Reviewerly/exHarmony huggingface dataset:
Privacy-Preserving Verifiable Collaborative Learning with Chain Aggregation

As many countries have promulgated laws on the protection of user data privacy, how to legally use user data has become a hot topic. With the emergence of collaborative learning, also known as federated learning, multiple participants can create a common, robust, and secure machine learning model aimed at addressing such critical issues of data sharing as privacy, security, and access, etc. Unfortunately, existing research shows that collaborative learning is not as secure as it claims, and the gradient leakage is still a key problem. To deal with this problem, a collaborative learning solution based on chained secure multi-party computing has been proposed recently. However, there are two security issues in this scheme that remain unsolved. First, if semi-honest users collude, the honest users' gradient also leaks. Second, if one of the users fails, it also cannot guarantee the correctness of the aggregation results. In this paper, we propose a privacy-preserving and verifiable chain collaborative learning scheme to solve this problem. First, we design a gradient encryption method, which can solve the problem of gradient leakage. Second, we create a verifiable method based on homomorphic hash technology. This method can ensure the correctness of users' aggregation results. At the same time, it can also track users who aggregate wrong. Compared with other solutions, our scheme is more efficient.
Research on Integrated Algorithm of Target Detection and Tracking

In the field of automatic target recognition and tracking, long-term tracking for marine target has been recently seen with great interest. Although Siamese neural network and correlation filter trackers provide competitive results in terms of performance, they not only ignore the issue of heavy occlusion, but also fail to focus greater attention in the objective re-detection area. To cope with these issues, an integrative algorithm of kernel correlation filter and single-stage detector is proposed. A one-dimensional scale filter and feature fusion method are added. At the same time, a second-level tracking confidence discrimination mechanism is established. Meanwhile, the re-detection and positioning function of target loss is introduced. The algorithm can effectively deal with the occlusion situation and maximize the balance accuracy and real-time performance. The algorithm is tested on the marine ship tracking data set and compared with other six tracking algorithms. Compared with the Kernelized Correlation Filter (KCF) tracking algorithm, the accuracy is improved by 9.9% and the success rate is improved by 32.6%. It solves the problem that the KCF algorithm can't track stably for a long time, and the effectiveness and feasibility of the tracking model are verified by experiments.
Covid-19 digital Contact-tracing: a doorway to well-being or a backdoor to security vulnerabilities?

Digital Contact-tracing through mobile applications require gathering of location and other personal information of an individual by the government or private organizations and became an essential solution for moderating the pandemic and slackening lockdown measures. However, the moral and legal boundaries for such privacy-sensitive information reconnaissance procedure and the ambiguity in the security measures of such technologies has gained controversial reputation.In this work, we performed static profiling of 10 different Android Contact-tracing applications, developed by the health departments of 10 different states within the United States and studied possible security threats posed by them. To the best of our knowledge, our work is the first to heuristically analyze the users’ attitude towards these applications to understand the user-perceived contribution of these apps towards their well-being. We collected user feedback for each of the apps and trained a logistic regression classifier on cleaned, pre-processed and vectorized texts to identify positive or negative outlook towards these apps. Using the confusion matrix, our predictive model showed up to 85% accuracy, 94% precision, 93% recall and 83% f1 score. in predicting the sentiments. The sentiment prediction shows, users in some states did find the apps to be helpful where some other states found them wasteful. Whereas, our static analysis shows none of the apps are malicious themselves but all of them request permission that can be abused to gain escalated privileges.
Binary Cat Swarm Optimization Feature Selection and Machine Learning Based Ensemble for the Classification of the Daily Living Activities of the Elders

The classification of the daily living activities (DLAs) is a challenging research topic as the analysis of the DLAs data can help to improve the general health condition of the monitored subjects, to identify patterns that indicate various types of diseases such as the Alzheimer’s disease and to prevent the falls caused by the poor health condition. The DLAs classification is approached in this article using a machine learning methodology as follows: (1) the standard Cat Swarm Optimization (CSO) algorithm is adapted to a binary version (BCSO) applicable for features selection, (2) the features are selected using the BCSO algorithm, (3) the cats are evaluated considering the sum of square errors of the DLAs and (4) the DLAs data is classified by an ensemble based on six machine learning classification algorithms. The data used in experiments is the PAMAP2 Physical Activity Monitoring dataset from the UCI Machine Learning Repository.
Increasing Awareness for Cyber Security in the Corporate Sector

The age of computer advancement has caused a revolutionary change in the corporate sector. From on-campus working hours to remote work from home scenarios, from meetings in a conference room to meeting online in a virtual environment, things are changing continuously in the corporate environment. This paper tries to educate and generate awareness about cyber security in the non-technical human resource and try to make them understand the potential risks to their organization which can be caused because of not giving much attention to smaller details. This paper concentrates on those attacks which can be mitigated by any non-technical employee and which are easy to understand and give preventive measures for the same.
Development of an Intelligent Win-Win Negotiation Model

