Reviewerly/exHarmony huggingface dataset:
Parallel Computer For Face Recognition Using Artificial Intelligence

We implemented a facial recognition application with AI. We used the VGGFace model for our neural net to identify faces. The application includes training and recognizing. The training part is to add new faces to our system, while the recognizing part is to determine the identity of a face. The application runs on multiple cores and able to scale with different numbers of cores. The implementation for parallelism uses tensorflow. For performance measurements, we used the Task Manager application found in Windows with special option known as `affinity to choose the number of cores to run the application. The results show that our system scales in performance with number of processors up to twelve.
A Study on Methods of Automatic Extraction of Korean-Language Modification Relationships for Sentiment analysis

The most of sentiment analysis on the user reviews only aim to classify the review as overall positive, negative, and neutral sentiment in terms of the overall polarity score. Such overall sentiment score cannot provide detailed information. This study proposes a method of automatic extraction of features and their modifying sentiment words in user reviews, thus a recommendation system provide more detailed sentiment and information. To parse such sentiment and information, we propose a general method for extracting modification relations between nouns and adjectives in Korean sentences, which are corresponding to features and their modifying (sentiment) words, respectively.
Artificial Neural Networks (ANN)-based Seismic Signal Source Identification from MEMS Accelerometer Measurements

This paper reports initial findings on Artificial Neural Networks (ANN)-based methods to identify seismic signal source using only low-quality MEMS accelerometer signals. We collected annotated signals sampled at 50 Hz from several mobile devices and built an open source linear acceleration dataset. From this dataset, we performed and compared several feature extraction techniques based on signal length and spectrogram parameters. We trained the resulting features using three-layer neural networks with a hidden layer of 32 neurons and a dropout layer. Training was performed using Tensorflow 2.0 and Google Colaboratory with GPU backend. The results show 95.83% validation accuracy from spectrogram signals with length of 2 seconds and 3 seconds, and 79.17% validation accuracy from raw amplitude inputs.
Moodle-Based Speaking Learning Model

Abstract This study is aimed to developing a Moodle-based speaking learning model for Students of Technology Department at Teacher Training and Education Higher School of Muhammadiyah Kuningan. Moodle is one of the Learning Management System. It is developed by lecturers in making teaching and learning well. The English materials given is English for Specific Purposes. It tends to the English learning given to the non-English department students. Teaching ESP is started by diagnosing the situations of class by means of need analysis based on Dudley-Evans and St. John’s Model. Dudley-Evans and St John’s model consists of seven aspects; personal information about learners, language information about target situation, learners’ lack, learners’ needs from course, language learning needs, how to communicate in the target situation, and professional information about learners. The steps are based on Borg and Gall’s model. The steps are collecting data, planning, developing first draft of product, testing the developed product, revising the developed product and revising the final draft of product. The data is collected by questionnaire, interview, group discussion, observation, documentation, and speaking test. Quantitative and qualitative are used in analyzing data. The result of this research is the effectiveness of learning speaking by using Moodle. Theexperimental research is used to investigate the effectiveness of Moodle in learning. It indicates that the Moodle-based Speaking Learning Model improves the students’ speaking skill at ESP Lecture for the Students of Teacher Training and Education Higher School of Muhammadiyah Kuningan.
A Study on QoS based Task Scheduling using Meta Heuristic Algorithms in Cloud Environment

Cloud computing is considered to be a predominant technology in the course of events occurring and has an enchanting contribution in software and hardware setup. In cloud environment the performance improvement is highly dependent on features like load balancing and task scheduling. The major issue in cloud computing is task scheduling which leads to reduction in the performance of the system. Efficient resource scheduling algorithm is required in order to resolve this low performance issue. Through which the clients and users can demand services based on pay-as-you-go basis. There are numerous algorithms proposed especially for explaining load balancing and task scheduling. Since cloud infrastructure is based on huge client’s requirement, appropriate decision is required for each and every scheduled job. This paper illustrates a detailed study on huge algorithms which are explained to resolve the common issues taking place in various scheduling of resources.
Research on Optimizing Recovery Strategy of Distribution Network Communications System in Disaster Based on Mobile Ad-Hoc Networks

Typhoon and other natural disasters will cause great damage to the distribution network communication system in the area where it passed, affect the damage statistics of distribution network power, and also affect the speed of repair of distribution network. To realize the rapid recovery of the distribution network communication system, an optimal recovery strategy based on Mobile Ad-Hoc Networks is proposed. Firstly, the multivariate data of distribution network is analyzed to determine the state of distribution network communication in disaster. Secondly, according to whether the distribution network communication system needs to be temporarily restored, the restoration method of distribution network communication system is decided. Then, the optimal recovery strategy of distribution network communication system is studied by using Mobile Ad-Hoc Networks characteristics, and the target models are established. Furthermore, the optimal recovery scheme is found according to each target. The proposed method is applied to the IEEE-14 structure of communication grid. The result demonstrates that the proposed method is effective and efficient and is flexible in application.
Application of virtual reality systems to psychology and cognitive neuroscience research

