Reviewerly/exHarmony huggingface dataset:
An Overview of String Processing Applications to Data Analytics

Data analytics may conveniently be divided into four stages: preparation, preprocessing, analysis, and post-processing. Especially in the second and third of these, where the data is cleaned, filtered and analyzed, string processing algorithms are fundamental. Applicable string methodology especially includes pattern matching (dozens of competing algorithms) and algorithms that compute repetitions and other forms of regularity. These are supported by powerful data structures (suffix array, prefix table, Burrows-Wheeler Transform, Lyndon array, and many others), developed and refined over the last 50 years. In this paper we provide an overview of three central methodological areas: · pattern matching; · repetitions (of both adjacent and non-adjacent repeating substrings); · string covering and compression. Each of these methodologies deals with both exact and approximate matches in the data provided. We outline several current applications to data analytics, in particular bioinformatics, information security and image analysis - all of them therefore positioned for future extension as string methodologies continue their rapid development.
Behavior Analysis and Prediction of Disabled in Smart Home

People with disabilities face a lot of inconvenience in daily life, even in their homes where are the most familiar environment. They are difficult to complete the operation of simple household equipment, and they rely heavily on others. In order to solve the above problems, we presented an operation behavior model for people with disabilities, analyzed their operation behavior and proposed a prediction method for them in smart home. This method can effectively reduce the dependence of disabled people on others and improve their self-reliance. Different from the existing operation behavior prediction methods in smart home, our method specialized in the operation behavior characteristics of the people with disabilities. In the process of modeling, some special elements of disabled groups are added in, such as types of disabilities and interaction modes. At the same time, the collaborative operation between users and collaborative operation relationship between various devices are also taken into our model. The experimental results shown that our method proposed in this paper has higher accuracy.
On the Minimum of True Matches in Exact Graph Matching with Simulated Annealing

Graph matching is one of the most important problems in graph theory and combinatorial optimization, with many applications in various domains. Although meta-heuristic algorithms have had good performance on many NP-Hard and NP-Complete problems, but for graph matching problem, there were not reported superior solutions by these sort of algorithms. The reason of this inefficiency  has not been investigated yet. In this paper it has been shown that Simulated Annealing (SA) as an instance of a meta-heuristic method is unlikely to be even close to the optimal solution for this problem. Mathematical and experimental results showed that the chance to reach to a partial solution, is very low, even for small number of true matches. In addition to theoretical discussion, the experimental results also verified our idea; for example, in two sample graphs with $10000$ vertices, the probability of reaching to a solution with at least three correct matches is about $0.02$ with simulated annealing.
A Deep End-to-end Hand Detection Application On Mobile Device Based On Web Of Things

In this paper, a novel end-to-end hand detection method YOLObile-KCF on mobile device based on Web of Things (WoT) is presented, which can also be applied in practice. While hand detection has been become a hot topic in recent years, little attention has been paid to the practical use of hand detection on mobile device. It is demonstrated that our hand detection system can effectively detect and track hand with high accuracy and fast speed that enables us not only to communicate with each other on mobile devices, but also can assist and guide the people on the other side on the mobile device in real-time. The method used in our study is known as object detection, which is a working theory based on deep learning. And lightweight neural network suitable for mobile device which can has few parameters and easily deployed is adopted in our model. What's more, KCF algorithms is added in our model. And several experiments were carried out to test the validity of hand detection system. From the experiment, it came to realize that the YOLObile-KCF hand detection system based on WoT is considerable, which is more efficient and convenient in smart life. Our work involving studies of hand detection for smart life proves to be encouraging.
Difficulties and Countermeasures of Cultivating Students' Practical Ability in College Physical Education Experiment Teaching

With the continuous progress of society, people pay more and more attention to the important role of physical exercise, and the content of physical education in Colleges and universities is also more and more valued. Through physical education experiment teaching, students can master better practical ability and skills, and how to give full play to the training effect of Physical Education Experiment Teaching on students' practical ability has become an important research topic of physical education major in Colleges and universities. In order to study how to better promote the sports experimental teaching on the cultivation of students' practical ability. Through the questionnaire survey and field investigation of two domestic colleges and universities, the statistical analysis of the survey results shows that: in the physical education experimental teaching work of domestic colleges and universities, there are generally cognitive errors, specific programs are not in place, lack of hardware facilities, experimental teaching management system is not perfect and other problems, which have a great negative impact on the cultivation of students' practical ability. Therefore, it is necessary to improve the relevant hardware facilities of physical education, change the wrong cognition of practice teaching, and perfect and implement the management system of physical education. Only in this way, can we effectively play the role of Physical Education Experimental Teaching on students' practical ability, and achieve the teaching goal of cultivating students' practical ability in the training program of physical education experiment teaching.
Enhanced Load Balancing Approach for Cloud Data Center Load Optimization

