Reviewerly/exHarmony huggingface dataset:
New Instances of Quantum Error-Correcting Codes for Single Deletion Errors

This paper provides new instances of quantum error-correcting codes. According to recent results by Nakayama and Hagiwara, by using two sets of binary sequences that satisfy three conditions, we can construct quantum error-correcting codes for single deletion errors. However, only two instances of two sets that satisfy the three conditions have been found to date. The present study constructs two infinite series of sets that satisfy the three conditions. In other words, this paper constructs new infinite series of quantum error-correcting codes for single deletion errors.
USING AUGMENTED REALITY TO INTRODUCE THE BOHR ATOMIC MODEL IN 3D IN THE CLASSROOM – A STUDY ABOUT LEARNING EFFECTS AND PERCEPTION OF STUDENTS

Information about the paper titled "USING AUGMENTED REALITY TO INTRODUCE THE BOHR ATOMIC MODEL IN 3D IN THE CLASSROOM – A STUDY ABOUT LEARNING EFFECTS AND PERCEPTION OF STUDENTS" at IATED Digital Library
WorldSkills Competition as an Efficient Engineer Training Technology

The article covers the phenomenon of the positive impact of the WorldSkills championship movement on improving the training of engineers at universities. Technologies of training young professionals developed in the WorldSkills championship movement have been effectively applied in universities in the last years. This applies to improve the content of educational programs and curricula . WorldSkills movement allows future engineers to improve their professional skills in working professions related to the future profession of engineer. The training base and training ground for students are being improved, direct contacts with different companies and representatives from the government are being formed during competitions. Experts claim that approximately every country in the world will encounter a lack of personnel in key sectors of the economy by 2025. We observe a rapid shift to the digital economy and see how new competencies are emerging and existing ones are developing. In 2020 the situation with coronavirus confirms the relevance of a rapid shift to the digital economy and improvement of existing competencies. Therefore, WorldSkills movement plays an important role in these processes. The consequences of existing trends in the development of the global economy are job cuts, the appearance of new people, and retraining staff to the digital economy and destruction of the usual mechanisms «guarantees of the future» (career guidance, long-term employment, and decent pension). These statements were cleared up with the support of Young professionals [1]. On the other hand, there is a declining interest in engineering professions all over the world. Nowadays only a small percentage of young people choose technical specialties and engineering education. Therefore, one of the most important problems is the awakening of young people's interest in engineering specialties from school days and while studying in colleges [2] . All over the world technological and engineering universities try to persuade young learners and students to study technical studies and engineering professions as a possible future career. That is why it is important to make new informal and other methods of developing an interest in engineering professions [3, 4]. International Young professionals WorldSkills movement has a crucial role in the process of training and teaching youngsters to adapt to new realias. And also provide engineering universities with effective methods and technologies for attracting talented young people to engineering professions and training of future engineers.
Becoming immersed: Using Virtual Reality technologies in academic libraries to expand outreach and enhance course curricula

In the fall of 2018, the Binghamton University Libraries purchased Virtual Reality (VR) headsets and accessories to provide an innovative service on campus and extend its outreach. The availability of the technology through the Libraries’ allowed a nursing course to incorporate it into their teaching. The Libraries also hosted monthly VR outreach events and included the technology in a Digital Humanities Research Institute (a four-day intensive workshop). This article explains these uses, describes VR’s ability to be adopted into various learning spaces, and discusses the future integration of VR into course curricula. Limitations encountered and potential challenges are also discussed.
Early Detection of Sepsis on Clinical Data Using Multi-layer Perceptron

Sepsis is activated by the immune system present in your body that works all the time in order to prevent the infection from entering. During this stage, the enormous number of synthetic substances discharged into the blood causes broad irritation. For the patient, the practicality of detecting sepsis disease occurrence in development is an important factor in the result. The primary goal of this work is to build, train, and test a Multi-Layer Perceptron (MLP) model using data that is available in the form of electronic clinical health data and predicts the outcome of class labels as sepsis or no-sepsis for unseen health records. The secondary goal is to compare the accuracy of the MLP against many other models like Ada-Boost, Gradient Boosting, etc., using the metrics accuracy and log loss.
Optical-image communication through random-phase propagation channels using phase-shifting coding

