Reviewerly/exHarmony huggingface dataset:
UPAR Challenge: Pedestrian Attribute Recognition and Attribute-based Person Retrieval - Dataset, Design, and Results

In civilian video security monitoring, retrieving and tracking a person of interest often rely on witness testimony and their appearance description. Deployed systems rely on a large amount of annotated training data and are expected to show consistent performance in diverse areas and gen-eralize well between diverse settings w.r.t. different view-points, illumination, resolution, occlusions, and poses for indoor and outdoor scenes. However, for such generalization, the system would require a large amount of various an-notated data for training and evaluation. The WACV 2023 Pedestrian Attribute Recognition and Attributed-based Per-son Retrieval Challenge (UPAR-Challenge) aimed to spot-light the problem of domain gaps in a real-world surveil-lance context and highlight the challenges and limitations of existing methods. The UPAR dataset, composed of 40 important binary attributes over 12 attribute categories across four datasets, was extended with data captured from a low-flying UAV from the P-DESTRE dataset. To this aim, 0.6M additional annotations were manually labeled and vali-dated. Each track evaluated the robustness of the competing methods to domain shifts by training on limited data from a specific domain and evaluating using data from unseen do-mains. The challenge attracted 41 registered participants, but only one team managed to outperform the baseline on one track, emphasizing the task's difficulty. This work de-scribes the challenge design, the adopted dataset, obtained results, as well as future directions on the topic.
Hand Guided High Resolution Feature Enhancement for Fine-Grained Atomic Action Segmentation within Complex Human Assemblies

Due to the rapid temporal and fine-grained nature of complex human assembly atomic actions, traditional action segmentation approaches requiring the spatial (and often temporal) down sampling of video frames often loose vital fine-grained spatial and temporal information required for accurate classification within the manufacturing domain. In order to fully utilise higher resolution video data (often collected within the manufacturing domain) and facilitate real time accurate action segmentation - required for human robot collaboration - we present a novel hand location guided high resolution feature enhanced model. We also propose a simple yet effective method of deploying offline trained action recognition models for real time action segmentation on temporally short fine-grained actions, through the use of surround sampling while training and temporally aware label cleaning at inference. We evaluate our model on a novel action segmentation dataset containing 24 (+background) atomic actions from video data of a real world robotics assembly production line. Showing both high resolution hand features as well as traditional frame wide features improve fine-grained atomic action classification, and that though temporally aware label clearing our model is capable of surpassing similar encoder/decoder methods, while allowing for real time classification.
Exploiting Inter-pixel Correlations in Unsupervised Domain Adaptation for Semantic Segmentation

“Self-training” has become a dominant method for se-mantic segmentation via unsupervised domain adaptation (UDA). It creates a set of pseudo labels for the target do-main to give explicit supervision. However, the pseudo la-bels are noisy, sparse and do not provide any information about inter-pixel correlations. We regard inter-pixel cor-relation quite important because semantic segmentation is a task of predicting highly structured pixel-level outputs. Therefore, in this paper, we propose a method of transfer-ring the inter-pixel correlations from the source domain to the target domain via a self-attention module. The module takes the prediction of the segmentation network as an in-put and creates a self-attended prediction that correlates similar pixels. The module is trained only on the source domain to learn the domain-invariant inter-pixel correlations, then later, it is used to train the segmentation network on the target domain. The network learns not only from the pseudo labels but also by following the output of the self-attention module which provides additional knowledge about the inter-pixel correlations. Through extensive ex-periments, we show that our method significantly improves the performance on two standard UDA benchmarks and also can be combined with recent state-of-the-art method to achieve better performance.
Impact of Cultural Diversity in Classrooms of Secondary Schools

Students spend their real life hours in a classroom learning and getting an education.But occasionally they were left with an unpleasant memory of that location.A varied classroom experience usually has a different influence on the students.Because of the diversity of cultural backgrounds represented in the class.By using the skills and teachings of the teacher, that might be managed and corrected.The study's goal is to provide guidance in a multicultural school, culturally different groups are acknowledged and supported.Multicultural ideas may coexist in the educational setting with teaching methods that value variety as a resource and an added benefit, such as studying cultural diversity or using material that is sensitive to different cultures in the classroom.Using open-ended research questionnaires, school instructors who are the variables in a qualitative study and the major source of data collection are contacted directly.Interviews were done by the researcher with 12 respondents who were three different school instructors.The semi-structured questionnaire that served as the interview's instrument was utilized to conduct the semi-structured interview.Interviews with a specific population of lower secondary teachers were the main method of my research.
DSFL: Dynamic Sparsification for Federated Learning

