Reviewerly/exHarmony huggingface dataset:
Quantifying Event Impact on the Bitcoin Blockchain

As the pioneer of blockchain technology, Bitcoin is the most famous cryptocurrency to date. Given its dramatic price swings along with constant news and events, the dynamics of cryptocurrencies are difficult to quantify. These dynamics are believed to be affected by various political, security, financial, and regulatory events. This paper presents the Impact-Score metric, an efficient framework that attempts to quantify the impact of events on the Bitcoin blockchain. We have applied our framework to 16 major worldwide events and the Bitcoin blockchain network (defined as Bitcoin transaction and users, blockchain data, and memory pool data) from 2016-2018. The results show that the majority of the events are correlated with quantifiable changes in the Bitcoin blockchain and network. We also observed correlations between event types (e.g. financial events) and event subtypes on certain Bitcoin blockchain network temporal features.
Event or Emergency Case Detection by Human Running

Recognizing the difference between walking and running is certainly an essential part of standard sports activity classification. Moreover, that can be commented on a security problem in daily life. If someone is running on the street that may show something goes wrong. The purpose of this study is to recognize the everyday actions of the human, whether walking as regular movement or running in the case of emergency or event. In recent years, significant progress has been made in computer vision and machine learning. CNN, a deep learning algorithm for image processing was used for the model. The dataset, a thousand of images, of the study were collected from different sites of the internet or extracted from videos. Classify frequent human movements, whether a regular walk or running action, were separated by 86.85% success in the research.
Semantic Extraction for Sentence Representation via Reinforcement Learning

Many modern Natural Language Processing(NLP) systems rely on word embeddings, previously trained in an unsupervised manner on large corpora, as base features. Several attempts of learning unsupervised sentence representations have not achieved satisfactory performance and have not been widely adopted. In this work, we present a Semantic Extraction Reinforcement learning Model(SERM) for encoding sentences into embedding vectors, which transforms the learning process into intent detection and named entity recognition tasks. Intent detection is mainly related to the semantics of the whole sentence, while named entity recognition pays more attention to local entities. Given a sentence, SERM builds a sentence representation by extracting the most important words and removing irrelevant words in a sentence. Unlike the mainstream approach, SERM compresses sentences from semantic and entity perspectives and this allows us to efficiently learn different types of encoding functions. The experimental results show that SERM can learn high quality sentence representation. This paper demonstrates that our sentence representations have sufficient competitiveness with the best performing model on text classification task.
A Database of Students' Actions Based on Real Classroom Environment

Nowadays, human action recognition has exerted a profound influence on many subjects. With the help of it, recognizing students' actions automatically in the classroom has become possible. A standardized database is indispensable for training and evaluating different action recognition algorithms. However, there are few public action databases for classroom environment at present. This study contributes to this problem in the following two aspects. First, a students' actions database in real classroom environments is established. The database consists of 4,917 images of students' learning in the classroom, including ten kinds of students' actions in a smart classroom. Second, based on this database, a benchmark experiment is conducted to evaluate the usability of this database using the recognition algorithm of VGG16. A method of data augmentation to solve the problem of overfitting and a method of super resolution to improve the image resolution are introduced. The experimental results show that such a spontaneous database established in this study has good usability for research of action recognition, providing a good data basis for student action recognition algorithms in learning environment.
A Hybrid Intelligent System to Detect Anomalies in Robot Performance

Although self-diagnosis is required for autonomous robots, little effort has been devoted to detect software anomalies in such systems. The present work contributes to this field by applying a Hybrid Artificial Intelligence System (HAIS) to successfully detect these anomalies. The proposed HAIS mainly consists of imputation techniques (to deal with the MV), data balancing methods (in order to overcome the unbalancing of available data), and a classifier (to detect the anomalies). Imputation and balancing techniques are subsequently applied in for improving the classification performance of a well-know classifier: the Support Vector Machine. The proposed framework is validated with an open and recent dataset containing data collected from a robot interacting in a real environment.
Research on Modulation Recognition of Communication Signal

