Reviewerly/exHarmony huggingface dataset:
English Article Style Recognition and Matching by Using Web Semantics

With the explosion of internet information, people feel helpless and difficult to choose in the face of massive information. However, the traditional method to organize a huge set of original documents is not only time-consuming and laborious, but also not ideal. The automatic text classification can liberate users from the tedious document processing work, recognize and distinguish different document contents more conveniently, make a large number of complicated documents institutionalized and systematized, and greatly improve the utilization rate of information. This paper adopts termed-based model to extract the features in web semantics to represent document. The extracted web semantics features are used to learn a reduced support vector machine. The experimental results show that the proposed method can correctly identify most of the writing styles.
A Novel Transmission Line Insulator Detection Method Based on Enhanced YOLO Model

Aerial photography patrol has become the main method of electric power patrol on transmission lines instead of manual patrol, and the integrity of insulators on transmission lines directly affects the power supply reliability. Under the interference of complex background, traditional insulator detection method tends to have a low ability to perform the detection tasks. To solve this problem, we proposed a new method based on enhanced YOLOv4. Based on watershed algorithm and data augmentation combinations, the problem of accurate image detection of aerial photo insulator is studied. Combined with the watershed algorithm and data augmentation combinations, the enhanced YOLO model has robust ability against different distances, various lighting conditions in complex environments. It is shown that our proposed method achieved competitive performance in the experiments.
Modeling IoT and Big Data Impacts to Business Performance

With the existence of Internet of Things that have ability to connect various devices through censors, the growth of data volume is increasing rapidly known as Big Data. Through this Big Data, many companies gained its insights as basis for better decision making to gain competitive advantage. However, past literatures have shown that these empirical results are still fragmented. Therefore, this paper aims to propose a model on how IoT Big Data impacts business performance. For modeling purposes, some elements are included such as: 1) Business Process Improvement, 2) Marketing Strategies, 3) Business Management Innovation, 4) Business Models and Organizational Culture, 5) Privacy and Ethics, 6) Business Performance. Furthermore, sampling of managers in manufacturing industry are gained to answer several questions regarding the model development. For analysis purposes, Smart PLS 3.0 is run to evaluate the fitness of the model with requirement of Goodness of Fit (GoF) above 0.38. After careful conduct, the model is robust and accurate. From this model it can be said that 1) Business Models & Organizational Culture positively influence Business Process Improvement while Privacy and Ethics negatively influence it, 2) Business Process Improvement positively influences Marketing Strategies, Business Management Innovation and Business Performance, and 3) both Marketing Strategies and Business Management Innovation positively influence Business Performance.
Fuzzy Logic Evaluation of Teacher Competencies Through Students Perceptions

Today, the importance of foreign language teaching and learning has increased, countries have participated in the race to teach their language as a foreign language. In this study is evaluated teacher competencies through students perceptions of teaching Russian as a foreign language. A 5-point Likert style questionnaire having 21 items was applied to measure the opinion of the international students taking Russian as a foreign language. Results of measurement are evaluated using the fuzzy-logic approach. This approach reduces a subjective error introduced by survey participants in the process of measurement and to grades the competency of each teacher individually.
A Voltage Producing Smart Wheelchair Development with Heartbeat Monitoring System

In this paper the development of a voltage producing smart wheelchair is presented. This wheel chair can use the rotational kinetic energy generated from wheel spin to charge a power bank. In this wheelchair a motor-controller is connected with an Arduino. It receives power from source and controls the motors connected with the wheels according to the signals given by Arduino mega. A dynamo is connected with the wheels to convert the rotational kinetic energy generated from wheel spins. This wheelchair will help us to save the kinetic energy generated from wheel spin and use this energy to recharge a power bank. Features like heart beat monitoring system and emergency calling system are also added in this wheel chair.
Automatic Content Creation Mechanism and Rearranging Technique to Improve Cloud Storage Space

The storage of electronic data as well as the demand for it has become a major problem in today’s society. Data is becoming increasingly centralized in order to provide the flexibility to use it anywhere, at any time, and on any device. Due to the increasing mobility in modern devices, data productivity and accessibility in cloud storage are increasing. Data versatility is expanding on a daily basis, posing a management challenge. All methods of dumping data regularly over a period of time necessitated the deletion and rearrangement of a few data items in order to achieve greater efficiency in the data retrieval process. Currently, the researchers are focusing on the efficient searching algorithms and not on the combined technique of data prioritization, deletion, and rearrangement. The proposed automatic content creation mechanism (AACM) system will create new document after deleting unwanted contents and by merging few existing documents based on the top key words. Each and every document is associated with particular keywords. The proposed system leads to two outputs by considering the text, first to form core points with voting count and then to create new documentary on it. The proposed system can also focus on video, audio, and image in addition to text but however the major focus is given to text, which is the complex one of the four. The mechanism will move from lower priority/older one to higher priority/newer one on the basis of success rate with a particular cluster. This mechanism will save the valid information even from lower priority and older documents. It will also free up the space by deleting the unwanted sentences from older files, and all these depend on the threshold (confidence) value, which is auto-adjusted by the proposed mechanism on the basis of success rate. This will lead to a better memory management and prevention of core historical data.
Blind Symbol Rate Estimation using Convolutional Neural Networks for PSK Modulated Signals

