Reviewerly/exHarmony huggingface dataset:
Supervised Learning for Human Action Recognition from Multiple Kinects

The research of Human Action Recognition (HAR) has made a lot of progress in recent years, and the research based on RGB images is the most extensive. However, there are two main shortcomings: the recognition accuracy is insufficient, and the time consumption of the algorithm is too large. In order to improve these issues our project attempts to optimize the algorithm based on the random forest algorithm by extracting the features of the human body 3D, trying to obtain more accurate human behavior recognition results, and can calculate the prediction results at a lower time cost. In this study, we used the 3D spatial coordinate data of multiple Kinect sensors to overcome these problems and make full use of each data feature. Then, we use the data obtained from multiple Kinects to get more accurate recognition results through post processing.
Development of an Intelligent Decision Support System for Electrical Equipment Diagnostics at Industrial Facilities

This paper presents the description of the basic principles of designing an Intelligent Decision Support System (IDSS) for diagnosing electrical equipment (EE) of industrial facilities while in operation based on the data received from the measurement technology using soft computing methods and their combinations, as well as fuzzy cognitive modeling. Since the development of an IDSS for diagnosing EE is a complex task that requires the study of a large number of interconnected modules, the work includes detailed information on the IDSS architecture, IDSS operating principles and basic capabilities of the system. By way of example, some objective-settings solved by the system, as well as fragments of screen forms of the developed system have been shown. The proposed IDSS will make it possible not only to assess the EE condition at a given time under conditions of a wide range of monitored parameters, but also to predict their values under conditions of statistical and fuzzy data. That will help to identify EE defects and failures at an early stage of their development; to prevent emergencies and reduce the risk of man-made disasters; to increase the validity of making decisions on EE faults and the equipment as a whole, as well as to give troubleshooting recommendations.
Textual Connector “And Now about...”: Lexical Variants and Specificity of Functioning

The paper aims to reveal specificity of functioning of the textual connector “And now about …” and to identify its lexical variants. The article briefly describes the existing linguistic terms designating connectors, such as a “textual connector” and a “connecting phrase”. Relying on the conducted analysis, the authors differentiate predicative and non-predicative connectors and show that the connector under study is more frequent in comparison with its lexical variants. Scientific originality of the research lies in the fact that the authors justify the existence of lexical variants of the textual connector “And now about …” and identify differences in their contextual usage. The conclusion is made that non-predicative connectors link isolated text fragments, whereas predicative connectors can be used to link adjacent text fragments.
Information-Theoretic Based Clustering Method for High-Dimensional Data

Abstract Clustering is one of the important techniques for pattern recognition. Traditional clustering methods often group data objects by distance measurement. However, it is difficult to measure data in high-dimensional space. This paper proposes a clustering method for high-dimensional data. It combines the information theory criteria to establish clustering rules. The improved of K-Means is used to generate basis clustering, then the clustering ensemble is used to integrate the initial clusters to obtain a more stable final result. Our proposed method is tested on standard datasets, and its performance is compared with K-means ensemble. The experimental results indicate effectiveness for high-dimensional data clustering.
Blockchain Applications in Logistics and Supply Chain Management: Problems and Prospects

The growing interest and expectations from the blockchain applications attract many analysts to this issue. In what spheres of logistics and supply chain management blockchain is appropriate? What blockchain software solutions are available to companies now? This paper investigates the basic functionality of the existing software solutions on the market and the comparative analysis of blockchain platforms used for developing the solutions for logistics is also carried out. The main trends of blockchain applications are identified, based on the analysis of the project experience on the use of blockchain, in logistics and supply chain management, in different countries. The problems, limitations and conditions of blockchain implementation are also determined.
Modified Mutated Firefly Algorithm

The strength of the Firefly algorithm depends on the attractiveness of fireflies with lesser intensity towards the superior firefly with brighter intensity. The mutated firefly algorithm (MFA) explores the search space by enhancing the features of less glowing fireflies from more luminous fireflies. The MFA converges faster than the standard FA using the 40:40 formula. With this, only the top 40 percent and bottom 40 percent of the fireflies join in the mutation process. Consequently, there has to be an added sorting algorithm to achieve such a preferred method. However, the sorting algorithm entails processing time depending on its applied technique, size of data, and CPU. A Modified Mutated Firefly Algorithm (MMFA) was developed to optimize the convergence time, employing a stochastic approach with increased convergence time. The results of the simulations show a significant improvement in terms of convergence time.
SoftMax Neural Best Approximation

