Reviewerly/exHarmony huggingface dataset:
Gaussian Mixture Model-Based Clustering of Multivariate Data Using Soft Computing Hybrid Algorithm

In this study, a new hybrid-clustering algorithm is proposed for model-based clustering of multivariate data. In order to dimension reduction with variable selection in multivariate data, heterogeneous (mixture) structures in the variables are determined according to the univariate Gaussian mixture models (GMM). Component numbers of variables in heterogeneous data are determined comparatively based on univariate Gaussian mixture distribution and k-means algorithms. Based on the number of components (subgroup) in the variables, the number, orientations and structure of the clusters are determined by the mixture models created. The number of candidate mixture models that can be determined by the soft calculation method developed for model-based determination of cluster structures consisting of mixture distributions is determined. Information criteria are used to select the best model that fits the data among candidate mixture models. In selecting the best model, information criteria based on likelihood function, such as AIC (Akaike Information Criteria) and BIC (Bayesian Information Criteria) are used.
A Concept of E-Medical Record Storage and Sharing Based on Blockchain Technology

Medical data contains sensitive information and can have big impact if stolen. The term medical identity theft is coined for such thefts. Medical records are collected from various sources like hospitals, diagnostic labs, physicians, pharmacy and health insurance companies and includes all details of patient including his demographic information, test reports like X – rays, CT scans, MRIs, etc. With the advent of digitization, these medical records are now stored in digital form to make access and sharing easier. However, storing and sharing these data electronically opens the threat of data theft and misuse. Health insurance companies often bear the brunt of fraudulent claims based on stolen medical data. So, the current need is to enable storing and sharing these data with security and make a prohibition on making copies of such data. Hence, considering the importance of healthcare data, the use of blockchain technology can be promising to maintain the security, privacy, immutability, authentication, and reliability of the data to the intended users. The aim of this paper is to investigate the current state of blockchain technology and its diverse applications. The paper also discusses the application of the blockchain technology in the domain of secure medical health record sharing.
Multi-modal action segmentation in the kitchen with a feature fusion approach

In this paper, we propose a “Multi-modal Action Segmentation approach” that uses three modalities: (i) video, (ii) audio, (iii) thermal to classify cooking behavior in the kitchen. These 3 modalities are assumed to be features related to cooking. However, there is no public dataset containing these three modalities. Therefore, we built the original dataset and frame-level annotation. We then examined the usefulness of Action Segmentation using multi-modal features. We analyzed the effects of each modality using three evaluation metrics. As a result, the accuracy, edit distance, and F1 value were improved by up to about 1%, 2%, and 8%, respectively, compared to the case when only images were used.
Automatic Detection of Crimes Captured in CCTV Images for Safety of Senior Citizens

Closed circuit television systems (CCTV) are getting widely popular and are being deployed in many workspaces, housing estates and in most public spaces. Efficiency of CCTV surveillance can be improved by incorporation of image processing and object detection algorithm into monitoring process. In this project, we specialize in the task of automated detection and recognition of dangerous incidents for CCTV systems. We propose solutions that are able to alert the human operator when a weapon is visible in the image through e-mail. We have shown that it's possible to make a system that's capable of an early warning during a dangerous situation, which can cause faster and more effective response times and reduce in the number of potential victims. Face Detection and Face recognition of individuals is an intricate problem which has garnered much attention during recent years because of its ever-increasing applications in numerous fields. In this project the facial detection has been carried out using Viola Jones algorithm.
Overcast: Running Controlled Experiments Spanning Research and Commercial Clouds

The Chameleon project developed a unique experimental testbed by adapting a mainstream cloud implementation to the needs of systems research community and thereby demonstrated that clouds can be configured to serve as a platform for this type research. More recently, the CloudBank project embarked on a mission of providing a conduit to commercial clouds for the systems research community that eliminates much of the complexity and some of the cost of using them for research. This creates an opportunity to explore running systems experiments in a combined setting, spanning both research and commercial clouds. In this paper, we present an extension to Chameleon for constructing controlled experiments across its resources and commercial clouds accessible via CloudBank, present a case study of an experiment running across such combined resources, and discuss the impact of using a combined research platform.
Despeckling in echocardiographic images using a hybrid fuzzy filter