Cloud computing is a most popular technology that has huge response in markets. Cloud computing has the potential to access applications and their related data via the Internet anywhere. Most companies already pay for the use of cloud resources for storage purposes and ultimately reduce the costs of infrastructure spending. They can make use of this technology for accessing to company applications like pay-as-you-go approach. One of the major obstacles associated with cloud computing technology is to better optimization of resource allocation. Assigning of workloads to the servers using load balancing techniques is used to achieve less response time and better resource optimization across the server. Resource control and balance of load are the major conflicts in the cloud environment, which is why there are different load balancing algorithms, each with its own advantages and disadvantage. In order to achieve a better economy and mutual benefit, efficient algorithms can be derived simultaneously by optimizing servers, green computing and better utilization of resources. The objective of this paper is to analyze and enhance existing load balancing algorithms.
Blended Learning Technique in the Educational Process

One of the reasons for the existing crisis in the education system and the decline in the quality of students’ professional training is the “catching up” type of higher education arrangement. In the context of life and public communications virtualization, the need for introducing new more adapted to the needs of today’s students approaches to learning and the changing role of a teacher becomes obvious. The “blended learning” technology, or the so-called Academician Suleiman Khan technology, which is a form of blended learning, has become widespread in educational institutions of developed countries of late. This article presents the main characteristics of this technology and analyzes the various possibilities of its implementation in Russia, as well as its advantages and disadvantages.
E-boarding KAI access summative usability analysis with adoption of USE questionnaire

Abstract PT Kereta Api Indonesia (KAI) is a State-Owned Enterprise that regulates and manages railroad transportation services in Indonesia. PT KAI focused on becoming the best railroad service provider by meeting stakeholder’s expectations. In its development, PT KAI launched the KAI Access Application in 2014. KAI Access is an Official Mobile Application to help prospective passengers obtain information and make train ticket reservations online. E-Boarding Pass is one of the features in the KAI Access application that can be operated two hours before the train departure schedule. Summative usability analysis is applied to measure the level of compatibility between the functional system and its focus. This study uses the USE Questionnaire Method as a parameter in usability measurement. The assessment criteria used are usefulness, ease of use, ease of learning, and satisfaction. The results showed that usability was in the “high” standards with a value of 79.6%. The study conducted in 9 Indonesian Railroad Operation Areas (DAOP) of PT KAI with the results of the usefulness criterion 80.9%, the ease of use criterion 77.7%, the ease of learning criterion 80.4%, and the satisfaction criterion 79.4%.
A Fast Epipolar Rectification Method Based on Fundamental Matrix

Construction machines are in a state of vibration during operation, which causes the misaligned horizontally of image pairs taken by the binocular camera installed on machines. Therefore, epipolar rectification must be carried out before stereo-matching. We propose a novel approach for epipolar rectification, which can be divided into two steps: the rectification of the left image and the corresponding rectification of the right image. The rectification of the left image consists of the transformation of mapping the epipole to infinity and the shear transformation. The rectification of the right image can be achieved on the constraint of zero vertical disparity. The images can be resampled by the transformation matrix, which realizes epipolar rectification of the image pairs. The experimental results show that the proposed algorithm has good accuracy under the premise of minimal geometric distortion, and the linear least square method is used only once in the algorithm, which shows the fewer computing cost and better stability.
Storage Capacity of Quaternion-Valued Hopfield Neural Networks With Dual Connections

A complex-valued Hopfield neural network (CHNN) is a multistate Hopfield model. A quaternion-valued Hopfield neural network (QHNN) with a twin-multistate activation function was proposed to reduce the number of weight parameters of CHNN. Dual connections (DCs) are introduced to the QHNNs to improve the noise tolerance. The DCs take advantage of the noncommutativity of quaternions and consist of two weights between neurons. A QHNN with DCs provides much better noise tolerance than a CHNN. Although a CHNN and a QHNN with DCs have the samenumber of weight parameters, the storage capacity of projection rule for QHNNs with DCs is half of that for CHNNs and equals that of conventional QHNNs. The small storage capacity of QHNNs with DCs is caused by projection rule, not the architecture. In this work, the ebbian rule is introduced and proved by stochastic analysis that the storage capacity of a QHNN with DCs is 0.8 times as many as that of a CHNN.
Review of the Role of IoT in Managing COVID-19 in Saudi Arabia

