Reviewerly/exHarmony huggingface dataset:
Problems and Opportunities of Blockchain Technologies Implementation in Public Authorities

Blockchain is one of the most innovative technologies that is currently used in many areas as part of the spread of web3 technologies. Although the origins of blockchain are linked to the emergence of cryptocurrencies such as Bitcoin, today’s application of blockchain technology focuses on a wider range of issues where trust is required for a large number of transactions. For this reason, many researchers today believe that these technologies have great potential for use in the public sector. The public sector can use these technologies to fulfill its role as a generator of innovation and progress, which is part of its responsibilities, and to modernize projects that will enable citizens to receive the services they need. Therefore, this work aims to determine the main factors of success or failure in the implementation of blockchain technology in public authorities based on the analysis of existing experience in this area, in particular, in EU member states. Blockchain has proven useful for government agencies for several reasons. First, blockchain is a technology characterized by the ability to generate trust in information and processes in a heterogeneous context with a large number of participants and users. Second, blockchain facilitates governance by leaving no traces and acts as a tool to promote transparency, as it allows people to know who did what and when. Third, it eliminates the need for a centralized certificate authority to control access and use of services by large groups of people. Fourth, the successful development of blockchain-based systems can lead to significant cost savings. However, the analysis shows that authorities are not yet ready to widely use and offer blockchain-based services. In many cases, a lack of understanding of the benefits and opportunities, as well as a still unclear interpretation of the legal and regulatory environment, prevent senior executives from making decisions that may involve greater risk. With this in mind, we believe that the implementation of blockchain technology in the public sector can be facilitated by the implementation of blockchain systems as a complement to existing tracking and transparency systems and processes, as well as increasing their efficiency while simultaneously reducing the cost of services provided. This will require the development of adaptive management structures in public institutions aimed at developing reasonable and acceptable solutions to solve real problems.
Neural Network Field Theories: Non-Gaussianity, Actions, and Locality

Abstract Both the path integral measure in field theory and ensembles of neural networks describe distributions over functions. When the central limit theorem can be applied in the infinite-width (infinite-$N$) limit, the ensemble of networks corresponds to a free field theory. Although an expansion in $1/N$ corresponds to interactions in the field theory, others, such as in a small breaking of the statistical independence of network parameters, can also lead to interacting theories. These other expansions can be advantageous over the $1/N$-expansion, for example by improved behavior with respect to the universal approximation theorem. Given the connected correlators of a field theory, one can systematically reconstruct the action order-by-order in the expansion parameter, using a new Feynman diagram prescription whose vertices are the connected correlators. This method is motivated by the Edgeworth expansion and allows one to derive actions for neural network field theories. Conversely, the correspondence allows one to engineer architectures realizing a given field theory by representing action deformations as deformations of neural network parameter densities. As an example, $\phi^4$ theory is realized as an infinite-$N$ neural network field theory.
CrowdCache: A Decentralized Game-Theoretic Framework for Mobile Edge Content Sharing

Mobile edge computing (MEC) is a promising solution for enhancing the user experience, minimizing content delivery expenses, and reducing backhaul traffic. In this paper, we propose a novel privacy-preserving decentralized game-theoretic framework for resource crowdsourcing in MEC. Our framework models the interactions between a content provider (CP) and multiple mobile edge device users (MEDs) as a non-cooperative game, in which MEDs offer idle storage resources for content caching in exchange for rewards. We introduce efficient decentralized gradient play algorithms for Nash equilibrium (NE) computation by exchanging local information among neighboring MEDs only, thus preventing attackers from learning users' private information. The key challenge in designing such algorithms is that communication among MEDs is not fixed and is facilitated by a sequence of undirected time-varying graphs. Our approach achieves linear convergence to the NE without imposing any assumptions on the values of parameters in the local objective functions, such as requiring strong monotonicity to be stronger than its dependence on other MEDs' actions, which is commonly required in existing literature when the graph is directed time-varying. Extensive simulations demonstrate the effectiveness of our approach in achieving efficient resource outsourcing decisions while preserving the privacy of the edge devices.
Investigation of student experiences with ChatGPT-supported online learning applications in higher education