Ultrasound or sonographic imaging is a pain-free diagnostic technique that makes use of sound waves to view internal organs of the body. It plays a significant role in diagnostic purposes. The major concern with ultrasound imaging is speckle noise, which severely affects the visual information and automatic analysis of ultrasound images. In this chapter, we propose three hybrid fuzzy filters (HFFs) that integrate a fuzzy filter into a nonlocal means (NLM) filter to reduce speckle noise. We compare the quality of the proposed method with fifteen despeckling filters using both standard test images and echocardiographic images. Given the quantitative assessment and clinical approval, we show that one HFF in particular, HFF3, outperforms other filters in terms of image edge protection and reduction of speckle noise.
Analysis of Variable Learning Rate Back Propagation with Cuckoo Search Algorithm for Data Classification

For the data classification task back propagation (BP) is the most common used model to trained artificial neural network (ANN). Various parameters were used to enhance the learning process of this network. However, the conventional algorithms have some weakness, during training. The error function of this algorithm is not explicit to locate the global minimum, while gradient descent may cause slow learning rate and get stuck in local minima. As a solution, nature inspired cuckoo search algorithms provide derived free solution to optimize composite problems. This paper proposed a novel meta-heuristic search algorithm, called cuckoo search (CS), with variable learning rate to train the network. The proposed variable learning rate with cuckoo search algorithm speed up the slow convergence and solve the local minima problem of the backpropagation algorithm. The proposed CS variable learning rate BP algorithms are compared with traditional algorithms. Particularly, diabetes and cancer benchmark classification problems datasets are used. From the analyses results it show that proposed algorithm shows high efficiency and enhanced performance of the BP algorithm.
Sparse Spatial Attention Network For Semantic Segmentation

The spatial attention mechanism captures long-range dependencies by aggregating global contextual information to each query location, which is beneficial for semantic segmentation. In this paper, we present a sparse spatial attention network (SSANet) to improve the efficiency of the spatial attention mechanism without sacrificing the performance. Specifically, a sparse non-local (SNL) block is proposed to sample a subset of key and value elements for each query element to capture long-range relations adaptively and generate a sparse affinity matrix to aggregate contextual information efficiently. Experimental results show that the proposed approach outperforms other context aggregation methods and achieves state-of-the-art performance on the Cityscapes, PASCAL Context and ADE20K datasets.
Adaptive Recursive Circle Framework for Fine-Grained Action Recognition

Intuitively, distinguishing fine-grained actions in videos requires recursively capturing subtle visual cues and learning abstract features. However, existing deep neural network based methods are counter-intuitive in that their network layers do not explicitly model the recursive feature abstraction. Therefore, we are motivated to propose an Adaptive Recursive Circle (ARC) framework that equips common neural network layers with recursive attention and recursive fusion. ARC layer inherits the same operators and parameters as the original layer, but, most critically, it treats the layer input as an evolving state, thus explicitly achieving recursive feature abstraction by alternating the state update and the feature generation. Specifically, at each recursive step, the input state is firstly updated via both recursive attention and recursive fusion from the previously generated features, and then the feature abstraction is performed with the newly updated input state. Significant improvements are observed on multiple datasets. For example, an ARC-equipped TSM-ResNet-18 outperforms TSM-ResNet-50 on the Something-Something V1 and Diving48 datasets with only half over-heads. Code will be available at: https://github.com/0HaNC/ARC-ActionRecog.
Application of Information and Communication Technologies in the Network Interaction of Educational Institutions, Enterprises and Organizations

The article considers the features of the use of information and communication technologies in the network interaction of educational institutions, enterprises and organizations on the example of the interaction of a university and a large enterprise.
Factor Analysis of Continuous Use of Car Services in Japan by Machine Learning

The automotive industry is an important industry for Japan and has a great impact on the Japanese economy. According to the Automotive Industry Strategy 2014 announced by the Ministry of Economy, five problems will occur in the next 10 to 20 years, and countermeasures are required. Five is-sues are increasing environmental and energy constraints, population growth and personal income growth, aging, urban overcrowding and depopulation, and changing values. Moreover, it is affected by external factors which is consumption tax increase, soaring gasoline and so on. As a strategy to increase the number of cars sold, it is possible to develop products that meet the needs of customers. However, in the car industry, the same type of cars of the same manufacturing company are also sold by other sales corporations, and product development that makes a difference between products may involve enormous costs and risks. Therefore, in this re-search, we focus on customers who use car-related services rather than products to differentiate their services. The purpose of this study is to build a Bayesian network that can handle many missing values and difficult elements of observed data and can introduce expert knowledge into the network structure. In addition, we also show that Bayesian networks are useful by comparing them with multiple machine learning models. It used Logistic regression, Decision tree, XGBoost, and Random Forest. The conclusions of this study show that the Bayesian network is more accurate than other analyzes.
Internet and Social Networks As a Tool for Cleaning Business Development

