Reviewerly/exHarmony huggingface dataset:
Theoretical and Methodological Aspects of Anti-Corruption Mechanisms Formation in the System of Higher Education

Modern higher education in the Russian Federation are increasingly approaching the critical state. Despite attempts to reform and use of successful foreign practices, our country is still lagging behind in the role. In the events that occurred in the country over the past two decades, we can conclude that Russia is characterized by unpredictable ‘rules of the game’ and, as a consequence, the future unpredictable. This led to the fact that Russian society is developing in a legalized falsification of higher education. This is reflected in the media and formally legalized the issuance of diplomas of higher education to persons not mastered the full competencies, required not only educational standards, but also the labor market. Indicators such falsifications are ‘custom-made’ evaluation and qualification works of students. Plagiarism, rough compilation when writing papers, the active use of cribs in the control of knowledge of students – a systemic phenomenon in the modern high school, that is, in fact, the falsification of knowledge. Thus, in today's society formed the ‘cult of the diploma’ the presence of higher education, and not the real competence, with the help of which the professional career and the life of a citizen in a civilized world.
Correction to: Ambient vibration measurements to support morphometric analysis of a pyroclastic cone

During the correction phase, the publisher inadvertently changed the order of figures 4 and 5.
Enhanced TCP Sequence Number Steganography using Dynamic Identifier

Network steganography is a branch of steganography that hides information through packet header manipulation and uses protocols as carriers to hide secret information. Many techniques were already developed using the Transmission Control Protocol (TCP) headers. Among the schemes in hiding information in the TCP header, the Initial Sequence Number (ISN) field is the most difficult to be detected since this field can have arbitrary values within the requirements of the standard. In this paper, a more undetectable scheme is proposed by increasing the complexity of hiding data in the TCP ISN using dynamic identifiers. The experimental results have shown that using Bayes Net, the proposed scheme outperforms the existing scheme with a low detection accuracy of 0.52%.
The Problem of Preparation of Future Specialists of Pedagogical Specialties to Work in Conditions of Inclusion

The article deals with the peculiarities of preparation of future specialists of pedagogical specialties to work in the conditions of inclusion and the problems that arise in this case. Based on the analysis of scientific sources, it was found that the most common shortcomings in the process of implementation of inclusive education include the lack of training of professionals to engage in professional activities in an inclusive educational environment, the lack of highly qualified specialists who are able to provide training in psycho-inclusion, as well as teachers' unwillingness to work with children with special needs. The foreign experience of introduction of inclusion and directions of improvement of secondary education are analyzed. Foreign scholars see the main problem of inclusion as increasing the psycho-emotional burden on the teacher and, as a consequence, his early professional burnout. Both domestic and foreign researchers see the solution of the outlined problems in forming the teacher's professional readiness to work with children with special needs during the professional training stage. Accordingly, it was concluded that it is necessary to improve the process of professional training of future employees of pedagogical specialties in the direction of forming readiness to work in the conditions of inclusion. Studies in this area were also analyzed, their advantages and disadvantages identified. Prospects for further research are the development of an organizational and structural model and appropriate methodological support for the formation of the readiness of future specialists of pedagogical specialties to work with children with disabilities.
The Impact of Using Domain Specific Features on Lexicon Based Sentiment Analysis on Indonesian App Review

The number of smartphone users today has reached a staggering number. This fact leads to the growth of the mobile application market. It is essential for each developer to maintain their engagement with their user by understanding the users’ needs. Sentiment analysis is an approach that can be used to classify reviews from users to help to understand the users’ need. Recent method using lexicon based approach which is proposed by previous study still has poor performance due to domain limitation problem on lexicon based approach. One way to handle this domain limitation problem is by using domain specific features. This study tries to explore the impact of the use of domain specific feature in the realm of app review to the performance of the classifier. The domain specific features which are explored in this study are star rating and special words. The result shows that the use of star rating and special words can increase the performance of the classifier both on accuracy and f-measure evaluation score.
Implementation of Scrum Work Framework in the Development of Quality Assurance Information System

