Reviewerly/exHarmony huggingface dataset:
Obtaining Factors of Q&amp;A Statements with the Consideration of Categories

In order to avoid the problem of mismatch between questioner and respondent, an impression evaluation experiment was conducted and nine factors were obtained as a result. The factors were then used to estimate the factor scores of all other statements through multiple regression analysis and to select the most appropriate respondents. The factors, however, have been obtained without taking into consideration the categories of the Q&A statements. In this paper we therefore investigated whether the consideration of categories could lead to obtaining more apparent tendencies and precise results to select the most appropriate respondents. In this paper, factor analysis is applied to the experimental results with a consideration of the following categories: Auction, PC, Love, and Politics. As a result of factor analysis, both similarity and difference could be observed for each category of Q&A statements. Comparing the new results with the previous one, two similar main factors could be obtained regardless of the category considered. On the other hand, factors other than the two have were also obtained for each respective category. This shows that the number of factors obtained for each category varies.
Self-assessment of learning results by future teachers of primary school

The article describes the peculiarities of self-assessment by students of their own learning results; highlights the state and some areas of use of self-assessment in the process of preparing future teachers of primary school. The self-assessment of learning results is regarded by researchers as a manifestation of a person’s attitude toward himself, an element of critical self-knowledge, an important regulator of behavior, a means for self-improvement of the student’s personality, and others like that. The student acts as the subject of control activities (internal control) in the process of self-control, in particular: independently plans it, implements, verifies and evaluates its learning outcomes. Formation of the ability to self-assess the learning results of future primary school teachers becomes a means and goal during their professional training so that they can further use the experience gained to self-assess the activities of junior pupils. Self-assessment of own learning results is important for the development of the student’s personality, forms the ability to critically and adequately assess their own educational achievements, as well as readiness for pedagogical activity. The results of empirical research show that most of the university’s teachers use oral and written control methods, and in forms – frontal and individual examination. Selfcontrol and self-assessment have not been so widespread in higher education. Students used formation portfolio (content analysis of concepts, formation of methodological apparatus of research, fixation of research results and their processing, mathematical calculations of results, multimedia presentation of the results of an individual study and research task) during independent work, as well as methods of interactive learning during practical training (discussion, heuristic conversation, brainstorming, etc.) for development self-assessment of their own learning results. The article is prezented that most students positively evaluate their own ability to self-control learning results, they have high and middle level reflexivity.
Automatic Training Data Construction and Extractive Supervised Summarization for NTCIR-14 QA Lab-PoliInfo

On the summarization task at NTCIR-14 QA Lab-PoliInfo, participants of the task need to generate a summary corresponding to an assembly member speech in assembly minutes within the limit length. Our method extracts important sentences to summarize an assembly member speech in the minutes. Our method applies a machine learning model to predict the important sentences. However, the given assembly minutes’ data do not contain information about the importance of the sentences. As a result, we cannot directly utilize machine learning techniques for the task. Therefore, we construct training data for the importance prediction model using a word similarity between sentences in a speech and those in the summary. In addition, we apply the sentence reduction process. In the process, we consider characteristics of summaries of assembly minutes to avoid removal of important words in extracted sentences. On the evaluation, all the scores by our supervised method with the constructed data outperformed unsupervised and supervised baseline methods. The result shows the effectiveness of our method.
TagEmbedSVD: Leveraging Tag Embeddings for Cross-Domain Collaborative Filtering

Cross-Domain Collaborative Filtering (CDCF) mitigates data sparsity and cold-start issues present in conventional recommendation systems by exploiting and transferring knowledge from related domains. Leveraging user-generated tags (e.g. ancient-literature, military-history) for bridging the related domains is becoming a popular way for enhancing personalized recommendations. However, existing tag based models bridge the domains based on common tags between domains and their co-occurrence frequencies. This results in capturing the syntax similarities between the tags and ignoring the semantic similarities between them. In this work, to address these, we propose TagEmbedSVD, a tag-based CDCF model to cross-domain setting. TagEmbedSVD makes use of the pre-trained word embeddings (word2vec) for tags to enhance personalized recommendations in the cross-domain setting. Empirical evaluation on two real-world datasets demonstrates that our proposed model performs better than the existing tag based CDCF models.
Characteristics of influence of individual-personal factors of professional socialization of students of secondary vocational educational institution