This paper proposes using Artificial Intelligence (AI) to resolve optimality of negotiation outcomes in an incomplete information negotiation context. The model proposed with the experiment implemented proves that AI is a beneficial and convenient tool to predict negotiators’ preference models to be used in offers’ assessment and therefore ensuring optimality of win-win negotiation outcomes. Moreover, it develops a new concept in negotiation, the Zone of Predicted Profitability (ZOPP), in which the Zone of Possible Agreement (ZOPA) is minimized to a more relevant zone. Although the ZOPP range is not explicitly determined, but the models predicted from AI are used to check whether a negotiator offered from outside his ZOPP range that helps to ensure optimality of an outcome. Therefore, the ZOPP concept which became applicable through AI contributes to designing an intelligent win-win negotiation outcome although information is incomplete.
Graph Convolutional Network Based on Higher-Order Neighborhood Aggregation

The graph neural network can use the network topology, the attributes and labels of nodes to mine the potential relationships on network. In paper, we propose a graph convolutional network based on higher-order Neighborhood Aggregation. First, an improved graph convolutional module is proposed, which can more flexibly aggregate higher-order neighborhood information in convolution kernel. It alleviates the over-smooth problem caused by high-order aggregation to a certain extent. Then, we propose two different neighborhood aggregation models. The first model trains multiple embeddings independently for the network and obtains the final embedding through connection, that is, focuses on processing the overall information of the neighborhood. The other is analogous to the three-dimensional convolutional neural network on image. It concatenates features in each layer which pays more attention to processing the hierarchical information of the neighborhood. Our algorithm is a general network model enhancement algorithm which is suitable for different network architectures. Experiments on 6 real network datasets show that our algorithm has good results in node classification, link prediction, etc.
Recommender Systems in e-Commerce

With the rapid development of e-commerce, more and more users have poured into e-commerce platforms. In order to provide users with a better shopping experience, e-commerce platforms often recommend products they may like or need. A recommendation system is a kind of system that can recommend products that may be satisfactory to users by collecting and analyzing user product data. In this way, the match between goods and consumers can be established. The recommendation system plays an important role in the e-commerce platform. In this article, starting from the recommendation system itself, it will explain what a recommendation system is and its goals; how to evaluate the pros and cons of a recommendation system, that is, the metrics of its performance; the types of mainstream recommender systems are analyzed by combining with e-commerce platforms Their respective advantages and disadvantages. Their respective advantages and disadvantages; finally analyze the possible development direction of the recommendation system in the future. And the recommender system in e-commerce will be described through the above four aspects.
Learning Language to Symbol and Language to Vision Mapping for Visual Grounding

Visual Grounding (VG) is a task of locating a specific object in an image semantically matching a given linguistic expression. The mapping of the linguistic and visual contents and the understanding of diverse linguistic expressions are the two challenges of this task. During the past decade, visual grounding performance is consistently improved by deep visual features. While deep visual features contain rich information, they could also be noisy, biased and easily over-fitted. In contrast, symbolic features are discrete, easy to map and usually less noisy. In this work, we propose a novel modular network learning to match both the object’s symbolic features and conventional visual features with the linguistic information. Moreover, the Residual Attention Parser is designed to alleviate the difficulty of understanding diverse expressions. Our model achieves state of-the-art performance on three popular datasets of VG.
The Online Criminal Trial as a Public Trial

This Essay proposes that an online trial is fundamentally “public” for Sixth Amendment purposes. “public trial” was understood to be one where the audience could see and hear the trial, as is possible online. Moreover, the purposes of the right to a public trial are easily satisfied by conducting a trial online, with audio and video of the proceedings available to interested members of the public. If those purposes are satisfied, there should be no insistence on the presence of bodies in a room
Transactional Knowledge Graph Generation To Model Adversarial Activities

A Knowledge Graph (KG) is a formal and structured representation of entities, relationships, and their semantic descriptions. Traditionally, KGs are used to describe metadata about entities and provide additional context to a target application. Many real-world domains also involve temporal interactions between entities, in addition to the metadata. Modeling these attributed transactions is a critical requirement when using KGs in complex real-world applications, such as modeling adversarial activities. Adversarial activity modeling requires methodology and tools to produce realistic large-scale background graphs that include embedded Weapons of Mass Destruction (WMD) activity patterns [1]. We present a novel framework for constructing transactional knowledge graphs from a diverse set of sources. We present cloud-scale architecture of the framework, core components, and a real-world use case to demonstrate the generation of a background knowledge graph and WMD activity templates to evaluate network alignment and subgraph matching algorithms.
ToFM: Topic-specific Facet Mining by Facet Propagation within Clusters