&lt;p class="JGI-AbstractIsi"&gt;Quality Assurance Information System Development is required to accellerate accreditation achievement. This Information System application is an integrated model of quality assurance information systems based on the integration of BAN-PT accreditation and ISO 9001: 2008. The purpose of this research is to develop a quality assurance information system by implementing the Scrum Framework. Scrum is one of the popular frameworks in Agile Development Methodology. In this way, the development of productivity increases significantly. In this Applied Research the Action Research approach is used. This Multi-Year Applied Research is the final research of previous studies. The results of this study presented the quality assurance information system that was produced using the complete Scrum framework. This information system is expected to contribute significantly to ISO-certified higher education in increasing the BAN-PT Accreditation assessment for their study programs.&lt;/p&gt;
Automatic CAC Voxel Classification with Multi-scale CNN Architecture

Coronary Artery Calcification (CAC) score is one of the most important measures in determining the degree of cardiovascular disease. It is time-consuming to do this manually or semi-automatically, so automatic CAC scoring methods are being studied. Most methods classify the calcified pixels(2D) or voxels(2.5D or 3D) and calculate the CAC score. We present a new automatic CAC voxel classification model with multi-scale CNN architecture which can reflect the advantages of large receptive CNN and small receptive CNN. This study used a cardiac CT dataset of 98 patients from Asan Medical Center in South Korea. The dataset consisted of a cardiac CT DICOM raw data and a CAC label data annotated by a cardiac radiologist. A total of 10,000 voxels were selected for each calcified artery(LM, CX, LAD, RCA) and background(BG), so a total of 50,000 voxels were used in training and testing. Our proposed model showed an accuracy of 89.58% with cardiac CT dataset of Asan Medical Center. The performance of our model is generally better when compared to other automatic CAC classification models.
Optimization of Information-Seeking Dialogue Strategy for Argumentation-Based Dialogue System

Argumentation-based dialogue systems, which can handle and exchange arguments through dialogue, have been widely researched. It is required that these systems have sufficient supporting information to argue their claims rationally; however, the systems do not often have enough information in realistic situations. One way to fill in the gap is acquiring such missing information from dialogue partners (information-seeking dialogue). Existing informationseeking dialogue systems were based on handcrafted dialogue strategies that exhaustively examine missing information. However, these strategies were not specialized in collecting information for constructing rational arguments. Moreover, the number of system’s inquiry candidates grows in accordance with the size of the argument set that the system deal with. In this paper, we formalize the process of information-seeking dialogue as Markov decision processes (MDPs) and apply deep reinforcement learning (DRL) for automatic optimization of a dialogue strategy. By utilizing DRL, our dialogue strategy can successfully minimize objective functions: the number of turns it takes for our system to collect necessary information in a dialogue. We also proposed another dialogue strategy optimization based on the knowledge existence. We modeled the knowledge of the dialogue partner by using Bernoulli mixture distribution. We conducted dialogue experiments using two datasets from different domains of argumentative dialogue. Experimental results show that the proposed dialogue strategy optimization outperformed existing heuristic dialogue strategies.
Multi-Precision Policy Enforced Training (MuPPET): A precision-switching strategy for quantised fixed-point training of CNNs

Large-scale convolutional neural networks (CNNs) suffer from very long training times, spanning from hours to weeks, limiting the productivity and experimentation of deep learning practitioners. As networks grow in size and complexity, training time can be reduced through low-precision data representations and computations. However, in doing so the final accuracy suffers due to the problem of vanishing gradients. Existing state-of-the-art methods combat this issue by means of a mixed-precision approach utilising two different precision levels, FP32 (32-bit floating-point) and FP16/FP8 (16-/8-bit floating-point), leveraging the hardware support of recent GPU architectures for FP16 operations to obtain performance gains. This work pushes the boundary of quantised training by employing a multilevel optimisation approach that utilises multiple precisions including low-precision fixed-point representations. The novel training strategy, MuPPET, combines the use of multiple number representation regimes together with a precision-switching mechanism that decides at run time the transition point between precision regimes. Overall, the proposed strategy tailors the training process to the hardware-level capabilities of the target hardware architecture and yields improvements in training time and energy efficiency compared to state-of-the-art approaches. Applying MuPPET on the training of AlexNet, ResNet18 and GoogLeNet on ImageNet (ILSVRC12) and targeting an NVIDIA Turing GPU, MuPPET achieves the same accuracy as standard full-precision training with training-time speedup of up to 1.84$\times$ and an average speedup of 1.58$\times$ across the networks.
Bayesian Inverse Reinforcement Learning for Demonstrations of an Expert in Multiple Dynamics