Federated Learning (FL) is considered the key, enabling approach for privacy-preserving, distributed machine learning (ML) systems. FL requires the periodic transmission of ML models from users to the server. Therefore, communication via resource-constrained networks is currently a fundamental bottleneck in FL, which is restricting the ML model complexity and user participation. One of the notable trends to reduce the communication cost of FL systems is gradient compression, in which techniques in the form of sparsification are utilized. However, these methods utilize a single compression rate for all users and do not consider communication heterogeneity in a real-world FL system. Therefore, these methods are bottlenecked by the worst communication capacity across users. Further, sparsification methods are non-adaptive and do not utilize the redundant, similar information across users' ML models for compression. In this paper, we introduce a novel Dynamic Sparsification for Federated Learning (DSFL) approach that enables users to compress their local models based on their communication capacity at each iteration by using two novel sparsification methods: layer-wise similarity sparsification (LSS) and extended top- <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$K$</tex> sparsification. LSS enables DSFL to utilize the global redundant information in users' models by using the Centralized Kernel Alignment (CKA) similarity for sparsification. The extended top- <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$K$</tex> model sparsification method empowers DSFL to accommodate the heterogeneous communication capacity of user devices by allowing different values of sparsification rate <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$K$</tex> for each user at each iteration. Our extensive experimental results <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> All code and experiments are publicly available at: https://github.com/mahdibeit/DSFL. on three datasets show that DSFL has a faster convergence rate than fixed sparsification, and as the communication heterogeneity increases, this gap increases. Further, our thorough experimental investigations uncover the similarities of user models across the FL system.
Study on the Power Data Assets and Trading System Design of Blockchain

Abstract The sensitive data such as electric power data suffer from the easily abuse in the traditional centralized data trading system. To solve this problem, the blockchain-based power data’s capitalization and trading system were proposed in this study. Given the trust for blockchain technology, this work established a multi-party trusty network between multiple parties in the distributed ledger with cryptographic algorithms, and distributed multi-party consensus protocol. The system development with Hyperledger’s consortium blockchain framework Fabric made the power data safe, transparent and immutable. The local development environment for testing included a operating system MAC OS 10.14.6, six-core CPU 3.0 GHz, RAM 16 GB. A single machine is configured with multiple block chain network nodes, an ordering node, and two organizations Org0 and Org1, each of which has two nodes Peer0 and Peer1, and adopts the Solo consensus mechanism. The experimental results show that the system run well in the integrated test, satisfied the design requirements, and had low maintenance costs and improved security. Compared with the traditional data trading system, the blockchain-based power data’s capitalization and trading system demonstrated the comprehensive functionality and security advantages in the study.
Linking Contexts from Distinct Data Sources in Zero Trust Federation

An access control model called Zero Trust Architecture (ZTA) has attracted attention. ZTA uses information of users and devices, called context, to verify access requests. Zero Trust Federation (ZTF) has been proposed as a framework for extending an idea of identity federation to support ZTA. ZTF defines CAP as the entity that collects context and provides it to each organization (Relying Party; RP) that needs context for verification based on ZTA. For precise verification, CAPs need to collect context from various data sources. However, ZTF did not provide a method for collecting context from data sources other than RP. In this research, as a general model for collecting context in ZTF, we propose a method of linking identifiers between the data source and CAP. This method provides a way to collect context from some of such data sources in ZTF. Then, we implemented our method using RADIUS and MDM as data sources and confirmed that their contexts could be collected and used.
Tianji: Securing a Practical Asynchronous Multi-User ORAM

