Reviewerly/exHarmony huggingface dataset:
Design of Software Architecture for Neural Network Cooperation: Case of Forgery Detection

Recent technological advances in media tampering has been the cause of many harmful forged images. Tampering detection methods became major research topics to cope with it in the neural network community. The methods almost always aim at detecting a specific forgery. That is, a general detecting method to find any tampering has not been invented so far. This paper concerns about a software architecture for organizing multiple neural networks to detect multiple kinds of forgeries. The key issue here is to construct, from the meta-level, a mechanism for an ensemble of front-end neural networks to select a neural network which makes a decision. Under this architecture, we implemented a prototype for detecting forged images resulted from multiple tampering methods of copy-move and compression. In order to demonstrate that our architecture works well, we examined a case study with a total of 120,000 patches which consist of three classes of copy-move, compression and untampered data, 40,000 patches for each. The result shows our proposed method successfully classified 108,954 out of 120,000 patches with 90.82 &#x0025; accuracy. We also give discussions on our architectural implication to avoid concept drift. Our architecture is designed to be a context-oriented and meta-level, which has a two-layered structure: meta and base. The neural networks can be categorized into base-level components, whereas a component coordinating the networks is addressed in meta-level. The architecture explains that the concept drift can be handled in the meta-level. Through the discussions on the techniques of transfer learning, online learning, and ensemble learning in terms of the architecture we constructed, it is concluded that we could construct a universal architecture to coordinate machine learning components.
Modeling of Optimal Bidirectional LSTM Based Human Motion Recognition for Virtual Reality Environment

Recently, virtual reality (VR) has received significant attention among researchers to accomplish realistic interaction between humans and computers. Among the different application areas of VR, multimedia human computer interaction requires accurately collect and identify the motions of human body in real time. For enabling highly realistic and efficient communication among humans and computers, design of human motion recognition system becomes necessary to determine diverse complex and distinct human actions. Therefore, this study designs a new optimal bidirectional long short term memory (BiLSTM) with fully convolution network (FCN), called OBiLSTM-FCN model for human motion recognition in VR environment. The proposed OBiLSTM-FCN model comprises different processes namely feature extraction, classification, and hyperparameter optimization. Primarily, kernel based linear discriminant analysis (LDA) approach is employed as a feature extraction technique. In addition, the BiLSTM-FCN technique is applied as a recognition model to determine human motions. Finally, the Adam optimizer is applied to optimally tune the hyperparameters involved in the BiLSTM-FCN model. The performance validation of the OBiLSTM-FCN model take place and the resultant values portrayed the improved performance over the other compared methods.
Weighted 2-sections and hypergraph reconstruction

In the paper we introduce the notion of weighted 2-sections of hypergraphs with integer weights and study the following hypergraph reconstruction problems: Given a weighted graph (G,w), is there a hypergraph H such that (G,w) is its weighted 2-section? Given a weighted 2-section (G,w), find a hypergraph H such that (G,w) is its weighted 2-section.
THE PROBLEM OF PERSONALIZATION AND PERSONIFICATION OF PROFESSIONAL TRAINING AND ITS IMPLEMENTATION IN HIGHER EDUCATION INSTITUTIONS AS A NEED OF TIME

Professional training in the context of reforming the educational sphere acquires new aspects and forms of implementation of educational programs, taking into account the personality-oriented trajectory of individual professional growth of each subject in the educational process. The solution to the problem is relevant in our time: what is important in the process of personal and professional development – personalization or personification of the educational process? The paper reflects the features of personalization and personification of specialists’ professional training, discloses and clarifies their essence, presents an analysis of scientific publications dedicated to this problem; it emphasizes the necessity of introducing a personalized approach, which is an important means of achieving this goal. The theoretical foundations of the personalization and personification of professional training, the peculiarities of the organization of educational activities, covering the cognitive, informational, scientific and innovative, social, managerial spheres, have been characterized. It has been proved that the important conditions for the personalization of the educational process at higher educational establishments is a cultural impulse in the idea of democratization and construction of society and a human, the tradition of social and humanitarian thought, individualization of education, etc.
Deep Kernel Network Embedding

This paper concerns the problem of network embedding (NE), whose aim is to learn a low-dimensional representation for each node in networks. We provide a new train to solve the sparsity problem where most of nodes including the new arrival nodes have little knowledge with respect to the network. A novel paradigm is proposed to integrate the multiple information from the subgraph covering the target node instead of only the target node. Particularly, to epxress the distinctive feature over the vertex domain, a probabiltiy distribution over subgraph space is constructed for each node. The distribution is more effective to express the distinctive characteristic and feature in a higher dimension compared to the latent representation vectors. One of the primary goals of this paradigm is to define the convolution operation over the distributions, which are efficient to evaluate and learn. Experiments on four real-world network datasets demonstrate that our approach significantly outperforms state-of-the-art methods, especially on the representation learning for the nodes newly joining in the network.
An alternative proof of the minimality of the Hamming weight of width-3 non-adjacent form

