Reviewerly/exHarmony huggingface dataset:
Efficient Attribute Based Encryption Outsourcing in Cloud Storage with User Revocation

By providing a function called Cloud Storage, the company let participants outsource their confidential data to a third party and use the on-demand services and applications of data on the organization&#x0027;s cloud storage server. With this, researchers would be able to encrypt details, which would will be crucial in preventing security breaches of confidential details. In this exchange conference, businesses have to encrypt their data before they process it to the Cloud framework. Attribute Based Encryption (ABE) system is a symmetric key dependent cryptosystem used in cloud system that lets device users, software, data and programmers access managed digital data. Unfortunately, BAE suffers from a performance downside with outsourcing the operation of decrypting the secret. There has been a lot of suggestions put forward as to how to boost the performance of the method. It would be to the same investigation that was stated in the research study. We take the case of the Robust Paraphrasing and conclude there is a new implementation of the Electronic Referencing tool, even though it depends on the Actuals. The load testing strategy is used to minimize the expense of outsourcing the decryption phase to a third-party data decryption service provider. Load balancing may occur by considering features such as file space, memory, hard drive disc usage, etc. For the intent of revocation of key for a community, we often discuss the problem of the key consumer quitting the group. Therefore, in the case of the key user leaving the group, the latest key to open a group should be modified and circulated to all current key holders. The experimental findings of this proposed method proves that the time and memory consumptions of this proposed system were comparable to, if not higher than, the current system of time consumptions and memory usage.
Fast Implementation of Digital Signatures Using Parallel Techniques

Digital signatures are widely used to check the authenticity of the identity of the signatory of the message/document and the integrity of the message sent. They are also used by the receiver for ensuring non-repudiation by the sender. They play an important role in making day-to-day processes electronic and paperless. Digital signatures are based on public key infrastructure (PKI). The message digest (hash) of the file is signed by the sender using a private key and appended to the file. The recipient extracts the signature, decrypting it with the sender&#x0027;s public key, and verifies if the received digest matches its own hash calculations. However, complex calculations for secure signatures imply that digital signatures are time consuming for large files. Hashing is the basic security mechanism used in digital signatures that is performed by all the parties and consumes most of the time. This paper presents a solution to this problem by using parallel hashing to achieve fast digital signatures, discussing two possible approaches. The first one uses only parallel hashing, keeping the rest of the algorithm the same as the reference algorithm based on RSA. The second approach parallelizes the entire reference algorithm. Both were implemented using the OpenMP framework, and the experimental results show a significant decline in the execution time in both the cases.
Secure Data Contribution and Retrieval in Social Networks Using Effective Privacy Preserving Data Mining Techniques

Privacy Preserving Data-Mining is the major use of data- mining methods for ensuring privacy of personal information. Data-mining algorithms search for the information that is most valuable. A major aspect of Privacy Preserving Data Mining is the safeguarding of sensitive information against unauthorized access. The Secure Data Contribution Retrieval algorithm assigns a privacy policy and security is assigned depending on the application needs and compatibility. This method is capable of meeting the requirements for numerous datasets. Currently, social media sites such as Facebook, Twitter, and YouTube are quite popular. Then, the expanded attribute-based encryption methodology allows users to transfer data contents across orbit software networks. Data leakage occurs during the gathering and storage of user Orbit Software Networks in an insecure distributed or centralized system. Third, the suggested Level by Level Security Optimization and Content Visualization algorithm helps prevent privacy problems when sharing information and visualizing data. They use privacy levels at the individual level following the assessment of the privacy compatibility of orbit software networks application. Experimental analysis employs the data from social datasets.
Software Development Effort Estimation Using Machine Learning Techniques: Multi-linear Regression versus Random Forest

The software development industry has lately become quite intricate, at a global level. As the tools and technologies used keep changing, so does the approach of developing a software. Thus, software effort estimation plays a critical role in doing so. This arises a challenge of accurately estimating the software development effort, and then proceeding with the plan of development. The history shows various algorithmic cost estimation models like Boehm&#x0027;s COCOMO model, Putnam&#x0027;s SLIM, Multiple Regression, Statistical models, and many non-algorithmic soft computing models]. Despite multiple techniques, achieving a higher accuracy of effort estimation has always been challenging. This paper is concerned with a comparison between two algorithmic regression models, one using Multiple Regression, and another model using Random Forest Regression, to predict the estimation of software development effort. It is observed that Random Forest Regression is successfully able to model the complex, by closely matching the effort estimated in the dataset, providing a better accuracy.
Spatial Domain Texture Synthesis for Data Embedding