The purpose of this study was to determine university students' experiences with the use of ChatGPT in online courses. The sample consisted of 84 associate degree students from a state university in Turkey. A multi-method approach was used in the study. Although quantitative data were collected using the Chatbot Usability Scale, qualitative data were collected using a semi-structured interview form that we developed. The data were analysed using descriptive and content analysis methods. According to the findings, ChatGPT exhibits advantages such as a user-friendly interface and fast, concise, relevant responses. Moreover, emphasizing its contribution to the learning process, the information provided was sufficient and topic-oriented. The understandability of the chatbot’s functions and the clarity of their communication were emphasized. However, there are disadvantages such as performance issues, frequency of errors and the risk of providing misleading information. Concerns have also been raised about the potential difficulties chatbots may face in ambiguous conversations and providing insufficient information on privacy issues. In conclusion, ChatGPT is recognised as a potentially valuable tool in education based on positive usability impressions; however, more research is needed for its safe use. Implications for practice or policy Based on positive usability impressions, students and instructors can use ChatGPT to support educational activities. ChatGPT can promote and enhance students' personalised learning experiences. ChatGPT can be used in all higher education courses. Users should be cautious about the accuracy and reliability of the answers provided by ChatGPT. Decision-makers should take precautions against risks such as privacy, ethics, confidentiality and security that may arise from using artificial intelligence in education.
Review of strategic alignment: Accounting and cybersecurity for data confidentiality and financial security

In the contemporary landscape of rapidly evolving technological advancements and the increasing prevalence of cyber threats, organizations face a critical imperative to align their accounting practices with robust cybersecurity measures. This review explores the symbiotic relationship between accounting and cybersecurity in safeguarding data confidentiality and ensuring financial security. Focusing on the intersection of these two domains, we examine the strategic alignment required to fortify organizations against the escalating challenges posed by cyber threats to sensitive financial information. The review begins by delving into the intricate connection between accounting processes and the protection of financial data, emphasizing the pivotal role of accurate financial reporting and transparent disclosure in maintaining stakeholder trust. Subsequently, it scrutinizes the evolving threat landscape, identifying cyber risks that specifically target financial systems and data. The analysis underscores the need for a comprehensive strategic approach that integrates accounting practices with cybersecurity protocols to effectively mitigate these risks. Furthermore, the review investigates the contemporary tools and technologies that facilitate the integration of accounting and cybersecurity, enhancing organizations' ability to detect, prevent, and respond to cyber threats. It explores the adoption of advanced encryption methods, intrusion detection systems, and artificial intelligence-driven analytics to bolster data confidentiality and financial security. In examining case studies and best practices, this review highlights successful instances of organizations aligning accounting and cybersecurity strategies to achieve a cohesive defense against financial cyber threats. Lessons learned from these cases offer valuable insights for practitioners and decision-makers seeking to implement effective measures within their own organizational contexts. Ultimately, this review contributes to the evolving discourse on strategic alignment by emphasizing the imperative of synergizing accounting practices with cybersecurity initiatives. As organizations navigate an increasingly complex and interconnected business environment, a holistic approach that unifies financial integrity and cyber resilience becomes paramount for ensuring sustained success and safeguarding against the multifaceted challenges of the digital age.
Healthcare 4.0 Enabled Lightweight Security Provisions for Medical Data Processing

The recent progress of the Internet of Things (IoT) for various smart city applications into the Industry 4.0 resolution offers different advantages and challenges. E-healthcare is one of the vital applications of Industry 4.0 emerging as the Healthcare 4.0 standard for remote health monitoring. Healthcare 4.0 is originally a sub-class of Industry 4.0 standard. The Healthcare 4.0 standard consists of different layers such as edge layer, fog layer, cloud storage layer, and blockchain layer. For E-healthcare systems, the main challenges are concerning medical data security and privacy-preserving while processing the data from edge-layer to cloud storage layer via fog computing. The development of blockchain technology connected with cloud storage and edge layer offers strong security provisions. However, there are not enough experimental works available to address lightweight cryptography with blockchain implementation. In this paper, the robust framework of securing the medical data processing using blockchain connected with the cloud storage system has proposed. The medical data collected at the edge layer first encrypted using Elliptic Curve Cryptography using Elliptic Curve Diffie Hellman (ECDH). The encrypted data has stored in cloud storage, and then it is reflected in the blockchain. For signature generation and authentication of medical data, the Elliptic Curve Digital Signature Algorithm (ECDSA) has been designed. The experimental outcome of the proposed framework outperforms the state-of-art solutions.
Artificial intelligence and Blockchain technology enabling cybersecurity in telehealth systems

