Reviewerly/exHarmony huggingface dataset:
Cyber Crime Investigation and Law

Today we are living in the era of information technology where most of the activities of the private and public are dealt with the online transactions with the help of World Wide Web (www.) Anybody can access another via internet through online transaction. The entire world has become one global village. The people are connected closely with the help of internet and intranet. The internet has both uses and misuses. It is unfortunate to note that the internet is being misused by some of the criminals who use the hacking methods and blackmail the World Wide Web organisers and internet protocol users. Such criminals resort to the cyber crimes that use unauthorised access to the other’s networks. Such criminal activities come under the concept of cyber crimes [1]. At this juncture the ‘Cyber Law’ comes to the rescue of the aggrieved. Hence, the Cyber Law was introduced in the Indian legal domain. Cyber Law deals with the crimes through internet, cyberspace and addresses the computer related legal issues and corresponding punishments. Cyber Law also addresses the sub themes such as freedom of expression, access and utilization of internet, security via online including privacy. To put it in a short way it is termed a ‘the Law of the Web’ [2].
Accurate Detection of Facial Landmark Points Using Local Intensity Information for Emotion Detection

Face is the gateway to mind. Understanding facial expressions gives the affective state of human mind. Accurate detection of facial expressions is one of the open challenges in machine vision. The fulcrum of facial expression detection and classification depends on extraction of salient points on the face. The accurate detection of these points is a mandatory step for emotion detection as these facial points undergo tremendous change as emotion expressed by human changes. This paper proposes an accurate adaptive algorithm for extraction of the distinct facial points which will make identification of various expressions a feasible task. Viola–Jones face detection algorithm was used for detection of face. The extracted face region was decimated into various regions of interest (ROIs) based on prior knowledge of human face anatomy. Eye centers were located using horizontal and vertical projections of histogram of eye ROI. The ROI for eyebrows was located using the location of eye centers. The eyebrows were detected by taking advantage of the sign of double edges produced by second-order differential used for edge detection. Lip corners were extracted from the bottom half of the face by obtaining the highest contrast points from the contrast image. The algorithm was tested on the UNBC Pain Archive and achieved 100%, 100%, and 99.87% accuracy for eye centers, lip corners, and eyebrow corners, respectively. The algorithm is learning free, computational inexpensive, and can be modeled into embedded platforms.
Application Of Optical Flow In Determining Movements For Realistic Roller Coaster Ride Using A 360° Video And Two Degrees Of Freedom Motion Chair

Virtually created roller coaster rides are computer generated scenario that mimic real life roller coasters. But most virtual roller coaster rides are dependent on artificially made environments that seem fake and are not at all realistic. This study focuses on the use of motion chair, 360° roller coaster videos, and the use of optical flow to create a roller coaster system that is more realistic. Using optical flow to determine movements in the 360° video that is sent to the created motion chair, while the video is played on the connected android phone for viewing of the video. The solution proved that acquiring 3D movements of roller coaster in 360° video is possible through the proposed method by the researchers but provides the problem of level of accuracy of movements. The optical flow is used to determine the movement of pixels from its current to previous movement, the gathered data in the whole video that is processed with optical flow is filtered to acquire the estimated angle of the movement of the roller coaster int the video. It was also proven that the created motion chair can execute the calculated movements of the program but relating it to the 15° maximum and -15° minimum movement of the created motion chair. Finally, the realism of the created system is evaluated by respondents that have previously experienced riding rollercoaster rides in real life.
Assessment Model for Perceived Visual Complexity of GIS System

Although the visual complexity of GIS interface information display has increased in recent years, few researchers have examined the impact of this visual complexity. Based on the characteristics of the GIS system, we divide the interface into window information areas and map areas for research. The complexity of the window is divided into the number, type, and relationship corresponding to the number of colors and characters, the presence or absence of word map combinations, and the presence or absence of visual partitions. Map complexity focuses on the complexity of the target stimulus and combines it with semantic complexity to explore the visually complex evaluation model of the entire interface. The experimental results confirm the rationality of interface complexity partitioning and provide a reference for the study of more complex human-computer interaction systems.
A Fast SNR-based Vibration Events Detection Algorithm for AETA Geo-acoustic Data

