Reviewerly/exHarmony huggingface dataset:
Sentiment Analysis Model Based on Text to Solve Work Stress Using Deep Learning

In workplaces, stress is normal. Stress is both physical and mental. It may be caused by major events such as a change in responsibilities or expectations at work, and job promotions, loss, or changes. Smaller, daily events also cause stress. This stress is not that apparent, but the constant and cumulative impact of the small stressors adds up to a big impact. This big impact affects a worker's physical and mental sense of well-being. Too much stress can cause physical, emotional, and behavioural problems. It can cause defensiveness, lack of motivation, difficulty concentrating, accidents, reduced productivity, and interpersonal conflict. Minor problems such as sleep-loss, irritability, backaches, or headaches may also happen. When employees suffer from the problems mentioned above, they are unable to work with high efficiency. Sometimes, managers may discover that the productivity of the company has declined but do not know the reasons. Since the causes of stress are various among employees, it is hard for managers to solve all the potential problems without receiving reviews from employees, which is an essential ingredient for the long-term success of a company or an organisation.
Advertisement monitoring system based on C++

According to the characteristics and requirements of advertising monitoring, in order to automatically monitor the advertisements broadcast on radio and TV, this paper mainly studies the design of the advertising monitoring system based on voice recognition and the design of the voice recognition algorithm of advertisements. This paper discusses the design method of advertising monitoring system based on speech recognition technology, and studies the speech recognition algorithms used in the speech recognition process, including the preprocessing of speech signals, endpoint detection, feature value extraction, and speech recognition algorithms. At the same time, based on the design requirements of the advertising monitoring system, this system is developed based on C++.
AnyTRNG: Generic, High-Throughput, Low-Area True Random Number Generator Based on Synchronous Edge Sampling

In this paper, we present a generic, high-throughput, and low-area true random number generator (TRNG) architecture based on synchronous edge sampling. Our approach exploits the entropy source from the jitter introduced by the clock generator with high precision sampling. The substantial gain in throughput is mainly contributed by the novel idea of using a synchronous tapped delay, ensuring the sampling is performed precisely on the signal edge. Furthermore, the jitter source can be utilized from any clock source, making our architecture generic and compatible with cloud-based field-programmable gate array (FPGA), which prohibits combinatorial loops circuits conventionally used to build a digital oscillator. We evaluate our design by implementing it on the Xilinx Artix-7 FPGA, which yields a throughput of 100 Mbps and requires only four look-up tables (LUTs) and seven flip-flops (FFs). Lastly, our proposed architecture, namely the AnyTRNG, is further validated using the NIST 800-90B entropy assessment, as well as NIST 800-22 statistical test and Dieharder, which achieves an approximate min-entropy of 0.9996 per bit.
'Could You Describe the Reason for the Transfer?'

With the booming of the Internet finance and e-payment business, telecom and online fraud has become a serious problem which grows rapidly. In China, 351 billion RMB (approximately 0.3% of China's GDP) was lost in 2018 due to telecommunication and online fraud, influencing tens of millions of individual customers. Anti-fraud algorithms have been widely adopted by major Internet finance companies to detect and block transactions induced by scam. However, due to limited contextual information, most systems would probably mistakenly block the normal transactions, leading to poor user experience. On the other hand, if the transactions induced by scam are detected yet not fully explained to the users, the users will continue to pay, suffering from direct financial losses.
Role and Significance of Reflection in Educational Management

espanolEl articulo examina la reflexion como fenomeno socio-psicologico de la gestion educativa. Se demuestra que la reflexion, como proceso basico de las actividades de gestion, esta asociada a las actividades, el pensamiento y la comunicacion. Se estudian los componentes de la estructura de motivacion y fijacion de objetivos, como la motivacion y la fijacion de objetivos, el marco informativo de las actividades, la toma de decisiones y la ejecucion de las actividades, la reflexion sobre el plan de accion, la ejecucion de las acciones de gestion y la reflexion sobre los resultados obtenidos. EnglishThe article examines reflection as a socio-psychological phenomenon of educational management. It is demonstrated that reflection, as a basic process of management activities, is associated with activities, thinking and communication. Such components of motivational and goal-setting structure as motivation and goal-setting, information framework for activities, decision making and implementation of activities, action plan reflection, management action implementation, reflection of achieved results are studied.
The Advantage of Augmented Reality Technology for Supporting Learning Media in Ethics Learning

