Reviewerly/exHarmony huggingface dataset:
A Novel Approach to Predict Personality of a Person

The main objective of this paper is to suggest a novel approach to predict personality of a person in online hiring processes, with the goal of automating candidate pre-screening. In this initiative, we propose a series of procedures and strategies that will make the recruiting process easier, more reliable and more efficient. Our key goal is to restrict the rounds of interviews and background analysis of the applicants exclusively to those capable of being recognized by the organization in terms of their personal attributes and the criteria of the organization. The organization is now more interested in the applicant's personality. As a result, the system runs a personality prediction test to determine the candidate's personality traits. Finally, it reports the results of the candidates to the HR manager for further screening.
Stylistic and typological features of students ' thinking as a factor of psychological readiness for innovative activity

This article discusses the quantitative characteristics of the features of the mental activity of students with different psychological readiness for innovation. The study involved 100 students of 2-4 bachelor's and 1-2 master's courses studying at the Faculty of "Agro-industrial" of DSTU, aged from 17 to 27 years (M=21.6, SD=4.4 (76.4% men), studying at different levels of education (bachelor's degree – 66.8% (M=19.8, SD=1.6, 54.8% men), master's degree – 34.2% (M=22.6, SD=3.5, 53.5% men). The following methods were used: The questionnaire "Psychological readiness for innovation" (V. E. Klochko, O. M. Krasnoryadtseva); The method of measuring the style of thinking (A. K. Belousova); The questionnaire "Determining the type of thinking and the level of creativity" according to the method of J. Bruner; The questionnaire "Individual styles of thinking" (A. Alekseeva, L. Gromova); statistical methods. The study established the style and typological characteristics of students with different levels of psychological readiness for innovation, and also found that styles and types of thinking, as well as creativity affect the level of readiness for innovation. The prospects of the research are aimed at further studying the factors and predictors of readiness for innovation and developing recommendations for teachers to form universal competencies among students.
Semantic Analysis on Product Review Headlines Based on Review Association Mechanism and Convolutional Neural Network

With the development of online shopping, product reviews and star ratings have an important impact on users' shopping behaviors, and the reviews can also urge merchants to improve their products. This article explores the internal connection between product reviews and star ratings, analyzes the key factors which deeply affect the star ratings, and constructs a review association mechanism based on high-frequency keywords to provide supplementary information for products with missing text reviews, which offers some valuable suggestions to merchants. At the same time, this article uses the headline which highly summarizes each product review to construct a convolutional neural network (CNN), and predicts the star ratings with high accuracy, which can also help users and merchants better understand those product reviews without star ratings.
Cybersickness and Its Implications for Using Virtual Reality Head Mounted Displays in Transport Psychology Research

Cybersickness (CS), described mainly as unpleasant symptoms of nausea, eye strain, and headache, often occurs during usage of virtual reality (VR) head mounted displays (HMD). CS possesses a barrier for broader usage of VR not just in entertainment industry but also in research. This paper aims to review important findings about the prevalence and severity of CS, and main factors affecting CS. Further, opportunities and effectivity of VR usage in traffic psychology research are assessed.
A Framework for Mobile Forensics Based on Clustering of Big Data

With the rapid development of the wireless network and smart mobile equipment, many lawbreakers employ mobile devices to destroy and steal important information and property from other persons. In order to fighting the criminal act efficiently, the public security organ need to collect the evidences from the crime tools and submit to the court. In the meantime, with development of internal storage technology, the law enforcement officials collect lots of information from the smart mobile equipment, for the sake of handling the huge amounts of data, we propose a framework that combine distributed clustering methods to analyze data sets, this model will split massive data into smaller pieces and use clustering method to analyze each smaller one on disparate machines to solve the problem of large amount of data, thus forensics investigation work will be more effectively.
Analytical Framework for National Cyber-security and Corresponding Critical Infrastructure: A Pragmatistic Approach

Countries are putting cyber-security at the forefront of their national issues. With the increase in cyber capabilities and infrastructure systems becoming cyber-enabled, threats now have a physical impact from the cyber dimension. This paper proposes an analytical framework for national cyber-security profiling by taking national governmental and technical threat modeling simulations. Applying thematic analysis towards national cybersecurity strategy helps further develop understanding, in conjunction with threat modeling methodology simulation, to gain insight into critical infrastructure threat impact.
Testbed Design and Performance Analysis for Multilayer Blockchains