The article analyzes the influence of the individual personality factors of professional socialization of the students studying at the vocational educational institution. Existing factors &#x0D; affecting the productivity of professional socialization of the students have been considered. There has been conducted a survey by means of questioning the students of engineering specialties of the Department of secondary vocational education in Federal State Educational Institution of Higher Education ASTU. Conclusions have been drawn about the influence of individual personality &#x0D; factors of students' professional socialization.
An Improved Indoor Image Registration Algorithm Based on Shallow Convolutional Neural Network Descriptor

At present, the application demand of indoor simultaneous localization and mapping (SLAM) technology increases greatly, among which, image matching is the most basic and critical content. Compared with traditional image registration, indoor image registration has higher requirements on the real-time and robustness of the algorithm. The shallow convolutional neural network is a deep machine learning model based on supervised learning with the characteristics of centralized and automatic learning from data. Aiming at the problems of slow processing and strong rotation failure of feature descriptors in traditional registration algorithms, this paper proposed an improved algorithm of local feature descriptor of triple-sample shallow convolutional neural network, which has strong feature expression ability. In addition, the performance of our improved algorithm was compared with that of three traditional algorithms (SIFT, ORB and SURF) in rotation change of indoor image matching. The results show that the improved algorithm performs better than the other three traditional methods and has a certain antagonistic effect on image rotation.
Visual content summarisation for instructional videos using AdaBoost and SIFT

Research contributions in video retrieval field are rising to propose solutions for automatic understanding and retrieval of video content. The aim is to make the user able to retrieve specific video sequences in a large database, based on semantic information. In this paper, we process a special case of videos, instructional videos, where text presents very rich semantic information for understanding video content. Indeed, lecture videos are the source of information used in learning systems by educators and students for archiving and sharing knowledge. However, users usually have difficulties to access accurate parts in instructional videos. In our paper, we propose a method to summarise the visual content in instructional videos. For that, first, we segment the video into shots based on SIFT. Then, key frames which are rich in text and figures are extracted from each shot based on entropy measurement. These keyframes are classified using AdaBoost to eliminate non-text frames. The text content in the lecture video summary can be detected and recognised to identify keywords for indexing and classification.
An efficient level set method based on global statistical information for image segmentation

In this paper, an improved region-based active contour model in a novel variational level set formulation employing global information constraints is proposed for image segmentation. Using the image global characteristics, the proposed model can accurately segment objects with weak or blurred boundaries in the presence of noise or intensity inhomogeneity and achieve robustness against different kinds of images. We present a global intensity fitting energy functional based on the differences between the original image intensity and the global intensity means. This energy is then integrated into a geodesic variational level set formulation, from which a curve evolution equation is derived for energy minimization. The level set function is regularized by the Gaussian filter which keeps it smooth and eliminates the reinitialization. Experimental results on different images demonstrate the performance of our method in terms of accuracy and robustness. Compared with the well-known active contour models, our method is less sensitive to the initial contour and more computationally efficient.
Implementation and Performance Assessment of Enhanced RC5 Cryptosystem Applied to Different File Types

This paper focuses on the implementation and performance assessment of the Enhanced RC5 Algorithm (ERC5a). A software implementation on Windows platform was written in C language as File Encryption Utility with a toggle switch for ERC5a and classic RC5. The design of the file structure is very simple and uses only one (1) byte of extra header as part of the underlying encrypted format. Performance assessment has been conducted based on encryption and decryption speed, throughput, and power consumption. Result shows an average performance increase of 32.3% on encryption and 22.1% on decryption of the proposed ERC5a over classic RC5. This can significantly boost the RC5 cryptographic viability especially in lightweight environments.
Predicate Encryption from Bilinear Maps and One-Sided Probabilistic Rank

In predicate encryption for a function f, an authority can create ciphertexts and secret keys which are associated with ‘attributes’. A user with decryption key $$K_y$$ corresponding to attribute y can decrypt a ciphertext $$CT_x$$ corresponding to a message m and attribute x if and only if $$f(x,y)=0$$. Furthermore, the attribute x remains hidden to the user if $$f(x,y) \ne 0$$. We construct predicate encryption from assumptions on bilinear maps for a large class of new functions, including sparse set disjointness, Hamming distance at most k, inner product mod 2, and any function with an efficient Arthur-Merlin communication protocol. Our construction uses a new probabilistic representation of Boolean functions we call ‘one-sided probabilistic rank,’ and combines it with known constructions of inner product encryption in a novel way.
Combined usage of the optical and radar remote sensing data in territory monitoring tasks

