Reviewerly/exHarmony huggingface dataset:
Research on Sports Video Tracking Technology Based on Mean Shift Algorithm and Color Histogram Algorithm

The research on sports video tracking technology based on mean shift algorithm and color histogram algorithm is a research work focusing on the automatic tracking algorithm of sports events. The purpose of this research project is to develop an effective method to track moving objects in real time using video sequences. The purpose of this study is to find out whether there are any differences between the two algorithms in tracking objects in video. The purpose of this study is also to find out whether there are any differences between the two algorithms when tracking objects in video. The main purpose of this study is to know whether there is any difference between the mean shift algorithm and the color histogram algorithm for object detection in video. In order to achieve this goal, we use mean shift algorithm and color histogram algorithm as our method. This paper presents the results obtained from these two methods.
E-Certificate Generation using MVC Model

With the advancement in digitalization over the internet, web development and its applications came into play. A web application is built as an interactive app using web development technologies. The purpose of developing this system is to replace the paper-based system with a digital system. This change in the system has allowed students to apply for their required certificates from anywhere in the world and track the status of their application. The end objective is to provide a dynamically generated certificate to the student.
YOGA posture detection using machine learning

Yoga has proved to be the oldest and the most effective practice to attain a healthy life. Over the years its popularity is only increasing day by day. A lot of young people feel fascinated and find it an effortless way to per sue a good mental health. It helps in energizing and calming of the one who is constantly practicing it. That is why there is a need for a yoga position detection system. We tend to come with a new solution because the already available ones only instruct how the poses can be done it does not have the ability to monitor the user while doing. So, it forms a drawback that the user cannot be assisted or been notified weather he is practicing for the right manner that it is supposed to be performed. So, our proposed system is used to detect the position infer the accuracy of the positions practiced to significantly lead the user to outreach the right posture while one is frying to extract the best outcomes from doing the asanas. The proposed model uses Google's pose net model to categorize the poses by forming its own skeleton image and then comparing it with the pre trained dataset consisting of various yoga asanas. This will keep up the time, continuous monitoring, and accuracy measure to find and drive the user to follow the right positions. This model will help the people extract the maximum outcome without the need for any trainer or external assistance. We tend to come with a new solution because the already available ones only instruct how the poses can be done it does not have the ability to monitor the user while doing.
Graph Theory Matrix Approach in Cryptography and Network Security

Graph Theory Matrix Approach (GTMA) is moving with great speed into the main stream of computer design, Information sciences, Information and Computer programming, Artificial Intelligence and design, Artificial Intelligent and various field of research. Application of GTMA is in diverse area such as Data structures, Communication networks and their security. A Graph-based approach centres on conserving the environment of security events by breaking down factors of observable data into a graph representation of all cyber vestiges, from all data aqueducts, counting for all once and present data. For secret communication, Ciphers can be converted into graphs. The Application of Graph Theory plays a vital role in various field of Engineering and Sciences. Especially Graph theory is commonly used as a tool of encryption. In this article some survey has been work done in the field of Cryptography and Network security is given.
A federated learning framework based on transfer learning and knowledge distillation for targeted advertising

The rise of targeted advertising has led to frequent privacy data leaks, as advertisers are reluctant to share information to safeguard their interests. This has resulted in isolated data islands and model heterogeneity challenges. To address these issues, we have proposed a C-means clustering algorithm based on maximum average difference to improve the evaluation of the difference in distribution between local and global parameters. Additionally, we have introduced an innovative dynamic selection algorithm that leverages knowledge distillation and weight correction to reduce the impact of model heterogeneity. Our framework was tested on various datasets and its performance was evaluated using accuracy, loss, and AUC (area under the ROC curve) metrics. Results showed that the framework outperformed other models in terms of higher accuracy, lower loss, and better AUC while requiring the same computation time. Our research aims to provide a more reliable, controllable, and secure data sharing framework to enhance the efficiency and accuracy of targeted advertising.
State of the art review of seismic hazard approaches in Indian context

Numerous researchers have carried out seismic microzonation of India using various criteria.Seismic zonation was conducted at the national and regional levels using the conventional approaches i.e., either a probabilistic or a deterministic approach.Between the computed peak ground acceleration and the real peak ground acceleration observed during the seismic event, significant differences have been detected.Different approaches (conventional and new) have been used in the literature to conduct seismic hazard analysis and site-specific analysis of India in order to address this issue.The goal of the current work is to evaluate critically various researchers' approaches to seismic hazard analysis and compare those to Indian codal provisions already in place with a highlight of need for further research and modifications.
Modeling Educational Financing for Private Vocational High Schools in Deli Serdang Regency in the Context of Optimizing School's Marginal Contribution

