Reviewerly/exHarmony huggingface dataset:
Human Action Recognition for Boxing Training Simulator

Computer vision technologies are widely used in sports to control the quality of training. However, there are only a few approaches to recognizing the punches of a person engaged in boxing training. All existing approaches have used manual feature selection and trained on insufficient datasets. We introduce a new approach for recognizing actions in an untrimmed video based on three stages: removing frames without actions, action localization and action classification. Furthermore, we collected a sufficient dataset that contains five classes in total represented by more than 1000 punches in total. On each stage, we compared existing approaches and found the optimal model that allowed us to recognize actions in untrimmed videos with an accuracy 87%.
Afterword: In Conversation with Richard Saul Wurman

Richard S. Wurman, architect, designer, inventor of the TED conference and author of many books, including Information Architects, reveals in an interview with Dan Klyn his dislike of public speaking early in his career when he felt he didn’t have much to say. He contrasts this to his great passion of discovering how important it is for him to understand concepts at their basic, root level so as to be able to then communicate that understanding to others. It was this, more than anything, that has driven his career in many ways, and how he gave birth to the notion of systematic explaining, which turned into architecting information.
Strengthening Digital Literacy toward Students in Facing Education Era 4.0

This article discussed about the importance of strengthening digital literacy toward students in facing education era 4.0. In this technological acceleration era, the ability of digital literacy becomes so important, because by having good digital literacy students would be able to get valid informa
Analysis of Community Response to Disasters through Twitter Social Media

Abstract Disasters that occur in a place can give a variety of reactions in the community. Social media users, the dissemination of Twitter with followers, makes it easy and fast to share emergency information like a disaster. Some disasters often occur in Indonesia, such as earthquakes, volcanic eruptions, weather anomaly, and flooding. To find out the public response to an accident, researchers conducted twitter data crawling on July 26, 2019, to July 29, 2019, using the keyword “gunung tangkuban parahu erupsi”, crawling data produced a dataset of 14,045 records. This study calculates the number of tweets before the disaster, to several days after the accident. The results of the survey indicate the response of the community to disasters relatively increased on the first day of the crash and decreased after two or three days after the disaster.
A Model-Agnostic Framework to Correct Label-Bias in Training Data Using a Sample of Trusted Data

The importance of usage of massive datasets in machine learning is continuously growing. Such massive datasets pose serious challenge to robustness of the model to label-noise which is a critical property for any classifier. This challenge primarily stems from the fact that such datasets often contain biases which unfairly disadvantage certain groups. Non-random label-noise can be introduced into the data because of automatic labeling, lack of domain expertise while manual labeling and data poisoning attack by adversaries. Classifiers trained on such datasets can inherit these biases. In most of the prior work it is assumed that the entire training data are potentially corrupted. However it is always possible to curate small amount of trusted data for testing and validation and a part of it can be used up for denoising training data. Recently a methodology has been proposed to de-noise label-corrupted data using a sample of trusted data. The core of the methodology is based on the assumption that adversarial process has no access to true labels of the data. This is quite strong an assumption as it underestimates the capacity of the adversarial process. As part of this work, we proposed a more generic framework to correct label noise without any assumption on the access of the adversarial process to true labels of the data. Experimental results suggest performance improvement by our method over existing methods.
A data fusion approach to improving moving object measurements using phase-shifting profilometry

Phase-shifting profilometry (PSP) is one of the mainstream fringe projection techniques for object surface reconstruction. Thanks to its multi-shot nature, PSP is less sensitive to ambient light and reflectivity on the object surface. For static objects, the performance of PSP can be improved by projecting and capturing more phase-shifted fringe patterns. However, when applied to dynamic objects, PSP suffers from motion-induced errors due to the loss of correspondence. This paper proposes a new approach to improving the performance of measuring objects with a general three-dimensional (3D) movement. Firstly, instead of employing a large number of fringe patterns for PSP, we apply three-step PSP, which suffers less from motion errors, to obtain multiple coarse measurements of the object. Then the moving object is segmented, and the iterative closest point (ICP) algorithm is applied to estimate the motion parameters. Finally, the multiple measurements of the object are fused using adaptive weights. The proposed scheme alleviates the motion errors of PSP with a large number of fringe patterns and enhances the accuracy of three-step PSP. Simulations and experiments verify the feasibility of the proposed scheme.
The Promise of Deep Learning for Indian Roads: A Comparative Evaluation of Architectures

