Reviewerly/exHarmony huggingface dataset:
Examining the Factors of Korean Coast Guard officer Job Satisfaction: The Role of Family Harmony

Majority of the police officer job satisfaction research in the field of Criminal Justice began with an examination of demographic factors. As the research progressed, job satisfaction research has expanded to work-related factors to predict job satisfaction. Although work-related factors have benefited the job satisfaction research tremendously, further examination of job satisfaction is needed. Hence, the current study focuses on familial factors as a predictor of job satisfaction. Are officers with happier and more harmonious family have higher levels of job satisfaction? To answer this, we examine Korean Coast Guard police officers and their job satisfaction levels. In brief, the results indicate that work-related factors such as work condition, work unsafety, organizational cohesion, and work pride were significant predictors of job satisfaction. Most importantly, the familial factors such as the amount of family conversation, general relationship with family members, and family-work conflict were significant predictors of job satisfaction.
Eye Controlled Wheelchair Using Raspberry Pi

The purpose of this eye-controlled wheelchair is to eliminate the need for support for people with disabilities. The proposed wheelchair control technique depends on eye movements. The camera is mounted on the user’s head to take eye photos and track eye pupil movements using face landmarks recognition technology. Based on eye pupil movements, the wheelchair motor moves left, right and forward. For security, an ultrasonic sensor is introduced at the front of the wheelchair to identify obstruction and consequently stop the wheelchair. The complete system is controlled by Raspberry Pi.
Semi-supervised Learning using Differentiable Reasoning.

We introduce Differentiable Reasoning (DR), a novel semi-supervised learning technique which uses relational background knowledge to benefit from unlabeled data. We apply it to the Semantic Image Interpretation (SII) task and show that background knowledge provides significant improvement. We find that there is a strong but interesting imbalance between the contributions of updates from Modus Ponens (MP) and its logical equivalent Modus Tollens (MT) to the learning process, suggesting that our approach is very sensitive to a phenomenon called the Raven Paradox. We propose a solution to overcome this situation.
Reading room of architect V. I. Sobolshchikov of the Imperial Public Library

In November 1862, a great event took place in the Library - the fi rst in its history a special reading room for 250 seats was opened, built according to the project of V. I. Sobolshchikov and his assistant I. I. Gornostaev, laid down under the director M. A. Korf , but completed already in the directorship of I. D. Delyanov. The appearance in the Public Library of a new, spacious and comfortable hall for receiving visitors with a special study for artists and a room for ladies ushered in a new era. In the hall itself, a reference library of several thousand books was organized, and the supply of books from departments to the reading room was accelerated. In the reading room, instead of one general catalog for a subsidiary fund, there were seven printed systematic catalogs. The article recreates the history of the reading room in the pre-revolutionary period, provides information about its managers, gives a description of the organization of services for readers in it.
An Expert Judgment in Source Code Quality Research Domain—A Comparative Study between Professionals and Students

In scientific research, evidence is often based on empirical data. Scholars tend to rely on students as participants in experiments in order to validate their thesis. They are an obvious choice when it comes to scientific research: They are usually willing to participate and are often themselves pursuing an education in the experiment’s domain. The software engineering domain is no exception. However, readers, authors, and reviewers do sometimes question the validity of experimental data that is gathered in controlled experiments from students. This is why we will address this difficult-to-answer question: Are students a proper substitute for experienced professional engineers while performing experiments in a typical software engineering experiment. As we demonstrate in this paper, it is not a “yes or no” answer. In some aspects, students were not outperformed by professionals, but in others, students would not only give different answers compared to professionals, but their answers would also diverge. In this paper we will show and analyze the results of a controlled experiment in the source code quality domain in terms of comparing student and professional responses. We will show that authors have to be careful when employing students in experiments, especially when complex and advanced domains are addressed. However, they may be a proper substitution in cases, where non-advanced aspects are required.
ANALYSIS OF THE COMPETITIVE ACTIVITIES OF THE WORLD STRONGEST BELT WRESTLERS

International potential and consequent greater competition in belt wrestling increase the significance of assessing quantitative and qualitative indicators of competitive activity of the strongest wrestlers in the world. It is important to identify the sport development trends and to search for effective ways and tools for achievement of the sport excellence. Purpose: to reveal the features of efficient competitive activities of the world leading wrestlers on the basis of analysis of group differences in technical and tactical excellence indicators. Materials and methods of research. We analyzed videos of 285 events with participation of 197 wrestlers competing at the Belt Wrestling World Championship 2019 in Kazan. We registered the following indicators of competitive activity: the total and average number of fighting techniques, including techniques executed to the right and to the left side within 4 minutes of combat; number and types of technical actions of competition winners among men and women. Research results and discussion. The paper focuses on the comparative analysis of technical and tactical skills of men and women, the winners of the Belt Wrestling World Championship. The research revealed distinguishing features of the winner’s technical toolkit including the diversity of technical and tactical actions, a balance in the knowledge of the right and left-handed techniques. At the same time, the analysis of technical and tactical actions in women wrestling shows the backlog of female athletes in these components of technical fitness.
Microgrid System Design Based On Model Based Systems Engineering And Goal-Oriented Requirements Engineering