The purpose of this research is to develop an educational financing model that best suits the needs in optimizing the marginal contribution value of private vocational high schools in Deli Serdang Regency. This study employs a qualitative research method. The author develops an educational financing model for vocational high schools using a descriptive qualitative method aimed at identifying phenomena occurring in the field to be presented as an educational financing management model. The research was conducted in private vocational high schools in Deli Serdang Regency. In this study, the author selected 31 respondents, including the Head of the Vocational High School Division of the Deli Serdang Education Office, ten Principals, ten Vice Principals for Facilities and Infrastructure, and ten School Treasurers from 13 private vocational high schools. This research produces a Model expected to be effective and efficient in financing private vocational high school education in Deli Serdang Regency. The resulting Educational Financing Model can serve as a standard for implementing school management, especially for private vocational high schools
Media resources in video information systems

The article describes methods for increasing the contrast of images in video information systems, the main compression of the video stream is provided by eliminating inter-frame redundancy using methods for compensating the movement of image fragments of adjacent frames. However, the use of motion compensation methods requires the formation of additional data (metadata) containing information about the types of image blocks used, their movement coordinates, etc. At the same time, in order to increase the compression of the video stream without sacrificing its quality, a higher accuracy of motion compensation is required, which leads to an increase in the number of blocks and, accordingly, an increase in the volume of the video stream metadata that reduces the effectiveness of motion compensation. This is the main problem of compressing streaming video without compromising image quality. In addition, higher positioning accuracy of blocks with motion compensation dramatically reduces the speed of image processing, which is not always feasible in real-time systems.
Truncated Differential Attacks On Symmetric Primitives With Linear Key Schedule: WARP And Orthros

Abstract In truncated differential cryptanalysis of symmetric primitives, a generalized framework is to search a distinguisher concerning part of output differences, like truncated differential distribution (TDD) on certain bits (e.g. a nibble) first, and then append several rounds before and after it to recover the secret key. The logarithmic likelihood ratio statistic with respect to the TDD is usually used to distinguish guessed key bits. In this paper, we study how to improve the effect of truncated differential cryptanalysis by considering key schedules of the attacked ciphers. It turns out that for a cipher with a simple key schedule, certain guessed subkey bits may reveal information of the master key, which will help build a stronger TDD distinguisher and reduce the key recovery complexity or attack more rounds. As a result, we explore heuristic techniques to search key-recovery-friendly TDDs and construct automatic search models based on MILP. The refined methods are applied to two recent designs of symmetric primitives, WARP and Orthros, together with peculiarities of their structures as well. For WARP, after making two observations on relations between certain differences with key bits, we propose an algorithm that can find TDDs with low complexities and having potentialities to cover more rounds. Consequently, we launch key recovery attacks on 24 to 27 rounds of WARP. When it comes to Orthros, we present a two-step search algorithm to balance the number of guessed key bits and TDDs, obtaining a key recovery attack on a 7-round variant of it in the weak-key setting. Finally, we perform several verification experiments on round-reduced versions of WARP and Orthros, and the experimental results are consistent with the theoretical distributions and the analysis of generalized key recovery attack framework.
Explicit expressions for stationary states of the Lindblad equation for a finite state space

Abstract The Lindblad equation describes the time evolution of a density matrix of a quantum mechanical system. Stationary solutions are obtained by time-averaging the solution, which will in general depend on the initial state. We provide an analytical expression for the steady states of the Lindblad equation using the quantum jump unraveling, a version of an ergodic theorem, and the stationary probabilities of the corresponding discrete-time Markov chains. Our result is valid when the number of states appearing the in quantum trajectory is finite. The classical case of a Markov jump-process is recovered as a special case, and differences between the two are discussed.
Brakedown: Linear-Time and Field-Agnostic SNARKs for R1CS