Oblivious Random Access Machines (ORAMs) allow cloud users to access remote data without leaking access patterns. Current ORAM solutions achieve this goal at expense of either increasing bandwidth consumption by a factor of <inline-formula><tex-math notation="LaTeX">$O(\log N)$</tex-math></inline-formula> , where <inline-formula><tex-math notation="LaTeX">$N$</tex-math></inline-formula> is the number of data blocks, or relying on homomorphic encryption for bandwidth amplification reduction to <inline-formula><tex-math notation="LaTeX">$O(1)$</tex-math></inline-formula> . Furthermore, most ORAMs are only effective for a single user, while the solutions for multi-user scenarios often induce security or performance problems. This article introduces <i>Tianji</i> — an asynchronous multi-user Shamir-based ORAM system — which supports asynchronous network access scenarios for multiple users with improved security and performance. <i>Tianji</i> is implemented on top of <i>S<inline-formula><tex-math notation="LaTeX">$^{3}$</tex-math><alternatives><mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msup><mml:mrow/><mml:mn>3</mml:mn></mml:msup></mml:math><inline-graphic xlink:href="wang-ieq4-3241184.gif" xmlns:xlink="http://www.w3.org/1999/xlink"/></alternatives></inline-formula>ORAM<inline-formula><tex-math notation="LaTeX">$^+$</tex-math><alternatives><mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msup><mml:mrow/><mml:mo>+</mml:mo></mml:msup></mml:math><inline-graphic xlink:href="wang-ieq5-3241184.gif" xmlns:xlink="http://www.w3.org/1999/xlink"/></alternatives></inline-formula></i> —an extension of the state-of-the-art Shamir-based S <inline-formula><tex-math notation="LaTeX">$^{3}$</tex-math></inline-formula> ORAM with a new non-eviction data write-back scheme to achieve <inline-formula><tex-math notation="LaTeX">$O(1)$</tex-math></inline-formula> consumption in both bandwidth amplification and storage capacity. Our experimental results show that the proposed <i>Tianji</i> with <i>S<inline-formula><tex-math notation="LaTeX">$^{3}$</tex-math><alternatives><mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msup><mml:mrow/><mml:mn>3</mml:mn></mml:msup></mml:math><inline-graphic xlink:href="wang-ieq8-3241184.gif" xmlns:xlink="http://www.w3.org/1999/xlink"/></alternatives></inline-formula>ORAM<inline-formula><tex-math notation="LaTeX">$^+$</tex-math><alternatives><mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msup><mml:mrow/><mml:mo>+</mml:mo></mml:msup></mml:math><inline-graphic xlink:href="wang-ieq9-3241184.gif" xmlns:xlink="http://www.w3.org/1999/xlink"/></alternatives></inline-formula></i> can significantly outperform the state-of-the-art multi-user <i>TaoStore</i> in terms of access latency and client scalability. Additionally, its average response time is relatively stable when client loads increase.
Partisan Conflict Over Content Moderation Is More Than Disagreement about Facts

Social media companies have come under increasing pressure to remove misinformation from their platforms, but partisan disagreements over what should be removed have stymied efforts to deal with misinformation in the US. Current explanations for these disagreements center on the “fact gap”—differences in perceptions about what is misinformation. We argue that partisan differences could also be due to “party promotion”—a desire to leave misinformation online that promotes one’s own party—or a “preference gap”—differences in internalized preferences about whether misinformation should be removed. Through an experiment where respondents are shown false headlines aligned with their own or the opposing party, we find some evidence of party promotion among Democrats and strong evidence of a preference gap between Democrats and Republicans. Even when Republicans agree that content is false, they are half as likely as Democrats to say that the content should be removed and more than twice as likely to consider removal as censorship.
Digital Transformation and Sustainability Factor Cases from UAE

The research addresses the increased focus by organizations in adopting sustainability measures as necessary strategies to improve their performance and be more responsible for the impact their operations have on the surrounding economy, societies, and environment.The article reviews studies on the correlation between adopting digital transformation programs and delivering sustainability goals and deriving global examples.Interviews are conducted with four UAE-based companies (DU, FEWA, Etisalat, and DEWA).The results shed light on their sustainability goals, how they motivated their respective digital transformation and the significant outcomes of digitization regarding sustainability.Reduced waste, more efficient and cost-effective processes, and a higher quality of service to customers were some of the key results of the digital transformation that facilitated a more sustainable operation by these companies.
Approaching an Optimal Bitcoin Mining Overlay

Bitcoin builds upon an unstructured peer-to-peer overlay network to disseminate transactions and blocks. Broadcast in such a network is slow and brings inconsistencies, i. e., peers have different views of the system state. Due to the delayed block propagation and the competition of mining, forking, i. e., the blockchain temporarily diverges into two or more branches, occurs, which wastes computation power and causes security issues. This paper proposes an autonomous and distributed topology optimization mechanism to reduce block propagation delay and hence reduce the occurrence of blockchain forks. In the proposed mechanism, a node can autonomously update his neighbor set using the information provided by his current neighbors, since each neighbor will recommend a peer from his own neighbor set, i. e., a neighbor’s neighbor, to this node. Each recommendation is based on a peer’s propagation ability, which is characterized as a criteria function obtained through a combination of empirical analysis and machine learning. We further propose some metrics to evaluate a Bitcoin network topology. Experiment results reflect the effectiveness of the proposed mechanism and indicate the correlation between block propagation time and fork rate. Thus, we analyze the relation between block propagation time and fork rate by applying an epidemic model to capture the block propagation process. We prove that a Bitcoin network topology with a relatively small network delay variance among all nodes produces a lower fork rate than another topology if its average block propagation time to 84% of the entire network is shorter.
Variational Latent-State GPT for Semi-Supervised Task-Oriented Dialog Systems

