Reviewerly/exHarmony huggingface dataset:
A Coordinated and Optimized Mechanism of Artificial Intelligence for Student Management by College Counselors Based on Big Data

The purpose of this article is to perform in-depth research and analysis on the artificial intelligence coordination and optimization mechanism of college counseling student management using big data technology. This study places the collaborative ideological and political work of colleges and universities in the context of big data, and by analyzing its basic connotation and changes in the real situation, it explores the development progression of colleges and universities making full use of big data resources to cultivate a collaborative education model, which is conducive to promoting colleges and universities to cultivate a whole staff, whole process, and all-round accurate ideological education and value-led services and to shape excellent young college students with comprehensive growth. The first is to scientifically build a multilevel linked big data management platform for counselor professionalization construction, plan the technical architecture of the organizational platform, build a cloud database of counselor career files, and extract valuable information and data from the organizational activities at the macrolevel and personal activities at the microlevel with counselor professionalization construction activities; the second is to realize the integrated application of information resources for counselor team construction. The second is to realize the integrated application of counselor team construction information resources, visualise and accurately analyze and evaluate the counselor group’s focus on career development and individual counselors’ feedback on career capacity construction, and improve the overall construction, personalized education management level, and self-improvement development ability. Fourth, in the professionalization of counselors, attention should be paid to the scientific selection and prevention of risks of big data application, ensuring the authenticity and reliability of data and leakage prevention and control, etc.
Federated Social Recommendation with Graph Neural Network

Recommender systems have become prosperous nowadays, designed to predict users’ potential interests in items by learning embeddings. Recent developments of the Graph Neural Networks (GNNs) also provide recommender systems (RSs) with powerful backbones to learn embeddings from a user-item graph. However, only leveraging the user-item interactions suffers from the cold-start issue due to the difficulty in data collection. Hence, current endeavors propose fusing social information with user-item interactions to alleviate it, which is the social recommendation problem. Existing work employs GNNs to aggregate both social links and user-item interactions simultaneously. However, they all require centralized storage of the social links and item interactions of users, which leads to privacy concerns. Additionally, according to strict privacy protection under General Data Protection Regulation, centralized data storage may not be feasible in the future, urging a decentralized framework of social recommendation. As a result, we design a federated learning recommender system for the social recommendation task, which is rather challenging because of its heterogeneity, personalization, and privacy protection requirements. To this end, we devise a novel framework Fe drated So cial recommendation with G raph neural network ( FeSoG ). Firstly, FeSoG adopts relational attention and aggregation to handle heterogeneity. Secondly, FeSoG infers user embeddings using local data to retain personalization. Last but not least, the proposed model employs pseudo-labeling techniques with item sampling to protect the privacy and enhance training. Extensive experiments on three real-world datasets justify the effectiveness of FeSoG in completing social recommendation and privacy protection. We are the first work proposing a federated learning framework for social recommendation to the best of our knowledge.
Untrained deep learning-based fringe projection profilometry

Deep learning-based fringe projection profilometry (FPP) shows potential for challenging three-dimensional (3D) reconstruction of objects with dynamic motion, complex surface, and extreme environment. However, the previous deep learning-based methods are all supervised ones, which are difficult to be applied for scenes that are different from the training, thus requiring a large number of training datasets. In this paper, we propose a new geometric constraint-based phase unwrapping (GCPU) method that enables an untrained deep learning-based FPP for the first time. An untrained convolutional neural network is designed to achieve correct phase unwrapping through a network parameter space optimization. The loss function of the optimization is constructed by following the 3D, structural, and phase consistency. The designed untrained network directly outputs the desired fringe order with the inputted phase and fringe background. The experiments verify that the proposed GCPU method provides higher robustness compared with the traditional GCPU methods, thus resulting in accurate 3D reconstruction for objects with a complex surface. Unlike the commonly used temporal phase unwrapping, the proposed GCPU method does not require additional fringe patterns, which can also be used for the dynamic 3D measurement.
Blockchained service provisioning and malicious node detection via federated learning in scalable Internet of Sensor Things networks