Though a reinforcement learning framework has numerous achievements, it requires a careful shaping of a reward function that represents the objective of a task.There is a class of task in which an expert could demonstrate the optimal way of doing, but it is difficult to design a proper reward function.For these tasks, an inverse reinforcement learning approach seems useful because it makes it possible to estimates a reward function from expert's demonstrations.Most existing inverse reinforcement learning algorithms assume that an expert gives demonstrations in a unique environment.However, an expert also could provide demonstrations of tasks within other environments of which have a specific objective function.For example, though it is hard to represent objective explicitly for a driving task, the driver could give demonstrations under multiple situations.In such cases, it is natural to utilize these demonstrations in multiple environments to estimate expert's reward functions.We formulate this problem as Bayesian Inverse Reinforcement Learning problem and propose a Markov Chain Monte Carlo method for the problem.Experimental results show that the proposed method quantitatively overperforms existing methods.
Citation Detection on Scientific Journal Using Support Vector Machine

Scientific journals are scientific works that are published regularly by an organization or institution, writing carelessness in scientific work can be considered as a form of plagiarism. So writing quotations in scientific work is important, because quotations are able to provide recognition of reference sources. In finding out the source of the citation, the method used in this study is SVM. In the process, two experiments were made, including the first experiment using a combination of SVM and TF-IDF, where the TF-IDF method was used to reduce the number of dimensions of the data, so that data could be processed optimally using SVM, while the second experiment used SVM only. The results of the study for the first experiment obtained accuracy and f-measure values of 98.79% and 98.44%. While the second experiment obtained accuracy and f-measure values of 98.1% and 97.64%. From these results it can be seen that the use of the TF-IDF method is able to support SVM as evidenced by the value of accuracy and f-measure which has increased by 0.69% and 0.8%.
In-Process Hole and Fastener Inspection Using a High-Accuracy Laser Sensor

&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;Electroimpact has produced a new in-process inspection system for use on drilling and fastening systems. The system uses a high-accuracy, non-contact, laser system to measure the flushness of installed fasteners. The system is also capable of measuring part normality and providing feedback to the machine for correction. One drawback to many automatic inspection systems is measurement error. Many sources of measurement error exist in a production environment, including drilling chips, lubrication, and fastener head markings. Electroimpact’s latest system can create a visualization of the measured fastener for the operator to interpret. This allows the operator to determine the cause of a failed measurement, thus reducing machine downtime due to false negatives.&lt;/div&gt;&lt;div class="htmlview paragraph"&gt;Electroimpact created a custom C# WPF application that queries the point-cloud data and analyzes the raw data. A custom “circle Hough transform” scoring algorithm is used to find the center of the nosepiece (pressure foot). A best fit plane is calculated from the point cloud data to find the panel surface. This plane is then used to output panel normality in the A and B axes. Flushness is determined by computing the distance of each point in the fastener point-cloud to the best fit plane previously calculated. Finally, the point cloud is made into a surface and displayed on the screen using HelixToolkit open source 3D libraries. This allows the user to rotate, zoom, and center the 3D image on the PC.&lt;/div&gt;&lt;/div&gt;
Kernel Sharing in the Channel Dimension to Improve Parameters Efficiency

In many real-time applications, the deployment of deep neural networks is constrained by its high computational cost. Therefore, reducing redundancy and designing efficient neural networks are widely concerned. In this paper, we propose that the convolution kernel can be shared in the channel dimension, which can be seen as the same convolution kernel sliding on the channel. A novel efficient convolution is proposed by using group convolution and kernel sharing, which is named as kernel sharing group convolution (KSGC). KSGC is more parameter efficient than standard convolution and general group convolution (GGC). Subsequently, the KSGCNet is constructed by utilizing KSGC. We compare KSGCNet with ResNet, MobileNet and ResNeXt on CIFAR dataset. The results show that KSGCNet has higher accuracy than ResNet and MobileNet. KSGCNet is more flexible and parameter efficient than ResNeXt because of two flexible hyper-parameters, the number of input feature maps in each group and kernel sliding stride. Moreover, the two hyperparameters can make a trade-off between accuracy and model size.
Multi-Resolution Like Data Transmission Security Using Diffie-Hellman Protocol

