Reviewerly/exHarmony huggingface dataset:
Deep Visual Attention Based Transfer Clustering

In this paper, we propose a methodology to improvise the technique of Deep Transfer Clustering (DTC) when applied to the less variant data distribution. Clustering can be considered as the most important unsupervised learning problem. A simple definition of clustering can be stated as “the process of organizing objects into groups, whose members are similar in some way”. Image clustering is a crucial but challenging task in the domain machine learning and computer vision. We have discussed the clustering of the data collection where the data is less variant. We have discussed the improvement by using attention-based classifiers rather than regular classifiers as the initial feature extractors in the Deep Transfer Clustering. We have enforced the model to learn only the required region of interest in the images to get the differentiable and robust features that do not take into account the background. This paper is the improvement of the existing Deep Transfer clustering for less variant data distribution.
A Survey of Big Data Techniques for Extracting Information from Social Media Data

Data mined from social media can be used in a variety of methods.The goal of this paper is to explore some of the various methods of mining data from social media and the different areas of its applications.From the analysis of other studies, it was clear that methods such as text analysis, classification, clustering, mapping, testing/validity methods, regression, and research methods were the overarching themes of the previously done research.Pros, cons, and possible extensions were examined for the current research evaluated in the social media data mining area.At the conclusion of this survey, our research team found that text analysis, sentiment analysis, and support vector machine classifiers were among the most common themes of the research methods in this field.In most cases, multiple methods were attempted for each topic to be able to cross compare results.
Information tools: opportunities of bi platform to evaluate education quality

This work is aimed at determination of opportunities of BI platforms as a tool of quality evaluation of higher education. Main results. This article determines the trends of application of BI platform as an information tool to evaluate education quality. It has been established that BI provides uniformity of metadata for all tools and unified user-friendly methods of searching, acquisition, saving, reusing, and publication of metadata objects. OLAP enables end users to analyze data by means of rapid requests and predictions, whereas the simulation and Data Mining allow to classify and to rank information on the basis of regulations. As exemplified by separate BI platforms, the BI opportunities are analyzed, as well as comparative criteria of various platforms, and the trends are revealed to use BI platform as information system of decision making for managers in educational sphere.
Artificial datasets for hierarchical classification

Hierarchical classification (HC) is a especial type of multilabel classification, where an instance can be associated to multiple labels, but in HC the labels are arranged in a predefined structure, commonly a tree but in its general form a Directed Acyclic Graph (DAG). HC includes up to eight different problems, and when a method is proposed to solve one of them, the real world datasets for each problem is limited. Thus, a way to extend the evaluation of a method is to generate Artificial Datasets (ADs). ADs are useful to evaluate a method in different conditions that could not be present in the available datasets. Thus, in this work is proposed a method that is able to generate artificial datasets for up to four of the different hierarchical classification problems, which makes use of distributions to generate the instances. Furthermore, two groups of ADs were generated using the proposed method, Tree and DAG hierarchies, which are made available to the scientific community; also the source code is provided so that you can generate your own datasets. Finally, standard and state of the art methods were evaluated with the generated artificial datasets. The best performance in the datasets was obtained by a couple of methods of the state of the art which make use of Bayesian networks and chained classifiers. The proposed method for generating HC datasets provides a flexible and general alternative to evaluate different hierarchical classification methods.
DeepWORD: A GCN-Based Approach for Owner-Member Relationship Detection in Autonomous Driving

It’s worth noting that the owner-member relationship between wheels and vehicles has an significant contribution to the 3D perception of vehicles, especially in the embedded environment. However, there are currently two main challenges about the above relationship prediction: i) The traditional heuristic methods based on IoU can hardly deal with the traffic jam scenarios for the occlusion. ii) It is difficult to establish an efficient applicable solution for the vehicle-mounted system. To address these issues, we propose an innovative relationship prediction method, namely DeepWORD, by designing a graph convolution network (GCN). Specifically, we utilize the feature maps with local correlation as the input of nodes to improve the information richness. Besides, we introduce the graph attention network (GAT) to dynamically amend the prior estimation deviation. Furthermore, we establish an annotated owner-member relationship dataset called WORD as a large-scale benchmark, which will be available soon. The experiments demonstrate that our solution achieves state-of-the-art accuracy and real-time in practice.
A Correlation Analysis Between ISO 25010 based Modularity and CK Metrics in Object-Oriented Software

