Reviewerly/exHarmony huggingface dataset:
Performance Evaluation of GATE Coaching Institutes in India: A Fuzzy-MCDM Approach

It becomes inevitable to impart more attention to the education sector, specially the technical education sector as technology is the pillar of ultramodern civilization of 21 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">st</sup> century. Graduate Aptitude Test in Engineering (GATE) is considered to be one of the most critical and standardized tests conducted at the national level in India and it aims in examining the technical aptitude and practical ability of engineering students. Numerous institutes offer quality coaching for GATE/IES aspirants through online and classroom courses. The prime objective of this research is to evaluate the performance of six leading institutes which provide GATE exam preparation coaching across the country. A literature review has been carried and six influential parameters were identified for the assessment of institutions. Relevant data have been gathered from expert's opinion and simulated numerically. This study propounds a hybrid fuzzy-multi criteria decision making (MCDM) approach where fuzzy-analytical hierarchy process (AHP) is used to determine the priority weights of criteria and fuzzy-technique for order of preference by similarity to ideal solution (TOPSIS) is used to prioritize, rank and benchmark the institutions. In this study, fuzzy set theory is incorporated with MCDM to eliminate the vagueness and discrepancy present in the human judgement. To the best of our knowledge it is one of the few studies that measure the performance of educational institutions in India.
Generation of a dictionary of abstract/concrete words by a multilayer neural network

Abstract Large dictionaries of abstract/concrete words were compiled for several languages by interviewing native speakers. The Russian dictionary contains only one thousand words. This article proposes a new method for automatic generation of a large (tens of thousands of words) Russian dictionary of abstract/concrete words by using neural networks trained on the Google Books Ngram corpus. Estimates of the quality of the dictionary compiled using this method are obtained. The correlation coefficient between the estimates obtained using a neural network for the level of word concreteness and the estimates obtained based on the native speakers‵ responds is 0.778.
Low-Slow-Small Target Tracking Using Relocalization Module

With the gradual opening of airspace, tracking of noncooperative low-altitude slow-speed small size (LSS) targets is important for the maintenance of security. It is still a challenging problem, especially for complex scenarios and real-time constraints. In this letter, an efficient tracking by relocalization (TRL) framework is proposed for small flying object tracking, aiming to alleviate the issue of losing moving targets in a complex background. Our designed relocalization module consists of a feature-aggregated module and a global search module. On the one hand, a feature-aggregated module is integrated into the designed framework to increase the ability to locate small targets. On the other hand, a global search module is developed to update the tracking performance, which attempts to address missed targets in long-term small object tracking tasks. What needs to be declared is that the basic tracking module cooperates with the relocalization module we designed to achieve the tracking of small targets. Performance evaluation of two small-flying target data sets and comparison with several state-of-the-art approaches demonstrate the effectiveness of the proposed framework.
Hardware accelerator systems for artificial intelligence and machine learning

Abstract Recent progress in parallel computing machines, deep neural networks, and training techniques have contributed to the significant advances in artificial intelligence (AI) with respect to tasks such as object classification, speech recognition, and natural language processing. The development of such deep learning-based techniques has enabled AI-based networks to outperform humans in the recognition of objects in images. The graphics processing unit (GPU) has been the primary component used for parallel computing during the inference and training phases of deep neural networks. In this study, we perform training using a desktop or a server with one or more GPUs and inference using hardware accelerators on embedded devices. Performance, power consumption, and requirements of embedded system present major hindrances to the application of deep neural network-based systems using embedded controllers such as drones, AI speakers, and autonomous vehicles. In particular, power consumption of a commercial GPU commonly surpasses the power budget of a stand-alone embedded system. To reduce the power consumption of hardware accelerators, reductions in the precision of input data and hardware weight have become popular topics of research in this field. However, precision and accuracy share a trade-off relationship. Therefore, it is essential to optimize precision in a manner that does not degrade the accuracy of the inference process. In this context, the primary issues faced by hardware accelerators are loss of accuracy and high power consumption.
A Survey of Blockchain Solutions for Autonomous Vehicles Ecosystems