The problem of symbol rate estimation in Phase Shift Keying (PSK) modulated signals is addressed. We show that machine learning based techniques can be used to estimate the symbol rate blindly from the baseband received signal. A Convolutional Neural Network was trained to obtain a fast and coarse estimate of the symbol rate compared to classical symbol rate estimators. This is beneficial for real time applications such as situational awareness where a large bandwidth needs to be analysed quickly. The network was trained using an input feature vector derived based on cyclostationarity theory. This feature vector is used in blind signal detectors and symbol rate can be estimated by observing the peak value of the feature vector. We show that symbol rate estimation accuracy can be significantly improved by using deep learning techniques, specially at low signal to noise ratios compared to classical signal processing techniques. The proposed method does not require prior knowledge of the PSK modulation order or pulse shaping filter and robust against time, phase and small frequency offsets.
Simple Computations

Now that we have some values, classified into types, we can start to compute with them. Starting with something familiar, let’s use Haskell to do some simple arithmetic calculations.
ARShape-Net: Single-View Image Oriented 3D Shape Reconstruction with an Adversarial Refiner

In this paper, we propose a novel method of reconstructing 3D shapes from single-view images based on an adversarial refiner. Generative Adversarial mechanism is adopted between the coarse-volumes generation stage and the refinement stage. In the coarse-volumes generation stage, a Context Aware Channel Attention Module (CA-CAM) and a VoxFocal Loss are designed to reconstruct the coarse-volume shapes as complete as possible. Furthermore, an adversarial refiner is proposed to adaptively optimize the coarse-volume shapes and remove redundant voxels by adding the discriminator into refiner. On the challenging large synthesis-image dataset ShapeNet and the real-world dataset Pix3D, ARShape-Net demonstrated significant quantitative and qualitative improvements compared with the state-of-the-art methods.
Characterizing and Injecting Faults in Executable Models Specified with fUML

Fault Injection (FI) is a well-known system verification technique, in which faults are artificially introduced into a system, to assess its behavior in exceptional conditions. FI can be applied at different levels, including physical, hardware and software. FI has also been applied at model level, although the amount of work in this direction is limited. However, the importance of models in the development of complex systems is growing, pushing towards model-level verification and sim-ulations. The Foundational UML (fUML) is a specific subset of UML, released as an OMG standard, that has a precise execution semantics and can therefore be executed. In this paper we investigate the application of FI to executable models specified with fUML. We first characterize the kind of fault that may be applied to fUML models, and then we realize an injection mechanisms based on automated model transformation. We apply the methodology to a simple example in the space domain, adapted from a real satellite system. We believe this is an important first step for the adoption of FI techniques on fUML models, for an early detection of design flaws. We conclude discussing some challenges and directions for future work.
ВИХОВНИЙ ЗМІСТ МУЗИКОТЕРАПІЇ

The paper highlights the impact of art and music on people medical and pedagogical practice. The concept is revealed / musictherapy, methods, forms, means and how it affects the emotional sensory sphere and physiological state of people. Techniques are considered musictherapy used in the classroom.
A Blockchain-based Protocol for Evidence Preservation in Hengxing vs. Zhenhai Case

In 2006, Hengxing and Zhenhai signed a transportation service agreement via fax. Then the two parties disputed the authenticity of the agreement and went to court. This involves the legality, factuality, and relativity of the evidence provided by the double-issue, and we can use the advantages of the blockchain to assist in the collection of judicial evidence. We confirm the identity through a security authentication protocol, and then store data including timestamps and hashes in the blockchain after verification, so as to provide judicial validity to the evidence data.
Smoothing Weight Distance to Solve Euclidean Distance Measurement Problems in K-Nearest Neighbor Algorithm