Secrete information is embedded in some cover medium through steganography. The contents of information as well as existence of information must be undetectable to attackers. Steganography is normally done by slightly altering the pixel values of cover image. We have used texture synthesis process for embedding the data instead of changing pixel values. Correlation at joining edges of patches to be stitched is considered for suitable patch selection. Energy of candidate patches is the parameter used to verify uniqueness of candidate patches and to identify patch in data extraction process. Along with energy, other parameters like mean as well as mean, variance, kurtosis and skewness combined are experimented. The data extraction rate in presence of different stego attacks is observed.
Construction and Performance Analysis of a Groomed Polarity Lexicon Derived from Product Review Source Datasets

Using a large, publicly-available dataset [1], we extract over 51 million product reviews. We split and associate each word of each review comment with the review score and store the resulting 3.7 billion word- and score pairs in a relational database. We cleanse the data, grooming the dataset against a standard English dictionary, and create an aggregation model based on word count distributions across review scores. This renders a model dataset of words, each associated with an overall positive or negative polarity sentiment score based on star rating which we correct and normalise across the set. To test the efficacy of the dataset for sentiment classification, we ingest a secondary cross-domain public dataset containing freeform text data and perform sentiment analysis against this dataset. We then compare our model performance against human classification performance by enlisting human volunteers to rate the same data samples. We find our model emulates human judgement reasonably well, reaching correct conclusions in 56% of cases, albeit with significant variance when classifying at a coarse grain. At the fine grain, we find our model can track human judgement to within a 7% margin for some cases. We consider potential improvements to our method and further applications, and the limitations of the lexicon-based approach in cross-domain, big data environments.
Network eavesdropping

The subject of the article is the surveillance of ICT networks, commonly known as network eavesdropping or network wiretapping.First, the author presents the basic technical aspects of wiretapping.The main part of the article discusses international regulations (the Convention on Cybercrime), European Union regulations (Directive 2013/40 on attacks against information systems) and Polish regulations (Penal Code) concerning network eavesdropping.The last part of the article contains conclusions from the comparison of the provisions of the Cybercrime Convention, Directive 2013/40 and the Polish Penal Code.
The e-learning survey analysis in Universitas Sarjanawiyata Tamansiswa: Should it be improved?

E-learning is not a new issue in education. This research aims to analyze the readiness of early-semester students to implement e-learning at Universitas Sarjanawiyata Tamansiswa. The readiness of students in facing lectures in this pandemic period is considered to be an important issue in the implementation of online lectures. This study is qualitative research using survey methods to describe students' responses to university e-learning in an even semester. This research was conducted by disseminating survey instruments using the RASE model to students using SIPEDAR (university e-learning platform) and academic portal. The research subjects were students in the Educational Research and Evaluation Post-Graduate program and undergraduate science education program in Universitas Sajanawiyata Tamansiswa. The finding of this study stated that Students' responses on e-learning resources had an average of 3.29 (excellent). The students' responses to the activity features in e-learning reached an average of 3.25 (excellent). The student's response to the support features in e-learning with an average of 3.16 (excellent), and The student's response indicator on evaluation in e-learning by 3.21 (excellent).
Study of the influence of behavioral skills on the development of media education of future specialists

In the article, the study of the influence of behavioral skills on the development of media education of future specialists is considered as an actual problem. It is proved that behavioral skills are a real manifestation of activity and communication. It is proved that future specialists, combining theory and practice in their practice, along with mastering professional skills, find what is necessary among a huge amount of information, analyze, formulate and use it in their practice in a new way. It is established that the concepts of "Media Education", "Media literacy", "Psychological health" and "Psychological immunity" are interrelated. It is analyzed that the media is not only a carrier of information, but also provides the process of information perception. Using a special technique, it is shown that Coping is a type of behavior that reflects a willingness to solve life problems and is aimed at adapting to a specific situation. The methodology made it possible to identify priority coping strategies.
A Study of Limitations of Artificial Intelligence and Creative Education of Future Information