In elliptic curve cryptography (ECC for short), point multiplication (or scalar multiplication) is the dominant operation. It is a very important matter to improve the efficiency of point multiplication for practical use. In ECC, recoding methods of the scalars play an important role in the performance of the algorithm used. One such example is the width-ω non-adjacent form (ω-NAF). Bosma (2001) proved that the Hamming weight of the NAF (2-NAF) is minimal, and Muir and Stinson (2005) proved that the Hamming weight of the ω-NAF is minimal. As other examples, the generalized non-adjacent form (GNAF) and the τ-adic non-adjacent form (τ -NAF) are known. Clark and Liang (1973) proved that the Hamming weight of the GNAF is minimal by constructing an injective map. A similar strategy was adopted by Hakuta, Sato, and Takagi (2010) to prove the minimality of the Hamming weight of the τ -NAF. In this paper, we shall give an alternative proof of the minimality of the Hamming weight of the 3-NAF (ω-NAF with ω = 3). We also discuss that our alternative proof may not work in the case ω ≥ 4.
Machine Reading Comprehension-Enabled Public Service Information System: A Large-Scale Dataset and Neural Network Models

With the rapid development of 5G and machine learning, various innovative information systems continue to emerge. Among them, the public service information system (PSIS) has attracted extensive attention from researchers. However, the existing public service information system can only handle simple tasks and cannot answer complex questions raised by citizens. In this paper, we explore the application of machine reading comprehension to the field of Chinese public service information systems from scratch. Machine reading comprehension is a new research hotspot in the field of machine learning, which has great application potential in PSIS systems. However, work in this area is still lacking, with neither large scale datasets nor neural network models available. To address the above issue, first, we create a large scale machine reading comprehension dataset of Chinese public service information, including public service affair guidelines, policies, etc. Next, we propose several new neural network models which were continually pretrained on the Chinese public service corpus. The experimental results show that the proposed models achieve a significant improvement over several previous SOTA models on the new dataset. However, they are still far below human performance, indicating that the proposed dataset is challenging.
ENTROPY CODING FOR H.264 VIDEO COMPRESSION

H.264/AVC standard of the ITU-T Video Coding Experts Group and the ISO/IEC Moving Picture Experts Group. The H.264/AVC standardization is now achieving signicant improvement in rate-distortion efciency for digital systems worldwide. This paper provides an overview of the technical features of H.264/AVC, describing its baseline proles, motion estimation for inter, and intra prediction. To improve the human perceptual quality of videos deblocking lter and perceptual quality assessments are described in detail.
Analysis of electricity generation in domestic photovoltaic systems, from the perspective of energy cost savings

Currently, it is increasingly common to find distributed generation systems in Mexico, especially in places where the cost of electricity becomes high, such as in cities with extreme temperatures that require air conditioning systems throughout the year. The present work aims to analyze four photovoltaic solar systems for domestic use, to determine if there is an economic benefit as a result, in the city of Nogales, Sonora. For which it was necessary to interpret the data of the payment receipt, visit the locations to obtain the installation characteristics and make a comparison between the monitored data and the calculated one. One of the outstanding results was that the difference in inclination angle in a range of ± 10 degrees basically did not affect the efficiency of the systems. In addition to a case with a high energy cost, due that the consumption is greater than the generation of the SSFV. This work contributes with important information to the community, so that better decisions can be made regarding how to reduce the cost of energy used in homes, as it can be approached from the point of view of installing an SSFV and awareness of energy use.
Unsupervised Kernel-Induced Fuzzy Possibilistic C-Means Technique in Investigating Real-World Data

Abstract The goal of this study is to break down a large dataset into meaningful groupings. Due to the vast dimension and significant resemblance seen among data, exploring divided clusters in real-world datasets is the most difficult assignment. As a result, this work proposes a fuzzy set-based unsupervised effective clustering technique that includes possibilistic memberships, and fuzzy membership degrees into the membership, weighted Cauchy kernel-based similarity measure and center equations. The empirical findings demonstrate the feasibility of the proposed effective clustering technique.
Learning and Revising Dynamic Temporal Theories in the Full Discrete Event Calculus

