Reviewerly/exHarmony huggingface dataset:
An Interactive Approach to the Next Generation Quantum Computing

An Interactive Approach to the Next Generation Quantum Computingperformance statistics over traditional computers.The mentioning of Qubits changes the fate of computing where we have the delay in finding the solutions for much complex problems.The phenomenon used in this approach resembles the Quantum theory in which it imparts the features of superposition and entanglement. By holding all the bit positions may lead to storage issues but with the superposition we may find the combination at any one the state. Sometimes it even given drastic changes to the computing community. The top level MNC's are also trying to have a quantum computer to reduce error rates, achieving faster response times and tracking of satellites etc. It is equivalent to the performance of supercomputer in a cost-effective way. By the process of annealing I will produce better results. On periods it creates the dilemma by the feature of superposition. The way it operates the feature of de coherence is remarkable. But with the existing hardware performing robust error detections and achieving better performance is miles away.it has a special combination of Qubit organisation where it combines the with adjacent 8-bit combos. To make effective algorithms to work with level of high expectations with this normal hardware is disastrous.A different hardware has been accompanied to design the strong algorithms which moves the traditional computer computational strategy to the knees of this.Moreover, it will take some years of time to sustain as a general computer, researchers striving hard to make this happen.
Framework Model of Personalized Learning Recommendation System Based on Deep Learning Under the Background of Big Data

In the context of big data, information overload has emerged in various resources, especially the various learning resources available today. In order to effectively improve the recommendation accuracy of learning resources under the background of big data, firstly, the existing research on the recommendation system is analyzed. Then, based on the deep learning model, the deep learning model framework and the personalized learning recommendation framework are constructed. How to effectively obtain user and learning resource information. Finally, through the proposed framework model, the accuracy of personalized learning recommendations can be effectively improved.
Graph-Based Node Finding in Big Complex Contextual Social Graphs

Graph pattern matching is to find the subgraphs matching the given pattern graphs. In complex contextual social networks, considering the constraints of social contexts like the social relationships, the social trust, and the social positions, users are interested in the top-K matches of a specific node (denoted as the designated node) based on a pattern graph, rather than the entire set of graph matching. This inspires the conText-Aware Graph pattern-based top-K designated node matching (TAG-K) problem, which is NP-complete. Targeting this challenging problem, we propose a recurrent neural network- (RNN-) based Monte Carlo Tree Search algorithm (RN-MCTS), which automatically balances exploring new possible matches and extending existing matches. The RNN encodes the subgraph and maps it to a policy which is used to guide the MCTS. The experimental results demonstrate that our proposed algorithm outperforms the state-of-the-art methods in terms of both efficiency and effectiveness.
Decentralized Electronic Health Record System

With a view to overcome the shortcomings of traditional Electronic Health Record (EHR) system so as to assure the interoperability by providing open access to sensitive health data, while still preserving personal data privacy, anonymity and avoiding data misuse, Decentralized Electronic Health Record System was developed. The aforementioned issue concerning traditional EHR system can be addressed by implication of emerging technology of the era namely Block chain, together with Inter Planetary File System (IPFS) which enables data sharing in decentralized and transactional fashion, thereby maintaining delicate balance between privacy and accessibility of electronic health records. A block chain based EHR system has been built for secure, efficient and interoperable access to medical records by both patients and doctors while preserving privacy of the sensitive patient’s information. Patients can easily and comprehensively access to their medical records across providers and treatment sites using unique properties of block chain and decentralized storage. A separate portal for both the patients and doctors has been built enabling the smart contracts to handle further interaction between doctors and patients. So, in this system, it is demonstrated how principles of decentralization and block chain architectures could contribute to EHR system using Ethereum smart contracts and IPFS to orchestrate a suitable system governing the medical record access while providing patients with comprehensive record review along with consideration for audit ability and data sharing.
European Science Editing is in full open access now