Abstract Since the beginning of the 2010s major car manufacturers progressively started to invest in Autonomous Vehicles(AV). Notable examples are Tesla, Ford, and Toyota. Small startups and tech giants like Google and Yandex are now also entering the competition to propose new solutions. Most of them have already developed their prototypes of driverless vehicles that you can normally observe in the streets of New-York, USA, or Innopolis, Russia. Despite the significant hype on this technology, current solutions are not always optimal and multiple challenges are left open in a domain such as security, data integrity, privacy, and communication. Blockchain is one of the most appealing technologies to be used in this domain since it provides solutions for some of these challenges. In this paper, we describe, categorize, and evaluate different solutions of the Autonomous Vehicles Industry that makes use of Blockchain. We use a software engineering approach to organize the existing work in multiple categories such as challenges addressed, quality attributes promoted. This work is intended to provide researchers in the field with a well-defined and structured categorization, plus insights into the existing literature.
Comparing Time-to-Solution for In Situ Visualization Paradigms at Scale

This short paper considers time-to-solution for two in situ visualization paradigms: in-line and in-transit. It is a follow-on work to two previous studies. The first study [10] considered time-to-solution (wall clock time) and total cost (total node seconds incurred) for a single visualization algorithm (isosurfacing). The second study [11] considered only total cost and added a second algorithm (volume rendering). This short paper completes the evaluation, considering time-to-solution for both algorithms. In particular, it extends the first study by adding additional insights from including a second algorithm at larger scale and by doing more extended and formal analysis regarding time-to-solution. Further, it complements the second study as the best in situ configuration to choose can vary when considering time-to-solution over cost. It also makes use of the same data corpus used in the second study, although that data corpus has been refactored with time-to-solution in mind.
Urban Scene Recognition via Deep Network Integration

Scene understanding remains a challenging task due to the complex and ambiguous nature of scene images in defiance of several networks pre-trained on large-scale benchmark datasets are available. In this paper, we proposed a multi-scale, discriminative integrating method to aggregate both context and multi-scale object information of urban scene images. Our model is formulated as the integration of both patches and networks. Our scene-centric network is built upon the network fine-tuned on scenery dataset; our object-centric is an aggregation of multi-scale networks pre-trained on object-centric networks. We show that the integration of networks leads to an improvement in performance and achieved a 6.63% boost on overall accuracy comparing with the best-performed base model.
Deep Learning and SVM-Based Method for Human Activity Recognition with Skeleton Data

In recent years, research related to the analysis of human activity has been the subject of increased attention by engineers dealing with computer vision, and particularly that which utilizes deep learning. In this paper, we propose a method for classification of human activities, composed of 3D skeleton data. This data is normalized beforehand and represented in two forms, which are fed to a neural network with parallel convolutional and dense layers. After the network is trained, the training data is propagated again to infer the output from the second last layer. This output is used for training a Support Vector Machine. All hyperparameters were found using the Bayesian Optimization strategy on the PKU-MMD dataset. Our method was tested on the UTD-MHAD dataset, achieving an accuracy of 92.4%.
Objective assessment of image quality based on image content contrast perception

Image quality assessment (IQA) plays a very important role in acquiring, storing, transmitting and processing image and video. Using the characteristics of human visual perception and the features of the gray, gradient, local contrast, and blurring of image, an IQA method based on the image content contrast perception is proposed in the paper, which is called MPCC. In the proposed method, firstly, combining with the characteristics of human visual perception, based on the definition of the contrast in physics, a novel definition for image quality and its calculation method are proposed. Then, based on the gray gradient co-occurrence matrix, a novel concept, namely the gray-gradient entropy of image, and its calculation method, are proposed. And based on the gray-gradient entropy, local contrast and blurring of image, a method of describing the image content and their visual perception are proposed. Finally, based on the image content features and the image quality definition, an IQA method and its mathematical model are proposed by comprehensive analysis. Further, the proposed IQA model MPCC is tested by using 119 reference images and 6395 distorted images from the five open image databases (LIVE, CSIQ, TID2008, TID2013 and IVC). Moreover, the influences of the 52 distortion types on IQA are analyzed. In addition, in order to illustrate the advantages of the MPCC model, it is compared with the seven existing typical IQA models in terms of the accuracy, complexity and generalization performance of model. The experimental results show that the accuracy PLCC of the MPCC model can achieve 0.8616 at lowest and 0.9622 at most in the five databases; among the 52 distortion types, the two distortion types, namely the change of color saturation and the local block-wise distortions of different intensity, have the greatest influence on IQA, and the accuracy PLCC values of the seven existing IQA models are almost all below 0.6, but the PLCC of the MPCC model can reach more than 0.68; and the comprehensive benefit of the performance of the MPCC model is better than those of the seven existing IQA models. These results of test and comparison above show that the proposed IQA method is effective and feasible, and the corresponding model has an excellent performance.
Viewport Margins for 360-Degree Immersive Video