At the present time, a lot of problems in a sphere of fundamental sciences as well as technical and applied tasks can be solved only with the use of satellite images, since their usage reduces material, financial and time costs significantly in comparison with traditional methods. One of the modern integrated approach remote sensing processing is to join the measurements obtained from the various sources, such as optical and radar sensors, allowing to achieve a gain in comparison with independent processing due to the extension of the information volume and the opportunities of data acquisition (weather conditions, spectral ranges, etc.). However, methods of digital processing and interpretation of radar data, as well as qualitative and proven methods and algorithms for joint processing of optical and radar satellite images, has not sufficiently been well developed yet. Therefore, the development of new methods and information technology of joint analysis and interpretation of optical and radar data which are a major issue of the current paper, are certainly relevant. The paper presents an information technology for joint processing of optical and radar satellite imagery, based on training the processing procedure based on the reference values of data from sensors of the one type (optical data), followed by applying to both data types: optical and SAR data.
Dynamic Dwell Time Determination and Optimization based on Motion Detection Algorithm

Dwell time, as an important parameter in timetabling, is also a significant temporal component in rail transit operation. In general, the dwell time is a fixed value, so that passengers have to wait on board before the doors close. Therefore, dynamic dwell time, which adjust itself according to the actual passenger boarding and alighting activities, is proposed in this paper. In other words, the train would close the door and depart when these passengers' activities are recognized actual finish. Acquisition device herein is used to collect the video date of passenger activities around the door area. The time point to close the doors can be determined according to the change of detected motion value (DMV) in video through dynamic motion detection algorithm using inter-frame subtraction method. This algorithm was conducted to one metro line in Beijing partly with 5 stations. The results show that the time points, determined by dynamic motion detection algorithm, better correspond to the actual passenger boarding and alighting activities. This study contributes a unique method to dynamic adjust dwell time in real operation with the support of video technology. It shortens passenger travel time, improves the quality of passenger service and increase the through capacity for operation.
Fusing Direct Visibility Diagram with Clothoid Curves for Motion Planning

Autonomous driving technology improves the vehicles' usage in terms of minimizing the consumed energy, time and distance. Motion planning, as the main influencer of the moving efficiency of an autonomous agent, defines how the vehicle moves and interacts with other agents. Despite the fact that motion planning for autonomous robots is a well-explored field, optimizing the path planning in terms of the computation time and path length is still a topic under research. This paper introduces an innovative approach of using the Visibility Diagram to plan the optimum holonomic path to the desired target position and at a subsequent step we propose an innovative scheme of regularizing, the path shape through using Clothoid curves as path smoother to provide the optimum non-holonomic path to the same target position. The proposed methodology has been proven to reduce the computation time of finding the shortest holonomic path by a factor of sixty percent (60%) in comparison to the normal Visibility Diagram (VD). This method was implemented in several use-cases and could efficiently plan the optimum path for all the provided scenarios.
Dual-Cross Patterns with RPCA of Key Frame for Facial Micro-expression Recognition

Fast and discriminative feature extraction has always been a critical issue for spontaneous micro-expression recognition applications. In this paper, a micro-expression analysis framework based on new facial representation is proposed. Firstly, to remove redundant information in the micro-expression video sequences, the key frame is adaptively selected on the criteria of structural similarity index (SSIM) between different face images. Then, robust principal component analysis (RPCA) obtains the sparse information of the Key frame, which not only retains the expression attributes of the micro-expression sequence, but also eliminates useless information. Furthermore, we use Dual-cross patterns (DCP) to extract features of sparse key frame. Repeated comparison experiments were performed on the SMIC database to evaluate the performance of the method. Experimental results demonstrate that our proposed method achieves promising performance for micro-expression recognition.
The Implementation of Data Mining to Analyze the Consumer which is divided Into Class to Support the Decision Support System (DSS) in TB. 80 Majalengka