New technologies like artificial intelligence (AI) and Blockchain have significantly transformed the telehealth industry. AI can revolutionize healthcare, improving treatment outcomes, enabling personalized medicine, and electronic health record (EHRs) management systems. However, this trend also poses many cybersecurity risks, making telehealth systems an easy target for hackers. Data breaches in telehealth are most often caused by inadequate cybersecurity measures, insider threats, and third-party breaches, which cost economies heavily on the taxpayer&apos;s revenue per incident. Telehealth systems&apos; most common types of cyberattacks include ransomware, phishing, and malware attacks. This chapter exhaustively presents various telehealth concerns triggered by cybersecurity from a universal telehealth system perspective, its vulnerability, and what the future could hold for telehealth systems at the facility, patients&apos; level, and other stakeholders. Identifiable attacks and imperatives for telehealth systems, over 15 global significances for medical data, are demonstrated and presented. The effects of these attacks can be identically complex, like data leaks, system downtime, financial losses, and damage to a person&apos;s reputation. In order to deal with these risks, the study further demonstrates a privacy and security framework that telehealth organizations can adopt to ensure that privacy and security are attained amidst better quality of services using a multi-pronged cybersecurity strategy that includes regular risk assessments, adequate cybersecurity measures, employee training, and incident response plans. Lastly, highlight how Blockchain can store, secure, and share patient data, including Electronic Health Records (EMR) and medical research data.
CLIPTER: Looking at the Bigger Picture in Scene Text Recognition

Reading text in real-world scenarios often requires understanding the context surrounding it, especially when dealing with poor-quality text. However, current scene text recognizers are unaware of the bigger picture as they operate on cropped text images. In this study, we harness the representative capabilities of modern vision-language models, such as CLIP, to provide scene-level information to the crop-based recognizer. We achieve this by fusing a rich representation of the entire image, obtained from the vision-language model, with the recognizer word-level features via a gated cross-attention mechanism. This component gradually shifts to the context-enhanced representation, allowing for stable fine-tuning of a pretrained recognizer. We demonstrate the effectiveness of our model-agnostic framework, CLIPTER (CLIP TExt Recognition), on leading text recognition architectures and achieve state-of-the-art results across multiple benchmarks. Furthermore, our analysis highlights improved robustness to out-of-vocabulary words and enhanced generalization in low-data regimes.
MeMOTR: Long-Term Memory-Augmented Transformer for Multi-Object Tracking

As a video task, Multiple Object Tracking (MOT) is expected to capture temporal information of targets effectively. Unfortunately, most existing methods only explicitly exploit the object features between adjacent frames, while lacking the capacity to model long-term temporal information. In this paper, we propose MeMOTR, a long-term memory-augmented Transformer for multi-object tracking. Our method is able to make the same object's track embedding more stable and distinguishable by leveraging longterm memory injection with a customized memory-attention layer. This significantly improves the target association ability of our model. Experimental results on DanceTrack show that MeMOTR impressively surpasses the state-of-the-art method by 7.9% and 13.0% on HOTA and AssA metrics, respectively. Furthermore, our model also outperforms other Transformer-based methods on association performance on MOT17 and generalizes well on BDD100K. Code is available at https://github.com/MCG-NJU/MeMOTR.
Instruct-NeRF2NeRF: Editing 3D Scenes with Instructions

We propose a method for editing NeRF scenes with text-instructions. Given a NeRF of a scene and the collection of images used to reconstruct it, our method uses an image-conditioned diffusion model (InstructPix2Pix) to iteratively edit the input images while optimizing the underlying scene, resulting in an optimized 3D scene that respects the edit instruction. We demonstrate that our proposed method is able to edit large-scale, real-world scenes, and is able to accomplish more realistic, targeted edits than prior work. Result videos can be found on the project website: https://instruct-nerf2nerf.github.io.
LERF: Language Embedded Radiance Fields

Humans describe the physical world using natural language to refer to specific 3D locations based on a vast range of properties: visual appearance, semantics, abstract associations, or actionable affordances. In this work we propose Language Embedded Radiance Fields (LERFs), a method for grounding language embeddings from off-the-shelf models like CLIP into NeRF, which enable these types of open-ended language queries in 3D. LERF learns a dense, multi-scale language field inside NeRF by volume rendering CLIP embeddings along training rays, supervising these embeddings across training views to provide multi-view consistency and smooth the underlying language field. After optimization, LERF can extract 3D relevancy maps for a broad range of language prompts interactively in real-time, which has potential use cases in robotics, understanding vision-language models, and interacting with 3D scenes. LERF enables pixel-aligned, zero-shot queries on the distilled 3D CLIP embeddings without relying on region proposals or masks, supporting long-tail open-vocabulary queries hierarchically across the volume. See the project website at: https://lerf.io.
Treating Pseudo-labels Generation as Image Matting for Weakly Supervised Semantic Segmentation