We propose an optical-image communication system robust to random-phase propagation using phase-shifting (PS) image coding. That is, this optical-image communication system is based on digital PS interferometry principles. Each pixel of the parallel transmitted image is coded as the phase of a sequence of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mi>N</mml:mi> </mml:math> phase-shifted fringe patterns. The temporal fringe patterns may be displayed on a TV screen (or a multimedia projector) for transmission through the random-phase channel. At the receiver, the PS fringe patterns are digitized with a telescopic digital camera. The received fringes are phase-demodulated using an <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mi>N</mml:mi> </mml:math> -steps least-squares PS algorithm (LS-PSA). We show that the received, phase-demodulated images are less blurred and have better contrast than any received image without PS coding. We propose and analyze a mathematical model for the received PS fringes degraded by random-phase propagation. This PS communication system can also be used for robust optical communications through random refractive media such as underwater, air–water, or random-thickness textured glass. In particular, we show experiments for LS-PSA imaging through textured-glass, obtaining sharper images. As far as we know, this is the first time that PS interferometry has been used for parallel optical-image communication through random-phase channels.
THE THEORETICAL BASIS OF STRA TICAL BASIS OF STRATEGIC M TEGIC MANAGEMENT OF HIGHER EDUCATION SYSTEM

The article discusses the theoretical foundations of strategic management of the higher education system. In the case of strategic management, the organization looks at the present from the future to achieve the goals set in the future due to changes in the environment and living conditions of the organization, identifies and implements current actions that provide a certain future for the organization. Strategic management has advantages as well as disadvantages and limitations, which show that this type of management, like other types, does not have universal application in solving any tasks and problems
PoW Blockchain Network's Short-term Self-correction Mechanism

This paper empirically analyzes PoW blockchain network’s short-term self- correction mechanism through examining the impact of daily network hashrate fluctuation on miner per hashrate income. Using data from Bitcoin blockchain network, we find that network hashrate fluctuation negatively affects miner per hashrate transaction fee income and positively affects miner per hashrate block re- ward income. Further tests show that network hashrate fluctuation affects miner transaction fee income through negatively influencing both per hashrate transaction number and average transaction fee. In robustness tests, we find that user trans- action fee might have heterogenous price rigidity. Our paper provides empirical evidence for theory on PoW blockchain network’s short-term stability.
Applying natural language processing to analyze customer satisfaction

The aim of this paper is to analyze customer satisfaction by applying natural language processing (NLP). We have collected over 50,000 airline reviews from TripAdvisor data in the period from 2016 until 2019. This analysis demonstrates the capability of discovering the pain points of the customers by using data science techniques related to NLP. Our study shows that in today`s world, data-driven decisions must be taken quickly in order to maintain customer satisfaction and prevent customer churn.
The Current Status of Forensic Science and its Impact on Administration of Criminal Justice System in Pakistan: An Analytical Study

Every contact leaves a trace. The existence of duties of citizen in society and sanctions for the wrongdoings expressed in the statutes are meaningless if the courts cannot properly enforce and ensure conformity to them. The courts are responsible for administering justice and relies upon evidence gathered through different mean. One way of gathering evidence is through the use of forensic science. This research emphasizes on the significance of forensic evidence for the administration of justice in Pakistan. This research also aims to investigate the question whether the current status of forensic science has affected the administration of criminal justice in Pakistan. It focused on the key components of CJS as well as the concepts and nature of forensic science. This study concludes that the crucial role which forensic science plays in the administration of criminal justice cannot be neglected. Knowledge of forensic tools and services provides the investigators with the ability to identify and seize on evidence opportunities that would not otherwise be possible. The underdevelopment of forensic science in most parts of the country can be traced back to the later introduction of forensic science in Pakistan. Having an effect on the administration of CJS it is necessary to improve the current status of forensic science. Forensic analysis can make the difference between solving a crime and it becoming a cold case.
Liquidity Co-Movements and Volatility Regimes in Cryptocurrencies

We find significant evidence of liquidity commonalities among cryptos, in particular when liquidity is estimated by relying on order-book-based proxies. Both the magnitude and pervasiveness of these co-movements are very similar to those estimated for US stocks 10 and 20 years ago. When we introduce volatility regimes based on the VCRIX volatility index on cryptocurrencies, we identify stronger liquidity co-movements in high volatility regimes across all cryptocurrencies and for all liquidity proxies. The magnitude and pervasiveness of commonalities are weaker in the low volatility regime when liquidity is arguably more idiosyncratic, affecting cryptos differently and leading to more disconnected movements relative to market liquidity. We conclude that the liquidity co-movements are not orthogonal to what was observed in the past for more centralized markets.
THE IMPACT OF THE TRANSFORMATION PROCESSES OF THE HIGHER EDUCATION SYSTEM ON THE NUMBER OF ITS PERSONNEL