Microgrids appear as a practical, clean and reliable solution to meet the demand of populations that, for various reasons, do not have access to electricity. The complexity of microgrid systems, requires considerable engineering eort in the design process. To design this type of complex systems, new approaches, methods, concepts and engineering tools are needed. Where, the requirements analysis has a preponderant role to better characterize, understand and specify the application domain and the problem that the microgrids must solve. This work proposes the introduction of a formal analysis of requirements in the life cycle of microgrid systems, using IEC 61850 as a reference architecture. The requirements would be represented in an Object Oriented Requirements Engineering (GORE) approach, using specically visual diagrams based on the KAOS (Keep All Objectives Satised) method, where the operation and control of the network will be formally represented. The requirements analysis is presented using a combined representation that uses the GORE and Petri Nets methodology for dynamic modeling and formal verication. A case study for small communities in the Amazon rainforest is used as a case study for the proposed method.
Reliability of wrist‐worn accelerometry devices and algorithms for sleep detection

Abstract Background Wearable devices have shown great potential for obtaining measures of real world evidence in clinical trials, but standardization and variability between different devices remain one of the barriers for systematic deployment. In this investigation, we present a neural network algorithm for sleep detection, Deep Learning Sleep (DLS), and compare its reliability across different datasets and two widely used sleep algorithms. Method A publically available dataset TUD (Borazio; ICHI 2014, N=45) and the CONTEXT dataset collected in Montpellier, IXI (Wolz; Alzheimer’s &amp; Dementia 2017, N=45) were used to compare algorithm performance. Each dataset provided one‐night accelerometry data alongside polysomnography (PSG) per participant. DLS performance was compared against two other algorithms for sleep segment detection; Cole‐Kripke, CK (Cole; Sleep 1992), and estimation of stationary segments or ESS (Borazio; ICHI 2014). Then, cross‐validations were implemented with the datasets; e.g. training with dataset TUD and testing on dataset IXI, and vice versa. Result The cross‐validations experiments showed no significant differences in performance (sleep detection: 85‐89% sensitivity, 43‐45% specificity and 72‐74% accuracy) for the DLS algorithm. On the contrary, CK and ESS showed significant differences in their performances across experiments, and CK showed the highest difference for specificity (mean specificity = 27‐52%), demonstrating that CK and ESS algorithms are susceptible to different devices and/or data sources (Figure 1). Conclusion The CK and ESS algorithms showed susceptibility to different devices or data sources (e.g. clinical groups) and this raises questions about their reliability. In conclusion, we presented a deep learning based algorithm (DLS) that showed high robustness against different data sources as well as high accuracy when compared against the gold standard PSG.
Secondary vocational education for IT-industry personnel training: achievements and prospects

Abstract The article explores the problem of personnel training for the IT-industry in the secondary vocational education system based on both the statistics analysis and national experience; determines the main trends and the reform outcomes; identifies the best practices for training and potential strategies as well. The conclusion emerges that the secondary vocational education is becoming increasingly important as a part of the lifelong learning strategy. Personnel training for the IT-industry under the incredibly accelerated technological changes requires the same rapid adaptation, combinations of high cost-effectiveness and training flexibility, traditional and advanced training aids, extensive use of the most sophisticated information and communicative resources, formation of not only professional competences but spiritual and personal qualities of the future specialists, creation of conditions for self-development and self-improvement. In this context, the need for the use of the best national and global training practices adapted to the Russian environment has been emphasized in the paper. The current experience analysis has shown that the extensive interaction between business and educational institutions would ensure the formation of staff capacities for high-tech companies.
RD2: Reward Decomposition with Representation Disentanglement