With the recent advances in the move towards autonomous vehicles, detecting vehicles through computer vision techniques is set to be an area of paramount importance. While existing studies apply artificial intelligence techniques for detecting vehicles in foreign road scenarios, most studies do not leverage the representational power of deep learning for Indian roads, wherein, the situation is often more challenging and complex. In this paper, we utilize an Indian road dataset to assess various deep learning models, focusing specifically on the condition of Indian roads at present. Alongside common vehicle classes like car, bus, motorcycle, etc., our dataset additionally consists of autorickshaws, trucks, person, and cycle, which are highly prevalent on Indian roads. Two detection algorithms: Faster RCNN and Single Shot Detector (SSD) have been incorporated with convolution neural net classifiers MobileNet, ResNet, and Inception Net, forming four detection models—Faster RCNN ResNet101, Faster RCNN ResNet50, SSD Inception NetV2, and SSD MobileNetV1. A comparative analysis of the algorithms shows that Faster RCNN ResNet 101 is the best performing model, achieving an accuracy of up to 57.60%, while SSD MobilenetV1 has the fastest inference time of 2.44 s. Our analysis shows that deep learning indeed has a bright future in the perspective of the Indian road
Mobile learning for social change

With technological innovations being introduced in the educational system, there have been various changes in the way of learners learning styles. Some authors argue that the old and well-acquired learning strategies cannot be replaced with learning through digital platforms and mobile devices. In this paper, I present ideas and facts supporting the initiation of mobile learning for adults based on scholarly papers and personal experience.
The Sound Sketchpad: Expressively Combining Large and Diverse Audio Collections

Software tools for media production have largely been adapted from physical media paradigms, offering blank canvases upon which to import, combine, and process content. In music production, this increasingly involves meticulous manual assembly of audio clips often carefully curated from diverse sources. As collections of audio content scale upwards in sample size, diversity, and number, creative projects require exponentially more time, effort, and attention to effectively shape them. New tools must find new ways to contend with this abundance of content. We propose the Sound Sketchpad, an algorithm-in-the-loop audio-graphical system and interface for combining sounds from a database into new music. It allows a user to sketch broad musical ideas by making sound, and then interactively modify and refine the resulting composition by drawing visual paths. We discuss the design, implementation, and advantages of this approach.
Generating Sport Summaries: A Case Study for Russian

We present a novel dataset of sports broadcasts with 8,781 games. The dataset contains 700 thousand comments and 93 thousand related news documents in Russian. We run an extensive series of experiments of modern extractive and abstractive approaches. The results demonstrate that BERT-based models show modest performance, reaching up to 0.26 ROUGE-1F-measure. In addition, human evaluation shows that neural approaches could generate feasible although inaccurate news basing on broadcast text.
Augmenting Graph Convolutional Neural Networks with Highpass Filters

In this paper, we propose a graph neural network that employs high-pass filters in the convolutional layers. To do this, we depart from a linear model for the convolutional layer and consider the case of directed graphs. This allows for graph spectral theory and the connections between eigenfunctions over the graph and Fourier analysis to employ graph signal processing to obtain an architecture that “concatenates” low and high-pass filters to process data on a connected graph. This yields a method that is quite general in nature applicable to directed and undirected graphs and with clear links to graph spectral methods, Fourier analysis and graph signal processing. Here, we illustrate the utility of our graph convolutional approach to the classification using citation datasets and knowledge graphs. The results show that our method provides a margin of improvement over the alternative.
Path and Energy Optimization for Autonomous Navigation

Autonomous vehicles are increasingly prevalent in many applications and their sophistication is rapidly increasing. While path planning for autonomous platforms like robots is well studied, many platforms are now equipped with multiple sensors that should be considered in optimal solutions. We develop methods to solve a class of problem in which multi-variate optimization is used to address not only path selection, but behaviors related to the sensor and communications energy resources of the autonomous platform.
Deep Hybrid Neural Networks with Improved Weighted Word Embeddings for Sentiment Analysis

Deep learning models such as Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) models have recently emerged as effective solutions to various NLP tasks with comparatively remarkable results. The CNN model efficiently extracts higher level features using convolutional layers and max-pooling layers while the LSTM model allows capturing long-term dependencies between word sequences. In this paper, we propose a hybrid CNN-LSTM model taking advantage of both models to overcome the sentiment analysis problem on Twitter data. We employ a multi-channel CNN that extracts local n-gram features in different sizes using several filters of different lengths. We also employ a weighted average word embeddings method which incorporates sentiment information in the continuous representation of words based on an adapted version of the delta TFIDF measure. These word representations will be the input for the CNN-LSTM model. Experiment results substantiate our intuition by reaching good macro average recall and accuracy scores beating several existing models as well as our same model using individual CNN and LSTM.
Study on Characteristics of Several Quantum Key Distribution Protocols