Abstract Modular identification of radio communication signals is widely used in commercial software’s as well as the country’s defence. With the phenomenal development of modern telecommunications in various countries. The process of converting data into radio waves by adding information to an electronic or optical carrier signal becomes more sophisticated and variable. As a result, there has been a great deal of research and development into signal interpretation, and a number of effective schemes have been devised. For Signal Modulation, recent research has used a variety of techniques such as clustering, neural networks, automatic modulation, and analytical parameters. A quick description and summary of signal recognition approaches is offered in this study. We also reviewed contemporary technological hurdles, as well as the future direction of modulation.
Graph Convolutional Neural Networks-based 3D Hand Pose Estimation over Point Clouds

In recent years we can find a multitude of approaches that aim to return the 3D pose of the hands. Most of them try to estimate the pose from RGB images or even include some geometrical information via depth maps. Furthermore, some proposals have shown promising results using point clouds as input data. However, the sparse nature of this type of data is often one of its drawbacks. To tackle this sparsity, different strategies have been brought to the table such as voxelizing or sorting the input data to impose a structure to the input domain. In this paper, we address this problem by means of a graph structure. This process implies that we should accommodate the point cloud onto a graph representation that connects its points. We connect each point to its neighborhood, a method that has been successfully used in similar proposals and whose clustering effect enables us to emulate an effect similar to kernels in image convolutions. The proposed architecture uses both graph and 2D convolutions. The first one aims to extract local features and build a feature map, from which the 2D convolutions will extract a second level of features used to estimate the pose. This proposal shows initial results to return a 3D pose of the hand from depth maps, which are projected on point clouds and redefined as graphs. Although the results diverge from other more established methods in the state of the art, it presents a proof of concept by which to address this problem without losing spatial information.
Temporal Image Forensics: Using CNNs for a Chronological Ordering of Line-Scan Data

We propose to use CNNs for obtaining a temporal ordering of line-scanner data. Excellent age classification accuracy is achieved only in case network training and testing is done with image patches taken from consistent spatial locations, i.e. temporal features exploited are bound to specific positions in the image. With spatially consistent patches, up to 100% classification accuracy can be achieved, whereas with spatially varying patches the accuracy stagnates at around 54%. We have also noted a result dependency on image content and have found, that a consistent patch position relative to the scanning line is not sufficient for good results.
PAT: Pseudo-Adversarial Training For Detecting Adversarial Videos

Extensive research has demonstrated that deep neural networks (DNNs) are prone to adversarial attacks. Although various defense mechanisms have been proposed for image classification networks, fewer approaches exist for video-based models used in security-sensitive applications like surveillance. In this paper, we propose a novel yet simple algorithm called Pseudo-Adversarial Training (PAT), to detect the adversarial frames in a video without requiring knowledge of the attack. Our approach generates ‘transition frames’ that capture critical deviation from the original frames and eliminate the components insignificant to the detection task. To avoid the necessity of knowing the attack model, we produce ‘pseudo perturbations’ to train our detection network. Adversarial video detection is then achieved through the use of the detected frames. Experimental results on UCF-101 and 20BN-Jester datasets show that PAT can detect the adversarial video frames and videos with a high detection rate. We also unveil the potential reasons for the effectiveness of the transition frames and pseudo perturbations through extensive experiments.
HOW TO MAKE LEARNING IN STEM MEANIGFUL FOR THE MILLENNIUM GENERATION

The education of today more and more encounters the teaching and learning problems of young adults therefore it is topical to find out how to make the teaching/learning of the Millennium generation meaningful. This issue is especially important in STEM education. The survey involved Grade 10-12 students of Latvia, in total 256 students. Spearman’s correlations and Kruskal-Wallis test were used in the data analysis. The obtained results showed that students- millennials as regards the learning of STEM subjects can be described as real-life oriented, digitally educated who want to participate actively in the teaching/learning process and who want to receive the feedback. Keywords: meaningful learning, Millennium generation, teaching and learning principles, STEM learning.
Research and Design of Visual Analytics System of Network Security Situation Based on Multi-source Log

Network security visualization takes advantage of the human perceptual and cognitive processes, provides an effective visual tool for analysts to understand the network status, find network anomalies, identify network attacks and solve other security problems. In this paper, with the help of visual analytics methods, uses its great advantages in network security analysis and massive data analysis, slices data in units of times, emphasizing network hierarchy, correlation, traffic timing analysis and collaborative interaction between views. A visual analytics system is developed to analysis the abnormal host asset communication, the abnormal analysis of database and user behavior and the analysis of abnormal traffic within 24 hours. Finally, the validity of the system in this paper is verified through the analysis of multiple set of cases.
Idea Development Method, Applying Systems Design Thinking in a Very Small Entity