Abundant information can be mined from AETA geo-acoustic data. In this paper, a fast SNR-based vibration events detection algorithm is proposed by improving traditional STA/LTA algorithm for these data. Background noise sampling and different update strategies during different states are applied to improve detection performance. Furthermore, we studied relative method to update the eigenvalues in O(1) time when an element in the sliding window is operated so data stream can be detected in O(N) time. Finally, in the experience part, white Gaussian noise with SNR range from 3 to 10dB is added to the geo-acoustic data of 72 AETA stations around the Longmenshan fault zone from October to November 2019 and result is measured by start point relative error, end point relative error and detected energy ratio. Result shows that the proposed method achieves considerable detection effect for vibration events in AETA geo-acoustic data with low computation complexity.
Research on Cataloging and Storage of Binocular Stereo Video Resources

Compared with the two-dimensional video, there are differences in cataloging and storage of stereo video. It is not conducive to acquisition and utilization accurately using the cataloging and storage method of two-dimensional video. In order to solve the problem of low efficiency in utilization caused by lacking of typical features and incomprehensive of file description, the cataloging and storage of binocular stereo video resources are studied. The special cataloging items of binocular stereo video resources are explored and analyzed, and the mode of storage is also explored. On this basis, a stereo vision resource management system is designed and developed, and the cataloging and storage of binocular stereo video resources are realized. The system can effectively realize the management of binocular stereo video resources, and improve the pertinence, accuracy and efficiency of acquisition and utilization.
Privacy Preserving Cloud Data Access using Control and Automation Technique

At gift the mankind are steadily relying extra on some of online garage stores to again up our records or for the use of it in real time which gives an anywhere, anytime get admission to. These kinds of services convey with it, worries of safety and privacy weaknesses for all of the services supplied via them for the reason that customers statistics are stored and maintained out of user’s premises. This paper portrays the various troubles associated to privacy even as storing the User’s statistics on one-third celebration provider vendors, that's extra typically termed as cloud Service. Without suitable protection and privateness solutions designed for clouds this computing paradigm should become a huge failure. There is a lot of studies being made to spot out the issues with those cloud service companies and cloud security in trendy. This paper is on regard of one of the key trouble-privacy that occur inside the context of Cloud computing and examine the numerous works being carried out to solve the problems in privateness and thus to make sure privacy to outsourced statistics on cloud garage. Keywords: Automation technique, cloud computing, cloud server, feasibility, software, threat model.
Statistical estimation of the object’s area from the image contaminated with additive noise

Area estimation of circular or ellipsoidal object on an image is a current issue in computer vision. Several methods that address this problem have been previously presented, but it turned out that they do not give satisfactory results when dealing with noisy images. As part of the research presented in this paper, a statistical model for estimating the width of uniform distribution for data contaminated with additive error was applied in order to approach the mentioned problem in an innovative manner. Initially, a method for length estimation of intersection of an object with an arbitrary line has been developed. It is possible to estimate the set of object's edge points using this method. Further, a circle or an ellipse is fitted in that set of points and its area is calculated, which approximates the area of the object itself. It is also possible to estimate the area of a circular or ellipsoidal object represented by a set of points in the plane.The presented method was implemented and publicly released as a package for the programming language R. The method has been extensively tested on the problem of estimating the area of objects recorded using RGB-D camera. Different noise levels were added to the captured images, and estimation results were compared with the ones obtained by several established methods. The test results showed that the method presented in this paper gives qualitatively the best results of area estimation when dealing with noisy images.
Hash Retrieval Method for Recaptured Images Based on Convolutional Neural Network

For the purpose of outdoor advertising market researching, AD images are recaptured and uploaded everyday for statistics. But the quality of the recaptured advertising images are often affected by conditions such as angle, distance, and light during the shooting process, which consequently reduce either the speed or the accuracy of the retrieving algorithm. In this paper, we proposed a hash retrieval method based on convolutional neural networks for recaptured images. The basic idea is to add a hash layer to the convolutional neural network and then extract the binary hash code output by the hash layer to perform image retrieval in lowdimensional Hamming space. Experimental results show that the retrieval performance is improved compared with the current commonly used hash retrieval methods.
Technical codes’potentialities in cybersecurity. A contextual approach on the ethics of small digital organizations in France

With the proliferation of malware and phishing attacks, information security has become a major challenge for companies. Organizations of all sizes have had to develop efficient cybersecurity strategies. Part of the literature has explained the growing cyber risks by the human factor such as the mistakes of users. Our aim is to analyze the user's cybersecurity ethics in their working context. We used the Feenberg's concepts of technical codes' potentialities to critically interpret the technical codes of users. We found three technical codes representing a variety of dimensions and combined a diversity of point of view about cybersecurity ethics: interest, values and priorities. These technical codes allow us to uncover alternative representations of user's ethics and to think critically on the way researchers, practitioners and information security specialists view and imagine the future of information security among small organizations. Our empirical findings are based on a critical qualitative research among small organizations with similar cybersecurity systems in France.
Anomaly Detection in Aerial Imagery Using Color and Texture Features