In this paper, a blockchained Beyond Fifth Generation (B5G) enabled malicious node detection model is proposed for the Internet of Sensor Things (IoSTs). Moreover, a secure service provisioning scheme using cascading encryption and feature evaluation process is also proposed for the IoSTs. The presence of malicious nodes causes severe issues in the localization and service provisioning, which discourages new entities to join the network. Therefore, it is very important to establish trust between all entities by detecting and removing such nodes. The proposed B5G enabled malicious node detection model uses federated learning for the detection of malicious nodes. The federated learning uses Support Vector Machine (SVM) and Random Forest (RF) classifiers to detect the malicious nodes. The malicious nodes are classified on the bases of their honesty and end-to-end delay. Moreover, the service provider nodes provide services to each other and get the reward. However, the service provisioning in the IoSTs has many issues like a repudiation of service providers as well as the clients. The feature evaluation and cascading encryption mechanisms are used to solve these issues. The digital signature in cascading encryption ensures the non-repudiation of the service provider. On the other hand, feature evaluation of service ensures that the client cannot repudiate about actually demanded services. Moreover, the conformance of services is also ensured by the feature evaluation process. The simulation results show the effectiveness of our proposed non-repudiation model. The SVM and RF classifiers are compared in terms of accuracy, precision, F1 score and recall. The accuracy, precision, F1 score and recall of SVM are 79%, 1, 0.8795 and 0.78, respectively. On the other hand, the accuracy, precision, F1 score and recall of RF classifier are 95%, 0.92, 0.96 and 1, respectively. The results show that RF has better accuracy than RF in malicious nodes detection.
Face Recognition by Elastic Bunch Graph Matching*†

Classification systems differ vastly in terms of the nature and origin of their knowledge about image variations. Systems in Artificial Intelligence and Computer Vision often stress specific designer-provided structures, for instance explicit models of three-dimensional objects or of the image-generation process, whereas Neural Network models tend to stress absorption of structure from examples with the help of statistical estimation techniques. Gabor wavelets are biologically motivated convolution kernels in the shape of plane waves restricted by a Gaussian envelope function. Some face recognition systems are based on user-defined face-specific features. Yuille, for example, represented eyes by a circle within an almond-shape and defined an energy function to optimize a total of 9 model parameters for matching it to an image. The manual definition of appropriate grid structures and the semiautonomous process of bunch graph acquisition will have to be replaced by a fully autonomous process.
A Dynamic Four-Step Data Security Model for Data in Cloud Computing Based on Cryptography and Steganography

Cloud computing is a rapidly expanding field. It allows users to access computer system resources as needed, particularly data storage and computational power, without managing them directly. This paper aims to create a data security model based on cryptography and steganography for data in cloud computing that seeks to reduce existing security and privacy concerns, such as data loss, data manipulation, and data theft. To identify the problem and determine its core cause, we studied various literature on existing cloud computing security models. This study utilizes design science research methodology. The design science research approach includes problem identification, requirements elicitation, artifact design and development, demonstration, and assessment. Design thinking and the Python programming language are used to build the artifact, and discussion about its working is represented using histograms, tables, and algorithms. This paper's output is a four-step data security model based on Rivest-Shamir-Adleman, Advanced Encryption Standard, and identity-based encryption algorithms alongside Least Significant Bit steganography. The four steps are data protection and security through encryption algorithms, steganography, data backup and recovery, and data sharing. This proposed approach ensures more cloud data redundancy, flexibility, efficiency, and security by protecting data confidentiality, privacy, and integrity from attackers.
The Border K-Means Clustering Algorithm for One Dimensional Data

Clustering has been widely used for data pre-processing, mining, and analysis. The k-means clustering algorithm is commonly used because of its simplicity and flexibility to work in many real-life applications and services. Despite being commonly used, the k-means algorithm suffers from non-deterministic results and run times that greatly vary depending on the initial selection of cluster centroids. To improve the k-means algorithm, we present in this paper a border k-means clustering algorithm. It combines concepts from the k-means algorithm with an additional focus on the concepts of the borders dividing clusters. Consequently, the resulting border k-means algorithm leads to deterministic results and a great reduction in run time when compared with the traditional k-means algorithm.
Homogeneous Decision Community Extraction Based on End-User Mental Behavior on Social Media

Aiming at the inadequacy of the group decision-making method with the current attribute value as interval language information, an interval binary semantic decision-making method is proposed, which considers the decision maker’s psychological behavior. The scope of this research is that this paper is based on localized amplification method. The localized amplification method used in this research may amplify physiological movement after removing unwanted noise, allowing the movement trend to be seen with the naked eye, improving the CNN network’s mental identification accuracy. These two algorithms analyze the input picture from various perspectives, allowing the CNN network to extract more information and enhance identification accuracy. A new distance formula with interval binary semantics closer to decision-makers thinking habits is defined; time degree is introduced. An optimization model is established to solve the time series weights by considering the comprehensive consistency of expert evaluation. Based on prospect theory, a prospect deviation value is constructed and minimized weight optimization model, using the interactive multiple attribute decision community making (TODIM) method based on the new distance measure to calculate the total overall dominance of the schemes to rank the schemes. Taking the selection and evaluation of supply chain collaboration partners as an example, the effectiveness and rationality of the proposed method are verified.
DIGITAL TECHNOLOGIES OF SUPPORT THE SPIRITUAL DEVELOPMENT OF STUDENTS