This paper presents the first automatic method for learning and revising dynamic temporal theories in the full-fledged Discrete Event Calculus (DEC), where fluents may be temporarily released from the law of inertia and subject to qualitative or quantitative domain laws. This is done by proposing a reformulation of the DEC, called the eXploratory Event Calculus (XEC), which can be more efficiently handled by state-of-the-art answer set solvers, and which supports a range of different logical semantics and policy options for resolving conflicts relating to the truth value or release status of fluents. The paper shows how XEC outperforms DEC on standard reasoning benchmarks, and how it can be used with an ILP system XHAIL to provide the first proof-of-principle demonstration of theory learning and revision in the full-featured DEC.
Recognition and Detection of Human Judgment About Influential Pairs Using Machine Learning Techniques

The wide selection of online media as means for communicating, sharing, or even for demanding estimation, testing sentiments has progressively drawn in light of a legitimate concern for associations that gives, or sell, items and administrations. Social media gives a stage for peer proposals to assume a larger part in adoption and purchase decisions. This paper deals with the identification of influencers in a special social network called Twitter using scikit tools. The existing system uses the follower count benchmark method. It is based on only the no of followers and achieves an accuracy of 70.2%. This paper presents a data mining modeling approach to forecast the human judgment about who is added powerful for pairs of persons. The model performance is shown using different performance metrics like accuracy, classification report, ROC curve, cross-validation accuracy, and AUC. We use machine learning algorithms such as logistic regression, naive Bayes, random forest, multilayer perception, decision tree and applying stacking to those classifiers. From all these classifiers, random forest gives the best results based on AUC and ROC curves. This paper will give good results as compared to the existing system in terms of accuracy.
Transparent Blockchain-Based Electronic Voting System: A Smart Voting Using Ethereum

The research work scrutinizes an e-voting concept that is on the platform ethereum blockchain. Ethereum is a distributed computing platform that is free, open source with the functionality of smart contracts. By utilizing this depiction, it is feasible to originate engrossing scientific prominence which enables the thoughtful in sober fact collaboration occurring in the blockchain. E-voting is the most accepted worldwide because it is a tool that every moment signifies the democracy of the election. Consequently, most of the countries persevere to experiment and development of the e-voting process. Blockchain technology is responsible for a decentralized design that designates advanced data simultaneously among the P2P network barring a central database. At last, the experiment addressed the debilitation of the existent e-voting method and successfully fruitful blockchain technology to unravel that feebleness.
A Heuristically Generated Metric Approach to the Solution of Chase Problem

In this work, heuristic, hyper-heuristic, and metaheuristic approaches are reviewed. Distance metrics are also examined to solve the “puzzle problems by searching” in AI. A viewpoint is brought by introducing the so-called Heuristically Generated Angular Metric Approach (HAMA) through the explanation of the metrics world. Distance metrics are applied to “cat and mouse” problem where cat and mouse makes smart moves relative to each other and therefore makes more appropriate decisions. The design is built around Fuzzy logic control to determine route finding between the pursuer and prey. As the puzzle size increases, the effect of HAMA can be distinguished more clearly in terms of computation time towards a solution. Hence, mouse will gain more time in perceiving the incoming danger, thus increasing the percentage of evading the danger. ‘Caught and escape percentages vs. number of cats’ for three distance metrics have been created and the results evaluated comparatively. Given three termination criteria, it is never inconsistent to define two different objective functions: either the cat travels the distance to catch the mouse, or the mouse increases the percentage of escape from the cat.
An Adaptive Classifier Based Approach for Crowd Anomaly Detection

Crowd Anomaly Detection has become a challenge in intelligent video surveillance system and security. Intelligent video surveillance systems make extensive use of data mining, machine learning and deep learning methods. In this paper a novel approach is proposed to identify abnormal occurrences in crowded situations using deep learning. In this approach, Adaptive GoogleNet Neural Network Classifier with Multi-Objective Whale Optimization Algorithm are applied to predict the abnormal video frames in the crowded scenes. We use multiple instance learning (MIL) to dynamically develop a deep anomalous ranking framework. This technique predicts higher anomalous values for abnormal video frames by treating regular and irregular video bags and video sections. We use the multi-objective whale optimization algorithm to optimize the entire process and get the best results. The performance parameters such as accuracy, precision, recall, and F-score are considered to evaluate the proposed technique using the Python simulation tool. Our simulation results show that the proposed method performs better than the conventional methods on the public live video dataset.
A Proposed Algorithm to Perform Few Shot Learning with different sampling sizes

Often times there is scarcity when it comes to model training of a quality dataset. Sometimes the data that is available is unlabelled, sometimes very few samples are available for some classes. In these cases, few shot learning comes in handy. There are two approaches to few shot learning Data Level approach and Parameter Level approach. The paper consists of analysis of the number of training samples using parameter level approach. Two classes have been used to perform few shot learning. Meta transfer learning is being used, by initialising the parameters of convolutional neutral networks (CNN) learner model from a model trained on ImageNet. It has been performed incrementally on datasets of various sizes. The results and performance of all the models are compared to the results when the entire dataset is used. As well as the advantages of using few shot learning. It has found its applications in a wide range of fields mainly computer vision, natural language processing etc.
Calibration of RGB-D Camera Using Depth Correction Model