Abstract Very Small Entities (VSE) delivers a substantial percentage of the world's product and services. These agile and innovative companies approach their market in a flexible, informal, and human‐centered approach. Their transformation from startup phase to an established company can be problematic. As they grow, the VSE must put more emphasis on incorporating formal organizational structure and processes to cope with the increased size and complexity. Failure in adapting often results in a negative market effect. Systems Design Thinking (SDT) is a combination of Systems Engineering, Systems Thinking, and Design Thinking. There is an increased awareness that the perspectives of SDT bring value to organizations. However, the advantage of SDT is not well understood for VSEs that are in the maturing phase. In this work, the researchers explore the value of SDT for very small enterprises transitioning from infancy to adolescence. The article presents a new method for idea development based on SDT principles and tailored to the needs of a VSE. Case‐based research is applied in a small company to understand the needs and verify the desirability, feasibility, and viability of our proposed method. The results suggest that an SDT approach improves the company's ability to capture and develop ideas and can help grow the company.
A General Machine Learning Framework for Solving Real Applications with a Texture Perception Case Study

Machine learning, especially deep learning, has made great achievements in application domains such as computer vision, speech recognition, machine translation, etc. This great success not only depends on the progress of model design but also on the emergence of large-scale annotated benchmark datasets. However, in some application domains, it is difficult to apply machine learning methods to solve the new arising problems due to the lack of standardized and annotated datasets. People have to find their way from the very beginning of the data collecting, cleaning, and labeling. Unfortunately, there is not a guiding framework to deal with such a situation. This paper pays attention to it and proposes a general machine learning framework to solve the pratical problems from the scratch. It contains two main stages implemented by an unsupervised clustering method and a semi-supervised learning method representatively. In the first stage, an unsupervised clustering method is utilized to find representative data samples which are more important and can be used to do data cleaning and manual labeling. In the second stage, a semi-supervised method is adopted to predict the labels for the rest data samples and to construct a larger annotated dataset. A case study in the texture perception field has been done to confirm the effectiveness and efficiency of the proposed application framework.
Health Evaluation Method of Power Information System Based on DEA-CCR

In view of the current situation that the power information system is becoming larger and larger and the operation and maintenance is more and more complex, this paper proposes an objective evaluation method of power information system index based on DEA-CCR. Apriori association rule algorithm is used to mine the association relationship between log information, and then select the important association rules that can express the health of power information system. Combined with the extracted association rules, using DEA-CCR evaluation model to get the comprehensive efficiency quantitative index of each decision-making unit, and then according to the extracted index, to evaluate the health of the system. Finall•y, an example is analyzed based on the log data of the host computer of the marketing system of a power company. The results show that the method in this paper is effective and objective, and has strong application value.
High Order Inverse Radial Lens Distortion

Abstract Lens distortion is one of the highest elements affecting camera calibration. In this paper, a new improved algorithm for calculating the inverse of the best radial distortion model is presented. The presented method provides a model of reverse radial distortion. The method involves lens of focal length (190mm) with a Sony G camera, and calibrating them using Zhang method which entails a chessboard pattern to be detected from several positions, and an exact formula to calculate the inverse radial distortion. The results show that the highest order radial distortion is the better model to be reverse, it was shown that the 8 th order radial distortion is the best model which contain a lowest value of distortion can be acquired after a number of iterations about 1.009842932 × 10 -24 , and this is the best value can be acquired without losing the accuracy, which produces low distortion performs as pincushion distortion. The new improved algorithm displays a good performance in obtaining least value for distortion coefficient.
Speaker Identification Method Using Bone Conduction and Throat Microphones

In recent years, the number of workers in Japan’s construction industry has been decreasing, and the working environment at construction sites and other facilities is being improved. For example, the use of professional headsets equipped with bone conduction microphones, which enable clear voice communication even under noise, is expanding. Therefore, we aim to improve the performance of speaker identification using bone conduction microphones. In this study, we propose a speaker identification method using the difference of spectrum between bone conduction and throat speech. In the proposed method, Mel log power spectrum of bone conduction and throat speech is calculated. Then, the difference of spectrums is calculated and SVM is used to identify the speaker. The proposed method using the difference of spectrum between bone conduction and throat speech achieves the highest identification rate, which confirms the effectiveness of the difference of spectrum between bone conduction and throat speech.
Balanced Whiteman Generalized Cyclotomic Sequences with Maximal 2-adic Complexity

