Reviewerly/exHarmony huggingface dataset:
A New True Random Number Generator Based on Differential Variable Ring Oscillator Robust Against PVT Variation

In this paper, a new true random number generator (TRNG) based on a new variable differential ring oscillator has been presented. In this structure, the ring oscillator’s jitter is considered a random phenomenon. The jitter of the proposed ring oscillator is driven by thermal noise and flicker noise so that the generated jitter is random. In this paper, a new variable differential ring oscillator has been designed. The stage of this ring oscillator is changed randomly in order to create random jitter. A new differential delay cell has been proposed to boost the speed of TRNG and random jitter. The proposed random number generator (RNG) is robust against temperature variations of [Formula: see text]40–120[Formula: see text] and voltage variations from 0.8 to 1.8[Formula: see text]V. The proposed system’s bitrate is 50[Formula: see text]MB/s and its efficiency is 30.03[Formula: see text]Mb/mJ. This process has been implemented in the 0.18[Formula: see text]um CMOS process. Simulation results have been presented in Hspice software and cadence. The output bit images have been displayed by Matlab software.
A Comparative Analysis of BTC Variants

The goal of image compression is to reduce the amount of information required to represent a digital image necessary for efficient storing, transmission, uploading, and downloading with less cost and time and at comparatively lower bandwidth. The block truncation coding method of image compression method is the simplest among all, in this paper, we have presented a comparative study of five popular BTC variants namely traditional BTC, AMBTC, two-parameter training method, and edge-based BTC and multi grouping BTC, in terms of objectives measures such as PSNR, SSIM, FSIM, and CR between original and reconstructed images as well as subjective measures through visible artifacts. All the algorithms rely on a common process of dividing the image into non-overlapping blocks and differ in the way of evaluating the quantization levels of the BTC technique. The different objective measures are evaluated on standard database images.
Equivariant estimation of Fréchet means

Summary The Fréchet mean generalizes the concept of a mean to a metric space setting. In this work we consider equivariant estimation of Fréchet means for parametric models on metric spaces that are Riemannian manifolds. The geometry and symmetry of such a space are partially encoded by its isometry group of distance-preserving transformations. Estimators that are equivariant under the isometry group take into account the symmetry of the metric space. For some models, there exists an optimal equivariant estimator, which will necessarily perform as well or better than other common equivariant estimators, such as the maximum likelihood estimator or the sample Fréchet mean. We derive the general form of this minimum risk equivariant estimator and in a few cases provide explicit expressions for it. A result for finding the Fréchet mean for distributions with radially decreasing densities is presented and used to find expressions for the minimum risk equivariant estimator. In some models the isometry group is not large enough relative to the parametric family of distributions for there to exist a minimum risk equivariant estimator. In such cases, we introduce an adaptive equivariant estimator that uses the data to select a submodel for which there is a minimum risk equivariant estimator. Simulation results show that the adaptive equivariant estimator performs favourably relative to alternative estimators.
Chinese Text-Oriented Interpersonal Emotional Relationship Mining Based on Event Knowledge Model

The extraction of emotional relationships between different persons in text is important for the study of social networks. Most current interpersonal relationship extraction methods focus on the extraction of explicit and direct interpersonal relationships text, ignoring indirect or even uncertain emotional relationships hidden in the text. In this paper, we propose an interpersonal emotional relationship mining algorithm based on event knowledge model. The event knowledge model describes the semantic relationship between different event classes, which are related different types of participants. Therefore, the event knowledge model contains the relationship between different roles in a specific event scenario. Based on the semantic relationship between events and event roles in event knowledge base, the relationship between persons involved in events could be inferred, and the emotional attitude of persons could be obtained by classification learning. Experiments show that our method has a greater effect than traditional relationship mining methods.
Notes on Monotone Recognition in Multi-Valued Grids

A novel method of monotone recognition based on the partitioning of the grid into discrete structures isomorphic to binary cubes (called “cube-split” technique) was proposed in our recent work, and a theoretical level description of two algorithms /algorithmic schemes/ solving this problem was also introduced. This paper provides implementation details of those algorithms, as well as focuses on the recognition of monotone binary functions with a small number of units
Smart Linear Algebraic Operations for Efficient Gaussian Markov Improvement Algorithm