This paper introduces a SNARK called Brakedown. Brakedown targets R1CS, a popular NP-complete problem that generalizes circuit-satisfiability. It is the first built system that provides a linear-time prover, meaning the prover incurs O(N) finite field operations to prove the satisfiability of an N-sized R1CS instance. Brakedown ’s prover is faster, both concretely and asymptotically, than prior SNARK implementations. It does not require a trusted setup and may be post-quantum secure. Furthermore, it is compatible with arbitrary finite fields of sufficient size; this property is new among built proof systems with sublinear proof sizes. To design Brakedown, we observe that recent work of Bootle, Chiesa, and Groth (BCG, TCC 2020) provides a polynomial commitment scheme that, when combined with the linear-time interactive proof system of Spartan (CRYPTO 2020), yields linear-time IOPs and SNARKs for R1CS (a similar theoretical result was previously established by BCG, but our approach is conceptually simpler, and crucial for achieving high-speed SNARKs). A core ingredient in the polynomial commitment scheme that we distill from BCG is a linear-time encodable code. Existing constructions of such codes are believed to be impractical. Nonetheless, we design and engineer a new one that is practical in our context. We also implement a variant of Brakedown that uses Reed-Solomon codes instead of our linear-time encodable codes; we refer to this variant as Shockwave. Shockwave is not a linear-time SNARK, but it provides shorter proofs and lower verification times than Brakedown, and also provides a faster prover than prior plausibly post-quantum SNARKs.
MT-BICN: Multi-task Balanced Information Cascade Network for Recommendation

Multi-task learning (MTL) is a promising research direction in recommender systems, whose prediction accuracy greatly depends on the quality of the modeling of the relationships among tasks. Much of the prior research focus on three tasks: predicting click-through rate (CTR), post-view click-through & conversion rate (CTCVR), and post-click conversion rate (CVR), which rely on inherent user action pattern of impression $$\rightarrow $$ click $$\rightarrow $$ conversion. Information cascade pattern, represented by Adaptive Information Transfer Multi-task (AITM), attempts to model such sequential dependencies in the feature space close to the output for the first time. However, we observe that the first task in the information cascade model usually tends to be the victim, which is not in line with expectations. To this end, we propose a novel architecture: Multi-task Balanced Information Cascade Network (MT-BICN). We set up both shared experts and task-specific experts for each task to provide a bottom-line guarantee for each task’s performance, which largely reduces the risk of each task falling victim to the seesaw phenomenon. Information transfer unit (ITU) is designed and set at the output layer of the top tower to explicitly model the sequential dependencies among tasks. In addition, to further improve the feature extraction capability of the bottom shared experts, task-specific experts, and task towers, we design individual optimization objectives for the BASE model without introducing ITUs, and a balanced marginal constraint to encourage the introduction of ITU to benefit the later tasks without harming the former ones. We conducted extensive experiments on open-source large-scale recommendation datasets from AliExpress. The experimental results show that our approach significantly outperforms the mainstream MTL learning approaches for recommender systems. In addition, the ablation study demonstrates the necessity of designing core modules in MT-BICN.
Hypergraph Enhanced Contrastive Learning for News Recommendation

With the explosion of news information, user interest modeling plays an important role in personalized news recommendation. Many existing methods usually learn user representations from historically clicked news articles to represent their overall interest. However, they neglect the diverse user intents when interacting with items, which can model accurate user interest. Moreover, GNN methods based on bipartite graph cause the over-smoothing effect when considering high-order connectivity, which declines the news recommendation quality. To tackle the above issue, we propose a novel Hypergraph Enhanced Contrastive Learning model, named HGCL, to incorporate the intent representation and the hypergraph representation with a cross-view contrastive learning architecture. Specifically, we design an intent interaction learning module, which explores user intents of each user-item interaction at a fine-grained topic level and encodes useful information into the representations of users and items. Meanwhile, the designed hypergraph structure learning module enhances the discrimination ability and enriches the complex high-order dependencies, which improves the presentation quality of the recommendation system based on hypergraph enhanced contrastive learning. Extensive experiments on two real-world datasets demonstrate the superiority of our model over various state-of-the-art news recommendation methods.
Task Inference for Offline Meta Reinforcement Learning via Latent Shared Knowledge

Offline Reinforcement Learning (RL) has emerged as a promising approach for learning from existing data without requiring online interactions. However, traditional offline RL algorithms often suffer from poor generalization and overfitting due to limited task diversity in the training data. In this paper, we propose a novel framework called Meta-Task (MeTask) for offline RL that leverages meta-learning techniques to learn a task representation from a diverse set of offline training tasks. Specifically, we introduce a task-shared meta-learning objective that extracts meta-knowledge from the context data of each task and uses it to learn a more generalizable task representation. Additionally, we design a task-infer module that restores the learned meta-knowledge and task-specific information between different tasks to achieve efficient transfer of knowledge. Experiments on a variety of benchmark tasks demonstrate that MeTask achieves state-of-the-art performance compared to traditional offline RL algorithms. These results suggest that leveraging task diversity and meta-learning techniques can significantly improve the efficiency of offline RL methods.
Photonic Integrated Circuits and Components for Quantum Key Distribution