In this paper we propose a k-nearest neighbors (kNN) classifier optimized, called swd-kNN, which can improve the performance of the original kNN classifier. kNN has a weakness in the distance measurement function using Euclidean, where each data attribute is considered the same. The attribute weighting obtained by the dummy data value generation method as a weight calculation, the performance of WKNN is proven to outperform the performance of kNN. swd-kNN integrates min-max normalization by integrating the WKNN weighting model which is used for parameter weighting in the feature weighting function at Euclidean distance. In this study using forest fire dataset from UCI Machine Learning Repository. Results of the accuracy measurement show that the performance of swd-kNN is better than the kNN and WKNN algorithms.
HKDP: A Hybrid Approach On Knowledge Distillation and Pruning for Neural Network Compression

A popular method for shrinking over-parameterized networks nowadays is pruning, which can efficiently reduce the number of computational parameters and computational cost of the network and has almost the same high accuracy as the original network. The general weighted pruning algorithm can only reduce the number of parameters based on the original network structure, but cannot reduce the width and depth of the pruned network. While the knowledge distillation algorithm can solve the problem by compressing the network structure, it cannot make further modifications on the processed network. To further reduce the network structure, we propose a model compression algorithm, HKDP, a hybrid method combining knowledge distillation and network pruning that can significantly reduce the overall size of the network and maintain substantial accuracy. This approach obtains the advantages of knowledge distillation and pruning, which achieves 10 times higher compression rate and 2 percent higher accuracy than using either algorithm alone. Concretely, we apply a stage-wise knowledge distillation algorithm in the front that can quickly and efficiently reduce the original model structure; we also apply a Stochastic Gradient Descent (SGD) based pruning method and introduce the concept of global sparsity, which allows us to customize the compression rate of the model. Our experiments on CIFAR-10 and MNIST show that our hybrid optimization algorithm has higher model accuracy and model compression ratio compared to other competitors' network compression algorithms.
Methodological Principles for Deep Learning in Software Engineering

There are increasingly deep learning approaches being used in software engineering domain. While, there are no agreement on how to adopt deep learning in this new domain. This work presents methodological principles for deep learning in software engineering. Firstly, this work summarizes how to evaluate deep learning results in software engineering. Secondly, this paper suggest methodological principle can be used in this new scenario. We construct these methodological principles as a guideline for deep learning research in software engineering.
HierarIK: Hierarchical Inverse Kinematics Solver for Human Body and Hand Pose Estimation

Estimating the 3D positions of the body and hand joints (keypoints) of human is widely investigated in computer vision. However, without knowing the joint rotations, the joint positions only cannot fully determine the body and hand motions and cannot drive a full geometry model of human to move. Thus the well-known Inverse Kinematics (IK) problem is proposed to solve for the full joint rotations based on some position constraints of the keypoints. Due to the noisy keypoint estimation and the ambiguity in twist rotations (Twist rotations cannot be solved by keypoint positions as they do not lead to position changes of any keypoint.), unnatural poses and jitters are commonly seen in current IK results. In this paper, we present a novel real-time IK solver where a deep multi-stage neural network takes the hierarchy of the human kinematic tree into consideration to robustly solve for the joint rotations in a depth-wise manner. Qualitative and quantitative results show the superiority of our hierarchical IK solver over the optimization-based methods and the solutions based on fully-connected neural networks.
Hierarchical Concept Bottleneck Models for Explainable Images Segmentation, Objects Fine Classification and Tracking

Improving explainability of Computer Vision models based on Deep Learning has recently become a compelling problem, both to ensure reliable predictions to the end-user and to enable more fine-grained classifications. Recently, Concept Bottleneck models have been proposed for images classification, partitioning the problem in two stages and thereby defining a hierarchy of concepts. So far, however, little work has been done to investigate the applicability of this approach to other datasets with higher intra-class variability and ambiguity, and to discuss its flexibility to tasks different from whole-images classification.In this work we develop and discuss a Concept Bottleneck model for images segmentation, objects fine classification and tracking, and compare it to more classical methods based on Mask R-CNN and images similarity algorithms.All our models are trained and tested on a dataset comprised of pictures of fridges filled with various objects.The proposed model makes full use of the hierarchy in concepts, exploiting the relationships between different categories at the same hierarchical level and relying on a novel method for handling multi-labels classifications. We show that the performance on fine classification is on par with a regular Mask R-CNN, but with a significant increase in explainability and in handling classes confusion. We also demonstrate the effectiveness of the derived Concept Bottleneck features on related tasks, i.e., the tracking of objects between consecutive pictures in a sequence.
The Construction of Smart Campus in the Environment of Internet of Things Cloud Computing and Big Data

