of active learning feedback loops. This paper first examines the current weaknesses of OCR models with a particular focus on symbol recognition, complex equation handling, and noise moderation. These limitations serve as a framework against which the subsequent research methodologies are assessed. Augmenting the training data with LaTeX syntax constraints is a crucial strategy for improving model precision. Incorporating symbol relationships, wherein contextual information is considered during recognition, further enriches the error correction. This paper critically examines the application of active learning strategies. The active learning feedback loop leads to progressive improvements in accuracy. This article underlines the importance of uncertainty and diversity sampling in sample selection, ensuring that the dynamic learning process remains efficient and effective. Appropriate evaluation metrics and ensemble techniques are used to improve the operational learning effectiveness of the OCR model. These techniques allow the model to adapt and perform more effectively in diverse application domains, further extending its utility.
Regulatory constraints, responsibilities and consultation (CRC) for legal institutionalization of cryptocurrencies in Pakistan

Purpose This study aims to synthesize the existing literature with insights gained from interviews conducted with regulatory experts. The objective is to analyse the challenges associated with incorporating cryptocurrencies into regulatory frameworks and to explore constraints in the regulatory institutionalization of cryptocurrencies. Design/methodology/approach The study methodology consists of two steps. The first step is to identify regulatory constraints in the literature review and in the next step, interviews are conducted with officials of the State Bank of Pakistan (SBP). The study used a qualitative case study methodology, in which a single case (regulatory constraint) was selected as a unit of analysis. Findings The findings show that lack of traceability, legal status, lack of governmental control due to decentralization, difficulty enforcing laws, volatility, lack of skills with regulators and difficulty integrating cryptocurrencies into the current financial system are the main obstacles to the introduction of a regulatory framework. Thus, on a broader conceptual level, the findings can be grouped into opportunism, lack of strategic capability and fragmented global laws. Research limitations/implications This study could inform global cryptocurrency regulation discussions, sharing a developing country’s views on balancing the government, central banks, the financial sector and public interests. This could guide countries to consider cryptocurrency adoption in similar situations. This could affect the cryptocurrency market, impacting demand, supply and investor trust in Pakistan. Practical implications The study has implications for policy making officials. The research aims to offer valuable insights to the SBP and other regulatory authorities, helping them identify potential risks and create an effective regulatory framework for cryptocurrencies. Social implications The study has implications for society in knowing about the volatile nature of cryptos and anonymity of their issuers, which poses regulatory constraints. This then implies its harmfullness to its traders and the huge losses that may arise from their trading due to its volatile nature. Originality/value This study contributes to the literature on the constraints, responsibilities and consultation framework of cryptocurrency regulations.
Determination of the number of clusters through logistic regression analysis

We advise a novel measure to determine the unknown number of clusters underlying a designated sample through implementation of the parametric logistic regression model. The regression analysis is carried out to estimate the probabilities of inclusion for every individual member from data, irrespective of its parent distribution, to each of the clusters under existence. The proposed one is shown to be superior to its well-known rivals by means of both synthetic and real-world data sets, while designed to significantly reduce the computational burden serving our desired purpose.