Abstract Neural networks have a great place in approximating nonlinear functions, especially those Lebesgue integrable functions that are approximated by FNNs with one hidden layer and sigmoidal functions. Various operators of neural networks have been defined and achieved to get good rates of approximation depending on the modulus of smoothness. Here we define a new neural network operator with a generalized sigmoidal function (SoftMax) to improve the rate of approximation of a Lebesgue integrable function L p , with p &lt; 1, to be estimated using modulus of smoothness of order k. The importance of choosing SoftMax function as an activation function is its flexible properties and various applications.
WProfX: A Fine-grained Visualization Tool for Web Page Loads

Web page performance is crucial in today's Internet ecosystem, and Web developers use various developer tools to analyze their page load performance. However, existing tools cannot be used to identify the critical bottlenecks during the page load process. In this work, we design an online tool called WProfX that allows Web developers to visually identify bottlenecks in their page structure. The key to WProfX is that unlike existing Web performance tools, WProfX not only visualizes the page load activity timings, but also extracts the dependencies between the activities. Using the dependency structure, WProfX identifies the critical bottleneck activities. This lets a developer quickly identify why their page is loading slow and conduct what-if analyses to study the effect of different optimizations. WProfX uses low-level tracing information exposed by most major browsers to extract the relationship between page load activities. The result is that WProfX works with most major browsers and newer browser versions. WProfX visualizes the page load process as a dependency graph of semantically meaningful Web activities and identifies the critical bottlenecks. We evaluate WProfX with 14 Web developers who perform three what-if analysis tasks involving identifying the page load bottleneck and evaluating the effect of a page optimization. All the participants were able to complete the tasks with WProfX, compared to less than 60% when using the popular developer tools available today. WProfX is currently being used by Web developers in a large telecom and at a Silicon Valley startup.
A Complex Approach to the Data Labeling Efficiency Improvement

In this paper the issue of labeling noise is considered. Data labeling is the integral stage of the most part of the machine learning projects, so the problem spotlighted in the paper is quite topical. According to the labeling noise source classification, a new approach is proposed, affecting both of the labeling noise sources. The first component of the approach is based on the distributed ledger technologies principles, including the automatic consensus between the experts. The second component includes the devices dependability improvement by means of fog- and edge-computing usage. Also some models are developed to estimate the approach and selected results of the simulation are presented and discussed.
Towards A Novel Conceptual Framework for Analyzing Code Clones to Assist in Software Development and Software Reuse

Cloning the codes is similar patterns or equivalent code fragments. The code cloning process in software systems has a great impact on software development. Code clones can be beneficial or harmful to the programmer depending on the type of clones detected. In this paper, a framework is proposed to highlight the impacts of cloning codes, which could be analyzed during the identification of code cloning process. Detected clones are segregated into beneficial and non-beneficial clones. By using the beneficial clones a database is created is used in the developing and as well as reusing software. Non-beneficial clones are discarded by refactoring. It is a new approach to software engineering. Effective use of code clones in new software will heighten the software quality.
Batch Target Recognition Count under Complex Conditions Based on Att-Unet

As we all know, the large number of counts is a challenging and time consuming task subject because of oversized number and complex conditions. However, the development of deep learning makes deep learning models very competitive in image segmentation. In this paper, we take cigarette filter rods as the research object. we first evaluate the standard Unet for the filter rod target recognition to separate target and background. Secondly, we use the focal loss function instead of the traditional cross-entropy function to solve the problem of imbalance between target and background area. Thirdly, we add a self-attention module in the traditional Unet convolutional layer to enhance the convolution effect. Fourth, we propose structural element detection criteria and round tangency matching strategy based on HMM (Hidden Markov Model) for the geometric relationship of filter rod position, which further improves the accuracy of the algorithm. We used Qu's [1], Mask-R-CNN [2], FCN [3], Deep-lab-V1 [4] and this paper's algorithm to test the performance of 30000 images from the industrial site. The performance of this paper's algorithm is completely better than the performance of the above algorithm.
Detection Efficiency Improvement in Multi–component Anti-spam Systems