Mining the facets of topics is an essential task for information retrieval, information extraction and knowledge base construction. For the topics in courses, there are three challenges: different topics have different facet, the labels of facets rarely appear in the topic description text and not all topics have enough textural information to mine facets. In this paper we propose a weakly-supervised algorithm for topic-specific facet mining (ToFM for short) based on our finding that similar topics in a cluster have similar facet sets. For example, topics Binary Search Tree, Suffix Tree and AVL tree in Tree cluster have example, insertion, deletion, traversal and other similar facets. ToFM first splits topics in a domain into several topic clusters based on the topic description text. Then ToFM extracts initial facet sets for all topics from the corresponding Wikipedia article pages. Finally, ToFM performs a normalized facet propagation within each topic cluster to acquire final facet sets of every topic. We evaluate the performance of ToFM on six real-world datasets and experimental results show that ToFM achieves better performance than the existing facet mining algorithms.
Development of a Framework for ‘Character’ in the Practical Arts Curriculum

이 연구는 교육과정 문서에서 실과교육이 추구해야 할 본질적 특성을 진술하는 방안을 제시하기 위해 수행되었다. 이를 위해 국외 실과 관련 교육과정 및 국내 타교과의 교육과정과 제1차부터 현행 2015 개정 차수까지 실과 교육과정을 분석하여 ‘성격’의 진술에 적합한 프레임워크를 개발하고, 그 예시를 제시하였다. 실과 교육과정 ‘성격’의 프레임워크로는 학문 배경 중심 전개, 현장 중심 전개, 학문-현장 연계 중심 전개 등 세 가지 안을 개발하였다. 세 가지 프레임워크를 바탕으로 각각 실과 교육과정 ‘성격’의 예시를 진술하였다. 이 연구에서 제안한 실과교육의 ‘성격’에서는 인공세계를 탐구의 대상으로 하고, 안목 형성을 탐구의 목적으로 삼으며, 창의노작을 탐구의 방법으로 삼는다. 또한 문제해결, 프로젝트, 실천적 추론 등의 교수·학습방법을 적용하고, 타교과와 연계하여 지도할 것을 강조하였다. 실과 교육과정 ‘성격’을 진술하는 내용과 방식은 교과를 이해하는 기초 자료가 된다는 점에서 중요하며, 교육과정 ‘성격’에 관한 연구는 실과교과의 본질을 공고하게 다지고, 학교 현장에서 실과교육에 대한 교사의 이해도를 높임으로써 학생들이 실과교육을 통해 온전히 세상을 바라보고 이해할 수 있도록 돕는 데 목적을 두고 수행되어야 한다.The purpose of this study is to suggest a way to state the nature that Practical Arts education should pursue in the curriculum. In order to achieve the purpose of the study, researchers developed a framework suitable for the ‘character’ statements in the Practical Arts curriculum by analyzing foreign documents, other domestic subjects and Practical Arts curriculum from the 1st to 2015 revised. The results of this study are as follows. As a framework for the Practical Arts curriculum character , three examples were developed: academic background-oriented, field-oriented and academic-field combined oriented framework. The statement of character proposed in this study was based on these frameworks. The object of inquiry is the artificial world, the purpose of inquiry is to form discernment, and the method of inquiry is creative work. It is emphasized that teaching and learning methods such as problem solving, projects, and practical reasoning are applied, and the instruction is provided to integrate with other subjects in various ways including integrated theme-based class. The contents and methods of stating ‘character’ in the Practical Arts curriculum are important in that they serve as the basic materials for understanding the curriculum. It should be carried out with the purpose of helping students to see and understand the world through Practical Arts education.
Study on Cluster-Based Pattern Recognition Using Recommendation System

The recommendations system has growing relevance to internet service and overload of information is one of the most critical incidents that users meet on the Internet. To make suggestions based on similar interests between users or items, Recommender Systems uses collective filtering. Clusters shall classify population or data points so that data points in the same category are more comparable to other data points in the same category and differ from data points in other categories. Pattern detection and matching include parameters such as average, correlation, and mutual information, etc. The PR can be used as a classification method for extracting and grouping patterns into various groups. PR is a classification method. PR is an intermediary process. In this review, we researched data mining, Clustering, Recommendation systems, Pattern Recognition, and described different types of recommendation systems as well as defined various methods of clustering & pattern recognition.
Exploring the Causal Relationship between Factors Affecting US Army Recruitment

Every year, United States Army Recruiting Command (USAREC) dedicates considerable resources to recruiting and accessing soldiers. As the largest branch of the United States Armed Forces, the Army must meet a high recruiting quota while competing in the free-labor market for quality recruits. Over the past two decades, the Army’s success in recruiting ebbed and flowed within the broader context of society and global events. While numerous studies have examined the statistical relationship between factors associated with recruitment, these studies are observational and definitively ascribing causality in retrospect is difficult. With this in mind, we apply fuzzy cognitive mapping (FCM), a graphical method of representing uncertainty in a dynamic system, to model and explore the complex causal relationships between factors. We conclude our paper with implications for USAREC’s efforts, as well as our model’s limitations and opportunities for future work.
A Novel Approach for Privacy Preservation in Blockchain Network Using Tensor Product and a Hybrid Swarm Intelligence