It has been more than a year when COVID-19 was declared global pandemic on 11th March 2020. Unfortunately, the pandemic is still persisting despite finding some vaccines to eliminate it. In this paper, we conduct a review of the role of the Internet of Things (IoT) in managing COVID-19 in the Kingdom of Saudi Arabia (KSA). Our review takes into account of the usefulness of the IoT tools and AI techniques, and Fog computing and the Location Based Services (LBS) databases. We discuss process for tracking people and determining the most venerable candidate to have infection with the help of the available technologies. In particular, fog computing enables applications of contact tracing procedure while dealing with the huge amount of data without significant delay. This procedure uses historical information in conjunction with healthcare data to predict the number of potential infections. We shall also suggest some important services and applications to enhance the level of awareness to reduce the number of infections.
Research On Teaching And Research Methods Of Junior College Mathematics Course

With the continuous development of the education industry and the large number of teaching staff, mathematics teaching in junior college has become a controversial issue. In this paper, through the practice of practical teaching, combined with the training criteria of junior college mathematics, puts forward some suggestions and ideas on the teaching methods of junior college mathematics.
Multi-Target Infrared–Visible Image Sequence Registration via Robust Tracking

To solve the problem of multi-target depth difference for infrared and visible image sequence registration, a registration framework based on robust tracking is proposed. Firstly, the curvature scale space corners of targets are extracted, and the descriptors based on the curvature distribution are created to complete feature matching. Since different targets lie on different depth planes, single global transformation matrix is no longer applicable. Robust target tracking algorithm is introduced to dynamically allocate and update the matching reservoir for each target. Finally, the precise registration of multiple targets is realized by computing transformation matrix independently for each target. Experiments on a public dataset of non-planar infrared and visible image sequences show that our framework achieves lower overlapping errors and improves the accuracy of multi-target registration.
Semantic Image Analysis for Automatic Image Annotation

The semantic image analysis is a process aimed to extract automatically the accurate semantic concept from image visual content. In this chapter, we shed light on a study conducted to boost the efficiency of semantic image analysis for improving automatic image annotation. We have explored, in this research study, different aspects of semantic analysis to overcome the semantic gap problem. In the first part of this research study, we evaluated the impact of the way in which the different low level features were associated on image annotation performance. In the second part, the benefit of combining two complementary classifiers for objects classification was investigated. The final part of the research study was devoted to the assessment of the usefulness of regrouping adjacent regions of segmented image for objects discrimination.
HIBE: Hierarchical Identity-Based Encryption

Cryptosystems fundamentally deal with the issue of securing data communication. Public key infrastructure (PKI) model requires an authenticated public key for encryption that has to be obtained prior to initiating communication. The identity-based encryption (IBE) essentially removes the public key distribution by using an arbitrary string, e.g., an email address or a phone number, as the public key. A private key generator (PKG) delegates private keys to corresponding users for decryption. In trivial identity-based encryption schemes, a single private key generator for all the users makes key generation computationally inefficient. The hierarchical identity-based encryption (HIBE) eliminates this bottleneck of verifying the proofs of identity, generating private keys and establishing secure paths to transfer these keys. In HIBE, a root PKG with a secret master key distributes the workload by mirroring an organization hierarchy, delegating the process of key generation, and authenticating identities of users to lower level PKGs.
Analyzing the Short-Term Dependency in Ultra-High Magnetic Response Systems - Modeling Sequential Data with Non-Recurrent Neural Networks

Recurrent neural network (RNN) is a popular modeling choice for sequential data. However, empirical experience shows RNNs are often difficult and time-consuming to tune and customize. It drives practitioners to replace RNNs with non-recurrent neural networks which presented comparable performances in some cases. The success of using non-recurrent neural networks to model sequential data indicates the short-term dependency among sequential data. In this paper, we systematically analyze the short-term dependency in ultra-high magnetic response systems (UHMR) with partial system knowledge based on the observability criterion of the dynamic systems. Moreover, we show that the sequential data in the UHMR system only have 2-step dependency. This result indicates that any consecutive three steps in an experiment form a datum to train a feed-forward neural network (FFNN). Therefore, sufficient data can be collected within a small number of experiments. Based on the analysis, we train a feed-forward neural network to model the UHMR system based on the sequential data from four experiments. Through proper data pre-processing, the FFNN model can predict the system performance with bounded mean absolute error.
Hate Crime in the News: The Media's Role in Agenda Setting