With the advancement of virtual reality, it is extensively used in the field of the cognitive neuroscience. They are used by many numbers of developers and researchers for the visualization of behavioral aspects of individuals with the help of programing in it. With the help of the virtual reality, things such as heartbeats, eye tracking, and gaming are visualized based on users’ emotional capability through spatial navigation techniques and anxiety disorder technique and others. The actions are visualized with the help of various tools creating a library of three-dimensional models. The Graphical User interface (GUI) and the custom systems differ on the level of their interaction and utility. Those are implemented using python, reducing the level of complexity for a user. The outcomes can be improved by the help of the principles like Artificial Intelligence and deep learning
An application of support vector machine to detect anomalies in time series data

Maintaining the equipment in a good performance is mandatory for business. It needs to reduce the production cost, minimize downtime, maintain product quality, safety and reduce risks. The maintenance department shall predict whether the equipment may fail in near future to minimize downtime. Parameters data which we used in this paper are temperature and vibration. We used anomaly detection to predict failure of the equipment. Anomaly detection is one of the methods which can be used to predict the failure. It detects anomalies in time series data with evenly time-spaced numerical values. Paper proposes to use Support Vector Machine method to detect anomalies. This testing was done by using Azure Machine Learning Development Studio simulation result shows that support vector machine method can detect anomalies with accuracy 0.906.
Multi-Bernoulli Filter for Tracking Multiple Targets Using Sensor Array

This paper presents a multi-Bernoulli filter for tracking the direction of arrival (DOAs) of time-varying number of targets using sensor array. Our method operates directly on the measurements of sensor array and does not require any detection. Firstly, more information is reserved and compared with the after-detection measurements using a finite set of detected points. It can significantly improve the tracking performance, especially in low signal-to-noise ratio. Secondly, it inherits the advantages of the multi-Bernoulli approximation which models each of the targets individually. This allows more accurate multi-target state estimation, especially when targets cross. The proposed filter does not need clustering step and simulation results showcase the improved performance of the proposed filter.
Increase Performance Genetic Algorithm In Matching System By Setting GA Parameter

Matching system is a system that finding an object base on what we are looking for. In this research, the object is a paragraph. Paragraph matching is a process of finding a paragraph which is inputted as a target paragraph. The previous worked on this filed is that focus on finding a sentence. It shows that it took 490 generations or iterations in order to find the sentence, which mean it is not so fast or takes more times. This paper intends to bring into expand research which means from sentence matching into paragraph matching. It means that there are two points to bring about that is paragraph matching and increase performance. On this research used genetic algorithm to get best speed of finding the sentence through several genetic algorithm components that is population, selection, crossover, and mutation. In implementation side, it sets the parameter value of genetic algorithm that is chromosome = 10, 20, 50, crossover probability=0.5 and mutation probability=0.05. The outcome of this research is to give a better approach of finding a paragraph accurately and also faster in term of speed. The final result is that the generation or iterations is 144.
An Autonomous UAV Navigation System for Unknown Flight Environment

Autonomous navigation systems on unmanned aerial vehicles (UAVs) equipped with multiple sensors are essential to various applications in the smart city and intelligent transportation. However, the general autonomous navigation models are markedly influenced by the prior knowledge from training environments, which in turn are not applicable in unknown environments. To address this issue, we propose an online autonomous UAV navigation system named as multi-sensor data-fusion-based autonomous navigation (MDFAN) system for unknown flight environments, including the collision avoidance and path planning. Specifically, first, the newly MDFAN system formulates the navigation problem as a decision-making path planning problem to reduce the dependence of prior knowledge of the flight environment. Secondly, we develop a multi-sensor data-fusion-based method to extract more effective local environment information for mining the inherent inter-relationship between the local environment information and the current state of the UAV. Thirdly, we propose a deep reinforcement learning method for handling uncertain situations of the unknown environment. Finally, we validated our method both on the simulated and real-world environments.
Deep learning based Character-level approach for Morphological Inflection Generation