Abstract Consumers are an important asset to the company. This is the reason why a company must design and uses a good strategy to serve the consumer. With numerous consumers in a company, thus, the problem which the company face is to decide which one is the potential consumer. Using clustering method on data mining, the company can identify a potential consumer by grouping the consumers. Aims of the grouping the consumers are to know the consumers’ behaviour and implementing an appropriate marketing strategy thus make the company earn a profit. This research discusses how the process of data mining from data of the consumers in TB. 80 which is a company run in selling building materials and has a goal to find a potential consumer, therefore, the company profit will increase. The process of Data Mining was started with pre-processing data (selecting, cleaning and transformation), then, on clustering phase use algorithm-means by deciding the number of clusters. The result of clustering from algorithm-means was used to group the consumers and formed a class based on frequency and monetary.
Kajian Pemilihan Jenis Kolam Olak Dari Segi Fisik Bendung Pada Bendung Di Dusun Darok

The occurrence of a hydraulic jump will cause scouring and damage to the energy damper in the form of an olak pond in the weir so that it can reduce the risk of the weir. Then it needs to be repaired olak pond in the weir by using a new olak pond. . In this case the researcher will conduct a Study of Pool Type Selection in terms of the Physical Weir of the Weir in the Dusun Darok. The objective to be achieved in this research is to study what kind of olak ponds are suitable in the weir in Dusun Darokt. From the results of research and research on the condition of the weir in the field, the weir structure was built with stone pairs, cross section of the weir in the form of a trapezoid with a slope of m = 0.5, the height of the dam lighthouse = +171.4 and the height of the roof floor of the room = +167, in accordance with the height between the olak floor to the lighthouse (D), which is: 171.4 - 167 = 4.4 m. The results of the analysis and discussion, olak pond types USBR I, USBR II, USBR III and USBR IV, do not meet the requirements and cannot be applied and the type of olak pond pool meets the requirements and can be applied to dams in Dusun Darok.
Track-Oriented MHT with Unresolved Measurements

This paper first validates that the track-oriented multiple-hypothesis tracking recursion holds in the case of state-dependent detection probabilities, as is generally assumed. Next, we seek to extend the track-oriented multiple-hypothesis tracking recursion to allow for unresolved measurements. The formulation requires some simplifying assumptions, including an assumption that targets be resolved at birth and a restriction on the size of unresolved target clusters. The tracking recursion requires some approximation to admit track-oriented (factored) form, and leads to a nonlinear optimization problem. We discuss a multi-stage architecture that provides a simpler and more robust processing approach for practical settings.
Proposal of a Method to Detect Size of Overlapping Cardboard Boxes Using Plural Lightings

The introduction of automatic guided vehicles is increasing in the world. However, it has a big problem of dropping luggage. As a method for preventing this, the automatic guided vehicle itself is required to measure the arrangement and size of the load and to automatically run according to the state of the load. As loads of automatic guided vehicles, cardboard boxes are well used and they are stacked up. In such a case, it is difficult to recognize the overlapping of objects by image processing, because the colors of them are similar. In order to solve this problem, a plurality of highperformance cameras are required, but it costs.
Propensity to pedagogical activity in the structure of professional choice of high school students

The relevance of the study of the problem of professional self-determination of high school students and features of the choice of profession by modern graduates is substantiated. The degree of expression of pedagogical inclinations in the structure of professional personality types and types of professional activity of graduates as a result of research of high school students is established. Methods were used: a questionnaire of professional inclinations (L. Yovaishi’s method in G. V. Rezapkina’s modification), the method “Determination of professional personality type” (J. V. Rezapkina’s method). Holland in the modification Of G. V. Resorcinol), methods - methods “Matrix choice of profession” G. V. Resorcinol. It was found that the most common professional personality type among high school students with pedagogical inclinations is the social type. Respondents with a tendency to pedagogical activity often have a harmonious structure of professional choice, namely, a high indicator in which all three components of professional choice (inclinations, interests, type) are clearly expressed.
Improving Quality Estimation of Machine Translation by Using Pre-trained Language Representation

Translation quality estimation (QE) has been attracting increasing attention due to its potential to reduce post-editing human effort. However, QE still suffers heavily from the problem that the quality annotation data remain expensive and small. In this paper, we focus on overcoming the limitation of QE data and explore to utilize the high level latent features learned by the pre-trained language models to reduce the model’s dependence on QE data and improve QE performance. Specifically, we propose two strategies to integrate the pre-trained language features into QE model: (1) a mixed integration model, where the pre-trained language features are fed into the QE mode combined with other features; and (2) a constrained integration model, where a constraint mechanism is used to adjust the reporting bias of our first integration model and enhance the robustness of the QE model. Experimental results on WMT17 QE task demonstrate the effectiveness of our approaches.