Binary sequences with high linear complexity and high 2-adic complexity have important applications in communication and cryptography. In this paper, the 2-adic complexity of a class of balanced Whiteman generalized cyclotomic sequences which have high linear complexity is considered. Through calculating the determinant of the circulant matrix constructed by one of these sequences, the result shows that the 2-adic complexity of this class of sequences is large enough to resist the attack of the rational approximation algorithm (RAA) for feedback with carry shift registers (FCSRs).
An analysis of the impact of subsampling on the neural network error surface

This paper empirically analyses the impact of changes to the set of training examples on the neural network error surface. Specific quantitative characteristics of the error surface related to properties such as ruggedness, modality and structure are measured and visualized as the set of training examples changes. Both the case of random subsampling and active learning from a fixed dataset are examined, producing ten different training scenarios. For each scenario eleven error surface characteristics are calculated for five common benchmark problems. The results demonstrate that the error surface characteristics calculated using only a subsample of the available data commonly do not generalize to that of the full dataset. The observed error surface characteristics are significantly impacted by the particular set of examples used to calculate error. Some error surface characteristics are significantly altered by small changes in the set of examples used to calculate error. The main finding from this study is that when the set of training examples may change during training the training of a neural network is in essence a dynamic optimization problem, suggesting that optimization algorithms developed specifically to solve dynamic optimization problems may be more efficient at training neural networks under such conditions.
Proverbs 4:10–19 and the growing spate of Internet fraud amongst Nigerian youths

This research article examines the increasing spate of youths who engage in fraudulent Internet activities in Nigeria in the light of Proverbs 4:10–19. Nigerian youths are fast becoming impatient with their quest for wealth. This had led many of them to engage in high-level fraudulent Internet activities. It has come to a point where Internet fraudsters opened schools to teach prospecting youths how to make money fast. The circle keeps expanding on a daily basis. Their victims include the rich, the poor and unsuspecting foreigners. The study findings revealed that the spate of Internet fraud has continued to increase because more youths are willing to engage in such activities and other cybercrimes to make fast wealth, so that they can feel pride among other youths and the society. Proverbs 4:10–19 provide a lucid response to this sad development. Contribution: In the light of Proverbs 4:10–19, Nigerian youths are advised not to join evildoers because their path of wickedness ends in darkness and unhappiness.
Eigenvalue Perturbation for Item-based Recommender Systems

Adding confidence estimates to predicted ratings has been shown to positively influence the quality of the recommendations provided by a recommender system. While confidence over single point predictions of ratings and preferences has been widely studied in literature, limited effort has been put in exploring the benefits provided by user-level confidence indices. In this work we exploit a recently introduced user-level confidence index, called eigenvalue confidence index, in order to provide maximum confidence recommendations for item-based recommender systems. We firstly derive a closed form solution to calculate the index, then we propose a new recommendation methodology for item-based models, called eigenvalue perturbation, founded on the strongly positive correlation between the index value and the accuracy of the recommendations. We show and discuss the accuracy results obtained with a comprehensive set of experiments over several datasets and using different item-based models, empirically proving that applying the new technique we are able to outperform the original recommendation models in most of the experimental configurations.
Adaptive Densely Residual Network for Image Super-Resolution

Many networks are designed to stack a large number of residual blocks, deepen the network and improve network performance through short residual connec-tion, long residual connection, and dense connection. However, without consider-ing different contributions of different depth features to the network, these de-signs have the problem of evaluating the importance of different depth features. To solve this problem, this paper proposes an adaptive densely residual net-work (ADRNet) for the single image super resolution. ADRN realizes the evalua-tion of distributions of different depth features and learns more representative features. An adaptive densely residual block (ADRB) was designed, combining 3 residual blocks (RB) and dense connection was added. It learned the attention score of each dense connection through adaptive dense connections, and the at-tention score reflected the importance of the features of each RB. To further en-hance the performance of ADRB, a multi-direction attention block (MDAB) was introduced to obtain multi-directional context information. Through comparative experiments, it is proved that theproposed ADRNet is superior to the existing methods. Through ablation experiments, it is proved that evaluating features of different depths helps to improve network performance.