This paper studies computational improvement of the Gaussian Markov improvement algorithm (GMIA) whose underlying response surface model is a Gaussian Markov random field (GMRF). GMIA's computational bottleneck lies in the sampling decision, which requires factorizing and inverting a sparse, but large precision matrix of the GMRF at every iteration. We propose smart GMIA (sGMIA) that performs expensive linear algebraic operations intermittently, while recursively updating the vectors and matrices necessary to make sampling decisions for several iterations in between. The latter iterations are much cheaper than the former at the beginning, but their costs increase as the recursion continues and ultimately surpass the cost of the former. sGMIA adaptively decides how long to continue the recursion by minimizing the average per-iteration cost. We perform a floating-point operation analysis to demonstrate the computational benefit of sGMIA. Experiment results show that sGMIA enjoys computational efficiency while achieving the same search effectiveness as GMIA.
CubeGen: Code Generation for Accelerated GEMM-Based Convolution with Tiling

In a convolutional neural network (CNN), the convolution layers typically dominate the execution time. Hardware accelerators have been designed to speed up convolution. One class of accelerators provide hardware support for matrix multiplication (matmul), allowing the reduction of convolution to matmul, a previously known approach we refer to as GEMM-based convolution. Manually writing and tuning tiled GEMM-based convolution is time consuming and error prone, especially in the presence of hardware constraints such as tensor layout. We propose a kernel compiler called CubeGen that generates fast convolution code and an auto-tiling tool that generates well-performing tiling plans. Our experiments show that for one large convolution problem, we generate code performing up to 93% compared to hand-tuned code. For various other convolution problems, we show that among the automatically generated plans, we are able to find plans with 2.02 $$\times $$ speed up on average compared to the median tiling plan.
Impact of Agile Methodology Use on Project Success in South African Banking Sector

Agile methodology has become the most used software development methodology in different software communities. Besides the extensive benefits that agile methods offer, it also posseses several critical issues. Most studies have reported that, when comparing structured methodologies with agile methodologies, agile improves software quality, whereas other studies have contradicted and questioned the claimed benefits. In contrast, others argued that it does not have enough empirical evidence. This study aims at investigating the impact that agile method implementation on project success in the South African banking sector. The survey was conducted through questionnaires. The theoretical model for analyzing the impacts of agile method implementation on project success was proposed. Reliability was tested using Cronbach’s alpha. The data were quantitatively analyzed by using correlation and regression approaches that use statistical package for social sciences. The results indicated that agile methodology implementation positively influences the project success. Furthermore, this study has indicated that reduced upfront planning, iterative delivery, environmental feedback and technical feedback as individual agile method use components positively impact project success in the South African banking sector.
The Context of the Covid-19 Pandemic and its Effect on the Self-Perception of Professional Competences by University Students of Business Administration

This article aims to determine to what extent the self-perception of acquiring professional skills has been affect ed, by the context of the Covid-19 pandemic in University Students ofBusiness Administration; For which, the results obtained in two satisfaction survey processes, carried out in academic semesters 2019-B and 2020-A, have been compared, marking a before and after in relation to the declaration of a state of emergency in Peru.Initially, it was determined that the indicators "To solve specialty cases" and "To assume self-education" are the most affected in this dimension, with a percentage of decrease in satisfaction of 3.42% and 3.82%, respectively.Then it was determined through the use of the statistical analysis of crossed tables, that the percentage of totally dissatisfied students, in the self-perception of having acquired the competences referred to the two indicators with the greatest negative impact, has remained invariant, almost constant, around of 44%.With this, it can be concluded that the self-perception of acquiring professional skills has been affected, by the context of the Covid-19 pandemic, decreasing by 3.1%, student satisfaction, as observed in the two indicators with the greatest impact negative "To assume self-education", and "To solve specialty cases".These results will allow the Public University of Peru to establish improvement plans, in order to advance towards the development of the teaching-learning model in a virtual way in higher education.
An AI-enabled future for Qatar and the region

research-article Share on An AI-enabled future for Qatar and the region Authors: Ashraf Aboulnaga Qatar Computing Research Institute Qatar Computing Research InstituteView Profile , Sanjay Chawla Qatar Computing Research Institute Qatar Computing Research InstituteView Profile , Ahmed Elmagarmid Qatar Computing Research Institute Qatar Computing Research InstituteView Profile , Mohammed Al-Mannai Ministry of Transport and Communications Ministry of Transport and CommunicationsView Profile , Hassan Al-Sayed Ministry of Transport and Communications Ministry of Transport and CommunicationsView Profile Authors Info & Claims Communications of the ACMVolume 64Issue 4April 2021 pp 59–61https://doi.org/10.1145/3447725Online:22 March 2021Publication History 0citation2,257DownloadsMetricsTotal Citations0Total Downloads2,257Last 12 Months617Last 6 weeks22 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Two-Image Approach to Reflection Removal with Deep Learning