In this paper, we present our work on morphological inflection generation of Sanskrit using a deep learning approach. Sanskrit is a morphologically rich language which came into use from the Vedic period. A basic understanding of the language formation is needed to study the abundant literature in it. Here a computational model for word formation in Sanskrit is proposed using deep learning based models. They are applied here to attain the morphological changes that a root word undergoes to result in the surface form. The approach is in character level so as to capture the character level transformations. The best performance was obtained from the Bidirectional Gated Recurrent Unit architecture with an accuracy of 98.42% and an F1-Score of 0.9838. This model is purely dependent on the dataset and does not require any external linguistic resources.
Deceptive Jamming Recognition Based on Wavelet Entropy And RBF Neural Network

Abstract The development of digital radio frequency memory has made the technology of deceptive jamming get a qualitative leap. The real-time and immersiveness are greatly improved, which makes it difficult to manually judge the type of deceptive jamming. Aiming at the problem of automatic identification of deceptive jamming, a method based on wavelet entropy for deceptive jamming is studied. Firstly, three mathematical models of deceptive jamming patterns are combed, then wavelet analysis is performed on the signal and wavelet entropy is extracted, and then sent to the radial basis function (RBF) neural network for automatic classification. The simulation results show that the proposed method has a high recognition rate for deceptive jamming patterns, which relatively less affected by the Jamming-to-Noise Ratio (JNR), and has a faster calculation velocity and better real-time performance.
EPSO enhanced by adaptive scaling and sub-swarms

This paper reports the positive results derived from adopting two variants for the EPSO - Evolutionary Particle Swarm Optimization method: variable's re-scaling and sub-swarms. Sub-swarms launched from the main swarm can be applied to intensify the search in promising regions of the space. Alternatively, the information regarding the dispersion of the particles along the search space can be used to create local landscapes with a spherical/ellipsoid form in an attempt to take advantage of the excellent convergence properties of metaheuristics for spherically-shaped optimization problems. The net improvement in reducing computing effort is observed in several unconstrained optimization problems and verified with ANOVA.
Evaluation of neural networks with data quantization in low power consumption devices

Image recognition is frequently carried out using a particular type of neural network called Convolutional Neural Networks (CNNs). Still, in general, any neural network requires powerful computational resources that are expensive and demand very high power consumption. It is a challenge to determine the best low power consumption devices and configuration parameters to train and run CNNs for image processing and recognition. These devices do not have the same capabilities as a computer, and the design and implementation of CNN algorithms should be adapted to reduce their size and computation time without affecting the performance and accuracy of results. Therefore, this research evaluates four CNN models with different data quantization to check their accuracy, processing, memory usage, model complexity, and inference time in four different low-power hardware platforms with varied computational capacity. The results showed that the efficiency obtained by each neural network in the classification tasks increases as the model complexity also increases, but the reduction of the model could also produce acceptable outcomes without critically affecting precision.
Application Analysis and Prospect of Cloud Platform in Operation Control of New Energy Power System

With the rapid development of large-scale renewable energy sources such as wind power and photovoltaic (PV) power generation, the power system presents a trend that renewable energy has become the main power source, named as the renewable energy power system. There are many kinds of power sources involved in the renewable energy power system, and the operation and control characteristics of different power sources vary greatly. The scale of system analysis and calculation is large, and the progress of calculation is difficult, symbolizing that cloud computing technologies and platforms are urgently needed. This paper firstly introduces the characteristics and architecture of a cloud computing platform. Secondly, it summarizes the frequency and voltage control technology for power system with renewable energy. Thirdly, it analyses the preliminary application modes of cloud platform in power system. Finally, it provides the follow-up research suggestions and prospects from five aspects: power flow calculation, frequency control, reactive power/voltage control, multi-energy system control and information security.
Research on Dynamic Task Planning of UAV Based on Ant Colony Algorithm

This paper utilizes the contract network protocol to achieve UAV dynamic mission assignment based on the basic biding procedure. Path planning is a very important sub-problem in UAV dynamic task assignment, therefore it is worth discussing. As a type of intelligent optimization algorithm, the ant colony algorithm is frequently used in UAV path planning because of its fine characteristics of self-organization, parallelism, positive feedback and robustness. This paper uses ant colony algorithm to replan the UAV flight path during the dynamic mission allocation according to the required length cost in the biding.
Experimental Progress on Quantum Communication with Quantum Dot Based Devices

Quantum communication is a key branch in the field of quantum information. Quantum dot devices have very promising applications in quantum communication due to the advantage in emitting single photon with good purity and high generation rate. In this chapter, a brief introduction of quantum communication, i.e., quantum key distribution, quantum teleportation, and atom–photon entanglement, is given. The experimental progresses in applying quantum dot devices in these directions are summarized. In each direction, a typical example is introduced in details to show clearly the main principles and technology involved in these experimental progresses.
Higher-Ranked Annotation Polymorphic Dependency Analysis