The detection of anomalous regions in digital images can be used in many applications, such as security, search and rescue operations, hazard identification and industrial inspection. In this work, we present an anomaly detection method based on color and texture features applied to a non-linear one-class classifier, and show that it provides excellent results, even when compared to a two-class classifier. Our approach is lightweight and aimed at its implementation on an onboard computer for an Unmanned Aerial Vehicle.
Spatial and Multispectral Correlations for Joint Design of Color Filter Arrays and Demosaicing

CFA(Color filter array) is an essential component in the acquisition system, its adoption greatly decreases the acquisition size, however its design directly relates to the imaging quality. In this paper, a novel method of joint design of color filter arrays and demosaicing based on the spatial and multi-spectral correlation is proposed. The isotropic neighborhood is exploited for acquiring the spatial correlation, and the multi-spectral is utilized for the correlation cross channels. A convolutional network autoencoder is presented to design the color filter array patterns and demosaic, which comprehensively employs the spatial and multi-spectral correlation. Experiments shows that the method achieved high-quality image reconstructions, holding higher PSNR than the ones obtained by the state-the-art techniques on standard dataset.
Innovative Applications and Development of AI for Emergency Services

Abstract With the outbreak of 2019-ncov (NCP) virus, many AI technologies, equipment and platforms are emerging in the main battlefield of anti-epidemic, such as patrol UVA, 360 disinfection robot, infrared intelligent temperature measuring equipment, throat swab robot, whole lung quantitative analysis technology, medical robot, etc. In the future, AI will continue to play an important role not only in virus type identification, abnormal pathology detection, optimization of treatment methods, prediction of the flow of people, etc., but also in emergency services such as plugging loopholes, making up for shortcomings, strengthening weaknesses. It is suggested to integrate AI into the construction of the emergency system, strengthen the institutional guarantee and make a top-level design, build a coordination platform for AI emergency response, promote the research of key technologies in the field of emergency services and solve the problem of cross-platform coupling in order to build an efficient, scientific and intelligent emergency management system as soon as possible.
Automated Document Categorization Model

The aim of this work is to build a generic model of Document Clustering that automatically groups together the related documents. Model is built with unsupervised and supervised learning with the assumption of no prior knowledge of the given domain. No manual effort is required for creating the training document set, instead the proposed model automatically generates training document. After that, it uses those for categorizing text documents. In the proposed model, the entire process is broadly divided into two steps. First, the initial classification is done in an unsupervised way. Apply K-means algorithm on the unlabeled documents in order to prepare the training dataset. Text documents are represented here as feature vector format where keywords extracted are considered as a feature. Here the selected representative documents are considered as the initial centroids. In step 2, create a supervised classifier on the initially categorized set. The categorized documents resulted from the previous step are used to train the supervised classifier. Naive Bayes classifier will be used as a statistical text classifier which uses word frequencies as features.
A PREDISPOSITION USING MORPHOLOGICALLY-CHANGED ENGLISH WORDS IN WRITING BY INDONESIAN EFL LEARNERS

This study aimed at investigating the reasons of Indonesia EFL learners in using morphologically- changed English words in writing and finding out the number of  Morphologically Changed English Words tend to be used by Indonesian EFL learners at  English study Program at FKIP UMP. The number of sample was 36 Indonesian EFL Learners at the third semester of  English Study Program at FKIP UMP.  This research was a descriptive study. The technique used for collecting the data was distributing questionnaire. The data was analyzed by using percentage analysis and explained descriptively. The results of this study showed that the majority of respondents had more than one reason for a predisposition using morphologically changed English Words in writing. What triggers the use of them was classified into 9  categories; Entertainment, Habit, Efficiency; Saving time and space, Simplification (Easeness), Esthetics, Narsistics, Self-Contentment, Uniqueness, and Trend/Environment. Self-contentment Reasoning is the   most dominant reason that the respondents had. There were 1 sentence,  8 phrases, and 20 words tend to be changed morphologically by Indonesian EFL learners as language users. This research was hoped to give the contribution to sociolinguistic aspect that tries to investigate personal use of the language in society in written and it might be dealt with discourse analysis either. It also informed the language phonemenom happened among  Indonesian EFL learners as language users and hoped to help other people understand the original language intention by EFL learners when writing such forms for avoiding misunderstanding.
Toward Secured FPGA: Silicon Proven CLB With Reduced Information Leakage