Reflection in an image is not always desirable due to the loss of information. Conventional methods to remove reflection are based on priors that require certain conditions to be fulfilled. Recent advancements of deep learning in many fields have revolutionized these traditional approaches. Using input images more than one reduces the ill-posedness of the problem statement. Standard assumption in numerous methods assumes background is stationary and only reflection layer is varying. However, images at different angles have slightly different backgrounds. Considering this a new dataset is created where both reflection and background layer is varying. In this paper, a two-image based method with an end to end mapping between the observed images and background is presented. The key feature is the practicability of the method, wherein a sequence of images at slightly different angles can easily be captured using modern dual camera mobile devices. A combination of feature loss with MSE maintains the content and quality of the resultant image.
The agile frequency design of vector network analyzer excitation source

An agile frequency excitation source based on ADI frequency synthesis chip is introduced. The 48-bit fractional synthesis chip HMC983LP5E [1] achieves a frequency accuracy of 1 Hz, while using the high phase detection frequency and large loop bandwidth to achieve frequency agility. The frequency hopping speed in the 20 GHz frequency band is within 18 us, Phase-locking speed of 5us can be achieved in the frequency sweep state.
A Comparative Analysis of the Intonational Properties of Weifang Dialect and Harbin Dialect Using the M_ToBI labelling System

This paper compares the Weifang dialect and Harbin dialect in the Mandarin ToBI system. Through the ToBI images, the similarities and differences between the intonational properties of the two dialect systems have been revealed. This paper mainly has the following findings. Firstly, the tone becomes higher when the speaker intends to indicate the narrow focus on this part of the sentence or words. Secondly, in Weifang dialect, the topic of the topic utterance is not stressed, while the part after the topic has a higher tone. Thirdly, Harbin dialict is likely to make a higher tone at the beginning of the utterance. Finally, a pause exists between the topic and the after part in the topic utterance in both two dialects. These results can be applied to the second language teaching of Mandarin, so that learners will be more accessible to the intonational properties of the language and will enjoy the learning experience.
SoundexHindi: A Phonetic Matching Algorithm for Hindi Written in English

Phonetic matching plays key role in retrieving information in environment using multiple Indic languages. The purpose of retrieval of information involves searching of information from large database with different perspective so that further analysis can be done. In order to achieve these techniques such as indexing can be used along with ranking of matching. The strings actually represent the same keyword however due to language complexity it is possible that the same word may be spelt differently by different persons. Since in rural or urban areas, the selected word may be spelt or pronounced either wrongly or can be spelt differently. In cases of exact matching of words may not return correct result rather different techniques associated with phonetic matching can be used for retrieval. In this paper we proposed an approach, which provides a simple and efficient way of matching the strings in Hindi written in Roman. Our approach works on text-conversion technique for any Indian languages, especially Hindi, Marathi etc.
Effect of Absolute Cosine Value Regularization on VGG-19

The search for a more accurate Convolutional Neural Network (CNN) is a continuous process of unlimited possibilities. In order to simplify this process, the effects of algorithms on a network must be examined individually in order to determine their merits. The algorithm examined by this paper is a filter weight matrix regularizer designed to promote optimal information distillation. Absolute Cosine Value Regularization (ACVR) is a regularization technique hypothesized to increase the representational power of CNNs by using a Gradient Descent Orthogonalization algorithm to force the vectors that constitute their filters at any given convolutional layer to occupy unique positions in \mathbbR <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> . This method has previously been given a mathematical definition, implementation description, and in addition, has been demonstrated to be effective at producing high diversity filter vectors in \mathbbR <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> . However, the effect of this Regularizer on a full-scale CNN architecture has yet to be fully examined. This paper aims to determine the merits of this Regularizer by presenting experimental results generated by training the well-established CNN architecture VGG-19 with, and without its presence, using the CIFAR-10 image classification data set. This paper then goes on to propose the Dynamic-ACVR (D-ACVR) algorithm, demonstrating that at optimal configurations, this Regularization scheme can increase network accuracy by up to 3.12%.
THE MAIN MOTIVES OF STUDENTS WHEN CHOOSING AN EDUCATIONAL PROGRAM IN THE CONDITIONS OF THE PANDEMIC

This study examines the relevance of the problem of orientation of universities towards the student, shows the need to study the motivational foundations of educational and professional activities of students at the stage of entering a university, and presents the results of empirical research. The purpose of the study is to identify the motives for choosing an educational program and analyze the awareness of professional choice and students’ expectations from studying at a university in the context of a pandemic and the active development of distance education. It was attended by 147 first-year students. The results of the research testify to the awareness of the choice made by the students. The main motives for choosing an educational program are prestigious material motives and motives for self-realization.
A new method for profile identification using ontology-based semantic similarity