Modularity and the CK metrics are metric values that are associated with several other quality assessments, such as flexibility and complexity. However, no studies have proven that CK metrics and modularity are correlated with each other, especially objectively and scientifically. Therefore, in this study, we show the degree of relationship between metrics quantitatively using the Pearson and Spearman correlation method. We calculate the ISO 25010 based modularity aspect values and each metric on the CK metric of the dataset manually. These metrics are measured for the level of engagement to find out which factors influence each other. This research indicates that CBO, DIT, and LCOM are directly proportional to the CCC aspect, and DIT, NOC, and LCOM are directly proportional to the CC aspect. Meanwhile, WMC and RFC are the metrics that inversely proportional to the ISO 25010 based modularity.
Food Traceability for Smart Agriculture Through Block-chain

Blockchain is an emerging digital technology that allows for widespread financial transactions between underutilized groups, without the need for intermediaries such as banks. The report looks at why blockchain is better than the current traditional system, and discusses the effects, challenges and potential of overall asset management. Complete research shows that blockchain is a promising technology aimed at transparent food marketing, with many ongoing programs for various food products and food-related problems, but many challenges and challenges remain, preventing its widespread popularity among farmers and programs.
Low-Cost Multi-Agent Navigation via Reinforcement Learning With Multi-Fidelity Simulator

In recent years, reinforcement learning (RL) has been widely used to solve multi-agent navigation tasks, and a high-fidelity level for the simulator is critical to narrow the gap between simulation and real-world tasks. However, high-fidelity simulators have high sampling costs and bottleneck the training model-free RL algorithms. Hence, we propose a Multi-Fidelity Simulator framework to train Multi-Agent Reinforcement Learning (MFS-MARL), reducing the total data cost with samples generated by a low-fidelity simulator. We apply the depth-first search to obtain local feasible policies on the low-fidelity simulator as expert policies to help the original reinforcement learning algorithm explore. We built a multi-vehicle simulator with variable fidelity levels to test the proposed method and compared it with the vanilla Soft Actor-Critic (SAC) and expert actor methods. The results show that our method can effectively obtain local feasible policies and can achieve a 23% cost reduction in multi-agent navigation tasks.
Applying Mobile Intelligent API Vision Kit and Normalized Features for Face Recognition Using Live Cameras

This paper’s main contribution presents designing and implementing a face recognition system for the android mobile phone platform from the live mobile camera. The design methodology includes two main steps. The first step is the extraction of the face features, and the second one is the recognition according to the classification of patterns. The face detection is carried out using a face detector available in the Android called feature-based method machine learning (ML) Kit Software Development Kit (SDK). The face features include nose detection, mouth detection, eyes detection, and cheek detection. These features are detected based on their geometrical dimensions. Twenty-five geometrical raw distances are reduced to 23 normalized distances by referring them to their face width and height. The recognition step is done by computing the correlation coefficient ratio between the test image’s normalized distance and all normalized distances for authorized persons stored in the training database. The system will allow access to the mobile applications if the correlation coefficient is greater than a chosen threshold; otherwise, it rejects the person. The proposed approach’s recognition rate has achieved an accuracy more than 95% for appropriate chosen threshold. The time taken to recognize a face is approximately 13 s of other approaches.
Image Compression and Reconstruction Based on PCA

Abstract In view of the disadvantages of large image space, high dimension of feature representation and large storage, this paper uses principal component analysis to compress the data, which can effectively reduce the loss of information, reduce the dimension of data and extract features in all aspects of image compression. In this paper, based on the analysis of PCA algorithm dimension reduction, reconstruction, feature extraction principle, the PCA algorithm is applied to image compression and reconstruction. Through the principal component analysis of sample variables, the feature vector of sample variance is calculated, and its principal component is extracted, and the contribution rate is calculated to realize image PCA compression. By extracting different eigenvalues from the original image and calculating its compression ratio and contribution rate, we can see that the clarity of the image is positively correlated with the value of the eigenvalue, and the larger the value of the eigenvalue, the higher the contribution rate. PCA based image processing technology is simple to use, high compression rate and high quality of image reconstruction, which is unmatched by many other methods.
Improving Recommender Systems Performances Using User Dimension Expansion by Movies’ Genres and Voting-Based Ensemble Machine Learning Technique