The issue of providing the higher education system with scientific and pedagogical personnel is growing every year. With the transition of the Russian higher education system to the Bologna model, the issue of the availability of training for future teachers and the motivation of future scientists and researchers for obtaining the profession of a higher school teacher has worsened. In society, against the background of the growing importance of consumption, the value of intellectual labor has decreased, which played an important role in the devaluation of the profession of a higher school teacher in society, which negatively affects the motivation of young professionals to build a career in higher education. In the course of the study, it was found out that within five years there was a serious reduction in the number of personnel in the higher education system - by 147.5 thousand people among the main employees and by 13.3 thousand among external part-timers. The reduction was due to a decrease in the total number of employees in state educational organizations, but in percentage terms, the non-state educational sector suffered more. Of course, some of the staff left for natural reasons due to aging, but a large number of retired specialists were not at retirement age and, having mastered new niches in the economy, if there is a need to fill the personnel shortage in the industry, they are unlikely to agree to leave new jobs. Fill the human resource capacity through the young professionals does not take place for a number of reasons, among which the availability of postgraduate study and the lack of a significant motivational factors that stimulate youth to become a high school teacher.
Neural networks application to detect the facts of smoking in video surveillance systems

Abstract The article is devoted to the application of convolutional neural networks and cascade classifiers for tracking smoking facts in relation to video surveillance systems. Two methods of smoking detection are proposed. The first method involves detecting a cigarette against the background of a human face. The second method involves detecting a hand with a cigarette in it. On the basis of the proposed methods, software for a video surveillance system is being developed, which makes it possible to determine the facts of smoking in real time, and thereby prematurely prevent possible undesirable consequences.
Analysis of Various Sentiment Analysis Techniques of NLP

Sentiment Analysis and opinion mining complement each other. This approach is concerned with classifying the opinions of public regrading a product using NLP. This approach considers sentiments and viewpoint of individuals about the occurrence of an episode. Assessment mining is generally helpful in different domains such as business brand surveys, web-based media study, and movie analyses, and so forth. The sentiment analysis is an important method in formation of frameworks based on recommendations. The people give the content audits like web-based reviews, feedback or remarks on the web-based media and web-based business sites. This textual content is an essential wellspring of client's conclusions. In sentiment analysis, the main aim is to classify the users' sentiments into positive, negative and unbiased. This type of analysis indicates the ubiquity or importance the product in the marketplace. Every human being has their different opinion, feelings, thoughts, and emotion for an event this can be known with the help of sentiment analysis. The extraction of features and classification are the steps in sentiment analysis. This work is centered around implementing a fresh approach to sentiment analysis. The sentiment analysis techniques have various phases which include pre-processing, feature extraction and classification. The various machine learning algorithms for sentiment analysis are reviewed in terms of certain parameters.
Blockchain as a CA: A Provably Secure Signcryption Scheme Leveraging Blockchains

Although encryption and signatures have been two fundamental technologies for cryptosystems, they still receive considerable attention in academia due to the focus on reducing computational costs and communication overhead. In the past decade, applying certificateless signcryption schemes to solve the higher cost of maintaining the certificate chain issued by a certificate authority (CA) has been studied. With the recent increase in the interest in blockchains, signcryption is being revisited as a new possibility. The concepts of a blockchain as a CA and a transaction as a certificate proposed in this paper aim to use a blockchain without CAs or a trusted third party (TTP). The proposed provably secure signcryption scheme implements a designated recipient beforehand such that a sender can cryptographically facilitate the interoperation on the blockchain information with the designated recipient. Thus, the proposed scheme benefits from the following advantages: (1) it removes the high maintenance cost from involving CAs or a TTP, (2) it seamlessly integrates with blockchains, and (3) it provides confidential transactions. This paper also presents the theoretical security analysis and assesses the performance via the simulation results. Upon evaluating the operational cost in real currency based on Ethereum, the experimental results demonstrate that the proposed scheme only requires a small cost as a fee.
Cybersecurity Skills in EU: New Educational Concept for Closing the Missing Workforce Gap

Recruiting, retaining and maintaining a validated number of cybersecurity professionals in the workplace is a constant battle, not only for the technical side of cybersecurity, but also for the overlooked area of non-technical, managerial-related jobs in the cyber sector. For years, much of the focus within cyberspace has been on the technical needs of the underlying networks and services. Very little emphasis has been placed on the human dimension of cybersecurity. This lack of cybersecurity professionals is a major problem all over the world. To overcome it, current educational systems need to be re-shaped and cooperation introduced between the different stakeholders. This chapter presents and discusses the actions and the developments in the education concept of cybersecurity knowledge and skills intended to meet the needs of the labour market in the EU. The changes in the education prepared by the higher-education institutions and by professional training providers are presented and discussed.
An Empirical Analysis of Email Forensics Tools