Examining the extant literature on hate crimes shows that there has been an evolutionary process of Hate Crime legislation (HC). Similar to other social movements such as civil rights, the hate crime movement also had various waves which eventually lead to the passage of legislation. By and large, however, HC research has focused on victims and offenders of hate crimes as well as motivations of bias. Moreover, less research has been done on the media’s portrayal and coverage of HC. Since the media is a noted influencer in social issues (Culotta, 2002; Quisenberry, 2001), we sought to answer how the news media are reporting incidences of hate crimes – particularly LGBTQ+- and compare them with official crime statistics reported by law enforcement agencies. In order to answer these questions, our research utilized a qualitative content analysis using QSR NVivo 12.0 to identify potential themes and trends which may be overlooked in simple quantitative methods. Our dataset comes from the Hate Crime Index ("ProPublica," 2018), for the month of June 2018. Official FBI data is also utilized for comparison, spanning from 2012 to 2016. Our results suggest that the media reports HC within an overall internal Agenda Setting Orientation. During analysis, two main themes were identified that show the (i) media report both the failures and challenges of law enforcement in dealing with HC issue and, (ii) that media highlights various best practices some agencies engage in. Limitations and future research directions are discussed.
Sentiment analysis with covariate-assisted word embeddings

Sentiment analysis measures inclination of textual documents, aiming to extract and quantify their subjective sentiment polarity. In literature, most sentiment analysis methods first numericalize textual documents through certain word embeddings framework, and then formulate sentiment analysis as an ordinal regression or classification task. Yet it is often ignored that different people may have different preference of wording, and thus a uniform word embeddings often leads to suboptimal performance. In this article, to accommodate the heterogeneity among individual persons, we propose a covariate-assisted word embeddings in a margin-based ordinal regression framework, where covariates are incorporated through scaling factors to adjust the word embeddings. Moreover, we employ a block-wise coordinate descent scheme to tackle the resultant large-scale optimization task, and establish theoretical results to quantify the asymptotic behavior of the proposed method, guaranteeing its fast convergence rate in terms of prediction accuracy. Finally, we demonstrate the advantages of the proposed method over its competitors in both the Yelp Challenge dataset and synthetic datasets.
Crypto-assets in the EU and Polish regulatory framework regarding financial instruments

Crypto-assets are private, convertible, digital tokens that are managed in a decentralized way using technology called distributed ledger technology (DLT). The term is not limited only to decentralized virtual currencies, but also commodities, digital goods and services. As such they can be considered an addition to a financial investment portfolio, being an alternative to traditional instruments, such as stocks, bonds or cash. However, wide use of crypto-assets is being currently hampered by lack of regulations followed by uncertainties as to the legal qualification of crypto-assets and consequences of investments in such instruments. From that perspective, it seems crucial to answer a question whether crypto-assets can be considered financial instruments in the light of the current Directive on Markets in Financial Instruments (so called MiFID II) at the EU level and national acts that implemented relevant provisions of MiFID II (in Poland it is the Act on Trading in Financial Instruments). Since neither the EU nor Polish legislator provided a clear answer to that question, some indications can be found only in official statements of regulatory authorities and the doctrine, which however are not always consistent with each other. Furthermore, the Member States used different legislative techniques in the process of implementing MiFID II so the nomenclature can differ. The article discusses the diverse approaches to crypto-assets in the interpretation of the EU and Polish legislation concerning financial instruments as well as implications of such differences.
Blind deconvolution of images corrupted by Gaussian noise using Expectation Propagation

Blind image deconvolution consists of inferring an image from its blurry and noisy version when the blur is unknown. To solve this highly ill-posed inverse problem, Expectation Maximization (EM)-based algorithms can be adopted. In several previous studies, Variational Bayes (VB) approaches were deployed to approximate the intractable conditional probability distribution of the image that appears in the E-step of the traditional EM algorithm. In this paper, we propose to use an Expectation Propagation (EP) algorithm to derive an alternative approximation of the conditional probability distribution. The simulations conducted show that the resulting EP-EM approach can provide more reliable approximations, reflected by better image estimates and more reliable uncertainty maps than VB-EM for a comparable computational time.