Abstract This paper proposes a calibration method of RGB-D camera, especially its depth camera. First, use a checkerboard calibration board under auxiliary Infrared light source to collect calibration images. Then, the internal and external parameters of the depth camera are calculated by Zhang’s calibration method, which improves the accuracy of the internal parameter. Next, the depth correction model is proposed to directly calibrate the distortion of the depth image, which is more intuitive and faster than the disparity distortion correction model. This method is simple, high-precision, and suitable for most depth cameras.
An ML-based Citizen-Driven Traffic Violation Reporting Android App

As violations in the city are on the rise, we bring out a citizen-driven application to allow them to take responsibility for being a lawful Samaritan was the need of the hour. The Bangalore city Traffic Police has released an application ‘Public Eye’ which only partially serves this purpose. In this project, we attempt to contribute to this good initiative by enhancing the User Experience and adding the power of Machine Learning and Artificial Intelligence to simplify violation detection, while addressing privacy concerns. In our work, we have explored state-of-the-art models available for the task of detection and recognition in images. After studying the needs of users meticulously, we built an effective and meaningful User Experience in the App. The strengths of this App are its ease of use, flexibility, and privacy amongst others for an effective traffic violation reporting. Our approach to gamifying the experience will attract more users to take responsibility of being law abiding citizen. The efficacy of its usage is validated through volunteers.
A Data Processing Pipeline For Cyber-Physical Risk Assessments Of Municipal Supply Chains

Smart city technologies promise reduced congestion by optimizing transportation movements. Increased connectivity, however, may increase the attack surface of a municipality&#x0027;s critical functions. Increased supply chain attacks (up nearly 80 &#x0025; in 2019) and municipal ransomware attacks (up 60 &#x0025; in 2019) motivate the need for holistic approaches to risk assessment. Therefore, we present a methodology to quantify the degree to which supply-chain movements may be observed or disrupted via compromised smart-city devices. Our data-processing pipeline uses publicly available datasets to model intermodal commodity flows within and surrounding a municipality. Using a hierarchy tree to adaptively sample spatial networks within geographic regions of interest, we bridge the gap between grid- and network-based risk assessment frameworks. Results based on fieldwork for the Jack Voltaic exercises sponsored by the Army Cyber Institute demonstrate our approach on intermodal movements through Charleston, SC and San Diego, CA.
An Automated Attendance System Through Multiple Face Detection and Recognition Methods

In this paper, a face detection-based attendance system is proposed and the comparison between different algorithms is evaluated. Haar cascade is used for face detection and for face recognition is implemented through LBPH, fisher face and eigenface. Real-time images are captured by using a camera and stored in the dataset. During prediction, the person’s image is recognized and the database is updated with that person’s id and the changes will get reflected in the excel sheet as well. The SMS is also sent to an appropriate number given by the student. The comparison is done among the three methods, and the best one is chosen. This paper also tells about preferring of OpenCV over MATLAB.
Short-term Impact Load Forecasting Model Based on Multi-objective Antlion Optimization Algorithm

Abstract In this study, a novel multi-objective antlion optimization (MOALO) algorithm-based impact load prediction model is proposed to address forecasting problem in the area with a lot of impact load. To reduce the impact of glitch in impact load data, ensemble empirical mode decomposition(EEMD) is applied to decompose the primitive load data into a set of sub-layers. Then, a novel MOALO-based extreme learning machine (ELM) forecasting model is put forward to make short-term prediction by using the decomposed sub-series. Finally, superimpose the prediction results. According to case study, the proposed EEMD-MOALO-ELM model has the best prediction accuracy and stability.
Image Quality Predictor with Highly Efficient Fully Convolutional Neural Network

A highly efficient deep fully convolutional neural network (DFCN) for image quality assessment (IQA) is designed in this paper. The DFCN consists of two branches, one scoring local patches and the other estimating the weights of local patches to enhance quality prediction. Then, the DFCN outputs quality score of the whole image with aggregate weighted average pooling. There are no fully connected layers in the DFCN, resulting in far fewer parameters. In addition, the network model utilizes multiscale images as inputs to enrich the extracted distortion information. Furthermore, the parameters of the model are optimized in two steps to reduce the requirement for computing power and the risk of overfitting. The parameters of the shared layers and the quality module are optimized firstly, and then, the parameters of the weight module are optimized with the designed loss function. The extensive experimental results show that the proposed DFCN outperforms other competing IQA methods and has strong generalization ability.