Emails are the most common service on the Internet for communication and sending documents. Email is used not only from computers but also from many other electronic devices such as tablets; smartphones, etc. Emails can also be used for criminal activities. Email forensic refers to the study of email detail and content as evidence to identify the actual sender and recipient of a message, date/time of transmission, detailed record of email transaction, intent of the sender, etc. Email forensics involves investigation of metadata, keyword, searching, port scanning and generating report based on investigators need. Many tools are available for any investigation that involves email forensics. Investigators should be very careful of not violating user’s privacy. To this end, investigators should run keyword searches to reveal only the relevant emails. Therefore, knowledge of the features of the tool and the search features is necessary for the tool selection. In this research, we experimentally compare the performance of several email forensics tools. Our aim is to help the investigators with the tool selection task. We evaluate the tools in terms of their keyword search, report generation, and other features such as, email format, size of the file accepted, whether they work online or offline, format of the reports, etc. We use Enron email data set for our experiment.
Branch-and-Pruning Optimization Towards Global Optimality in Deep Learning.

It has been attracting more and more attention to understand the global optimality in deep learning (DL) recently. However, conventional DL solvers, have not been developed intentionally to seek for such global optimality. In this paper, we propose a novel approximation algorithm, {\em BPGrad}, towards optimizing deep models globally via branch and pruning. The proposed BPGrad algorithm is based on the assumption of Lipschitz continuity in DL, and as a result, it can adaptively determine the step size for the current gradient given the history of previous updates, wherein theoretically no smaller steps can achieve the global optimality. We prove that, by repeating such a branch-and-pruning procedure, we can locate the global optimality within finite iterations. Empirically an efficient adaptive solver based on BPGrad for DL is proposed as well, and it outperforms conventional DL solvers such as Adagrad, Adadelta, RMSProp, and Adam in the tasks of object recognition, detection, and segmentation. The code is available at \url{this https URL}.
Pseudodeterministic algorithms and the structure of probabilistic time

We connect the study of pseudodeterministic algorithms to two major open problems about the structural complexity of BPTIME: proving hierarchy theorems and showing the existence of complete problems. Our main contributions can be summarised as follows.
Optimization of RBF-SVM hyperparameters using genetic algorithm for face recognit

Manual grid-search tuning of machine learning hyperparameters is very time-consuming. Hence, to curb this problem, we propose the use of a genetic algorithm (GA) for the selection of optimal radial-basis-function based support vector machine (RBF-SVM) hyperparameters; regularization parameter C and cost-factor γ. The resulting optimal parameters were used during the training of face recognition models. To train the models, we independently extracted features from the ORL face image dataset using local binary patterns (handcrafted) and deep learning architectures (pretrained variants of VGGNet). The resulting features were passed as input to either linear-SVM or optimized RBF-SVM. The results show that the models from optimized RBFSVM combined with deep learning or hand-crafted features yielded performances that surpass models obtained from Linear-SVM combined with the aforementioned features in most of the data splits. The study demonstrated that it is profitable to optimize the hyperparameters of an SVM to obtain the best classification performance.&#x0D; Keywords: Face Recognition, Feature Extraction, Local Binary Patterns, Transfer Learning, Genetic Algorithm and Support Vector Machines.
Linguistic Skill Modeling for Second Language Acquisition

To adapt materials for an individual learner, intelligent tutoring systems must estimate their knowledge or abilities. Depending on the content taught by the tutor, there have historically been different approaches to student modeling. Unlike common skill-based models used by math and science tutors, second language acquisition (SLA) tutors use memory-based models since there are many tasks involving memorization and retrieval, such as learning the meaning of a word in a second language. Based on estimated memory strengths provided by these memory-based models, SLA tutors are able to identify the optimal timing and content of retrieval practices for each learner to improve retention. In this work, we seek to determine whether skill-based models can be combined with memory-based models to improve student modeling and especially retrieval practice performance for SLA. In order to define skills in the context of SLA, we develop methods that can automatically extract multiple types of linguistic features from words. Using these features as skills, we apply skill-based models to a real-world SLA dataset. Our main findings are as follows. First, incorporating lexical features to represent individual words as skills in skill-based models outperforms existing memory-based models in terms of recall probability prediction. Second, incorporating additional morphological and syntactic features of each word via multiple-skill tagging of each word further improves the skill-based models. Third, incorporating semantic features, like word embeddings, to model similarities between words in a learner's practice history and their effects on memory also improves the models and appears to be a promising direction for future research.