Ethics is defined as a set of moral principles that distinguish what is right and what is wrong. The unethical actions referred to here are actions that violate the ethics that apply in the environment. To find out about ethical violations in society, ethical awareness is needed to determine what to do to deal with offenses committed by a person or a group. Ethics learning is needed to increase ethical sensitivity. One of the most effective learning media is using augmented reality (AR) technology. AR is a concept that blends virtual reality with real-world reality (AR), and it is a natural way to experience 3D objects and data. Virtual things in two dimensions (2D) or three dimensions (3D) tend to be organic and unified with the real world. The purpose of this study is to answer the question of whether simulation-based AR technology can improve learning effectiveness and increase ethical sensitivity in students by conducting studies of relevant studies.
Locating and Identifying Codes in Circulant Graphs

Identifying and locating-dominating codes have been studied widely in circulant graphs. Recently, Ville Junnila et al. (Optimal bounds on codes for location in circulant graphs, Cryptography and Communications; 2019) studied identifying and locating-dominating codes in circulants <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <msub> <mrow> <mi>C</mi> </mrow> <mrow> <mi>n</mi> </mrow> </msub> <mfenced open="(" close=")" separators="|"> <mrow> <mn>1</mn> <mo>,</mo> <mi>d</mi> </mrow> </mfenced> </math> , <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <msub> <mrow> <mi>C</mi> </mrow> <mrow> <mi>n</mi> </mrow> </msub> <mfenced open="(" close=")" separators="|"> <mrow> <mn>1</mn> <mo>,</mo> <mi>d</mi> <mo>−</mo> <mn>1</mn> <mo>,</mo> <mi>d</mi> </mrow> </mfenced> </math> , and <math xmlns="http://www.w3.org/1998/Math/MathML" id="M3"> <msub> <mrow> <mi>C</mi> </mrow> <mrow> <mi>n</mi> </mrow> </msub> <mfenced open="(" close=")" separators="|"> <mrow> <mn>1</mn> <mo>,</mo> <mi>d</mi> <mo>−</mo> <mn>1</mn> <mo>,</mo> <mi>d</mi> <mo>,</mo> <mi>d</mi> <mo>+</mo> <mn>1</mn> </mrow> </mfenced> </math> . In this paper, identifying, locating, and self-identifying codes in the circulant graphs <math xmlns="http://www.w3.org/1998/Math/MathML" id="M4"> <msub> <mrow> <mi>C</mi> </mrow> <mrow> <mi>n</mi> </mrow> </msub> <mfenced open="(" close=")" separators="|"> <mrow> <mi>k</mi> <mo>,</mo> <mi>d</mi> </mrow> </mfenced> </math> , <math xmlns="http://www.w3.org/1998/Math/MathML" id="M5"> <msub> <mrow> <mi>C</mi> </mrow> <mrow> <mi>n</mi> </mrow> </msub> <mfenced open="(" close=")" separators="|"> <mrow> <mi>k</mi> <mo>,</mo> <mi>d</mi> <mo>−</mo> <mi>k</mi> <mo>,</mo> <mi>d</mi> </mrow> </mfenced> </math> , and <math xmlns="http://www.w3.org/1998/Math/MathML" id="M6"> <msub> <mrow> <mi>C</mi> </mrow> <mrow> <mi>n</mi> </mrow> </msub> <mfenced open="(" close=")" separators="|"> <mrow> <mi>k</mi> <mo>,</mo> <mi>d</mi> <mo>−</mo> <mi>k</mi> <mo>,</mo> <mi>d</mi> <mo>,</mo> <mi>d</mi> <mo>+</mo> <mi>k</mi> </mrow> </mfenced> </math> are studied, and this extends Junnila et al.’s results to general cases.
Face Recognition with Disguise and Makeup Variations Using Image Processing and Machine Learning