The article deals with the problem of finding new means of spiritual values of university students. An attempt was made to prove that information and communication technologies can be involved in the educational process, not only as educational supply, but also as a means of spiritual education. It is noted that the humanization of the educational process becomes practical in the case of specially organized communication, which is created by teachers at lectures, seminars, workshops, academic competitions, scientific and practical conferences, exhibitions of creative works, thematic days, etc. It is noted that students should be formed with interest to information and communication technologies that contain information capable to enrich the spiritual world of the individual. The leading role in the mentioned process is given to the tutor of a student group. Among the internet technologies that have significant educational influence, there should be pointed out digital libraries, virtual museums and the Internet versions of educational channels, social networks and websites of educational institutions which are focused on activating different forms and methods of interaction between teachers and students, resulting in the formation and development of leading personality traits that constitute spiritual originality and affirm the moral imperative of the individual. Forms characterization of process organization of inculcating spiritual values on the main features is presented. It is found that the development of the spiritual value quintessence of future specialists should be organized in three spheres: mastering knowledge of spiritual, moral, aesthetic things; self-knowledge and self-improvement; participation in socially significant activities. The authors emphasize that the effectiveness of the information and communication technologies usage is possible in the case of the compliance with a number of educational conditions and confirms that for the formation of spiritual students' values a coherent educational system is needed to be created.
A Comprehensive Study of XSS Attack and the Digital Forensic Models to Gather the Evidence

Cyber Crime and fraud are growing at a rapid rate and the law practitioners require proper methodology to collect the digital evidences. When the criminals use digital platform in order to commit the crime, forensic examiners apply practical frameworks and methods to collect digital evidences. Digital forensics method involves the analysis of network traffic for detecting intrusions and investigating them. Different models have been proposed for the cyber crime investigation with the merits and demerit. This research paper explores cross-site scripting (XSS) attacks, features of the existing digital forensic investigation models and discusses some of the issues related to digital forensic research.
Artificial Intelligence and Machine Learning Technologies in Cancer Care: Addressing Disparities, Bias, and Data Diversity

Summary: Artificial intelligence (AI) and machine learning (ML) technologies have not only tremendous potential to augment clinical decision-making and enhance quality care and precision medicine efforts, but also the potential to worsen existing health disparities without a thoughtful, transparent, and inclusive approach that includes addressing bias in their design and implementation along the cancer discovery and care continuum. We discuss applications of AI/ML tools in cancer and provide recommendations for addressing and mitigating potential bias with AI and ML technologies while promoting cancer health equity.
Challenges and Strategies in Cross-Cultural NLP

Various efforts in the Natural Language Processing (NLP) community have been made to accommodate linguistic diversity and serve speakers of many different languages.However, it is important to acknowledge that speakers and the content they produce and require, vary not just by language, but also by culture.Although language and culture are tightly linked, there are important differences.Analogous to cross-lingual and multilingual NLP, cross-cultural and multicultural NLP considers these differences in order to better serve users of NLP systems.We propose a principled framework to frame these efforts, and survey existing and potential strategies.
Identifying the Human Values behind Arguments

This paper studies the (often implicit) human values behind natural language arguments, such as to have freedom of thought or to be broadminded.Values are commonly accepted answers to why some option is desirable in the ethical sense and are thus essential both in realworld argumentation and theoretical argumentation frameworks.However, their large variety has been a major obstacle to modeling them in argument mining.To overcome this obstacle, we contribute an operationalization of human values, namely a multi-level taxonomy with 54 values that is in line with psychological research.Moreover, we provide a dataset of 5270 arguments from four geographical cultures, manually annotated for human values.First experiments with the automatic classification of human values are promising, with F 1 -scores up to 0.81 and 0.25 on average.
Enhancing Sequential Recommendation with Graph Contrastive Learning