Abstract The precision of a static analysis can be improved by increasing the context-sensitivity of the analysis. In a type-based formulation of static analysis for functional languages this can be achieved by, e.g., introducing let-polyvariance or subtyping. In this paper we go one step further by defining a higher-ranked polyvariant type system so that even properties of lambda-bound identifiers can be generalized over. We do this for dependency analysis, a generic analysis that can be instantiated to a range of different analyses that in this way all can profit. We prove that our analysis is sound with respect to a call-by-name semantics and that it satisfies a so-called noninterference property. We provide a type reconstruction algorithm that we have proven to be terminating, and sound and complete with respect to its declarative specification. Our principled description can serve as a blueprint for making other analyses higher-ranked.
A Noble Approach for Better Training with Neuro-Genetic System Using Apical Dominance Based Genetic Algorithm

Local minimum incorporated with premature saturation and slower convergence limits the performance of the Simple Genetic Algorithm based Neural Network (SGA-NN) algorithm. When the network reaches in local minima, the weights of the neural network become idle. To overcome this premature saturation and slow convergence a new neuro-genetic system named Apical Dominance based Genetic Algorithm based Neural Network (ADGA-NN) is proposed in this research work. As `Apical Dominance' is a natural genetic event in plants, this algorithm may accelerate the training by updating the stationary weights of the neural network. ADGA-NN is experimented on five actual world's classification problems which are breast cancer, glass, Australian credit card, heart disease and thyroid problem. ADGA-NN surpasses SGA-NN concerning convergence rate and generalization capability.
Analysis of the Informatization Construction of Library

Abstract In the era of the Internet, information technology has been widely used in various fields, to a large extent, it has greatly improved the quality and efficiency of work in various fields. Similarly, it is inevitable to develop towards the direction of informatization construction in the library management. The following is an in-depth analysis of the informatization construction of library management in the Internet era, hoping to effectively improve the quality and promote the long-term development of libraries.
Lempel-Ziv Compression with Randomized Input-Output for Anti-compression Side-Channel Attacks Under HTTPS/TLS

Security experts confront new attacks on TLS/SSL every year. Ever since the compression side-channel attacks CRIME and BREACH were presented during security conferences in 2012 and 2013, online users connecting to HTTP servers that run TLS version 1.2 are susceptible of being impersonated. We set up three Randomized Lempel-Ziv Models, which are built on Lempel-Ziv77, to confront this attack. Our three models change the deterministic characteristic of the compression algorithm: each compression with the same input gives output of different lengths. We implemented SSL/TLS protocol and the Lempel-Ziv77 compression algorithm, and used them as a base for our simulations of compression side-channel attack. After performing the simulations, all three models successfully prevented the attack. However, we demonstrate that our randomized models can still be broken by a stronger version of compression side-channel attack that we created. But this latter attack has a greater time complexity and is easily detectable. Finally, from the results, we conclude that our models couldn’t compress as well as Lempel-Ziv77, but they can be used against compression side-channel attacks.
Gender Recognition for Juvenile Unconstrained Faces Using Gabor-MeanPool-DCT Feature Model and SVM-Kernel Optimization

This paper presents novelty for facial gender analysis in unconstrained pose for children face where feature of gender categorization are immature. Adult face has matured feature for gender specification while face of juvenile age of group 2 to 14 year is very hard to recognize by system through digital face only. Face image having variation in illumination, pose (position, orientation, scale and expression) and obstructions as cuts, moles, eye color, birth marks, cap, scarves, bandage, spectacles etc. makes more hard to classify juvenile face in attribute of male or female. The paper contribute in feature engineering for illumination invariant, compact feature extraction for juvenile faces in unconstrained environment. Juvenile face has uniqueness feature in edges and texture pattern for different gender category. Gabor filter is illumination invariant and generate edge, textural features but redundant feature coefficients and huge dimensions. In the proposed method, the problem is solved using MeanDCT to generating compact and unique features for efficiency and accuracy respectively.
Spinal Atomic Lambda-Calculus

Abstract We present the spinal atomic $$\lambda $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>λ</mml:mi> </mml:math> -calculus, a typed $$\lambda $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>λ</mml:mi> </mml:math> -calculus with explicit sharing and atomic duplication that achieves spinal full laziness: duplicating only the direct paths between a binder and bound variables is enough for beta reduction to proceed. We show this calculus is the result of a Curry–Howard style interpretation of a deep-inference proof system, and prove that it has natural properties with respect to the $$\lambda $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>λ</mml:mi> </mml:math> -calculus: confluence and preservation of strong normalisation.