Face recognition is a research challenge continuous and has seen colossal development during the last two decades. While coming algorithms keep on achieving improved the performance, a greater part of the face recognition systems are receptive to failure under disguise and makeup variations that is one of the common challenging covariates of facial recognition. In past researches, some algorithms show promising results on the existing disguise datasets, still, most of the disguise datasets include images with limited variations (oftentimes captured in controlled settings). This not simulate a real-world scenario, wherever both the intended/ unintended unconstrained disguises and makeup are encountered by a face recognition systems. In this paper, the disguised and makeup faces database (DMFD) is used. In order to handle this problem, One of simple, yet efficient ways for extracting face image features is (LBPH), Principal Component Analysis (PCA) that was majorly utilized in pattern recognition. Also, the technique of Linear Discriminant Analysis (LDA) employed for overcoming PCA limitations was efficiently used in face recognition. Further, classification is employed following the feature extraction. The Naïve Bayes, KNN and Random forest RF algorithms are used. The results paper show the effectiveness and generalization of the proposed system on the Disguise and makeup face database (DMFD) and the features which are extracted by means of (LDA) with (RF) provided the better results of (F-measure, Recall, and Precision).
Neural Network based on Automatic Differentiation Transformation of Numeric Iterate-to-Fixedpoint

This work proposes a Neural Network model that can control its depth using an iterate-to-fixed-point operator. The architecture starts with a standard layered Network but with added connections from current later to earlier layers, along with a gate to make them inactive under most circumstances. These &#x201C;temporal wormhole&#x201D; connections create a shortcut that allows the Neural Network to use the information available at deeper layers and re-do earlier computations with modulated inputs. End-to-end training is accomplished by using appropriate calculations for a numeric iterate-to-fixed-point operator. In a typical case, where the &#x201C;wormhole&#x201D; connections are inactive, this is inexpensive; but when they are active, the network takes a longer time to settle down, and the gradient calculation is also more laborious, with an effect similar to making the network deeper. In contrast to the existing skip-connection concept, this proposed technique enables information to flow up and down in the network. Furthermore, the flow of information follows a fashion that seems analogous to the afferent and efferent flow of information through layers of processing in the brain. We evaluate models that use this novel mechanism on different long-term dependency tasks. The results are competitive with other studies, showing that the proposed model con-tributes significantly to overcoming traditional deep learning models&#x0027; vanishing gradient descent problem. At the same time, the training time is significantly reduced, as the &#x201C;easy&#x201D; input cases are processed more quickly than &#x201C;difficult&#x201D; ones.
Textual Adversarial Attacking with Limited Queries

Recent studies have shown that natural language processing (NLP) models are vulnerable to adversarial examples, which are maliciously designed by adding small perturbations to benign inputs that are imperceptible to the human eye, leading to false predictions by the target model. Compared to character- and sentence-level textual adversarial attacks, word-level attack can generate higher-quality adversarial examples, especially in a black-box setting. However, existing attack methods usually require a huge number of queries to successfully deceive the target model, which is costly in a real adversarial scenario. Hence, finding appropriate models is difficult. Therefore, we propose a novel attack method, the main idea of which is to fully utilize the adversarial examples generated by the local model and transfer part of the attack to the local model to complete ahead of time, thereby reducing costs related to attacking the target model. Extensive experiments conducted on three public benchmarks show that our attack method can not only improve the success rate but also reduce the cost, while outperforming the baselines by a significant margin.
“PollPub”: Secure and Dynamic Polling Platform Using Blockchain