Multi–layer spam detection systems frequently used in many SMTP servers often suffer from a lack of mutual communication between individual layers. The paper presents the construction of a feedback interconnection between two significant layers, namely Message contents check and Greylisting. The verification in a real SMTP server is performed, demonstrating considerable improvement of spam detection efficiency comparing the previous period with missing interconnection, while for a short testing period. Despite the limited generalizability of the result, it suggests the easy way how spam detection can be improved.
Estimating Grounding Sizes of Logic Programs Under Answer Set Semantics

Answer set programming (ASP) is a declarative logic programming paradigm geared towards solving difficult combinatorial search problems. While different logic programs can encode the same problem, their performance may vary significantly. It is not always easy to identify which version of the program performs the best. We present a system predictor (and its algorithmic backend) for estimating the grounding size of programs, a metric that can influence a program’s performance. We evaluate an impact of predictor when used as a guide for rewritings produced by the ASP rewriting tool projector. The results demonstrate potential to this approach.
Palma360: A 360-degree Web-based Multi-device Hypervideo

360-degree videos have recently grown in popularity thanks to the popularization of virtual reality and its adoption by major online video streaming platforms. Being watched on a diverse array of interfaces (virtual reality headsets, computers, mobile devices, etc.), user behavior needs to be analyzed for all these devices. A 360-degree hypervideo production, a virtual tour around the streets of a city, is played in an experiment by 30 users with multiple devices through a web application which collects their behavior following the Experience API standard. The distribution of viewport centers when playing these 360-degree videos is analyzed and compared between device and video types, showing an evident difference in behavior when playing videos of different type.
Online Product Grading using Sentimental Analysis with SVM

This paper implements a system that provides a user an authenticated score of any online product computed with the help of the NLP technique called Sentiment Analysis and Opinion Mining. Sentimental Analysis is the anatomization of the judgment of a user on a particular product. It scans through the sentences in search of keywords that deliver definite emotions. In our system, we have used a Support Vector Machine (SVM) classifier to generate a fine-grained Sentimental Analysis report. This classifier helps us to categorize the reviews collected from the product's website URL into predefined categories to affirm the user's thought process and generate a rating from 1 to 5 (1 being the lowest, 5 being the highest). The system makes use of this technique to gather all the accrued reviews and convert the textual meaning into a meaningful average grade. This process makes it possible for any prospective customer to have a definitive conclusion on the review of the product.
Exploring the Gradient for Video Quality Assessment

This paper presents an algorithm which incorporates spatial and temporal gradients for full reference video quality assessment. In the proposed method the frame-based gradient magnitude similarity deviation is calculated to form the spatial quality vector. To capture the temporal distortion, the similarity of frame difference is measured. In the proposed method, we extract the worst scores in both the spatial and temporal vectors by introducing the variable-length temporal window for max-pooling operation. The resultant vectors are then combined to form the final score. The performance of the proposed method is evaluated on LIVE SD and EPFL- PoliMI datasets. The results clearly illustrate that, despite the computational efficiency, the predictions are highly correlated with human visual system.
Analytical Decision of Adaptive Estimation Task for Measurement Noise Covariance Matrix Based on Irregular Certain Observations

The problem of adaptive estimation for measurement noise covariance matrix in Kalman filter is analytically solved based on accurate observations obtained irregularly. The results of numerical modeling are provided. These results illustrate the key advantages of state vector stochastic estimation algorithm based on proposed approach in comparison to conventional one.
Economic mechanisms of educational services export in the Russian Federation

The article is meant to analyze the economic mechanisms of educational services export in the Russian Federation. One of the most urgent tasks of Russian economy in the post-pandemic period will be filling the revenue part of the budget. The authors believe that the export of services, including education, is much more profitable for Russia and Russian companies than simple selling of raw materials and goods, since value-added taxes when the services are rendered on the territory of Russia remain in the country. Providing export services allows Russian economy to earn about twice as much as when exporting goods. Nevertheless, the potential for exporting educational services is not in high demand yet. The models for educational services export developed in our country provide various combinations of full-time and online education, which allow Russian universities to successfully get additional profit without requiring serious public investment. However, this work requires the revision of curricula and the creation of an appropriate world-class educational infrastructure.
Research on Pseudo-Random Noise Information Identification Technology of Printed Anti-Counterfeiting Image Based on Deep Learning