In this paper, special schema has been proposed for long message transmission which has been encrypted in efficient manner that is called (parallel multi-resolution like) encryption approach, that means multiple levels or sub-image represented in a matrix contains data of long message, each level of this matrix related with classical encryption methods with a secret keys that generated by (Diffie-Hellman) key exchange protocol that utilized in our cryptosystem.The statistical analysis that applied on encrypted data indicates that cipher text passed in each statistical test.Moreover, this cryptosystem removed an important problem which is (Man in the middle attack) in that protocol and (Brute force attack).Then efficiency of data transfer security has been increased when using the hash function (SHA256) and separating number of characters, the proposed method has improved the mechanism of protocol (D-H), making it possible to send long messages more confidentially.
An Analysis of Variables Affecting Korean ESL Students’ English Writing Performance

This study sought to investigate factors affecting the English writing performance of 30 Korean ESL students overseas. Also, examined were the relationship between L1 and L2 education levels and L2 writing performance, the relationship between English language proficiency and L2 writing performance and between length of residence and L2 writing performance. The participants were asked to write essays in their L1 and L2 along with a questionnaire. To analyze the data, two-way ANOVA and correlation were performed. The results showed there was no significant relation between L1 educational level and L2 writing performance, but there was a significant relation between L2 educational level and L2 writing performance. Also, English language proficiency and L2 writing performance were highly correlated. The length of residence of the participants in the U.S. and their L2 writing performance showed a weak correlation. Similarly, their L1 writing performance was not related to their L2 writing performance. These imply that students need to have adequate proficiency of English to write in their L2, and they need to receive a considerate number of years of formal education in their L2 for the success of L2 writing.
Leadership Qualities of a Modern University Student in the Reflexive Dimension

Leadership qualities of the individual are of particular importance in the context of globalization and integration trends of modern society.Leadership is the inner state of a person determining the strategy of her life.Thus, leadership qualities are related to its reflection, which gives the opportunity to carry out introspection and to analyze other people, that is meta quality.This circumstance makes it possible to consider the phenomenon of leadership qualities of students in the reflexive dimension.Leadership qualities of a modern University student have a complex structure, contains motivational-value, cognitive, operational-activity components.These components are associated with different types of reflection, namely, personal, intellectual, communicative, cooperative; retrospective, situational, perspective.The diagnostic tools presented in the article allowed to determine the level of formation of students' leadership qualities and their reflexivity and to statistically prove the relationship and interrelation of these phenomena.At the initial stage of education at the University dominated by leadership qualities of the individual, focused "on themselves" and, accordingly, personal, intellectual and retrospective types of reflection.At the stage of completion of the second (bachelor) level of higher education the priority is given to leadership qualities of the student, aimed "at others", which are determined by communicative, cooperative, perspective reflection.The results of the experimental study actualized the need for the formation of leadership qualities and different types of reflection of modern University students.
Trajectory tracking error using fractional order time-delay recurrent neural networks using Krasovskii-Lur’e functional for Chua’s circuit via inverse optimal control

This paper presents an application of a Fractional Order Time Delay Neural Networks to chaos synchronization. The two main methodologies, on which the approach is based, are fractional order time-delay recurrent neural networks and the fractional order inverse optimal control for nonlinear systems. The problem of trajectory tracking is studied, based on the fractional order Lyapunov-Krasovskii and Lur’e theory, that achieves the global asymptotic stability of the tracking error between a delayed recurrent neural network and a reference function is obtained. The method is illustrated for the synchronization, the analytic results we present a trajectory tracking simulation of a fractional order time-delay dynamical network and the Fractional Order Chua’s circuits
Pengembangan Sistem Informasi Pada Local E-Govervenance Untuk Peningkatan Kinerja Pelayanan Warga

The problems that often arise in the neighborhood of Neighborhood Unit (RT) and Rukun Warga (RW) are the distribution of information on citizen activities, administration, transparent financial management, and management of citizen databases as an effort to improve services and information facilities for residents. The purpose of this study is to support the government in implementing e-Government-based government systems, by developing Good Local Governance. The specific target of innovation to be achieved is the development of innovation in the form of a web-based Citizen Information System (SiWarga) application at the RT / RW level. The method used in this study uses the System Development Life Cycle (SDLC Waterfall) method, starting from the first is analyzing the needs by conducting a survey and the feasibility of developing the system, second is to make a detailed list according to system requirements specifications, third is to design the system by creating workflow designs and programming design, the fourth is the development of information systems with coding, the fifth is system testing, the sixth is the implementation and maintenance of the system