I am excited to announce that with this volume European Science Editing (ESE) has shifted from the print to a fully digital open access version. The journal underwent several changes last year. First of all, our publisher, the European Association of Science Editors (EASE) was generously offered &amp;ndash; and accepted &amp;ndash; a new ARPHA submission system (powered by PenSoft). Together with the EASE president Pippa Smart and EASE Council, we decided to transform ESE into a fully open access online journal. After several months of planning and re-thinking our strategy, a small working group (some members of the EASE Council and of ESE&amp;rsquo;s associate editors) prepared a proposal, the main idea of which was to divide the journal in two overlapping publications: European Science Editing and EASE Digest. The former will continue to publish original articles, reviews (formerly &amp;ldquo;essays&amp;rdquo;), viewpoints, and correspondence using the fully open access ARPHA submission system (flow publishing) but will drop the other sections, namely News notes, The editor&amp;rsquo;s bookshelf, This site I like, and EASE Forum Digest). These sections, which our readers consider particularly valuable, will now be published in EASE Digest with a few selected articles from ESE. The Digest will be available to EASE members only. As the proposal was accepted by the EASE Council in September 2019, the journal&amp;rsquo;s transformation is already under way. I wish to thank Silvia Maina (This site I like), Fiona Murphy (Book reviews), Elise Langdon-Neuner (EASE-Forum Digest), Anna Maria Rossi (The Editor&amp;rsquo;s bookshelf), and James Hartley and Denys Wheatley (members of the International Advisory Board) for the great work they have done and for their cooperation.
Research on the Influence of Interactive Teaching Method on the Learning Effect of College English

English has been a subject in the university classroom since the 1980s. This subject helps produce more excellent talents in economics, politics, and culture who are proficient in English language in China, and enable our country to better integrate with the global economy. At the same time, the relationship between our country and the international community has become closer. However, the social environment is constantly developing and changing. The traditional teaching methods can be said to be ineffective and associated with many shortcomings. Traditional teaching methods were used in the English lessons. Under this teaching mode, students were the passive subjects on the receiving end of what have been taught. The teacher was always talking in the classroom and the students were listening. This teaching style also affects the effectiveness of learning. Compared with traditional teaching, interactive teaching method has its unique advantages. It is a new teaching mode and under this teaching mode, students can actively communicate with teachers in the classroom to complete learning tasks. The initiative to perform self-directed learning is also greatly improved, and the students’ English proficiency can be improved.
Pengembangan Sistem Pendukung Keputusan Untuk Seleksi Penerimaan Beasiswa Dengan Metode Saw Dan Topsis : Studi Kasus Universitas Muhammadiyah Tangerang

Scholarships are a work program in every university. With the aim of reducing costs for underprivileged students. The problem faced during the selection process is that it takes a long time to recapitulate prospective recipient data and it is difficult to provide appropriate results so that it is not well targeted in determining the awarding of scholarships. The goal is to develop a decision support system design so that it is right on target and can help decision makers more quickly determine the recipients of scholarships. One method that can be used in the selection process is by using the Saw and Topsis methods. Because the concept is simple, easy to understand, its computation is efficient and has the ability to measure the relative performance of each decision alternative in a simple mathematical form. The criteria used are the GPA, income of parents, dependents of parents, participation in organizations and semesters. The results of the research by combining the Saw and Topsis methodshave been successfully built so that the results given are more targeted and can speed up the time in selecting scholarship data.
An Information Intelligent Search Method for Computer Forensics Based on Text Similarity

Data analysis is one of the research hotspots in the field of computer forensics. In the file system of the computer, there are files created and browsed by the user. The file contains information such as user information and transaction processing. This information can help forensic agency solve query valuable information, computer forensics, and other illegal and criminal activities. But how to find valuable information from a large amount of computer data is a major challenge. In order to achieve this goal, this paper proposes an information intelligent search method for computer forensics based on text similarity. This method is divided into two processes. First, information extraction technology is used to obtain the summary and keyword information of each text file on the computer. Then combine text similarity and keyword search algorithm to realize the search and analysis of semantic text. The experimental results show that this method can effectively solve the defects of traditional pattern matching algorithm and improve the effect of automatic data analysis.
Information Flow-Based Security Construction for Compositional Interface Automata

Information flow has been considered as a critical requirement to solve security related issues for complicated component-based system. However, security conditions are often fragile and general security properties may be not available to enforce the composition. Thus, this paper gives the computation model of interface automata (IA) and studies how the compositional interfaces behave to capture the information leakage with security process algebra (SPA) language. And we find that persistent bisimulation-based non deducibility property is preserved under composition, while it is not fully applicable to IA model. So several sufficient conditions for the property is developed to apply for composition of interface automata. Those conditions are given as theorems and proved efficiently to analyze security. Finally, we cite a classical instance to handle the composition and use an automatic verification software to test the correctness of our algorithms on compositional conditions.
An Exploratory Study of Requirements Engineering Practices in Indonesia â€“ Part 2: Efforts, Processes and Techniques