Both the number of blockchains and the discovered number of applications of blockchain are ever-increasing. Understanding how different blockchains compare in performance has become more important than ever, yet each blockchian paper uses its own inconsistent standards for performance analysis. There are few mechanisms that may allow for consistent performance analysis among multiple blockchains. This paper details the implementation of the first-ever multi-layer blockchain parameter tuning system: Parameter Space Exploration. Our system is primarily targeted at the performance issue but also takes into consideration power consumption. Multi-layer blockchains use a permissioned blockchain to solve performance deficits. We find that the side chain is the determining factor for the performance of a multi-layer system; our side chain is chosen among a set of Byzantine Fault Tolerance (BFT) and Crash Fault Tolerance (CFT) based consensus algorithms as they are more energy-efficient and have higher performance than their Proof of Work (PoW) counterparts. Our proposed Parameter Space Exploration has two elements: (1) The concept of a parameter space; (2) A side-by-side benchmarking system for balanced comparison among different multi-layer blockchains and consensus algorithms.
Rapid Approximate Aggregation with Distribution-Sensitive Interval Guarantees

Aggregating data is fundamental to data analytics, data exploration, and OLAP. Approximate query processing (AQP) techniques are often used to accelerate computation of aggregates using samples, for which confidence intervals (CIs) are widely used to quantify the associated error. CIs used in practice fall into two categories: techniques that are tight but not correct, i.e., they yield tight intervals but only offer asymptoticguarantees,makingthem unreliable, or techniques that are correct but not tight, i.e., they offer rigorous guarantees, but are overly conservative, leading to confidence intervals that are too loose to be useful. In this paper, we develop a CI technique that is both correct and tighter than traditional approaches. Starting from conservative CIs, we identify two issues they often face: pessimistic mass allocation (PMA) and phantom outlier sensitivity (PHOS). By developing a novel range-trimming technique for eliminating PHOS and pairing it with known CI techniques without PMA, we develop a technique for computing CIs with strong guarantees that requires fewer samples for the same width. We implement our techniques underneath a sampling-optimized in-memory column store and show how they accelerate queries involving aggregates on real datasets with typical speedups on the order of 10× over both traditional AQP-with-guarantees and exact methods, all while obeying accuracy constraints.
Research on Head Detection and State Estimation Algorithm in Classroom Scene

The penetration rate of mobile phones and tablet computers among college students is increasing, and the loose teaching environment has led to a large number of phubbers in college classrooms. The state of students' attendance in class is an intuitive indicator of classroom quality. Obtaining this data in real-time will bring great help to school evaluation and improvement of teaching standards. The data in this article comes from teaching videos collected by high-definition cameras in colleges. Through offline training, the face detector HDN can accurately extract the position coordinates of the student in the picture in the real teaching scene and pass the detected head information to the convolutional network responsible for judging the state of the student's head to obtain the student's current Class status. The HDN designed in this paper achieves a recall rate of more than 95% on the authoritative public dataset FDDB, and the accuracy of Wider Face's face dataset under three difficulty conditions is 93.9%, 93.2%, and 88.0%. The self-designed Raised Head Network achieves 88% accuracy on the RaisedHead dataset.
Secure and Cost Effective Public Cloud Storage for Managing Construction Firm Services

In the world of online business their comes a difficulty where we cannot assure the organization is trustworthy or except for the physical operational premises we look forward for the existence of the organization online i.e. a web site. Recently their area unit tremendous has begun where small and medium scale companies, UN agency take the responsibility to frame information in superhighway websites of organizations. These companies take just one occasion charges and develop a variety of static model which could show static knowledge. But, a corporation keeps on growing and dealing on further and extra fields for such state of affairs the data or illustration of information regarding organization should be changed that the net website should be turned off or passed in at a lower place construction mode that is-cost-efficient. To beat this we tend to stand live designing a model which could handle the illustration and modify it per user convenience. This might be achieved with the help of Dynamic net Application and MVC Structure.
Adversarial Complementary Attention-Enhancement Network For Fine-grained Image Recognition