When we discuss education under the background of the development of artificial intelligence, we must first analyze what algorithm-based artificial intelligence can and cannot do. Our education should focus on training people to learn and exercise the abilities that artificial intelligence is not good at or lacks. Only in this way can we give full play to the greatest potential of human intelligence under the blessing of artificial intelligence. Philosophy of information puts forward the idea of an information creation system to describe what kind of information creation ability real intelligence should have. Considering the functions of 9 subsystems, AI has completely or simply possessed some of the subsystem functions, but it still seriously lacks the capabilities of three subsystems. The future education of information creation is launched for the lack of abilities of these three artificial intelligences.
Hands Off: A Handshake Interaction Detection and Localization Model for COVID-19 Threat Control

The COVID-19 outbreak has affected millions of people across the globe and is continuing to spread at a drastic scale. Out of the numerous steps taken to control the spread of the virus, social distancing has been a crucial and effective practice. However, recent reports of social distancing violations suggest the need for non-intrusive detection techniques to ensure safety in public spaces. In this paper, a real-time detection model is proposed to identify handshake interactions in a range of realistic scenarios with multiple people in the scene and also detect multiple interactions in a single frame. The efficacy of the proposed model was evaluated across two different datasets on more than 3200 frames, thus enabling a robust localization model in different environments. The proposed model is the first dyadic interaction localizer in a multi-person setting, which enables it to be used in public spaces to identify handshake interactions and thereby identify and mitigate COVID-19 transmission.
Part II of On the Logic of Modalities

The ultimate goal of this part is to show that a proper understanding of the problem of modalities gives insight (i) into the conceptual presuppositions at the basis of the intuitionistic foundation of mathematics as well as (ii) into the relations between classical and intuitionistic logic. And, finally, (iii) to bring to light the relations between the alethic modalities, necessity and possibility, and the temporal modes of the present, on the one side, and on the past and the future of the other side.
Appendix

This Appendix is addressed in particular to the readers who are also interested in “technical” logical details. It is organized as follows.
Introduction

Oskar Becker was involved with Nazism—he was indeed, as is written in an official file of the SS Security Service, “not a party member but a loyal to National Socialism, who tries to consolidate the National Socialistic ideology”.
Part I of On the Logic of Modalities

Since Becker as well as MacColl and Lewis all refer to the old controversy about the right interpretation of conditional sentences, let us briefly dwell on this issue.
Combining Zeroth and First-Order Analysis with Lagrange Polynomials to Reduce Artefacts in Live Concatenative Granulation

This paper presents a technique addressing signal discontinuity and concatenation artefacts in real-time granular processing with rectangular windowing. By combining zero-crossing synchronicity, first-order derivative analysis, and Lagrange polynomials, we can generate streams of uncorrelated and non-overlapping sonic fragments with minimal low-order derivatives discontinuities. The resulting open-source algorithm, implemented in the Faust language, provides a versatile real-time software for dynamical looping, wavetable oscillation, and granulation with reduced artefacts due to rectangular windowing and no artefacts from overlap-add-to-one techniques commonly deployed in granular processing.
Alloy Sounds: Non-Repeating Sound Textures with Probabilistic Cellular Automata

Contemporary musicians commonly face the challenge of finding new, characteristic sounds that can make their compositions more distinct. They often resort to computers and algorithms, which can significantly aid in creative processes by generating unexpected material in controlled probabilistic processes. In particular, algorithms that present emergent behaviors, like genetic algorithms and cellular automata, have fostered a broad diversity of musical explorations. This article proposes an original technique for the computer-assisted creation and manipulation of sound textures. The technique uses Probabilistic Cellular Automata, which are yet seldom explored in the music domain, to blend two audio tracks into a third, different one. The proposed blending process works by dividing the source tracks into frequency bands and then associating each of the automaton's cell to a frequency band. Only one source, chosen by the cell's state, is active within each band. The resulting track has a non-repeating textural pattern that follows the changes in the Cellular Automata. This blending process allows the musician to choose the original material and the blend granularity, significantly changing the resulting blends. We demonstrate how to use the proposed blending process in sound design and its application in experimental and popular music.
One-to-Many Conversion for Percussive Samples