The recommender system's performance needs to be improved more than ever by increasing computer systems' usage in various applications. Recommender systems are a valuable tool in e-commerce websites. Their primary purpose is to generate accurate forecasts to access information in less time and energy for end-users. Classification optimizes information retrieval activity in these systems and reduces user search time. Besides, clustering tries to insert the new object in the best similar class, like using the k-nearest neighbor algorithm as a classifier. The proposing approach focuses on modeling categories by averaging rates of movie genres.Moreover, the user clustering will be improved by voting machine learning classifiers on multilayer perceptron (MLP) neural networks and k-nearest neighbors (kNN) algorithms. The experiments performed on the MovieLens dataset show that the proposed method is more successful than other previous methods in predicting user clusters with 93.81% accuracy, 94.45% precision, and 92.81% recall. Also, Davies Bouldin metrics indicates better clustering result using dimension expansion of movies' genres.
Attentive Update of Multi-Critic for Deep Reinforcement Learning

Unstable training and inefficient exploration remain challenging problems in Reinforcement Learning (RL), especially when handling continuous and high-dimensional state space. In prior works, deep RL methods based on the ensemble framework combine multiple models of the critics to stabilize the training process. However, these approaches still suffer from inefficient exploration due to the loss of diversity of critic caused by training the critics with the same samples. In this paper, we develop a new critic ensemble framework to train the critics with different sets of samples, so as to maintain the diversity of critic, and therefore explore more efficiently. The new critic ensemble framework, named Attentive Update of Multi-Critic (AUMC), is compatible with various off-policy actor-critic algorithms. Specifically, AUMC attentively trains the critics with masked samples, and then updates the actor with a randomly chosen critic to encourage efficient exploration. We couple AUMC with different off-policy actor-critic algorithms, and the consistent improvements on the suite of OpenAI Gym tasks demonstrate the effectiveness of our method.
Total Variation Regularized Low-Rank Model With Directional Information for Hyperspectral Image Restoration

Hyperspectral images (HSIs) are unavoidably polluted by various kinds of noise, which decrease the potential of subsequent processes in HSIs applications. Due to the diversity and complexity of HSIs mixed noise, including impulse noise, Gaussian noise, stripe noise and deadlines, traditional restoration technology cannot be used directly. In this paper, a novel HSIs restoration approach is proposed that integrates low-rank (LR) prior and spatial-spectral total variation with directional information. Specifically, by analyzing the characteristic of spatial-dependent edge and texture directional structure, a spatial-spectral directional total variation (SSDTV) regularization is defined. Then, considering the HSIs as a cube data, the proposed method utilizes SSDTV regularization to characterize spatial-spectral smoothness, as well as LR regularization to constrain spectral consistency. Finally, an extended alternating direction method of multipliers algorithm is designed to achieve simple and fast implementation, in which the complex optimization problem is separated into several easier subproblems. Both simulated and real-world HSIs experiments indicated that the proposed method is effective and numerically feasible for HSIs Restoration.
Hiding IoT Communication Using DCT Difference Modulation (DCTDM)

In the past few decades, the involvement of the Internet and technologies has become an integral part of our life. From morning till evening, we come across many automated devices which are connected via the Internet some of these devices share various information over the network. While making our life easy, these IoT devices also attract many malicious actors who exploit different vulnerabilities of the IoT devices. As IoT devices operate with limited power and computational resources. These devices lack a strong cryptographic protocol and compromising various data and opening many doors for malicious activities. One such device is IoT-based CCTV cameras, and these devices are made to keep us safe, but the very same devices are used to exploit various network flaws and harm our system and steal our data. There had been multiple incidents where the recordings or images captured by these devices are stolen or tampered for malicious reasons. To overcome this problem, authors have proposed a DCTDM-based arithmetic randomization method in this paper. This method conceals the images or recordings captured by the IoT-based CCTV cameras in a set of predefined images or videos and transmitted across the network for further processing or storing. This method not only protects the data but also protects the very existence of the data.
Multidimensional Team Communication Modeling for Adaptive Team Training: A Hybrid Deep Learning and Graphical Modeling Framework