Recently, two approaches, fine-tuning large pre-trained language models and variational training, have attracted significant interests, separately, for semi-supervised end-to-end task-oriented dialog (TOD) systems. In this paper, we propose Variational Latent-State GPT model (VLS-GPT), which is the first to combine the strengths of the two approaches. Among many options of models, we propose the generative model and the inference model for variational learning of the end-to-end TOD system, both as auto-regressive language models based on GPT-2, which can be further trained over a mix of labeled and unlabeled dialog data in a semi-supervised manner. Variational training of VLS-GPT is both statistically and computationally more challenging than previous variational learning works for sequential latent variable models, which use turn-level first-order Markovian. The inference model in VLS-GPT is non-Markovian due to the use of the Transformer architecture. In this work, we establish Recursive Monte Carlo Approximation (RMCA) to the variational objective with non-Markovian inference model and prove its unbiasedness. Further, we develop the computational strategy of sampling-then-forward-computation to realize RMCA, which successfully overcomes the memory explosion issue of using GPT in variational learning and speeds up training. Semi-supervised TOD experiments are conducted on two benchmark multi-domain datasets of different languages - MultiWOZ2.1 and CrossWOZ. VLS-GPT is shown to significantly outperform both supervised-only and semi-supervised self-training baselines.
Learning to Super-Resolve Blurry Images With Events

Super-Resolution from a single motion Blurred image (SRB) is a severely ill-posed problem due to the joint degradation of motion blurs and low spatial resolution. In this article, we employ events to alleviate the burden of SRB and propose an Event-enhanced SRB (E-SRB) algorithm, which can generate a sequence of sharp and clear images with High Resolution (HR) from a single blurry image with Low Resolution (LR). To achieve this end, we formulate an event-enhanced degeneration model to consider the low spatial resolution, motion blurs, and event noises simultaneously. We then build an event-enhanced Sparse Learning Network (eSL-Net++) upon a dual sparse learning scheme where both events and intensity frames are modeled with sparse representations. Furthermore, we propose an event shuffle-and-merge scheme to extend the single-frame SRB to the sequence-frame SRB without any additional training process. Experimental results on synthetic and real-world datasets show that the proposed eSL-Net++ outperforms state-of-the-art methods by a large margin. Datasets, codes, and more results are available at https://github.com/ShinyWang33/eSL-Net-Plusplus.
Object Detection on Scene Images: A Novel Approach

Convolutional Neural Networks (CNN) have played an important contribution to the significant development of Computer Vision. An important aspect can be the recognition of objects as it can be an essential part of Computer Vision. CNN's provides a one-step implementation for the detection and classification stages with an improved result. In both these stages, proper monitoring of object temporal will be attempted by observing the sequences in video, shape, size, presence, and location. In this paper, the authors have attempted to provide an implementation of the Object Detection method with a novelty along with text extraction from a scene image. A comparative analysis study of the performance evaluation of the proposed method has been made with various techniques at the end of the paper.
EVMPCSA: Efficient VM Packing mechanism in cloud computing using Chaotic Social Spider Algorithm

VM packing is one of prodigious challenge in cloud computing as it depends on the incoming workload onto virtual resources. Workload in the cloud is highly dynamic and it is difficult to predict the upcoming tasks and place them properly onto an appropriate VM is a challenging task. While scheduling workload onto VMs it is important to pack them in an appropriate physical machine because there is a chance of wastage of virtual resources and which leads to high energy consumption. Therefore, it is necessary to pack VMs into an appropriate Physical machines based on utilization of CPU. Many of authors proposed various consolidation techniques to assess parameters makespan, energy consumption, Througput but still there is a research gap and we can minimize energy consumption based on utilization of CPU in proposed approach i.e. EVMPCSA. Consolidation of VMs and chososing of VM for migration onto a Physical host is based on utilization of CPU. Chaotic Social Spider algorithm is used as a methodology for VM packing mechanism. EVMPCSA uses cpu utilization as constraint and used Chaotic Social Spider algorithm as methodology in this work to solve VM packing problem. It is simulated on Cloudsim and evaluated aganist existing algorithms named as PSO, CS and ACO. When it is compared with PSO, CS and ACO makespan is greatly minimizes by 30.25, 23.5% and 17.31% respectively and energy consumption is minimized for PSO, CS and ACO by 27.6%, 24.78% and 10.09% respectively.