Due to subtle visual differences among fine-grained subcategories only locate at local regions, how to locate and extract differentiated local features has become the primary contradiction to be solved in this field. In the early stage, many works based on weak supervision are inclined to focus only on the most discriminative feature while ignoring other features that play an equally important role in image recognition. To tackle this problem, this paper proposes an adversarial complementary strategy-based method that can urge the network to find a complementary region by removing the most meaningful parts in the image, which can extract more information with significant features to promote the precision of fine-grained image classification. At the same time, similarity loss was utilized to determine the similarity between the two regions to avoid obtaining similar features as much as possible. In addition, Bilinear Attention Pooling (BAP) is introduced to extract sequential part features, which can effectively merge several different levels of features together. Experiments show that our method achieves the competitive results in various challenging datasets, such as CUB-200-2011, FGVC-Aircraft and Stanford Car.
New Model of ARWCM Based on SDLC With Herman's Theorem Using Decision Tree Algorithm by Weka

In this paper, a new model is developed which is called the adaptive work theoretical model through the stages of building information systems and The New Haberman brain lobe method of remote adaptive work system classification using the dialogic decision support (ARWCM) model. Identifying intangible human behavior such as preference or motor aspect is very difficult because of the many factors characteristic in a person's personality, through the answers to a questionnaire that combines the stages of building and developing information systems and Herman's theory thus determining the mode of work of individuals especially for remote work and finding the right work approach consistent with Dealing with different individuals remotely with different thinking patterns and disciplines remains the critical issue for most research. This research introduced the ARWCM model that accommodates individuals in a comfortable remote work environment improving the remote work system by placing it in the adaptive remote work model. Decision support tree algorithm technology it has been used to classify and predict the remote working method of individuals based on the stages of building and developing various information systems with Herman's theory and advanced system structure.
The Evolution of Trust in Money

Trust is vital to the sustained existence of money. No currency can prevail without it. However, following the international crisis of 2008, the liability-side of trust became increasingly apparent. Blockchain and cryptocurrencies challenged the need to trust and proposed an alternative “trustless” system. In the context of rising interest and concern about cryptocurrencies, the authors intend to discuss the role of trust in the evolution of money, from ancient Mesopotamia to modern sovereign fiat currencies and cryptocurrencies, and whether cryptocurrencies are prompting a shift in the paradigm of money or not.
통사적 복잡성과 영어말하기 능력의 관계에 대한 학습자 코퍼스 연구

Research on the relationship between syntactic complexity and linguistic proficiency in writing, rather than speaking, has been conducted mainly until recently. The main concern of the current research includes: first, are the five factors presented by Lu (2010; 2011) consistent with the results obtained from a factorial analysis of the spoken corpus used in this study? Second, which measures are significantly related to English speaking proficiency?, Third, how much is the explanatory power of the selected factors for the regression model developed based on the proficiency in this study? To this end, the 14 syntax complexity indices from the corpus were measured as independent variables, and as dependent variables, holistic ratings of learners' overall L2 speech were used. As a result, the factorial analysis categorized the indices into four factors, which was virtually consistent with factors by Lu (2010; 2011) (i.e., with one exception in that two factors, Sentence complexity and Subordination, were integrated into one factor). Stepwise multiple-regression analysis shows that the factors affecting a learner‘s speaking ability were Subordination and Length of Production Unit, which explain 9.8% of the model. The results suggest that educators should design curriculum to better encourage students to use a larger variety of clauses and to try to create more complex sentences. Educators should also be aware that it will eventually affect L2 proficiency in an EFL context.
Large scale data mining for banking credit risk prediction

To become compliant with changing and stricter regulatory demands in the world, banks are focused on undertaking preventive measures for effective credit risk management. One of its drivers is to strengthen existing models to increase their predictive powers and help detect future defaults in advance. In this paper, we propose a new evaluation method to improve the overall performance of learning algorithms in the field of over-indebtedness prediction by using feature selection. Our objective is to find the most relevant subset of features related to the customer banking history for use in prediction model construction. Experimental results have shown that the proposed evaluation method is very useful to build a good predictor.
TOP MANAGERS CONCEPT OF RELATIONSHIP BETWEEN MANAGEMENT TEAM EFFICIENCY FACTORS AND THEIR VALUE ORIENTATIONS