Team communication modeling offers great potential for adaptive learning environments for team training. However, the complex dynamics of team communication pose significant challenges for team communication modeling. To address these challenges, we present a hybrid framework integrating deep learning and probabilistic graphical models that analyzes team communication utterances with respect to the intent of the utterance and the directional flow of communication within the team. The hybrid framework utilizes conditional random fields (CRFs) that use deep learning-based contextual, distributed language representations extracted from team members' utterances. An evaluation with communication data collected from six teams during a live training exercise indicate that linear-chain CRFs utilizing ELMo utterance embeddings (1) outperform both multi-task and single-task variants of stacked bidirectional long short-term memory networks using the same distributed representations of the utterances, (2) outperform a hybrid approach that uses non-contextual utterance representations for the dialogue classification tasks, and (3) demonstrate promising domain-transfer capabilities. The findings suggest that the hybrid multidimensional team communication analysis framework can accurately recognize speaker intent and model the directional flow of team communication to guide adaptivity in team training environments.
Machine learning algorithm for cluster analysis of mixed dataset based on instance-cluster closeness metric

Recently, ubiquitous studies need attention on clustering algorithms residual to their significance in machine learning. Most methods have been presented on clustering mixed datasets. However, these methods also suffer from certain issues. First, most of them directly implement existing machine learning methods used to cluster instances without adding any improvements. Second, some methods convert the categorical features into numerical features, which may lead to loss of information and the introduction of noise. Third, some methods convert the numerical features into categorical features but ignore the influence of initial inflow conditions. Accordingly, to address these issues, we propose an intelligent method for clustering categorical and numerical datasets based on the Instance Cluster Closeness Metric (ICCM) algorithm. More specifically, we first present a similarity metric for numerical features. Subsequently, we design a novel metric for categorical features. Moreover, we design a new learning algorithm to cluster mixed datasets. The proposed algorithm achieves the clustering accuracies of 89.2% for heart disease and 89.4%, 84.9%, 85.5%, 91.2% for kaggle, factors, kinase, UV of chemoinformatics datasets, respectively. Also, it is compared with the state-of-the-art approaches and the results demonstrate that the proposed method has the best efficiency.
Method of determining dynamic distance of an object from camera devices using ML and its applications

Measuring accurate dynamic distance of a moving object is one of the major challenges in implementing modern applications. In this paper, a solution is proposed to quickly get data related to position of a certain object in real time. Key technologies used are a high definition camera device, a deep learning model, supervised learning and a proposed calibration technique with a standard size object. This experiment was conducted successfully on available open source platforms. Based on the results of experiment conducted on human face as the object, distance with a high accuracy was obtained by using the proposed method.
Staff Management as a Tool for Building a Competitive Position of Universities

Today, the market for educational services is quite congested and requires more detailed external and internal analysis of the activities of universities in order to understand the principles of attracting students. The majority of prospective enrollees to universities have certain selection criteria and the quality of education is one of the main ones. That is why such a competitive factor as a level of preparation of professors requires more detailed consideration from the point of view of personnel management. The level of preparation of the scientific and pedagogical staff includes not only the selection of certain qualifications, but also the motivation and interest in conducting quality classes. Due to the nature of the work, teachers are most susceptible to emotional burnout, so management should always keep in mind ideas aimed at preventing burnout in the collective. The aim of the work is to consider personnel management as a tool for building a competitive position of higher education institutions, to solve the problem of teachers’ burnouts and to provide recommendations for preventing this condition in order to improve the level of preparation of professors. The study carried out the following tasks: the concept of competitive position and its characteristics has been examined; the main factors influencing the competitive position of higher education institutions have been identified; the necessity for quality personnel management to improve the preparation of professors has been established; the issue of burnout as a key issue in the work of university staff has been addressed; the effectiveness of the wellbeing concept and the necessity to apply it as an integral part of the personnel management process to improve the competitive position of universities has been proven. The scientific novelty of the work consists in the analysis of individual factors of competitive position – the quality of education and the level of preparation of professors, the identification of the importance of personnel management in improving the performance of these factors, In addition, recommendations have been made to introduce the concept of wellbeing and to diagnose burnout among teachers in order to strengthen the competitive position of universities