A filtering algorithm for generating subtle random variations in sampled sounds is proposed. Using only one recording for impact sound effects or drum machine sounds results in unrealistic repetitiveness during consecutive playback. This paper studies spectral variations in repeated knocking sounds and in three drum sounds: a hihat, a snare, and a tomtom. The proposed method uses a short pseudo-random velvet-noise filter and a low-shelf filter to produce timbral variations targeted at appropriate spectral regions, yielding potentially an endless number of new realistic versions of a single percussive sampled sound. The realism of the resulting processed sounds is studied in a listening test. The results show that the sound quality obtained with the proposed algorithm is at least as good as that of a previous method while using 77% fewer computational operations. The algorithm is widely applicable to computer-generated music and game audio.
Discrete-Time Hyperchaotic System Synchronization

Abstract Nonlinear systems with hyperchaotic dynamical behaviour can be used in signal encryption. The present contribution presents the design of hyperchaotic discrete-time emitter circuit the synchronizing receiver and their dynamics. Possible application in wide-band signal transmission is presented by using error dynamics method.
Seamless Remote Rendering of DICOM Images

Many solutions display DICOM images locally or in a web browser but require that the full DICOM images be transported over the network or on a physical medium. As DICOM image size and the number of DICOM images increase, their transport over the Internet becomes more difficult. Contemporary computer resources used by physicians cannot keep up with the ever-increasing amount of new DICOM data, which complicates and prolongs diagnosis. This problem is exacerbated in the COVID crisis, making it difficult to examine patients, communicate between patients and physicians, and have physical access to DICOM images and volumetric data. In this paper, we present a distributed architecture for remote rendering of DICOM images. We propose an innovative web-based solution hierarchically divided into several levels that can be used to manage patient data, DICOM images, and radiology reports based on those images. The architecture is organized to leverage existing hardware, storage, and telecommunications infrastructure. We have approached the solution in such a way that the images are stored in healthcare facilities, preferably where they were taken, and only the necessary data is securely and seamlessly transported to the physicians. Our carefully engineered solution shows significant improvements in terms of rendering time and speed of data transfer, which is about 110 ms for an image with a resolution of 1996 by 2457 pixels, achieving seamless user experience for at least Full HD images.
Evaluation Model of the Effect of Banning Bottled Water Sales Based on Analytic Hierarchy Process

Drinking bottled water has become a popular trend since it is very convenient and it is seems as having a better quality. While at the same time, the substantial consumption also leads to even larger harmful effect on the environment. Being conscious of this fact, communities commence to try to ban the sales of bottled water. As more and more bylaws appear, and the effect of ban appear differently, the objective evaluation of the impact became really significant. Firstly, in the light of personal understanding and various data from internet, we recognize eight potential impacts of the ban in respects of environment, economy, and individual. To solve the coefficients in the equations, we use the data of some specific cities or countries to determine. Having the solved functions, we develop a weight system using Analytic Hierarchy Process (AHP) to determine the relative significance of these impacts. Then, we apply them on the statistics of large cities in the Unities States that we found on Wikipedia. Even though the problem only required to consider Concord and San Francisco, we think a bigger sample - large cities - will be more appropriate. With the data of large cities, we first cluster it using k-means clustering and then we apply Entropy Method to each cluster to determine the weight. Thus, a refined scoring system is developed. Eventually, to enhance the generalizability of the model, the case of states is involved in our consideration. Similar to the basic model, clustering analysis is used first, and followed by Entropy Method. After that, we compare and contrast the different weight system. Besides, we also take the various effect of different beverages into consideration. Systematic clustering analysis is used to split the diverse beverages.
Learning causal theories with non-reversible MCMC methods

Abstract Causal laws are defined in terms of concepts and the causal relations between them. Following Kemp et al. (2010), we investigate the performance of the hierarchical Bayesian model, in which causal systems are represented by directed acyclic graphs (DAGs) with nodes divided into distinct categories. This paper presents two non-reversible search and score algorithms ( Q 1 and Q 2 ) and their application to the causal learning system. The algorithms run through the pairs of class-assignment vectors and graph structures and choose the one which maximizes the probability of given observations. The model discovers latent classes in relational data and the number of these classes and predicts relations between objects belonging to them. We evaluate its performance on prediction tasks from the behavioural experiment about human cognition. Within the discussed approach, we solve a simplified prediction problem when object classification is known in advance. Finally, we describe the experimental procedure allowing in-depth analysis of the efficiency and scalability of both search and score algorithms.