Immersive 360-degree video delivery is more and more widespread. New use cases are constantly emerging and make it a promising video technology for Extended Reality applications. Viewport Dependent Delivery (VDD) is an established technique used for saving network bit rate when transmitting omnidirectional video. One of the hardest challenges in VDD of 360-degree video is how to ensure that the video quality in the user's viewport is always the highest possible, independent of the user's head motion speed and span of motion. This paper introduces the concept of viewport margins. These can be understood as an extra high-quality spatial safety area around the user's viewport. Viewport margins provide a better user experience for the receiver by reducing the Motion to High Quality Delay and the percentage of low-quality viewport seen by the user. We provide simulation results that show the advantage of using viewport margins for real-time low-delay VDD of 360-degree video. In particular, for a head motion of 90 degrees, using a 10-30% margins can reduce the percentage of viewport at low quality by 5-10% and using 30% margins reduces the motion to high quality delay to zero for head speeds up to 360 degrees per second, when the viewport feedback is sent every 33ms.
IEEE SA Blockchain in Energy Standardization Framework: Grid and Prosumer Use Cases

Blockchain is an emerging technology that can be applied to many industries involving transactions. It is a fair, transparent and secure way to settle and record transactions between multiple parties without the involvement of a third party. In recent years, many blockchain applications in the field of energy have emerged, however, there is still no guideline or standard for blockchain applications in energy yet. In order to fill the gap, the paper aims to provide a brief review of grid and prosumer blockchain applications and present the standard development activities by the IEEE Standard Association (SA). The review shows that blockchain in energy is a maturing technology that can be used in many areas in the power and energy industry, facilitating the growth of different modern grid technologies. With the development of standards, guidelines for blockchain applications in energy will be provided so that this technology will be better utilized.
When it comes to cyber security, ignorance isn't bliss – it's negligence

Given the rise of cybercrime targeting many small to medium-sized businesses (SMBs), the vast majority find themselves completely unprepared, having laboured under the misguided belief that they are too small to become a target of cybercrime. Rather than being too big to fail, they think they are too small to be noticed. The rise of cybercrime targeting small to medium-sized businesses (SMBs) has left many of them unprepared, believing they are too small to be a target. Most SMBs are left in the dark as to their actual risk and ignore the threat until it's too late. By failing to understand their true cyber risk, these firms often neglect their obligations and spending when it comes to preventing cyber breaches. One solution may be to turn to managed detection and response (MDR) providers, but this requires care, explains Mark Sangster of eSentire.
Robust Texture Features For Emphysema Classification In CT Images

In this paper, we propose a novel feature extraction method based on local quinary patterns (LQP), multifractal features and intensity histograms for classifying emphysema into three subtypes in computed tomography images. Compared to local binary patterns, LQP method computes more image local patterns to represent texture features. Multifractal features which enhancing local textures are combined with other features to constitute a feature vector for this classification task. An autoencoder network and principal components analysis are used to reduce the dimensionality of the feature vector before using an SVM classifier. The proposed method is tested on an emphysema database containing 168 annotated regions of interest of three different subtypes. The experimental results demonstrate that our method outperforms most other state-of-the-art approaches with the best classification accuracy of 92.3% using the least dimensionality (15) of the feature vector.
A new class of generative classifiers based on staged tree models

Generative models for classification use the joint probability distribution of the class variable and the features to construct a decision rule. Among generative models, Bayesian networks and naive Bayes classifiers are the most commonly used and provide a clear graphical representation of the relationship among all variables. However, these have the disadvantage of highly restricting the type of relationships that could exist, by not allowing for context-specific independence. Here we introduce a new class of generative classifiers, called staged tree classifiers, which formally account for context-specific independence. They are constructed by a partitioning of the vertices of an event tree from which conditional independence can be formally read. The naive staged tree classifier is also defined, which extends the classic naive Bayes classifier whilst retaining the same complexity. An extensive simulation study shows that the classification accuracy of staged tree classifiers is competitive with that of state-of-the-art classifiers and an example showcases their use in practice.
Neuron-Like Optical Spiking Generation Based on Silicon Microcavity

