Reviewerly/exHarmony huggingface dataset:
Static Profiling of Assembly Code Performance and Optimization Effectiveness using Instructions Performed and Program Latency

Software program optimization for improved execution speed can be achieved through modifying the program. Programs are usually written in high level languages then translated into low level assembly language. More coverage of optimization and performance analysis can be performed on low level than high level language. Optimization improvement is measured in the difference in program execution performance. Several methods are available for measuring program performance are classified into static approaches and dynamic approaches. This paper presents an alternative method of more accurately measuring code performance statically than commonly used code analysis metrics. New metrics proposed are designed to expose effectiveness of optimization performed on code, specifically unroll optimizations. An optimization method, loop unroll is used to demonstrate the effectiveness of the increased accuracy of the proposed metric. The results of the study show that measuring Instructions Performed and Instruction Latency is a more accurate static metric than Instruction Count and subsequently those based on it.
Exploring the Privacy Issues of Smart-Grid Infrastructure – A Network Security Perspective

The proposed smart grid infrastructure aims to make use of the existing public networks such as internet for data communication between consumer premises to the public power utility network. The smart-grid adopts smart-meters which basically collect vast amount of data to provide a holistic view of the connected load behavior and preferences pattern related to power and water consumption. The smart-grids provide benefits to the utilities and consumers alike. For utilities the benefits are real time data collection, ease of power management, and reduced personnel requirement. The benefits for the users on the other hand include availability of real time usage data, providing information on ways to minimize power consumption, monetary savings and so on. Since, the smart-grid uses existing public networks the utilities do not have the burden of installing any new infrastructure (except for installing the smart-meters), thus an added advantage. But, the downside of using the public network is susceptibility to a variety of network attacks, if not guarded well against. This paper talks about the various network security vulnerabilities that exist and the measures to patch the same before employing in the smart grid networks.
Upcoming meetings

Artificial OrgansVolume 44, Issue 2 p. 197-197 UPCOMING MEETINGS Upcoming meetings First published: 21 January 2020 https://doi.org/10.1111/aor.13634Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume44, Issue2February 2020Pages 197-197 RelatedInformation
The art of domain classification and recognition for text conversation using support vector classifier

This paper presents an art for recognition of text conversation into multiple domain categories using SVC. The whole process of recognition includes diverse components as: lexical analysis, feature extraction, features normalisation, feature reduction and finally recognition. Useful words are extracted using lexical analysis from the input text paragraph and transformed it into a binary matrix for further processing through feature extraction. Feature normalisation is used to normalise the values of binary matrices while feature reduction is done through principal component analysis to extract the important features from the feature vector and now it is passed to different configurations of SVM, then the best one is selected for the final process of classification and recognition. The domain's categories are defined on the basis of various real life situations and conversation to train the system like education and research, personal, patriotism, terrorism, medical, religious, sports, and business. The experimental results demonstrate that the proposed approach works effectively with about 75% accuracy.
Upcoming meetings

Artificial OrgansVolume 44, Issue 5 p. 538-538 UPCOMING MEETINGS Upcoming meetings First published: 15 April 2020 https://doi.org/10.1111/aor.13694Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume44, Issue5May 2020Pages 538-538 RelatedInformation
Unsafe Tract Revelation and Inspection Using Machine Learning

Towns of diverse provinces are getting more unsafe day by day. The data following varied crime reports are available for these unsafe towns but, even such reports captioned are not perceived to 80% inhabitants of the town. The intent of this article is to decipher datasets which comprise of two distinct types of the dataset, one stands from real-life established crime dataset taken from police and another one stands from Safety Audit (survey) dataset which is performed by inhabitants of the town and using these pairs, anticipating tracts that may come to be unsafe in the future hinging upon numerous circumstances. In this article, we would be employing the procedure of Machine Learning and Data Science for unsafe tract revelation using the Indore city crime dataset as well as the Safety Audit dataset. The crime data has been obtained from the cyber website portal of Indore city’s police. It comprises of criminal parameter evidences like geolocation caption, classification of different criminal activities, duration i.e. date &amp; time. Before the building and training of the processing model, data pre-processing would be mounted. Onto the next step, drafting of useful extracted features and scaling up of them would be performed which on the precision obtained would be increased further. The various distinct algorithms (such as KNN, Linear &amp; Logistic Regression, SVC etc.) would be tested for unsafe tract revelation and an odd one with satisfactory precision would be opted for analysis. Anticipation of the dataset would be performed in terms of graphical depiction of many cases. For example, at what duration the frequency of criminal rates are high or at which sight the criminal undertaking are high. Safety Audit dataset contains information about various circumstances of a locale such as Light, Visibility, Transport, Security, Walk path, People, Time. The sole intent of this project is to give just an idea of how Machine Learning could be used to provide useful information about unsafe tracts to the user. It is not only restricted to Indore City but could also be used in other provinces being sure of upon the availability of the dataset.
Legal Trouble in 280 Characters or Less