Generating accurate pseudo-labels under the supervision of image categories is a crucial step in Weakly Supervised Semantic Segmentation (WSSS). In this work, we propose a Mat-Label pipeline that provides a fresh way to treat WSSS pseudo-labels generation as an image matting task. By taking a trimap as input which specifies the foreground, background and unknown regions, the image matting task outputs an object mask with fine edges. The intuition behind our Mat-Label is that generating trimap is much easier than generating pseudo-labels directly under weakly supervised setting. Although current CAM-based methods are off-the-shelf solutions for generating a trimap, they suffer from cross-category and foreground-background pixel prediction confusion. To solve this problem, we develop a Double Decoupled Class Activation Map (D2CAM) for Mat-Label to generate a high-quality trimap. By drawing on the idea of metric learning, we explicitly model class activation map with category decoupling and foreground-background decoupling. We also design two simple yet effective refinement constraints for D2CAM to stabilize optimization and eliminate non-exclusive activation. Extensive experiments validate that our Mat-Label achieves substantial and consistent performance gains compared to current state-of-the-art WSSS approaches.
IDiff-Face: Synthetic-based Face Recognition through Fizzy Identity-Conditioned Diffusion Models

The availability of large-scale authentic face databases has been crucial to the significant advances made in face recognition research over the past decade. However, legal and ethical concerns led to the recent retraction of many of these databases by their creators, raising questions about the continuity of future face recognition research without one of its key resources. Synthetic datasets have emerged as a promising alternative to privacy-sensitive authentic data for face recognition development. However, recent synthetic datasets that are used to train face recognition models suffer either from limitations in intra-class diversity or cross-class (identity) discrimination, leading to less optimal accuracies, far away from the accuracies achieved by models trained on authentic data. This paper targets this issue by proposing IDiff-Face, a novel approach based on conditional latent diffusion models for synthetic identity generation with realistic identity variations for face recognition training. Through extensive evaluations, our proposed synthetic-based face recognition approach pushed the limits of state-of-the-art performances, achieving, for example, 98.00% accuracy on the Labeled Faces in the Wild (LFW) benchmark, far ahead from the recent synthetic-based face recognition solutions with 95.40% and bridging the gap to authentic-based face recognition with 99.82% accuracy <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> .
BeLFusion: Latent Diffusion for Behavior-Driven Human Motion Prediction

Stochastic human motion prediction (HMP) has generally been tackled with generative adversarial networks and variational autoencoders. Most prior works aim at predicting highly diverse motion in terms of the skeleton joints' dispersion. This has led to methods predicting fast and divergent movements, which are often unrealistic and incoherent with past motion. Such methods also neglect scenarios where anticipating diverse short-range behaviors with subtle joint displacements is important. To address these issues, we present BeLFusion, a model that, for the first time, leverages latent diffusion models in HMP to sample from a behavioral latent space where behavior is disentangled from pose and motion. Thanks to our behavior coupler, which is able to transfer sampled behavior to ongoing motion, BeLFusion’s predictions display a variety of behaviors that are significantly more realistic, and coherent with past motion than the state of the art. To support it, we introduce two metrics, the Area of the Cumulative Motion Distribution, and the Average Pairwise Distance Error, which are correlated to realism according to a qualitative study (126 participants). Finally, we prove BeLFusion’s generalization power in a new cross-dataset scenario for stochastic HMP.
MotionBERT: A Unified Perspective on Learning Human Motion Representations

We present a unified perspective on tackling various human-centric video tasks by learning human motion representations from large-scale and heterogeneous data resources. Specifically, we propose a pretraining stage in which a motion encoder is trained to recover the underlying 3D motion from noisy partial 2D observations. The motion representations acquired in this way incorporate geometric, kinematic, and physical knowledge about human motion, which can be easily transferred to multiple downstream tasks. We implement the motion encoder with a Dual-stream Spatio-temporal Transformer (DSTformer) neural network. It could capture long-range spatio-temporal relationships among the skeletal joints comprehensively and adaptively, exemplified by the lowest 3D pose estimation error so far when trained from scratch. Furthermore, our proposed framework achieves state-of-the-art performance on all three downstream tasks by simply finetuning the pretrained motion encoder with a simple regression head (1-2 layers), which demonstrates the versatility of the learned motion representations. Code and models are available at https://motionbert.github.io/
A semi-supervised clustering approach using labeled data

Over recent decades, there has been a growing interest in semi-supervised clustering. Compared to the supervised or unsupervised clustering methods for solving different real-life problems, reviewed articles show that semi-supervised clustering methods are more powerful, and even a small amount of supervised information can signiﬁcantly improve the results of unsupervised methods. One popular method of incorporating partial supervised information is through labeled data. In this study, we propose a semi-supervised clustering algorithm called ConvexClust. The proposed method improves data clustering using a geometric view borrowed from the Lune concept in the connectivity index and 10% of labeled data. Clustering starts with the use of labeled data and the formation of a convex hull. It continues over the labeling of non-labeled data and the updating of the convex hull in an iterative process. Evaluations of three UCI datasets and sixteen artificial datasets show that the proposed method outperforms the other semi-supervised and traditional clustering techniques.