Recently, with the development of information technology, internet users are consuming and storing more data than ever before. This effect caused an explosion of data on the web. However, using the web to search for data that matches his expectations reflects his preferences. For this reason, several research have been developed to realize recommendation systems in various domains such as social networks, e-commerce, tourism, and others. The present document aims to treat and cover a new system in the domain of tourism in order to offer users of the system a set of interesting places and tourist sites according to their preferences. To this end, our work will focus on the design of a new profile identification method, and this method is based on a set of keywords as input. In the first step of our method, we find if there is a correspondence between these given keywords and the concepts of the ontology “DATAtourisme.” Otherwise, we define a semantic correspondence between these keywords and the concepts of the ontology using an external resource called (WordNet). Then, we store the most similar ontology concepts of “DATAtourisme” in a Composite Capability/Preference Profiles, which will be used to filter our interesting places and sights.
A real-time vehicle detection algorithm based on instance segmentation

Vehicle detection has achieved great progress in intelligent transport system, which has the advantages of low risk and high accuracy. In this work, we adopt one-stage algorithm YOLACT to train our model on dataset MS COCO 2017. We propose (Precise) P-CIoU loss to better predict bounding box regression in vehicle detection. In terms of the final candidate bounding box selection, this paper proposes skip-calculation NMS mechanism, in which IoU threshold γ and normalized central point distance method incorporated. This mechanism can effectively solve similar occlusion problem. The experimental results show that our approach achieves performance improvement with gains about 2%, without sacrificing inference efficiency.
Location and Cost Optimization of Datacenters and Solar Power Plants

The location deployment of datacenters and solar power plants involves various factors including local economic situation, land price, electricity price and so on. The selection and optimization of a large number of parameters make the decision-making process complicated and time-consuming. To solve this problem, an optimization framework is designed for the location deployment of datacenters and solar power plants in the power grid, considering transmission loss of the power grid system, the cost and revenue of datacenters and solar power plants. Under the constraint conditions to ensure the stable operation of the power grid, the appropriate deployment scheme of datacenters and solar power plants is found by solving the established optimization problem. Experimental results show that in the case of a single solar power plant and a single datacenter, the proposed strategy can reduce the cost of solar power plant by 11.6% and the transmission loss by 18.6%. In the case of a single solar power plant and two datacenters, the scheme found by our proposed method could increase the annual revenue of the datacenter and greatly reduce the total transmission loss.
Modeling a Decision Support System for Risk Management of COVID-19

A[aut]Bai, Shan decision support system that performs a comprehensive analysis on risks of the COVID-19 pandemic and its subsequent medium- and long-term impacts is highly desirable, especially in view of the still ongoing pandemic in the world. This paper focuses on the modeling process of such a decision support system. The modeling process includes the following steps: generating appropriate strategies that suit the specific situation concerned, where each strategy is composed of measures and each measure consists of a static (textual) part relating to the contexts abstracted from existing literature via techniques inspired by natural language processing and a dynamic part adapted by using scenario planning method and agent-based simulation; generating utility-based recommender based on soft systems methodology and recommending the Top-N strategies to the group of stakeholders based on the scores of the strategies using multiple-criteria decision analysis method. The proposed model is highly flexible in that it recommends appropriate strategies in respect of the various scenarios concerned and may thus be applied to various types of risk management.
Secure and Guarantee QoS in a Video Sequence: a New Approach Based on TLS Protocol to Secure Data and RTP to Ensure Real-time Exchanges

The continued development of networks has significantly contributed to increasing the quantity of information available to replace old intelligence-gathering methods faster and more efficiently. For this, it is necessary to implement services that meet the consumers' requirements and measure precisely the factors that can generate obstacles to any communication, among these causes we can cite strong security and high quality of services. In this work, we implement a secure approach useful in continuous communications in a time axis (video sequence, VOIP call...), the process consists in establishing a well-secured connection between two interlocutors (the server that broadcasts the video sequence and a client) using an AES encryption key of size 256. A step of jitter check (latency variation) periodically is essential for the customer in order to make a decision: If the jitter is within the standards (compared to the tolerable value), we continue to encrypt with the AES256 key, if no, both ends must go through an automatic and uninterrupted fast renegotiation of the video to switch to a small AES key (192,128) to reduce the bandwidth on the channel, this operation must be repeated in an alternative way until the end of the communication.