Blockchain-based technique is developed for privacy protection using tensor product and a hybrid swarm intelligence-based coefficient generation. Initially, the blockchain data with mixed attributes was subjected to the privacy preservation process, in which the raw data matrix and solitude and utility (SU) coefficient were multiplied through the tensor product. Thus, the derivation of the SU coefficient, which handles both sensitive information and utility, was formulated as a searching problem. Then, the proposed algorithm was introduced to evaluate the SU coefficient. The performance of the developed technique was evaluated by means of accuracy and information loss. The achieved results have shown that the developed hybrid sward intelligence reached a maximal accuracy of 0.840 and minimal information loss of 0.159 using dataset-2 compared to the existing system.
Concept of Robotic System for Assistance/Rehabilitation of Persons with Motor Disabilities

The paper explores an intensively researched field, linked to the role of robots in assisting and rehabilitating people with motor disabilities. Thus, the paper is focused on presenting a concept of a mechanical system, able to provide solutions for interaction between the human, with locomotors problems, and a mobile assistance/recovery robot, in order to improve the quality of life. The mechanical system proposed for development is customizable, based on human needs and physical capabilities. The paper presents the constructive characteristics and the kinematic control functions which are the base of the automation of the robot’s functionality.
Analysis of Image generation using Scene graphs

In this paper, we analysed the image generation using scene graphs. Three models - SGDET, SG2IM, and Stanford Parser were used for experimental purposes. We evaluated our design pipeline on MS COCO and Visual Genome datasets and compared the images generated using different metrics.
Computer Recognition and Conversion of the Painting Style of Characters in the Murals of the Tombs of the Northern Qi Dynasty

Based on the computer image recognition technology, this paper selects the images of the people in the tombs of the Northern Qi Dynasty as the research object. Through the investigation of the images of the people, including the subject matter, layout, and modeling characteristics, the computer recognition and image conversion technology are used to try to outline the image of the people in the tombs of the Northern Qi Dynasty. The overall style and outlook. This paper uses feature extraction to extract pixels from the image of people in the tombs of the Northern Qi Dynasty in different regions, and discusses the commonalities and differences among them. Then, in the vertical historical context and horizontal historical pattern, the images of people in different eras, regimes, and artistic categories were compared with those in the tombs of the Northern Qi Dynasty, and the difference was reduced to 4.5%
ConOffense: Multi-modal multitask Contrastive learning for offensive content identification

Hateful or offensive content has been increasingly common on social media platforms in recent years, and the problem is now widespread. There is a pressing need for effective automatic solutions for detecting such content, especially due to the gigantic size of social media data. Although significant progress has been made in the automated identification of offensive content, most of the focus has been on only using textual information. It can be easily noticed that with the rise in visual information shared on these platforms, it is quite common to have hateful content on images rather than in the associated text. Due to this, present day unimodal text-based methods won’t be able to cope up with the multimodal hateful content. In this paper, we propose a novel multimodal neural network powered by contrastive learning for identifying offensive posts on social media utilizing both visual and textual information. We design the text and visual encoders with a lightweight architecture to make the solution efficient for real world use. Evaluation on the MMHS150K dataset shows state-of-the-art performance of 82.6 percent test accuracy, making an improvement of approximately +14.1 percent accuracy over the previous best performing benchmark model on the dataset.
Security Framework for Supply-Chain Management

In recent times, cyber-attacks have been a significant problem in any organization. It can damage the brand name if confidential data is compromised. A robust cybersecurity framework should be an essential aspect of any organization. This chapter talks about the security framework for cyber threats in supply chain management and discusses in detail the implementation of a secure environment through various controls. Today, a systematic method is used for handling sensitive information in an organization. It includes processes, people, and IT systems by implementing a risk management method. Distinct controls dedicated to different levels of domains, namely human resources, access control, asset management, cryptography, physical security, operations security, supplier relations, acquisition, incident management, and security governance are provided. Companies, contractors, and any others who are part of the supply chain organization must follow this security framework to defend from any cyber-attacks.
Passive Eavesdropping Attack of Several Intruders on Deterministic Protocol with Pairs of Entangled Qubits

This work presented an analysis of the passive eavesdropping attack of two intruders on the original pingpong protocol with pairs of entangled qubits. A general recursive expression is obtained for the probability of detecting an attack made by random intruders number. It is shown that an increase in the number of intruders in a quantum channel leads to an increase in the probability of their attack detection by legitimate users.