Polls are simply a measurement tool that tells us how a population thinks and feels about any topic and getting an accurate polling result would be conducive to make the correct decisions in many domains. This research is aimed at providing a well secured and user-friendly online polling platform that has been developed to provide a sophisticated and reliable result compared to the paper-based surveys and other existing methods. The implemented platform is enabled for the public or non-probability sample of participants to express the views and thoughts in the form of voting. Blockchain technology, which is decentralized, ultra-transparent, based on distributed ledger technology (DLT) that records the provenance of a digital asset through the use of cryptographic hashing, has been used to improve the overall resilience of polling system. The proposed scheme satisfies the fundamental polling method criteria and achieves end-to-end verifiability. The Implemented system is built up by implementing a secured platform to avoid unauthorized access to prevent altering data and to remove fake accounts/logins by verifying the accounts. Additional features such as poll recommendation according to the user preferences and analysis of opinion polls have been implemented. This platform would be advantageous to many facets of society in governance and decision-making.
The Study of Automatic Picking of P and S Wave Arrival and Identification of Earthquake Sequence Pattern using Scalogram in Obspy (Python)

Abstract Initial identification on an earthquake record (seismogram) is something that needs to be done precisely and accurately. Moreover, the discovery of a series of unexpected successive earthquake events has caused unpreparedness for the community and related agencies in tackling these events. Determining the arrival time of the P and S waves becomes an important parameter to finding the location of the earthquake source (hypocenter) as well as further information related to the earthquake event. However, manual steps that are currently often used are considered to be less effective, because it requires a lot of time in the process. Continuous Wavelet Transform (CWT) analysis can be a solution for this problem. With further CWT analysis in the form of a scalogram, can help to determine the arrival time of P and S waves automatically (automatic picking) becomes simpler. In addition, further CWT analysis can also be utilized to help identify the sequence of earthquake events (foreshock, mainshock, aftershock) through the resulting scalogram pattern.
Steganography Algorithm for Voice Transmission in VHF Band

The paper presents a proposal of a steganographic algorithm for voice transmission in VHF (very high frequency) band. We also describe the terminology used in the field of information hiding, steganographic methods based on the signal domain in which information is embedded, and the human sense of hearing along with an analysis of the possibility of using it to hide data. As a result, we propose the steganographic method based on the algorithm of data encoding and extraction. The coding algorithm uses a mechanism of limited high-frequency sound reception by the human sense of hearing and is additionally exposed to white noise, which perceptually masks information. The proposed approach exploits two weaknesses of the human psychoacoustic model, which is a new way of hiding information. The decoder uses a fast Fourier transformation to detect embedded data and uses the Hamming time window to improve data recovery. Data embedding and extraction tests have been conducted for the encoder and decoder. The operation of the encoder and decoder was also tested in a real radio track.
Dual graph wavelet neural network for graph-based semi-supervised classification

Vertex classification is an important graph mining technique and has important applications in fields such as social recommendation and e-Commerce recommendation. Existing classification methods fail to make full use of the graph topology to improve the classification performance. To alleviate it, we propose a Dual Graph Wavelet neural Network composed of two identical graph wavelet neural networks sharing network parameters. These two networks are integrated with a semi-supervised loss function and carry out supervised learning and unsupervised learning on two matrixes representing the graph topology extracted from the same graph dataset, respectively. One matrix embeds the local consistency information and the other the global consistency information. To reduce the computational complexity of the convolution operation of the graph wavelet neural network, we design an approximate scheme based on the first type Chebyshev polynomial. Experimental results show that the proposed network significantly outperforms the state-of-the-art approaches for vertex classification on all three benchmark datasets and the proposed approximation scheme is validated for datasets with low vertex average degree when the approximation order is small.
Application of Constant-Weight Code ‘1-out-of-4’ while Synthesis of Self-Checking Combinational Devices