We propose a neuron-like optical spiking generation based on silicon microcavity. In the setup, the micro optomechanical oscillation modulates the silicon cavity, and combined with the coupling between the optical oscillation and spontaneous self-sustained oscillation. The silicon-based photon microcavity chip enters the nonlinear dynamical state, and finally output neuron like spiking pulses. We prove that the optical pulse have all the typical characteristics of nerve spiking pulse, including 4 stages: excited state, oscillated state, relative refractory period I and relative refractory period II. The spiking pulse width is only about 4ns, which is about 1 million times faster than typically biological neuron spiking. Compared with the traditional neuron scheme, the chip size of our optical microcavity has reached about 10 gm, being highly fast processing networks. Especially, the neuron-like spiking is implemented on silicon materials, owning the natural compatibility with CMOS process and good integration scalability. We have also established corresponding physical models for theoretical simulation, and the results show that neuron-like optical spiking can be generated in silicon-based photonic microcavity.
Sentiment analysis of tokopedia application review to service product recommender system using neural collaborative filtering for marketplace in Indonesia

Abstract Tokopedia is one of the leading e-commerce companies in Indonesia and ranks second in the top 10 e-commerce in Indonesia in 2018 based on Statista data. Large companies like Tokopedia need to find out what users think of the products or services offered. User opinions on the Tokopedia application can be seen in the review column on the Google Play Store, but processing a review is not easy. To overcome this we need a sentiment analysis method or technique. Sentiment analysis is performed using the Naive Bayes algorithm. The results of positive sentiments obtained are used as a reference to maintain service quality and the results of negative sentiments can be used as an evaluation material in improving Tokopedia services and applications.
Imparting Quality Education with Practical Approach: Using Cloud Computing for Education Case Study

Nowadays, technology has become an inherent part of our lives and has inevitably made its way into the field of education. It provides students with personalized learning and empowers them by giving them ownership of the way they learn. Embracing these technological advancements has changed many aspects of students, such as improved engagement, knowledge retention, individual learning, and collaboration. These advanced methodologies of incorporating technology into education have proven to be a better attention grabber and are cost-efficient methods of gaining knowledge. The case study chosen in this work discusses one such technology (i.e., cloud computing), which is used in imparting quality education at the convenience of both students and teachers.
A modified real-value negative selection detector-based oversampling approach for multiclass imbalance problems

A skewed distribution poses a major challenge in multiclass imbalanced problems and has attracted growing interest in the engineering and research communities. Conventional classifiers pose limitations in handling multiclass imbalanced data sets since they were originally designed to handle a balanced distribution. This paper proposes a modified real-value negative selection detector-based oversampling approach for the multiclass imbalance problem. Different from previous works, we have modified and introduced the traditional real-value negative selection algorithm to address the issue of the multiclass imbalance problem. First, the modified real-value negative selection technique is used to create detectors for each minority class. Then, a supervision mechanism is designed using these detectors to prevent overgeneralization. Furthermore, the method of selection weight based on crowding density and detectors is devised with the aim of reducing the within-class imbalance for each minority class. Finally, simulations were conducted on the public real-world multiclass imbalanced data sets from Knowledge Extraction based on Evolutionary Learning (KEEL) and the University of California Irvine (UCI). The experimental and statistical results have demonstrated that the proposed algorithm obtains better performance compared with seven well-known oversampling algorithms in terms of five metrics (precision, recall, F-measure, Multiclass G-mean (MG), and Multiclass Area Under the ROC (MAUC)) and four types of classifiers.
A Realistic Approach for Network Traffic Obfuscation Using Adversarial Machine Learning

Adversaries are becoming more sophisticated and standard countermeasures such as encryption are no longer enough to prevent traffic analysis from revealing important information about a network. Advanced encryption techniques are intended to mitigate network information exposure, but they remain vulnerable to statistical analysis of traffic features. An adversary can classify different applications and protocols from the observable statistical properties, especially from the meta-data (e.g. packet size, timing, flow directions, etc.). Several approaches are already being developed to protect computer network infrastructure from attacks using traffic analysis, but none of them are fully effective. We investigate solutions based on obfuscating the patterns in network traffic to make it more difficult to accurately use classification to extract information such as protocols or applications in use. A key problem of using obfuscation methods is to determine an appropriate algorithm that introduces minimal changes but preserves the functionality of the protocol. We apply Adversarial Machine Learning techniques to find realistic small perturbations that can improve the security and privacy of a network against traffic analysis. We introduce a novel approach for generating adversarial examples that obtains state-of-the-art performance compared to previous approaches, while considering more realistic constraints on perturbations.