Based on the security problems emerged in development process of quantum key distribution, it is introduced that the working principles and basic process of three classical protocols including BB84 protocol, decoy-state protocol, measurement-device-independent protocol, and two new protocols including twin-field protocol, phase-matching protocol as the main line. We deduce the relationship between the key generation rate and channel transmission efficiency of each protocol, and simulate security transmission performance of the above-mentioned protocol. Finally, the characteristics of each protocol are compared analyzed. Relevant research results show that the security transmission performance of phase-matching protocol is better than the others.
Role of Culture in Formation of General Competences of Art College Students

The paper aims to identify the role and place of culture when forming students’ general competences in the art college educational space. The article discusses the need to introduce cultural values to college students - future hair stylists. Scientific originality of the study involves justifying the necessity of using special pedagogical technologies to form general cultural competences of “Technology of Hairdressing” speciality students. The research findings are as follows: the authors present an additional educational program focused on forming the students’ general cultural competences, introduce a set of diagnostic tools to assess the program efficiency.
Hybridization of Adaboost with Random Forest for Real-Time Prediction of Online Shoppers’ Purchasing Intention

Recently, a real-time online shopper behavior prediction system based on random forest has been suggested to predict the visitor’s shopping intent as soon as the website is visited. The main focus of the current paper is to consolidate the previously suggested system by using hybridization of adaboost with random forest. As in the former study, the proposed system relies on session and visitor information and uses oversampling to improve the performance and the scalability of classification. The results show that the novel system based on adaboost and random forest ensemble classification outstrips the former one in terms of accuracy and F1 score.
A zeta function related to the transition matrix of the discrete-time quantum walk on a graph

We present the structure theorem for the positive support of the cube of the Grover transition matrix of the discrete-time quantum walk (the Grover walk) on a general graph G under some condition. Thus, we introduce a zeta function on the positive support of the cube of the Grover transition matrix of G, and present its Euler product and its determinant expression. As a corollary, we give the characteristic polynomial for the positive support of the cube of the Grover transition matrix of a regular graph, and so obtain its spectra. Finally, we present the poles and the radius of the convergence of this zeta function.
Importance of Intellectual Property Law: The McLibel Case

Publicity is important to corporations like McDonald’s, but as to how it is done and portrayed to the people through the media lies firmly to the corporation’s strategy. This helps the corporation to avoid having a repeat of another Mc Libel case in the future and damages to its corporate reputation. McDonald’s marketing efforts should remain interconnected with its competition efforts, but it has to be ethical and should not exhibit deception to both competitors and the public. Though the years of McDonald’s pleadings gained favor from the intellectual property law, and proven to its consumers and defamers that the corporation is not guilty of the allegations, admittedly, it gives no guarantee that the victory they enjoyed for years has the power the control to the effects of the case that has been on trial for more than two years.
Analysis of Image Steganography Techniques for Different Image Format

Steganography is the method of hiding one type of information into other type of information, hiding a secret a message in a cover so that others can't know the presence of the secret information. It provides an extra layer of security in communication and information sharing. Security is an important aspect of the communication process; everyone want security in communication. The main purpose of this paper is to introduce security of information that people share among them. In this paper we are presenting different methods of substitution techniques of image steganography and their comparison. Least significant bit and most significant bit substitution techniques are used. Information is hidden in an image file and then decoded back for the secret message. Hiding the presence of any hidden information makes this more secure. This implementation can be used by secret service agencies and also common people for secure communication.
Consolidating Online Real Estate Data Using Image Analysis and Text Processing

Property buyers often access online real estate websites to search for properties which they might be interested in. However, property sellers may in turn have their properties listed on several real estate agency websites to increase their chances of reaching a large audience. Consequently, same properties are often found on multiple websites. Problems arise when same properties are listed with different prices and different details on different websites. To users, such a situation impedes on the decision-making process. Ideally, having a single portal, where all data pertaining to the same property from different real estate agency websites are consolidated that would be useful for potential buyers prior for making a decision. Identifying similar real estate properties posts, however, is not a straightforward exercise. With different information available from different websites, additional processing is required. In this paper, we propose an approach that makes use of image analysis and text processing to detect similar real estate properties which advert from different websites posted for the Mauritius market.
A Systematic Evaluation for Paint Messages and Their Processing among C++/CLI Controls