The speed of transmission, individual control over, and breadth of communications in the twenty‐first century are unparalleled in history. Using widely accessible devices, any individual can within seconds broadcast a message across the globe. Moreover, such messages are, for all intents and purposes, permanent. While great advances in communications can be enjoyed through the use of social media, a failure to account for the legal exposure that such communications can bring may cause legal problems for any company.
Uniform Rules

A contract shall be governed by the law chosen by the parties. The choice shall be made expressly or clearly demonstrated by the terms of the contract or the circumstances of the case. By their choice the parties can select the law applicable to the whole or to part only of the contract.
Performance Analysis of Fault Tolerant Heuristic Algorithms for Determining Optimal

In this paper, the performance of different bio inspired algorithms are compared to obtain optimal path. Performance of different individual algorithms are analyzed for different topological weights. In networking, it is easy to find the route for static systems where once the optimal path is found, the same is used to communicate between two devices for a long time. Research has shown that it is difficult to find optimal paths for networks with dynamically changing network parameters. In this paper the focus is on performance of three fault tolerant heuristic routing algorithms for such networks. The simulation result shows that 60% of the time Ant Colony Optimization method outperformed Genetic and Beehive algorithms.
SQL

Structured query language, or SQL, is a formal query language used for interacting with relational databases. These interactions could include reading and writing data, creating or deleting new relations, or a host of other actions such as building indexes. SQL and relational databases are a broad topic area and there are entire careers focused on optimizing, maintaining, and representing data within relational systems. In this chapter, we will focus specifically on the use of SQL to retrieve information. Moreover, while it is equally possible to both insert data into and request data from relational databases using SQL, our discussion will focus heavily on requesting data from these databases as this is by far the most common mode of interaction for analytics use cases. After reading this chapter, you will be comfortable with the theory, concepts, and mechanics of the SQL language with an understanding of how to use it to interact with relational databases.
The effect of intensive authentic reading tasks on reading performance of academic ielts test takers

This study aimed at investigating the effect of employing intensive reading (IR) authentic tasks as supplementary materials on reading comprehension development of Academic IELTS test takers within the framework of schema theory and three instructional models of reading strategies suggested by Pearson and Duke (2009). Participants of this study consist of 81 individuals comprising 41 male and 40 female language learners at pre-advanced level of English proficiency with the age range of 25 to 37 among which 42 were categorized as the experimental group which received IR treatment and the rest of 39 ,who are considered as control group, that did not receive IR treatment. The experimental group was further divided into two subgroups which each received the same IR authentic tasks with different instructional methods to teaching reading strategies. The results of an ANCOVA analysis illustrated a significant positive influence of authentic IR treatments on the development of participants' reading comprehension skills. However, an independent one-way t-test revealed that Pearson's and Duke's (2009) various instructional methods were not significantly influential on participants' final reading proficiency development.
Energy-Efficient Heterogeneous Multi-Processor Environment in Cloud using Modern Artificial BEE Colony

Cloud Computing is an expansion in distributed, parallel as well as grid computing. The purpose behind cloud computing is the provision of dynamic hiring of server proficiencies as a virtualized and accessible service for customers and end-users. A key issue found in the cloud is the management of resources. Load balancing is a key problem in the management of resources. The job scheduling issue has charmed abundant courtesy in the field of operation research. There are various algorithms like Ant optimization, genetic algorithms, artificial bee colony which can be used to solve the problem of scheduling. No doubt, Parallelization is proved to be the best method that can be utilized for improving the concert of the above algorithms. In this article, a modified artificial bee colony is utilized in order to crack the problem of scheduling in a heterogeneous multi-processor environment. In this, ABC has various colonies located on dissimilar network hosts as well as the algorithm is accepted in several colonies in parallel fashion. The colonies communicate with each other, which is approved through exchanging immigrants. In order to determine the communication of colonies with neighbors, a dynamic strategy is followed up. The algorithm is useful in making the parallel environment more efficient by reducing energy consumption. The energy consumption is reduced for each job in the DAG. Scheduling with MABC in the heterogeneous environment becomes easy as well as effective
Working with JavaScript