Reward decomposition, which aims to decompose the full reward into multiple sub-rewards, has been proven beneficial for improving sample efficiency in reinforcement learning. Existing works on discovering reward decomposition are mostly policy dependent, which constrains diversified or disentangled behavior between different policies induced by different sub-rewards. In this work, we propose a set of novel policy-independent reward decomposition principles by constraining uniqueness and compactness of different state representations relevant to different sub-rewards. Our principles encourage sub-rewards with minimal relevant features, while maintaining the uniqueness of each sub-reward. We derive a deep learning algorithm based on our principle, and refer to our method as RD$^2$, since we learn reward decomposition and disentangled representation jointly. RD$^2$ is evaluated on a toy case, where we have the true reward structure, and chosen Atari environments where the reward structure exists but is unknown to the agent to demonstrate the effectiveness of RD$^2$ against existing reward decomposition methods.
The challenges of capability maturity model integration application in the dynamic environment

The capability maturity model integration for development (CMMI-DEV) model is a software process improvement (SPI) model designed to leverage the level of software products' quality. Through modelling highly organised, controlled and continuously improved processes, CMMI-DEV enhances the organisational and functional capabilities to guarantee a competitive advantage. However, software organisations face strong competitive dynamic environment challenges due to the fast-fierce changing environmental variables such as the instable customer perspectives. Without resolving such challenges, the sustainability of software organisations is questionable. CMMI-DEV v1.3 model cannot deal with the changing dynamic environment, thus, lacks dynamic capabilities (DC). These capabilities can handle dynamic environmental factors and thus adding economic value to the organisation. However, DC with CMMI-DEV v1.3 are either not integrated or are applied in isolation. To address this issue, we propose a new model that integrates CMMI-DEV v1.3 and the DC model. DC component adds knowledge absorbing and sensing processes, which helps organisations to reshape and reallocate their resources to seize opportunities and manage threats. The model was validated using knowledge literature review and experts' judgement via Delphi technique. Results show that the new model addressed the shortcomings of CMMI-DEV v1.3 and is applicable in today's environment.
Characteristics, qualities, and features of writing

These three areas are brought together, expanded, and discussed in detail using examples to illustrate how the Forensic Document Examiner, when conducting an examination and comparison, should evaluate them by focusing on their relative importance. It is vital that each are recognized both individually and collectively and assigned their proper weight and significance.
Penerapan PSO Over Sampling Dan Adaboost Random Forest Untuk Memprediksi Cacat Software

Abstract: The dataset of software metrics, in general, are not balanced (Imbalanced). Class imbalance in Dataset can reduce the performance of software defect prediction models, because it tends to produce majority class predictions from minority classes, the dataset used in this study uses the National Aeronautics and Space Administration (NASA) Metrics Data Program (MDP), dataset From Stages Pre-processing proposed the Particle Swarm Optimization (PSO). method to overcome the problem of attributes in the training data and the Random Over Sampling (ROS) Resampling method. to deal with class imbalances. This study proposes that the Random Forest method combined with Adaboost can estimate the level of disability of software through training data. The results of this study indicate that the Resampling + Adaboost + Random Forest algorithm can be used to predict software defects with an average accuracy of 94.70% and a value of AUC 0.939. While the PSO + Random Forest algorithm only has an average accuracy of 89.60% and AUC 0.636 the difference in the accuracy of the two models is 5.10% and AUC 0.303. Statistical tests show that there is a significant influence between the proposed model and the Random Forest model with a p-value (0.036) smaller than the alpha value (0.05), which means there is a significant difference between the two models.Keywords: Imbalanced Class, Resample, Particle Swarm Optimization, Random Forest, Adaboost, Software DefectAbstrak: Dataset dari software matrik secara umum bersifat tidak seimbang (Imbalanced). Ketidak seimbangan kelas yang ada dalam dataset dapat menurunkan kinerja model prediksi cacat software, karena cenderung menghasilkan prediksi kelas mayoritas dari kelas minoritas. Dataset yang digunakan pada penelitian ini menggunakan dataset National Aeronautics and Space Administration (NASA) Metrics Data Program (MDP). Dari tahapan pra pemrosesan diusulkan metode Particle Swarm Optimization (PSO) untuk mengatasi masalah attribute pada data training dan metode Resampling Random Over Sampling (ROS). untuk menangani ketidak seimbangan kelas. Penelitian ini mengusulkan metode Random Forest yang dikombinasikan dengan Adaboost dapat mengestimasi tingkat kecacatan suatu Software melalui data training, Dari Hasil penelitian ini menunjukan bahwa algoritma Resampling+Adaboost+Random Forest dapat digunakan untuk memprediksi cacat software dengan rata-rata akurasi 94,70% dan nilai AUC 0,939. Sementara algoritma PSO+Random Forest hanya memiliki rata-rata akurasi 89,60% dan AUC 0,636 perbedaan akurasi dari kedua model tersebut 5,10% dan AUC 0,303. Uji statistik menunjukan bahwa adanya pengaruh yang signifikan antara model usulan dengan model Random Forest dengan nilai p (0,036) lebih kecil dari nilai alpha (0,05) yang artinya terdapat perbedaan yang siginifkan antara kedua model.Kata kunci: Imbalanced Class, Resample, Particle Swarm Optimization, Random Forest, Adaboost, Kecacatan Software
Fetal Brain Segmentation Using Convolutional Neural Networks with Fusion Strategies