This paper is to propose a systematic evaluation for paint messages among C++/CLI controls, e.g., form, button, and picture box. Specifically, the occurrences of such messages and priorities of their processing are investigated to enhance the design of programming models for graphical user interface (GUI). To demonstrate the effectiveness of this approach, those models are progressively integrated with processing paint messages from various controls, in a graphically-demanding C++/CLI project.
Reform Path of Applied Courses of Assets Evaluation in the Information Age

This article focuses on current hot topics - Reform of Applied Curriculum, and apply it to the content of asset assessment courses. First, study domestic and foreign related literature on curriculum concepts, application-oriented curriculum construction, experimental course construction, etc. Draw preliminary research ideas, Conduct multi-party research and consultation according to the ideas, Comparing the degree of agreement between the curriculum philosophy, curriculum objectives and experimental courses of the applied curriculum of asset evaluation, Seek the collision point between the curriculum concept, curriculum goals and the information age of asset assessment applied courses, modular knowledge system and application skills, Finally explored the development path of asset evaluation application courses, clarify the main responsibilities of teachers and students. According to OBE, the final assessment mode is set to be a combination of regular curriculum assessment and experimental simulation assessment.
Quantum forced oscillator via Wigner transform

In this paper we review the basic results concerning the Wigner transform and critically discuss the problem of the reversibility. Then we completely solve the harmonic/inverted forced quantum oscillator in such a framework; eventually, the tunnel effect for the forced inverted oscillator is discussed.
Research on Software Resource Management Model Based on Cloud Service

With the development of intelligent industries such as "Internet plus", "cloud services" and "AI", etc. .New technologies and industries will be deeply integrated and comprehensively applied to the development of smart cities and smart enterprises. The core business of an enterprise increasingly depend on informationization. For the power industry, the new opportunities and development should be fully realized, in addition, new problems in software version management and intellectual property management should also be fully considered. The author of this paper puts forward a new management model, relying on cloud services, virtualization and other new technologies, unified release of integrated software and hardware resource pool, manage the whole process of "release change failure" of software, that improve the efficiency of using software resources as well as reduce the cost of software investment. It can also reduce the risk of intellectual property disputes.
The Fuzzy Origin–Destination Matrix Estimation for Planning Air Traffic

The work suggests an original approach, which allows to get a fuzzy origin-destination matrix on the basis of data on flows on the arcs of the transport column. For this purpose, a number of optimization models to determine fuzzy intervals of correspondence and search for correspondence with the maximum value of the membership function. Algorithmic and software for fuzzy correspondence search has been developed. Perspective of the given approach is shown on an example of a problem of the analysis of transport streams in passenger air transportation.
Revisiting the Jerash Silver Scroll: A new visual data analysis approach

This article revisits a complexly folded silver scroll excavated in Jerash, Jordan, in 2014 that was digitally examined in 2015. In this article we apply, examine and discuss a new virtual unfolding technique that results in a clearer image of the scroll's 17 lines of writing. We also compare it to the earlier unfolding and discuss progress in general analytical tools. We publish the original and the new images as well as the unfolded volume data open access in order to make these available to researchers interested in optimising unfolding processes of various complexly folded materials.
A Novel Clustering Based Undersampling Algorithm for Imbalanced Data Sets Using Artificial Bee Colony Algorithm

Datasets that follow imbalanced class distribution impose a critical challenge to the machine learning research community. Standard machine learning approaches assume that datasets have a balanced class distribution and can’t handle imbalanced datasets. There are several other methods to deal with imbalanced datasets. This paper proposed an Artificial Bee Colony (ABC) clustering-based undersampling approach for imbalanced datasets. The proposed approach has been applied on several benchmark datasets from the KEEL dataset repository, and the obtained AUC value is compared and analyzed. We strongly recommend the Artificial Bee Colony-based undersampling method for dealing with imbalanced classification problems based on obtained results.
The Role of Analyst Engineer in Algorithm Life and Social Cycle

The problems and perspectives of ethics based approach development of AI algorithms are presented in the article. The issue of ethical limitations and responsibilities of AI software developers during the life cycle of algorithms is examined. The analyses of corporate AI ethics guidelines and precedents in their application for analyst engineers' professional activities serve us empirical examples. The authors reveal potential impact of algorithms on different spheres of human lives such as education, medicine, enterprises, etc. in connection to rising control power of employer over the employees. Potential algorithms failures and engineers' illegal labour practices are also considered. Such examples and possibilities of ethical violations need to be accessibly documented and introduced to AI developers departments. The terms of potential engineers and employers interactions are to be set with the option of mutual assessments and tools for algorithm maintenance checkup.