TypeScript projects generally incorporate some amount of pure JavaScript code, either because the application is written in both TypeScript and JavaScript or because the project relies on third-party JavaScript packages installed using NPM. In this chapter, I describe the features that TypeScript provides for working with JavaScript. Table 14-1 summarizes the chapter.
SportsStore: Administration

In this chapter, I continue to build the SportsStore application in order to give the site administrator a way to manage orders and products. In this chapter, I use Blazor to create administration features. Blazor is a new addition to ASP.NET Core, and it combines client-side JavaScript code with server-side code executed by ASP.NET Core, connected by a persistent HTTP connection. I describe Blazor in detail in Chapters 32 – 35 , but it is important to understand that the Blazor model is not suited to all projects. (I use Blazor Server in this chapter, which is a supported part of the ASP.NET Core platform. There is also Blazor WebAssembly, which is, at the time of writing, experimental and runs entirely in the browser. I describe Blazor WebAssembly in Chapter 36 .)
Fault prediction for distributed computing Hadoop clusters using real-time higher order differential inputs to SVM: Zedacross

Hadoop distributed computing clusters are used worldwide for high-performance computations. Often various hardware and software faults occur, leading to both data and computation time losses. This paper proposes the usage of a fault prediction software called 'Zedacross' which uses machine learning principles combined with cluster monitoring tools. Firstly, the paper suggests a model that uses the resource usage statistics of a normally functioning Hadoop cluster to create a machine learning model that can then be used to predict and detect faults in real time. Secondly, the paper explains the novel idea of using higher order differentials as inputs to SVM for highly accurate fault predictions. Predictions of system faults by observing system resource usage statistics in real-time with minimum delay will play a vital role in deciding the need for job rescheduling tasks or even dynamic up-scaling of the cluster. To demonstrate the effectiveness of the design a Java utility was built to perform cluster fault monitoring. The results obtained after running the system on various test cases demonstrate that the proposed method is accurate and effective.
Redistributed Records Collections Personality Matching using Encrypted Cryptosystem

Sensitive information is gradually distributed in the cloud in this project's cloud computing and processing services to reduce costs, which raises concerns regarding data privacy. Encryption was a positive way to keep outsourced sensitive data secure, but it makes efficient use of data a very difficult process. In this paper, we focus on the issue of private matching in ide ntity-based cryptosystem over outsourced encrypted data sets that can simplify the management of certificates. To solve this proble m, we are proposing a private matching scheme based on identity
Parallel naïve Bayes regression model-based collaborative filtering recommendation algorithm and its realisation on Hadoop for big data

Collaborative filtering (CF) algorithms are widely used in a lot of recommender systems. However, space-time overhead and high computational complexity hinder their use in large-scale systems. This paper implements the parallel naïve Bayes regression model based collaborative filtering recommendation algorithm on Hadoop computing platform to scalability problem of CF. Firstly, this paper analysis the inherent parallelism of the naive Bayesian regression model and constructs the theoretical model of naive Bayesian parallelisation. Secondly, the parallel naïve Bayes regression model-based collaborative filtering recommendation algorithm is realised on Hadoop platform with distributed Hadoop distributed file system (HDFS) and MapReduce as the transparent distributed infrastructure. And its temporal-spatial overhead, speedup is discussed. Finally, applying parallel the naïve Bayes regression model-based collaborative filtering recommendation algorithm to a large dataset. The experiment results on Netflix dataset show that this method has high scalability and less space-time overhead, which is suitable for real-time recommendation on large dataset.
Conclusion

Machine translation investigates the use of software to automatically translate text or speech from one language to another language, with no human involved. Research works on machine translation have been widely studied since its first appearance in 1949. The invention of NMT makes related research studies about machine translation come to a new climax. The basic encoder-decoder framework of NMT has also been applied in other research fields, such as image caption [11] and sentence summarization [9]. Because of the substantial improvement of translation performances on human evaluation, many novel techniques about NMT have been successfully adopted in the industrial machine translation service [10].
Intensity modulator for secure, stable and high-performance decoy-state quantum key distribution