In this talk, we present designs of photonic integrated circuits and components for applications in quantum communications. The goal is to miniaturize the instrumentation while optimizing the security of various quantum key distribution protocols, such as BB84 with different qubit encodings, and measurement-device-independent protocols (MDI-QKD). The proposed components include diffraction gratings for polarization coupling, on-chip path-polarization interconverters, nanoantennas, and graphene nanocavities for coupling individual photon emitters. We simulate the behavior of the proposed circuits and components, and analyze the quantum key error for different parameters, comparing the advantages of single photons and weak coherent states.
Continual Contrastive Anomaly Detection under Natural Data Distribution Shifts

This article carries out a research on the topic of data-driven anomaly detection with a focus on its continual learning ability for nonstationary data streams. The study has two primary objectives: to process the precious label information in a semi-supervised paradigm, and to deal with the stability-plasticity dilemma caused by natural data distribution shifts. It means that the anomaly detector should adapt to the new data distribution while retaining and utilizing the previous knowledge. To address these objectives, a novel continual anomaly detection framework, called Continual Contrastive Anomaly Detection (CCAD), is proposed through the lens of contrastive learning. CCAD is highlighted by prototype learning through a continual contrastive loss function that includes both homologous sample-to-prototype and heterologous sample-to-sample contrastive components, which aim to find out the prototypical representation for new data and to constrain the updated representation space within the proximal region of previous ones, respectively. In addition, it provides a natural rehearsal selection method based on the affinity of samples to prototypes. The efficacy of CCAD is experimentally demonstrated through a case study over a network intrusion detection system spanning approximately a decade. The source code is available at https://github.com/JingyuYang1997/CCAD.
Exploring the effectiveness of Virtual Reality in teaching Maltese

Virtual Reality (VR) has gained popularity in education, but its potential in language learning, specifically for teaching a minor language like Maltese, has been understudied. To address this gap, a mixed-methods study was conducted to explore the effectiveness of VR in language learning and identify the advantages and challenges of teaching Maltese as a second language (ML2) with a focus on improving language skills. Qualitative research involved a focus group, while quantitative analysis utilized an online survey with a Likert scale and 25 Maltese teachers as participants. The results revealed that younger respondents were more inclined to believe in the significant impact of VR on improving learning outcomes compared to older respondents. VR was found to effectively engage students in immersive environments and enhance the learning context with entertainment features. However, challenges related to costs, bulky headsets, and technical issues were also uncovered. Although VR tools are not currently utilized in teaching Maltese, this research explored their potential effectiveness in ML2 instruction and provided suggestions for effective integration into language teaching. The findings provide quantitative evidence supporting the effectiveness of VR tools in education and their potential to facilitate ML2 learning. Most participants expressed a positive perception, emphasizing the importance of integrating VR technology into Maltese teaching practices to create more engaging and immersive learning experiences for ML2 learners.
Chapter 5 From evolution to revolution: the contemporary development of quantum computing

It is likely that you were familiar with quantum computing before beginning to read this book chapter. The fundamental building blocks of quantum computing are derived from the mind-boggling scientific field of quantum physics. Our society, particularly the fields of security, finance, and research, will be greatly impacted by the enormous potential effects of quantum computing. As a result, you may find articles about quantum computing in both specialized scientific journals and widely read lifestyle publications. But what does quantum computing mean to modern software developers? The prospective effects of quantum computing on developer’s lives were discussed in this book. In order to focus on the aspects of quantum computing that are pertinent to developers, we quickly discuss its principles and implications. The fundamental concepts are first introduced. Then, you will learn how to build a straightforward Java program that makes use of quantum computing. We also introduce the strange library, which enables you to continue using quantum principles while writing in Java (or other high-level languages). The qubit, a key component of quantum computing, is also introduced. Nothing is stopping you from beginning to explore quantum computation in your projects right now, thanks to quantum simulators and early prototypes. The likelihood that your software will be prepared by the time the hardware is available will rise as a result.
POLYPHONY OF N. I. BOROVSKY’S PAINTED IMAGES IN THE SYMBOLISM OF THE MOSAIC STRUCTURE OF THE WORLD

The article explores the diversity of artistic language based on the cognition of polyphonic, natural philosophical images created by the outstanding master of realistic art, academician of the Russian Academy of Arts, member of the Presidium of the Russian Academy of Arts, First Secretary of the Union of Artists of Russia, Chairman of the Moscow Branch of the Union of Artists of Russia, People’s Artist of the Russian Federation, Honored Artist of the Russian Federation N. I. Borovsky. With insuffi cient knowledge of the artist’s method of work in modern art studies, the interview with N. I. Borovsky reveals new aspects of the painter’s individual line of creativity in the socio-cultural space of modernity, gives relevance to the study and reveals its scientifi c novelty.