The sequential recommendation systems capture users' dynamic behavior patterns to predict their next interaction behaviors. Most existing sequential recommendation methods only exploit the local context information of an individual interaction sequence and learn model parameters solely based on the item prediction loss. Thus, they usually fail to learn appropriate sequence representations. This paper proposes a novel recommendation framework, namely Graph Contrastive Learning for Sequential Recommendation (GCL4SR). Specifically, GCL4SR employs a Weighted Item Transition Graph (WITG), built based on interaction sequences of all users, to provide global context information for each interaction and weaken the noise information in the sequence data. Moreover, GCL4SR uses subgraphs of WITG to augment the representation of each interaction sequence. Two auxiliary learning objectives have also been proposed to maximize the consistency between augmented representations induced by the same interaction sequence on WITG, and minimize the difference between the representations augmented by the global context on WITG and the local representation of the original sequence. Extensive experiments on real-world datasets demonstrate that GCL4SR consistently outperforms state-of-the-art sequential recommendation methods.
A Lightweight Image Encryption Algorithm Based on Chaotic Map and Random Substitution

Chaotic-maps-based image encryption methods have been a topic of research interest for a decade. However, most of the proposed methods suffer from slow encryption time or compromise on the security of the encryption to achieve faster encryption. This paper proposes a lightweight, secure, and efficient image encryption algorithm based on logistic map, permutations, and AES S-box. In the proposed algorithm, SHA-2 based on the plaintext image, a pre-shared key, and an initialization vector (IV) are used to generate the initial parameters for the logistic map. The logistic map chaotically generates random numbers, which are then used for the permutations and substitutions. The security, quality, and efficiency of the proposed algorithm are tested and analyzed using a number of metrics, such as correlation coefficient, chi-square, entropy, mean square error, mean absolute error, peak signal-to-noise ratio, maximum deviation, irregular deviation, deviation from uniform histogram, number of pixel change rate, unified average changing intensity, resistance to noise and data loss attacks, homogeneity, contrast, energy, and key space and key sensitivity analysis. Experimental results reveal that the proposed algorithm is up to 15.33× faster compared to other contemporary encryption methods.
zkBridge

Blockchains have seen growing traction with cryptocurrencies reaching a market cap of over 1 trillion dollars, major institution investors taking interests, and global impacts on governments, businesses, and individuals.
Entangled multiplets and spreading of quantum correlations in a continuously monitored tight-binding chain

We analyze the dynamics of entanglement in a paradigmatic noninteracting system subject to continuous monitoring of the local excitation densities. Recently, it was conjectured that the evolution of quantum correlations in such system is described by a semiclassical theory, based on entangled pairs of ballistically propagating quasiparticles and inspired by the hydrodynamic approach to unitary (integrable) quantum systems. Here, however, we show that this conjecture does not fully capture the complex behavior of quantum correlations emerging from the interplay between coherent dynamics and continuous monitoring. We unveil the existence of multipartite quantum correlations which are inconsistent with an entangled-pair structure and which, within a quasiparticle picture, would require the presence of larger multiplets. We also observe that quantum information is highly delocalized, as it is shared in a collective nonredundant way among adjacent regions of the many-body system. Our results shed light onto the behavior of correlations in quantum stochastic dynamics and further show that these may be enhanced by a (weak) continuous monitoring process.
Mining for equitable health: Assessing the impact of missing data in electronic health records

Electronic health records (EHR) are collected as a routine part of healthcare delivery, and have great potential to be utilized to improve patient health outcomes. They contain multiple years of health information to be leveraged for risk prediction, disease detection, and treatment evaluation. However, they do not have a consistent, standardized format across institutions, particularly in the United States, and can present significant analytical challenges– they contain multi-scale data from heterogeneous domains and include both structured and unstructured data. Data for individual patients are collected at irregular time intervals and with varying frequencies. In addition to the analytical challenges, EHR can reflect inequity– patients belonging to different groups will have differing amounts of data in their health records. Many of these issues can contribute to biased data collection. The consequence is that the data for under-served groups may be less informative partly due to more fragmented care, which can be viewed as a type of missing data problem. For EHR data in this complex form, there is currently no framework for introducing realistic missing values. There has also been little to no work in assessing the impact of missing data in EHR. In this work, we first introduce a terminology to define three levels of EHR data and then propose a novel framework for simulating realistic missing data scenarios in EHR to adequately assess their impact on predictive modeling. We incorporate the use of a medical knowledge graph to capture dependencies between medical events to create a more realistic missing data framework. In an intensive care unit setting, we found that missing data have greater negative impact on the performance of disease prediction models in groups that tend to have less access to healthcare, or seek less healthcare. We also found that the impact of missing data on disease prediction models is stronger when using the knowledge graph framework to introduce realistic missing values as opposed to random event removal.