With the progress of science and technology in modern society and the rapid development of information technology, colleges and universities now generally carry out information construction and gradually enter the era of big data information. In the new era, big data, as a new database processing technology, plays a very important role in the construction of smart campus. The information construction of colleges and universities has developed from the original digital campus to the present stage of intelligent campus. Internet of things, cloud computing, big data, mobile Internet, and other technologies are all popular in smart campus. In today's big data background, people need to build smart campus in the way of innovative and innovative thinking in work, life, and learning. On the other hand, the construction of smart campus will certainly make the reform of college education and teaching more in-depth and effective.
Identifying Competitive Attributes Based on an Ensemble of Explainable Artificial Intelligence

Competitor analysis is a fundamental requirement in both strategic and operational management, and the competitive attributes of reviewer comments are a crucial determinant of competitor analysis approaches. Most studies have focused on identifying competitors or detecting comparative sentences, not competitive attributes. Thus, the authors propose a method based on explainable artificial intelligence (XAI) that can detect competitive attributes from consumers’ perspectives. They construct a model to classify the reviewer comments for each competitive product and calculate the importance of each keyword in the reviewer comments during the classification process. This is based on the assumption that keywords significantly influence product classification. The authors also propose an additional novel methodology that combines various XAI techniques such as local interpretable model-agnostic explanations, Shapley additive explanations, logistic regression, gradient-based class activation map, and layer-wise relevance propagation to build a robust model for calculating the importance of competitive attributes for various data sources.
Simulating the Cognitive Thought Process with a Modified Genetic Algorithm

Cognitive Computing is a developing paradigm with its applications found in almost every field. It aims to develop computing methodologies and systems inspired by mind’s capabilities. A thought is the most fundamental capability of the mind. Hence it is important in the cognitive computing to understand the Thought Process. The purpose of this paper is to develop a computer model that simulates the Thought Process. The paper proposes that the Genetic Algorithm (GA) can be used for the same. Both cognitive model and computer model of the thought process with the help of GA has been given. In the computer model, a modification of GA has been implemented, which consists of a new crossover operator called Learning Crossover operator. The new crossover operator is not a replacement but is a supplement to the existing crossover operators. The GA is implemented over the Travelling Salesperson Problem (TSP), which is a classical NP problem and hence the algorithm is possible to be implemented on any other problem. The modification to the GA aims to improve the Thought Process Simulation. But it can also improve the performance of GA, when further explored.
Freedom of Conscience and COVID-19 Vaccination

Compulsory COVID vaccination is a timely question to ask as more and more countries introduce it. There is a growing body of case law and literature on child vaccination against a number of well-known diseases,[1] yet the current issue involving the compulsory vaccination of adults against COVID-19 presents a new case. I hypothesise as follows: (a) compulsory vaccination is constitutional, under certain conditions; (b) alternative behaviour must be tolerated if it produces the same end. I verify these hypotheses by analysing the role of conscience in vaccinations in general and in COVID-19 vaccination in particular. I consider the Hungarian context, but the conclusions might apply to other countries as well. The key issue is the extent to which the government should respect individual conscience during a pandemic. I first discuss what conscience is in legal terms. Second, I discuss the legal nature and background of COVID-19 vaccination. Third, I describe the decision of the Hungarian Constitutional Court on mandatory vaccination and compare the current situation with the previous one. Fourth, I analyse the outcome of the ‘comparative test of burdens’. Finally, I summarise my conclusions.
Design of Optical Fiber Goniometer Having Physiotherapeutic Application for Measurement of Tilt in Human Joints

The existing device used in the medical field to measure the extent of flexibility of a joint is a goniometer with a fixed hinge of rotation. As we know that the human body is not a flat surface, therefore the readings obtained from the goniometer must be approximated. The major focus here is to eliminate the fixed hinge of rotation and provide accurate readings. The project works on the principle of macrobending losses. Laser is monochromatic, directional, and coherent. When laser is passed through an optical fiber which is bent at a certain angle, attenuation in the signal is observed at the output. This attenuation can be correlated to the degree at which it is bent. The thought behind this project is to find the tilt of complex human joints primarily in the physiotherapeutic field and articular movement of the joints. The optical fiber losses will help in finding the precise reading of the angle.
Difficulties and Countermeasures of Information Literacy Promotion of Rural Specialized Teachers

As a group of teachers specially recruited and trained for the development of rural compulsory education, rural specialized teachers (RST) are of great significance for improving the level of rural compulsory education, the comprehensive quality of teachers and the quality of talent training. However, in the face of the sudden outbreak of COVID-19, the low information literacy of RST has been exposed, and the quality of rural teaching has been greatly impacted. Based on this, this article will explore the significance of improving the information literacy of RST and the reasons for the low level, reveal the impact of the epidemic on the information literacy of RST, and provide suggestions for improving the information literacy.