Halftone image with hiding pseudo-random information, which has excellent anti-duplication performance. It's used to against the problem of counterfeiting and copying QR Code. However, due to the mocro-size of pseudo-random image information, direct recognition with mobile phones often has high bit error rate, low reliability and badly user experience. This paper based on the method of deep learning, to through marking the noise data in the QR Code images and classify them. Then building a convolutional neural network model, training the features of the noise image. Proved by experiment, this method can effectively identify noise information and the result can satisfy the error-tolerance requirement of pseudo random noise image channel coding.
The Development of an Interactive Animation to Prevent Social Media Fraud

Abstract Technology is the creation, invention, methods or systems of something new that has the purpose to overcome human’s limitation, so that the creation can help human to complete the task that they cannot do. However, the world is getting advanced day-by-day, and so do the online frauds, where it is getting more types due to the ease of these technologies. People would use these advanced technologies to scam others, especially through social media. Thus, the number of online scam seems to be rising, and it is an appropriate action that someone does to bring benefits only for themselves. This paper is about the development of an interactive animation to increase the awareness of social media frauds among people and ways to prevent it. Subsequently, we created an animation that can raise the awareness of social media fraud among people, and hence they can also learn effective ways to prevent social media fraud from happening.
Introduction: Geosciences Supporting and Analyzing Military Operations

The natural environment affects almost all human activity, including military operations. While scientific study of the earth sciences represents a relatively recent development, people have always noticed that the landscape, surface processes, climate, and weather affected what can or cannot be accomplished, including for warfare. They did not need soil science to understand that bogs would severely hamper mobility, or climatology to know that the range of possible activity changed between winter and summer, or the dry and wet seasons.
A Correction Method of a Base Classifier Applied to Imbalanced Data Classification

In this paper, the issue of tailoring the soft confusion matrix classifier to deal with imbalanced data is addressed. This is done by changing the definition of the soft neighbourhood of the classified object. The first approach is to change the neighbourhood to be more local by changing the Gaussian potential function approach to the nearest neighbour rule. The second one is to weight the instances that are included in the neighbourhood. The instances are weighted inversely proportional to the a priori class probability. The experimental results show that for one of the investigated base classifiers, the usage of the KNN neighbourhood significantly improves the classification results. What is more, the application of the weighting schema also offers a significant improvement.
Constructing mutual context in human-robot collaborative problem solving with multimodal input

Abstract We describe a system that is designed for a human and a robot to solve problems in a shared space. This system uses speech and gesture to facilitate natural interactions with it. Our system uses a whiteboard-type architecture to represent and maintain information of different aspects in the problem-solving process. Our system represents information about a problem at a fairly high level, thereby making shared context accessible. We give an overview of the current status of our system and aspects currently under development. We explain the components of our system and provide details of the information produced by the various system components. Additionally, we explain how information is accumulated on the whiteboard and discuss and evaluate how various aspects of shared context are addressed by our system.
Inclusion through technology and education: A commitment to the improvement of museographic cultural spaces

In the following research, we intend to highlight the importance of inclusion in the museum spaces, being the key the educational value, with the use of the new tools (ICTs), as well as the interculturality showing the diversity of the current cities in its cultural aspect, and allowing the access to all type of public, with functional diversity. A systematic review has been carried out with a selection of 490 articles examined up to 56 articles from 2002 to October 2017. The search was conducted using ten databases: Esci, Circ, Miar, Eric, Isoc, Dialnet, Scopus, Google Scholar, Teacher Reference Center and Wos. The purpose is to provide elements to carry out good practices that facilitate inclusive education and avoid cultural exclusion. Within the conclusions obtained, the role of the museum space as a place of reflection and grouping of different intergenerational groups is evident. The incorporation of ICTs that improve interaction and learning should be encouraged, valuing cooperation between museums and schools.
Generating Bengali News Headlines: An Attentive Approach with Sequence-to-Sequence Networks

This age of data-driven innovation has made automated relevant and important data extraction a necessity. Automated text summarization has made it possible to extract relevant information from large amounts of data without needing any supervision. But the extracted information could seem artificial at times and that's where the abstractive summarization method tries to mimic the human way of summarizing by creating coherent summaries using novel words and sentences. Due to the difficult nature of this method, before deep learning, there hasn't been much progress. So, during this work, we have proposed an attention mechanism-based sequence-to-sequence network to generate abstractive summaries of Bengali text. We have also built our own large Bengali news dataset and applied our model on it to show indeed deep sequence-to-sequence neural networks can achieve good performance summarizing Bengali texts.