The ubiquity of FPGAs in security applications makes it imperative to design a secured configurable logic block (sCLB) that is resilient to side channel attacks. In this letter, we take a step toward secured FPGA by introducing new design circuit level methodology for the implementation of an sCLB. The security level of the sCLB is significantly enhanced by the design of its combinational part (LUT block) based on a dual-rail precharge MUX (DPMUX). The sCLB was implemented in a configurable array and fabricated in 65-nm CMOS technology. Silicon measurements proved the effectiveness of the approach. The security level was evaluated using advanced power analysis techniques. In particular, the number of secret bits that can be learned (mutual information) by a CPA attack dropped from 4 b (out of 4) after 1200 power traces for a conventional LUT design to only 2.56 b after 19.2-M power traces.
Deep Hashing with Attribute Guidance for Image Retrieval

Abstract Similarity-preserving hashing is an important method to solve approximate nearest neighbour search problem for image retrieval. Lots of works have been proposed for supervised hashing and image category labels are utilized as supervised information. However, current works ignore a fact that images can be described by a set of attributes. Intuitively, images of different categories with close visual features can be separated by their attribute features because attributes keep more detailed information. In this paper, we propose a novel supervised deep hashing method with image attribute guidance. Specifically, hash codes are learnt through image visual features and guided by image attributes by maintaining pair wise similarities between images as well as the corresponding attribute descriptions. Extensive experimental results on two benchmark datasets show that our proposed method achieves better performance compared with the state of the art hashing methods.
A Cost-Effective Automatic Dial Meter Reader Using a Lightweight Convolutional Neural Network

With the vigorous development of the Internet of Things technology, the government has gradually phased out the traditional meter and began the era of smart meters. However, the replacement of smart meters is expensive and the yield is too low, which has led to the slow deployment of smart meters. Our idea is to develop a low-cost alternative solution that uses an edge device with a camera to automatically identify traditional electric dial meters, and then uploads the identified value to cloud servers. In the past, there have been studies to automatically read dial meters through traditional image segmentation methods. However, because traditional electric meters are mostly set in an environment with high concealment, dim light, and dirt, it is difficult for traditional methods to obtain good identification results for unclear meter images. In this paper, we propose a cost-effective automatic dial meter reader with a lightweight convolutional neural network on edge devices. In order to easily deploy and improve the accuracy of dial meter recognition, the proposed meter reader has the ability to automatically adjust tilt meter images. Experimental results show that the proposed lightweight convolutional neural network achieves significant improvements in segmentation errors, false positives, and elapsed time compared with the relative approaches.
Unsupervised Deep Embedded Hashing for Large-Scale Image Retrieval

Hashing methods have proven to be effective algorithm for image retrieval. However, learning discriminative hash codes is challenging for unsupervised models. In this paper, we propose a novel distinguishable image retrieval framework, named Unsupervised Deep Embedded Hashing (UDEH), to recursively learn discriminative clustering through soft clustering models and generate highly similar binary codes. We reduce the data dimension by auto-encoder and apply binary constraint loss to reduce quantization error. UDEH can be jointly optimized by standard stochastic gradient descent (SGD) in the embedd layer. We conducted a comprehensive experiment on two popular datasets.
Research of Smart Substation Integrated Intelligent Test System

Nowadays for the devices of intelligent substations, testers only aim at testing single device instead of integrating whole substations. According to the “three-level and two-network” substation structure and based on the standard operation instructions, intelligent substation integrated intelligent test system targets at systematic and complete test and carries out the test work of project quickly and exactly through professional debugging tools supplied by integration system. This system can not only make the test work more perfect and effective, but also reduce the working strength of operators and improve the work efficiency and quality significantly.
Performance Evaluation of the Virtualization Environment of a Microservices-Based Payroll System

For better usage of idle resources in a symmetric multiprocessing environment, cloud computing providers often exploit the boundaries of parallelism by imposing high CPU subscription rates over their virtualization systems. Moreover, unsuitable resource allocation can significantly impair the performance during intensive workloads and increase infrastructure expenditures unnecessarily. It becomes an increasing challenge when dealing with microservices architecture and container encapsulated applications, for these are approaches that add more intricacy layers to the workload scheduling over the hardware. This paper presents a case study on a public agency private cloud, which aims to evaluate the performance of hypervisors and virtual machines, that are serving a Kubernetes container orchestration cluster and running a microservices-based Payroll System during CPU-bound behavior tasks. We also propose a model for sizing the virtual environment according to the best choices for the hardware characteristics and the workload needs.