Most of the Convolutional Neural Network (CNN) architectures are based on a single prediction map when optimising the loss function. This may lead to the following consequences; firstly, the model may not be optimised, and secondly the model may be prone to noise hence more sensitive to false positives/negatives, both resulting in poorer results. In this paper, we propose four fusion strategies to promote ensemble learning within a network architecture by combining its main prediction map with its side outputs. The architectures combine multi-source, multi-scale and multi-level local and global information collectively together with spatial information. To evaluate the performance of the proposed fusion strategies, we integrated each of them into three baseline architectures namely the classical U-Net, attention U-Net and recurrent residual U-net. Subsequently, we evaluate each model by conducting two experiments; firstly, we train all models on 200 normal fetal brain cases and test them on 74 abnormal cases, and secondly we train and test all models on 200 normal cases using a 4-fold cross validation strategy. Experimental results show that all fusion strategies consistently improve the performance of the baseline models and outperformed existing methods in the literature.
AN EXTENDED AND UNSCENTED KALMAN FILTERS SIMULATION AND DESIGN FOR A MOBILE ROBOT

This research analyzes the design and simulation of a mobile robot using Extended Kalman Filter (EKF) and Unscented Kalman filter (UKF). The mobile platform has a differential configuration, where each track of a wheel is associated with an encoder. The EKF and UKF methods are used to integrate the measurements of a novel odometric system based on the optical mice and the measurements of a localization system based on a map of geometric beacons. Two different types of simulations have been performed for validating the results, either using the mouse-based odometric system or using the conventional wheel encoder-based odometric system, to compare and evaluate the errors made by each system.
Computer aided art design Ps course teaching based on FPGA and embedded system graphics processing

Photoshop Spread through electronic media, the source of presentation to development, receipt and visual coordinators. The result of accessible media innovations has empowered visual planners to broaden these limitations of their training. In this setting, the planner is always achieving stylish success and is influenced by the modern embedded system's design and patterns in the existing method IOT (Internet of thing) based embedded system for PS (Photoshop) course teaching. In the existing method, wireless communication-based image processing is less accurate and less detection of image. So in the proposed method, art design PS (Photoshop) course teaching based FPGA (Field-programmable gate array) and embedded system graphics processing for Image Dissection Algorithm. The state of constant request, finding ways of exposure that lead to development and innovation, which are deeply respected standards for self-assessment. This exploration is based on the architect's perspective and performance observation of the wheels used in visual computerization. Contemporary planning philosophies are fundamentally examined, contradictory, and refined by the mirror action of the scientist. The refined approach to architects helps to advance the method work rehearsals, anticipating the importance of investigation and launch activities. Image Dissection Algorithm Exploration innovations exhibit some contradictions in identified approaches. Like many contemporary creators, they do not adhere to the customer base of expert planning practice, yet modern professionals influence self-produced configuration images. As FPGA (Field-programmable gate array) is confirmed by some parts of the more common planning actions, the expert finds that the architect's involvement with accidental disclosure and innovation has a critical impact on the planning cycle.
Track, trace and trust

Research being undertaken by three London universities suggests that public attitudes about contact tracing shows more support for an NHS centralised system than it does for a decentralised system. Writing on behalf of the team, Dr Julia Yesberg, Dr Krisztian Posch, Prof Jonathan Jackson and Dr Emmeline Taylor outline the findings that show that mass support will depend on limiting privacy intrusion while maximising public health effectiveness, trust in government and maintaining the sense of duty and solidarity that drove compliance in the first phase of lockdown.
Pedeutological aspects of using new technologies in education

New technologies that have been used in education are treated as the “golden mean” to remove most of the educational problems. Over time, it turns out that many of the original hopes have been disappointed. This paper tries to answer the question: Why is this happening? The historical-critical method allows analysing certain elements of the education system that are important to it and which turned out to be crucial when introducing additional technologies. That is the case with the position of a teacher in the education process. It turns out that the teacher's ideas about education can have a significant impact on his / her attitude to new technologies and thus their effectiveness. Hence, not only the preparation of teachers to use new technologies, but also shaping their ideas and building educational traditions is equally important in the effective application of new technologies in education.