In this article, using the example of cleaning companies, the features of positioning and methods of fi nding clients for micro and small enterprises and companies through various Internet resources are considered, the features of working on electronic trading platforms, the role of social networks in promoting the product of such companies, as well as ways of interaction between the seller and the buyer on the platforms of such networks.
Hypertextualization of library and information educational activities for the Federal state educational standard implementation

Information structuring is seen as an efficient way to develop social communication. The study task is to reveal theoretical and technological background of hypertextualization of library and information educational environment. The substance, functions, structural elements of hypertext and its advantages for social communication are described. Specifications of using hypertext technologies for education are discussed, along with principles of hypertext design and perception via digital academic complex, digital textbooks, digital bibliographies as the multifunctional open subsystems of digital learning materials interrelated structurally and methodologically in the library and information disciplines. The authors analyzee the experience of Belgorod State Institute of Arts and Culture in designing hypertext resource providing the completeness, accessibility and relevance of using information educational resources and information communication technologies in every type of learning activities. These efforts are seen by the faculty as one of the ways to support professional education methodologically.
Human Action Recognition Methods Based on CNNs for RGB Video Input

Human Action recognition is a complex problem that attracts more and more researchers from the scientific community due to its applicability in domains such as security and behavior analysis. At its core, this problem entails classifying an action into a finite set of classes. Neural network based approaches, and especially convolutional neural networks, are a good starting point for solving the problem of human action recognition. Due to their nature, they can recognize spatio-temporal features very well, making them ideal for working with sequences of RGB images. In this paper are proposed three types of convolutional neural network architectures that contribute to solving the problem of Human Action Recognition. The first one is based on 2D kernels, the second one on 3D kernels, and the third one on TCN (Temporal Convolutional Network) units. Each one is presented with its structure, advantages and disadvantages, along with metrics that measure their performance. The one based on 2D convolutions is the fastest, but it also has the lowest performances. The second one is a good middle ground, useful in certain situations which require a fast classifier operating on different action classes. Finally, the one based on TCNs performs close to some of the best existent models. It represents a viable solution to the proposed problem. It can classify many actions, using only RGB images of fairly low resolution, in real time. The three models have been tested on the RGB part of the NTU RGB+D dataset. The 2D convolution-based model obtained an accuracy of 7.43% on the Cross-Subject split and 10.28% on the Cross-View split. The 3D convolution-based model obtained 58.77% on Cross Subject and 56.11% on Cross-View. Finally, the TCN-based model obtained an accuracy of 80.45% on Cross-Subject and an accuracy of 82.57% on Cross-View.
Research Data Management Practices at Bindura University of Science Education

The study assessed the research data management (RDM) practices at Bindura University of Science Education (BUSE) with the aim of understanding how research data (RD) is managed. The study was prompted by lack of proper RDM policy. UK data archive research data lifecycle model was adopted in the study for benchmarking RDM practices at BUSE in line with international standards. The research used the interpretism approach and is qualitative in nature. Interviews were used to collect qualitative data from the Research and Postgraduate Centre (RPGC), deputy librarian, sub-librarian, and technology librarian. Quantitative data obtained from departmental chairpersons, assistant librarians, and chief library assistant was gathered by using questionnaires. The population was chosen using purposive sampling. The findings revealed that although respondents appreciated RDM practices, some researchers were managing their RD while RPGC was responsible for RD submitted to their office. The concept of RDM was relatively new to most researchers. The study recommended a policy guideline and training of researchers.
Blockchain Confidence Protection and Cloud Chain Management Support

Abstract Verification and completeness are main challenges for today’s ever more diverse supply chains. Even with the ability to counter blockchain technologies by offering a trail of manipulation-resistant audit It does not address the confidence issue associated with the source chain activities also information related to a produce life cycle Information itself. Reputation mechanisms are a promising solution for this faith problem. Yet existing structures of credibility are Not ideal for supply chain applications based on blockchain as centred on restricted findings, lack of granularity and their overhead was not discussed and automation. We recommend the system as a three-layer faith in this job. Management platform is using a blockchain consortium tracking relationships between actors in the supply chain and Assign trust and prestige dynamically dependent on these interactions. Its novelty is based on a Model for credibility assessing product quality and the trust of individuals based on many observations funding for credibility qualities in supply chain incidents separating the member in the supply chain from the goods, enables brand credibility to be reserved for smart contracts for straightforward use by the same participant, Effective, secure, and automatic credibility scoring measurement, and the latency and throughput minimum overhead as compared to a straightforward supply chain model based on blockchain.
BATAE-GRU: Attention-based Aspect Sentiment Analysis Model