This paper is being dedicated to Boolean Complement Method development reckoning synthesis of self-checking digital devices and systems. Here were highlighted specialties of constant-weight code ‘1-out-of-4’ application for synthesis of embedded control circuits of combined logical devices via Boolean Complement Method. We described basic organization structures of embedded control systems by means of Boolean Complement Method till constant-weight code 1-out-of-4’. Several options of control circuits arrangement were considered by us. Initial one is concentrated on devices control with disengagement of four exits’ and implementation of three exits transformation out of four per basic structure. Second option of control circuit organization is Boolean Complement Method till constant-weight code ‘1-out-of-4’, which means the search of triple exits’ with addition it to four-digit coded word ‘ ‘1-out-of-4’ via actual connection of logical control segment to tester input without signal transformation. Third alternative is related to application of basic structure of prior signal compression from being diagnosed site and control of reduced exits quantity. Within this paper several results of experimental research are being presented per each option of embedded control circuits with indices estimation. The event of insertion of single permanent malfunction will be discussed relating difficulties of technical structures realization as well as possibility of errors definition while data input procedure per being supervised site. In Boolean Complement Method for many cases allows us to fulfill synthesis for more simple self-checking devices, compare to implementation of Duplication Method with high level of errors definition on outputs of being monitored site
Application of Artificial Intelligence in Arrangement Creation

The growing maturity and innovation of information technology has promoted the development of artificial intelligence technology to a certain extent, and has been widely welcomed in all walks of life. Artificial intelligence technology can achieve a certain purpose by imitating people's thinking and behavior, which can make matter more intelligent. For example: smart home, driverless, speech recognition and automatic production are products of the era of artificial intelligence. In the field of music, more and more music engineers begin to apply artificial intelligence technology to music arrangement, which makes the form of music arrangement more diversified and intelligent. Therefore, we should strengthen the application of artificial intelligence technology in music composition, to ensure the maximum play out of its application advantages, and provide better services for the development of music.
Leakage-Resilient and Proactive Authenticated Key Exchange (LRP-AKE), Reconsidered

In [31], Shin et al. proposed a Leakage-Resilient and Proactive Authenticated Key Exchange (LRP-AKE) protocol for credential services which provides not only a higher level of security against leakage of stored secrets but also secrecy of private key with respect to the involving server. In this paper, we discuss a problem in the security proof of the LRP-AKE protocol, and then propose a modified LRP-AKE protocol that has a simple and effective measure to the problem. Also, we formally prove its AKE security and mutual authentication for the entire modified LRP-AKE protocol. In addition, we describe several extensions of the (modified) LRP-AKE protocol including 1) synchronization issue between the client and server's stored secrets; 2) randomized ID for the provision of client's privacy; and 3) a solution to preventing server compromise-impersonation attacks. Finally, we evaluate the performance overhead of the LRP-AKE protocol and show its test vectors. From the performance evaluation, we can confirm that the LRP-AKE protocol has almost the same efficiency as the (plain) Diffie-Hellman protocol that does not provide authentication at all.
Truncated attention mechanism and cascade loss for cross-modal person re-identification

Person re-identification with natural language description is a process of retrieving the corresponding person’s image from an image dataset according to a text description of the person. The key challenge in this cross-modal task is to extract visual and text features and construct loss functions to achieve cross-modal matching between text and image. Firstly, we designed a two-branch network framework for person re-identification with natural language description. In this framework we include the following: a Bi-directional Long Short-Term Memory (Bi-LSTM) network is used to extract text features and a truncated attention mechanism is proposed to select the principal component of the text features; a MobileNet is used to extract image features. Secondly, we proposed a Cascade Loss Function (CLF), which includes cross-modal matching loss and single modal classification loss, both with relative entropy function, to fully exploit the identity-level information. The experimental results on the CUHK-PEDES dataset demonstrate that our method achieves better results in Top-5 and Top-10 than other current 10 state-of-the-art algorithms.