This paper provides the second part of statistical research findings of an exploratory study of the requirements engineering practices implemented in software development processes in Indonesia. This second part attempts to reveal facts regarding efforts, processes and techniques exist in such requirements engineering practices. Such facts were captured in accordance with the first part which were surveyed through a comprehensive online questionnaire consisting of both closed- and open-ended questions. We invited 158 participant candidates representing industry and higher education institutions, however, 31 of them joined our web-based survey. Results which respect to efforts, processes and techniques are presented along with related interpretations.
What Does Recent Survey Data Say About the Effect of Corruption on Poverty in Africa?

Poverty is perpetuated by increased levels of corruption. It diverts resources, which denies the poor masses their right to enjoy necessities to improve their living standards. To estimate the impact of corruption on poverty, the study relied on the random effect, fixed effects and the instrumental variable regression techniques. The estimates from the instrumental variable regression show that OLS underestimates the effect of corruption on poverty levels in Africa. That's, it shows that the OLS estimates are biased downwards due to inconsistencies as a result of the endogeneity of the levels of corruption. While the instrumental variable technique produces an estimated effect ranging from .805-1.073 increased levels of corruption on lived poverty index, the OLS estimates an impact within a range of approximately .058-.168. This paper confirms the governance model of the effect of corruption on poverty through its effects on reducing the credibility of public institutions. The study thus recommends that public institutions must be strengthened, financed and be equipped to be able to apply the rule of law, thereby helping reduce corruption.
Formation of Leading Universities: World Practice and Russian Perspective

The subject of the research presented is the phenomenon of university leadership and its conditions and factors. The study is based on “success stories” of leading universities in various countries: Harvard and Stanford Universities (USA), Massachusetts Institute of Technology (USA), Shanghai Jiao Tong University (China), Indian Institute of Technology Bombay (India), Pohang University of Science and Technology (Korea), National Research University Higher School of Economics (Russia). We can conclude that universities become leaders if they manage to “ride the wave” of important changes for society and support the deployment of these processes, as well as use their energy and the emerging opportunities. An important factor is the activity of key partners that support the development of a leading university: professional communities, authorities, governmental institutions and large companies. The impact of leading universities on the development of the regions and countries where they are located is formulated as follows: they form elites; create an educated society, bring up professionals; produce and distribute new knowledge, technological, organizational and institutional solutions; directly develop the economy through the creation of new enterprises and jobs. Leading universities initiate cooperation on a regional and global scale; solving the problems faced by the humanity; they set new future-oriented objectives and goals. In the history of universities, we can observe various relations with the state: partnership of universities and authorities or state domination over universities. The implementation of the strategic interests of the state can be combined with the freedom and activeness of universities, modelling the promising practices of the future “on themselves”. At the same time, the situation in Russia is characterized by the actual absence of “universities as communities” demonstrating a high level of academic autonomy, their own vision of the prospects for the economy, technology, science, education and formulating their own independent strategies
A cost-effective 2-tier security paradigm to safeguard cloud data with faster authentication

The recent technological advancement has taken cloud computing (CC) infrastructure to a significant level where the increasing level of research interest laid upon cost-effective storage management. Owing to the potential distributed and pervasive storage facility, it lacks efficiency towards fully preserving the integrity of user data attributes. Thereby, the collaborative sharing of user data leads to a situation which opens up various forms of security loop-holes. In the past various forms of security protocols are witnessed which have attempted to solve this similar issue with cryptographic solutions but at the same time lacks sustainability and robustness from a computational perspective. Thereby, the study introduces a 2-tier framework which offers higher-degree of access control along with Virtual Machine (VM) storage security. The study basically optimizes the performance of the model by speeding up the authentication process. The performance validation of the system has been done with respect to conventional encryption standards. The outcome obtained demonstrate that the proposed solution outperforms the existing security standards in terms of processing time, time to generate a secret key and key size for encryption.
CREDIT CARD FRAUD DETECTION