The decoy-state method substantially improves the performance of quantum key distribution (QKD) and perfectly solves crucial issues caused by multiphoton pulses. In recent years, the decoy-state method has occupied a key position in practicality, and almost all the QKD systems have employed the decoy-state method. However, the imperfections of traditional intensity modulators limit the performance of the decoy-state method and bring side-channels. In this work, a special intensity modulator and its accompanying modulation method are designed and experimentally verified for the secure, stable and high-performance decoy-state QKDs. The experimental result indicates that its stable and adjustable intensities, convenient two-level modulation, inherently high speed, and compact structure is extremely fit the future trends and will help the decoy-state method to be perfectly applied to QKD systems.
A CNN-based temperature prediction approach for grain storage

Temperature prediction has a pivotal role in the grain storage phase. Accurate prediction results can optimise the effect of ventilation decisions and reduce the losses of stored grain. Most existing studies have only focused on layer temperature predictions whose predict particle size is very large. In contrast, this paper attempts to use convolutional neural network (CNN) to predict the point temperature of grain piles. The CNN-based approach uses multiple convolution kernels that share weights to capture the characteristics of grain temperature at different locations, which make full use of the temperature information around the target point. Experiments on real business data show that compared to other conventional algorithms, CNN has the best prediction effect on point temperature prediction problems.
Absurd Sounds

Absurd (derived from surdus, or deaf, in Latin): that which cannot be heard or is contradictory to reason. This artistic research project questions the ways we work with sound, using a methodology derived from the absurd as a tool for innovation. The scientific roots of sound as a physical phenomenon are rarely disputed, whether by sound artists, sound designers, sound technicians, or even composers. Yet this trust in the knowledge built by acousticians, so complex and yet so simple as it mostly limits the characteristics of sound to amplitude, pitch, timbre, and envelope, might lead to an incapacity to look any other way. Have we missed, as artists, the opportunity to question the underlying axioms beneath the science of sound? This research uses a method based on the rejection — or, better, a questioning — of established rules and ideas (e.g., ‘what if silence were a building tool with which we carved music and sounds into blocks of noise?’), followed by a series of logical developments until some new technique, idea, or even form of art emerges. Such a method somehow adopts the principles behind conspiracy theories and applies them to art: absurd premises followed by logical developments. The present exposition develops a set of three case studies built between 2017 and 2020, each one answering its own absurd question: the harvesting of rare sounds made by worms (what if silence were louder than noise?); an experiment carried out on humans, by loudspeakers sitting as members of the audience (what if there were no ‘sweet spot’?); and an attempt to blur the lines between the real and the virtual, while developing new ways to document sound art (what if there were no (virtual) reality?). Cover photo for the exposition 'Absurd Sounds' by Anders Sune Berg, 2017.
Internet Regulation and Online Censorship

This paper explores the development of Internet regulation policies worldwide since the birth of the World Wide Web, describes the advantages and disadvantages of the main filtering methods in use today, and presents two of the most important Internet Regulation Systems (IRS) implemented in authoritarian regimes and Western democracies around the globe. Moreover, the authors propose the conduction of well-designed surveys worldwide in order to measure Internet User's opinion and use such results as a starting point for developing a fair “Internet Regulation System” (fair IRS) in the future. Last, the authors introduce a new online tool for conducting related surveys, www.WebObserver.net project.
Magic

Chemistry & IndustryVolume 84, Issue 11 p. 18-21 Materials Magic First published: 10 November 2020 https://doi.org/10.1002/cind.8411_6.xAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Volume84, Issue11November 2020Pages 18-21 RelatedInformation
Encryption Schemes

An introduction to cryptographic primitives would not be complete without a chapter on encryption schemes. While encryption capabilities are not amongst the many properties that we often ascribe to hash functions,1 hash functions can be used in the design of encryption schemes and play a vital part in larger cryptographic protocols that deploy encryption as one component.
Abstract

An abstract class provides a partial implementation that other classes can build upon. When a class is declared as abstract, it means that the class can contain incomplete methods that must be implemented in child classes, in addition to normal class members.