Aim. The purpose of the study is to identify the peculiarities of the top management views on relationship between the factors of the management team effectiveness and their value orientations. Methodology. The values questionnaire by Sh. Schwartz, the methodology “Career anchors” by E. Shein, a specially developed questionnaire “Selection and formation of an effective management team by the chief executive” were used as diagnostic tools as well as analysis of mean values, Mann-Whitney test, factor analysis with varimax-rotation. Results. Analysis of the heads of the organization concept of the principles that should be relied on when selecting members of the management team showed that the majority of research participants in the selection rely on the principles of the resource approach, and also believe that the effectiveness of the management team is more determined by the professional competence of its members, than by their personal qualities. It was also shown that, in the opinion of research participants, professionalism is a constant criterion for selecting management team members, while similarity of beliefs and loyalty are variables. It has been established that, depending on the peculiarities of managers’ ideas about the principles and factors of forming an effective management team, as well as the factors of the effectiveness of its activities, there are significant differences both at the level of expressiveness of individual value orientations and at the level of their latent structure, i.e. at the level of value strategies. It is shown that both pro-social and pro-personal value strategies influence the opinion of managers about the factors of the effectiveness of the management team. Research implications. The significance of the results obtained is determined by the importance of the command principle in increasing the efficiency of management in all spheres of social life and the role of the leader in the processes of formation, development and activities of the management team. The research materials make it possible to understand more deeply how the value orientations of managers affect their views on the factors of the effectiveness of the management team, and can be used in the framework of personal and professional consulting of organization leaders on the problems of team building and improving the efficiency of team management.
Gradient Forward-Propagation for Large-Scale Temporal Video Modelling

How can neural networks be trained on large-volume temporal data efficiently? To compute the gradients required to update parameters, backpropagation blocks computations until the forward and backward passes are completed. For temporal signals, this introduces high latency and hinders real-time learning. It also creates a coupling between consecutive layers, which limits model parallelism and increases memory consumption. In this paper, we build upon Sideways, which avoids blocking by propagating approximate gradients forward in time, and we propose mechanisms for temporal integration of information based on different variants of skip connections. We also show how to decouple computation and delegate individual neural modules to different devices, allowing distributed and parallel training. The proposed Skip-Sideways achieves low latency training, model parallelism, and, importantly, is capable of extracting temporal features, leading to more stable training and improved performance on real-world action recognition video datasets such as HMDB51, UCF101, and the large-scale Kinetics-600. Finally, we also show that models trained with Skip-Sideways generate better future frames than Sideways models, and hence they can better utilize motion cues.
Quantum Machine Learning (Presentation Slides)

The presentation slides cover two promising machine learning applications of parameterised quantum circuits: Quantum Neural Network (discriminative QML model) and Quantum Circuit Born Machine (generative QML model).
The geology of Yelcho Station: a new high-resolution geological map at northwestern Antarctic Peninsula

&amp;lt;p&amp;gt;Yelcho Station is set on Doumer Island located in the southernmost section of Gerlache Strait between Anvers and Wienke Islands at the northwestern region of Antarctic Peninsula. This area is dominated by plutonic and volcanic deposits associated with the active margin developed during the Mesozoic and Cenozoic in the Antarctic Peninsula (e.g. Leat et al., 1995). Although Yelcho Station has been intensively visited since a few decades, the outcropping rocks have not been studied in detail. Furthermore, this location has hosted relevant contributions in the environmental and ecological sciences. We will present a detailed map (1:500) of the geological units outcropping in Yelcho Station based in fieldwork observations, which will be combined with drone and satellite images. Additionally, remote sensing spectral studies will be developed to support the geological mapping. This work will help to establish a geological baseline, which may serve for future studies in the area of Yelcho Station. This contribution will be a detailed geological study in the Antarctic Peninsula, which will also enhance our understanding of the geological units outcropping in Gerlache Strait. This material will also serve as an educational and outreach information for the polar community.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Leat et al. (1995). Geological Magazine 132 (4), 399-412 (DOI: 10.1017/S0016756800021464).&amp;lt;/p&amp;gt;
Combining Pipeline Quality with Automation to CI/CD Process for Improving Quality and Efficiency of Software Maintenance

In Taiwan, the regular software maintenance is the essential procedure in general commercial banks. However, during system maintenance, the banking system must be suspended and stop related services. Maintenance period usually make many inconveniences for banking customer. Even affect the transaction efficiency and market competitive advantages of enterprises. How to improve the maintenance efficiency and quality for software system is a critical job to increase the banking service quality and reduce customer lost. In this paper, we redefine the software maintenance workflow and apply the automation tool for constructing automated CI/CD workflow, and propose the CPQM model. CPQM model combines the pipeline quantity factors and automation tool factors of CI/CD process for identifying process defects and assistant improving process quality and efficiency. CI/CD process with high quality and high automation can concretely enhance the maintenance efficiency and quality for software system.