Now a day’s online transactions have become an important and necessary part of our lives. It is vital that credit card companies are able to identify fraudulent credit card transactions so that customers are not charged for items that they did not purchase. As frequency of transactions is increasing, number of fraudulent transactions are also increasing rapidly. Such problems can be tackled with Machine Learning with its algorithms. This project intends to illustrate the modelling of a data set using machine learning with Credit Card Fraud Detection. Fraud detection has become an important tool and probably the best way to stop such frauds. The Credit Card Fraud Detection Problem includes modelling past credit card transactions with the data of the ones that turned out to be fraud. This model is then used to recognize whether a new transaction is fraudulent or not. Our objective here is to detect 100% of the fraudulent transactions while minimizing the incorrect fraud classifications.
HIGHER SCHOOL TEACHERS’ DIGITAL COMPETENCE: STRATEGIES FOR SELF-ASSESSMENT AND IMPROVEMENT

The paper was aimed at examining higher school teachers’ strategies for self-assessing and improving digital competence and finding out the range of the most effective and innovative ones. The research sample consisted of 115 higher school teachers from four higher educational establishments in Ukraine. The research sample was selected with the use of a non-random sampling technique. Personal data of research sample were taken into consideration i.e. demography, gender balance, age and specialty. The research strategy for collecting empirical data combined a web-based questionnaire, individual interviews, conversations with respondents and analysis of reflexive texts. The collected empirical data were then analyzed both quantitatively and qualitatively. The data were processed by means of Microsoft Excel 2016 and verified by Chronbach’ Alfa (An Index of Reliability). During the development of a web-based questionnaire the authors defined seven cognitive content-components of higher school teachers’ digital competence and each of them was given a synthetic index. The obtained findings showed that higher school teachers had different levels of digital competence: ranging from low to high, and revealed the diversity in peculiarities of its manifestations and ways of thinking when operating the competence mentioned. The research results served as the basis for offering innovative strategies for self-assessing and improving higher school teachers’ digital competence.
Fast Forensic Triage Using Centralised Thumbnail Caches on Windows Operating Systems

A common investigative task is to identify known contraband images on a device, which typically involves calculating cryptographic hashes for all the files on a disk and checking these against a database of known contraband.However, modern drives are now so large that it can take several hours just to read this data from the disk, and can contribute to the large investigative backlogs suffered by many law enforcement bodies.Digital forensic triage techniques may thus be used to prioritise evidence and effect faster investigation turnarounds.This paper proposes a new forensic triage method for investigating disk evidence relating to picture files, making use of centralised thumbnail caches that are present in the Windows operating system.Such centralised caches serve as a catalogue of images on the device, allowing for fast triage.This work includes a comprehensive analysis of the thumbnail variants across a range of windows operating systems, which causes difficulties when detecting contraband using cryptographic hash databases.A novel method for large-scale hash database generation is described which allows precalculated cryptographic hash databases to be built from arbitrary image sets for use in thumbnail contraband detection.This approach allows for cryptographic hashes to be generated for multiple Windows versions from the original source image, facilitating wider detection.Finally, a more flexible approach is also proposed which makes novel use of perceptual hashing techniques, mitigating issues caused by the differences between thumbnails across Windows versions.A key contribution of this work demonstrates that by using new techniques, thumbnail caches can be used to robustly and effectively detect contraband in seconds, with processing times being largely independent of disk capacity.
2SRM: Learning social signals for predicting relevant search results

Search systems based on both professional meta-data (e.g., title, description, etc.) and social signals (e.g., like, comment, rating, etc.) from social networks is the trending topic in information retrieval (IR) field. This paper presents 2SRM (Social Signals Relevance Model), an approach of IR wh ich takes into account social signals (users' actions) as an additional information to enhance a search. We hypothesize that these signals can play a role to estimate a priori social importance (relevance) of the resource (document). In this paper, we first study the impact of each such signal on retrieval performance. Next, some social properties such as popularity, reputation and freshness are quantified using several signals. The 2SRM combines the social relevance, estimated from these social signals and properties, with the conventional textual relevance. Finally, we investigate the effect of the social signals on the retrieval effectiveness using state-of-the-art learning approaches. In order to identify the most effective signals, we adopt feature selection algorithms and the correlation between the signals. We evaluated the effectiveness of our approach on both IMDb (Internet Movie Databese) and SBS (Social Book Search) datasets containing movies and books resources and their social characteristics collected from several social networks. Our experimental results are statistically significant, and reveal that incorporating social signals in retrieval model is a promising approach for improving the retrieval performance.