Aspect-level sentiment classification aims to identify the sentiment polarity of a given aspect. However, most of the past methods do not analyze the role of words well, and the connection between context and a given aspects is not well realized, which greatly limits the effectiveness of the model. In this paper, we have designed a model based on the attention mechanism. First, the word embedding and aspect embedding are represented by pre-trained BERT coding. Next, we use the recurrent neural network to obtain the hidden state. Then, the context and aspect are related through the attention mechanism. Finally, the experiments were conducted on 3 data sets widely used in the field of sentiment analysis. The BATAE-GRU model was compared with several current advanced models. The results showed that the experimental results of the BATAE-GRU model were better than other models; Compared with the ATAE-LSTM model, the accuracy of the model in two comparative experiments has been improved by 6.9% and 9.9% respectively.
Several Problems of Semantic Engineering A Case Study of Humanoid Resolving the Primary Mathematics Application Problems

In this paper, the problems, such as engineering model, semantic description, basic algorithm, heuristic resolving and platform of semantic engineering are discussed. Taking the humanoid resolving of mathematical application problem as an example, the semantic instances’ aggregation, clause as semantic unit, formula variable's labels matching and space search methods are proposed; the scene's semantic description frame is used to describe context semantics, and the common data pool which includes the word segmentation chain, scene frame database, formula knowledge database and resolving rule database are designed; The basic algorithms such as scene frame matching, correspondence between data elements and formula variables, formula variable constraint algorithm and resolving operation mechanism are established; The common semantic description and selection of problems are realized, and the accelerated operation mechanism of heuristic resolving is also developed; The semantic data platform is built. Finally, the paper summarizes the general semantic ideas of humanoid resolving the primary mathematical application problems, and puts forward the next improvement plan.
Robotic Leg Experience

While a large body of research investigates upper limb bodily experience, knowledge concerning the lower limbs is similarly important and can benefit from human-in-the-loop experiments. This chapter inquires specific requirements, presents an appropriate system design, and confirms the transferability of robotic limb illusions to the lower limbs. An evaluation study shows the occurrence of a robotic leg illusion based on subjective embodiment as well as through measuring the proprioceptive drift. The study substantiates the importance of motion synchronization and that robot-related acoustical stimulation is possibly relevant in terms of multisensory integration and embodiment experience. Interrelations of these factors and the design and control of the human-in-the-loop experiment are discussed and alternative approaches are outlined.
Autonomous Obstacle Navigating System of Quadrotor UAV Based on Vision Positioning and Cascade Control Algorithm

To solve the problem of unstable obstacle circling process of UAV, an autonomous obstacle circling system of four-rotor UAV based on visual positioning and cascade control algorithm is designed in this paper. The system consists of a vision positioning module, an operation processing unit and a flight control module. The vision positioning module includes a monocular camera module and a laser ranging unit. After the relative position between the obstacle and the UAV is solved by the operation processing unit, the flight control module controls the four-rotor UAV to fly around the obstacle with a certain distance and complete the autonomous obstacle circling. Finally, an experiment of autonomous obstacle circumnavigation of UAV was designed based on the quadrotor platform, and the feasibility of the proposed obstacle circumnavigation scheme was verified.
A Latent Variable Augmentation Method for Image Categorization with Insufficient Training Samples

Over the past few years, we have made great progress in image categorization based on convolutional neural networks (CNNs). These CNNs are always trained based on a large-scale image data set; however, people may only have limited training samples for training CNN in the real-world applications. To solve this problem, one intuition is augmenting training samples. In this article, we propose an algorithm called Lavagan ( La tent V ariables A ugmentation Method based on G enerative A dversarial N ets) to improve the performance of CNN with insufficient training samples. The proposed Lavagan method is mainly composed of two tasks. The first task is that we augment a number latent variables (LVs) from a set of adaptive and constrained LVs distributions. In the second task, we take the augmented LVs into the training procedure of the image classifier. By taking these two tasks into account, we propose a uniform objective function to incorporate the two tasks into the learning. We then put forward an alternative two-play minimization game to minimize this uniform loss function such that we can obtain the predictive classifier. Moreover, based on Hoeffding’s Inequality and Chernoff Bounding method, we analyze the feasibility and efficiency of the proposed Lavagan method, which manifests that the LV augmentation method is able to improve the performance of Lavagan with insufficient training samples. Finally, the experiment has shown that the proposed Lavagan method is able to deliver more accurate performance than the existing state-of-the-art methods.