Reviewerly/exHarmony huggingface dataset:
Minimum Uniform Search Track Placement for Rectangular Regions

We consider the problem of search track placement over a rectangular region that considers a priori target information to maximize coverage. Our approach allocates uniformly spaced parallel tracks over sections of homogeneous sensor performance. We solve the search planning problem as a MILP optimization problem that is subject to a priori target distribution constraints, as well as minimum sectional and total effective coverage constraints. Additionally, we provide a derivation of a general method to evaluate the effective coverage that remains agnostic to the assumption of dependence or independence between tracks, as well as account for the inevitable overlap of parallel tracks. We include simulation results that demonstrate the efficacy of our approach.
Didactic Justification of Remote Learning for Master’s Degree Students in Engineering and Technology

Russian universities, like all global higher education institutions, experience difficulties because of the COVID-19 pandemic. The arrangement of the study process in a digital form and using remote education technologies turned out to be a serious challenge for teachers, students and university administrations, IT departments, and academic registries. With this increased onlinification, a risk arises that the quality of education may deteriorate. The quality of remote education depends on many factors. One of the most important of those is compliance with the didactic principles of this training format that set a benchmark for the best implementation of the study process. This article deals with the justification of the extension of general didactic principles of traditional education and complementing those with the principles addressing the features of remote education for master’s degree programs.
Improved Skeleton Extraction Based on Delaunay Triangulation

The skeleton, which is the center axis of the target shape, is a topological representation of the shape. It has been used in image processing and pattern recognition fields such as target recognition, target matching, text recognition, blood vessel detection, and crack detection. Since Blum first used the grass burning model to extract the skeleton as a shape descriptor, there has been a lot of research on image skeleton extraction algorithms. In view of the problems existing in the existing methods, such as inaccurate skeleton position, discontinuous structure, and sensitivity to noise and small deformation, this paper proposes an improved image skeleton extraction algorithm based on constrained Delaunay triangulation, which effectively improves the performance of the algorithm by means of burr pruning, image pyramid, and other measures. The improved method can meet the requirements of object skeleton extraction in various scenes and has a good effect on noise suppression.
Competencies of Widyatama University Graduates Based on the results of the Tracer Study

Alumni are the output of the quality of a college. But the quality of the alumni depends on the quality of the college. The task of higher education is not only to prepare qualified graduates but to maintain good relations between graduates is equally important. Widyatama University, which was founded in 1976, is certainly very aware of this considering the large number of graduates. With the increase in the number of graduates from year to year, it is necessary to develop new methods and media in developing alumni tracing. This study aims to find university graduates and evaluate the results of the 2017 graduate tracer study. The number of respondents who filled out the questionnaire was 384 out of 949 graduates in 2017. From the results of the overall analysis, the horizontal alignment was 79.5%, which means that the competencies obtained during their study at Widyatama is according to need. in the field of work, and the results of research show that there are 78% of graduates who work in accordance with their education level.
Determination of Giving ‘Tips’ Using Fuzzy Logic To Determine Restaurant Customer Service Satisfaction

Abstract Fuzzy logic can be used to represent obscurity based on terms such as high, old, hot, cold, and so on. Fuzzy logic can extend the range of truth values to all real numbers in intervals between 0 and 1. Numbers in this interval represent the possibility that the statement given is “true” or “wrong”. The use of fuzzy logic can be used in everyday life, such as in the fields of medicine, engineering, agriculture, transportation, environmental science and also in economic and business applications. As in this study, it was used to calculate the ideal amount of ‘tips’ for restaurant waiters based on the service provided by restaurant waiters. The method used in this study is to use Mamdani fuzzy logic with the process of defuzzyfication using the Centroid of area (COA). The results of this study are that it can be determined the value crisp of giving tips to restaurant waiters is 45 thousand rupiah provided that the service provided for service = 7 (Excellent) and food service = 8 (Delicious)..
Design Strategies for Integrating Artificial Intelligence into Systems Engineering Environment

The use of artificial intelligence capabilities for developing airborne safety-critical systems has been troublesome to the aerospace industry. This technology inserts new sources of non-determinism on process execution, increasing difficulty to ensure safety requirements. In this work, we evaluate the artificial intelligence capabilities for improving systems engineering methodology. From this analysis, we present design strategies to support the tool qualification process. The design strategies are a sound basis for applying artificial intelligence into the tools employed during the whole airborne systems life cycle.
Explanation-Driven Model Stacking

With advances of artificial intelligence (AI), there is a growing need for provisioning of transparency and accountability to AI systems. These properties can be achieved with eXplainable AI (XAI) methods, extensively developed over the last few years with relation for machine learning (ML) models. However, the practical usage of XAI is limited nowadays in most of the cases to the feature engineering phase of the data mining (DM) process. We argue that explainability as a property of a system should be used along with other quality metrics such as accuracy, precision, recall in order to deliver better AI models. In this paper we present a method that allows for weighted ML model stacking and demonstrates its practical use in an illustrative example.
Face Recognition from Depth Images with Convolutional Neural Network

In recent years, the rapid development of face recognition technology has made it a hot research field. Depth image has been widely studied in face recognition due to its advantages of three-dimensional information and light insensitivity. The traditional depth image recognition method mainly focuses on the design of manual features, and it is often difficult to achieve an ideal recognition effect. This paper proposes a Convolutional Neural Network (CNN) structure for face recognition in depth images. And experiments on the RGB-D-T face database show that the proposed CNN structure can significantly improve the face recognition accuracy, compared with traditional face recognition methods, such as LBP, moment invariant and PCA.
Tensor-based Polynomial Features Generation for High-order Neural Networks

This paper discusses the methods and algorithms for polynomial features generation. The polynomial features generation is the very first step for the High-order Neural Units evaluation. The algorithms for polynomial features generation based on recursive calls are memory effective; however, these algorithms can not benefit from the modern hardware optimizations for neural networks focused on fast tensor operations on GPUs (Graphic Processing Units) and TPUs (Tensor Processing units). Moreover, the recursive calls with many operations are limiting for the application of automatic differentiation algorithms. That makes the design of a high order neural network with HONU in the later than the first hidden layer challenging. The tensor-based algorithm for polynomial features generation that tries to leverage TPU and GPU hardware architecture is introduced in this paper. The tensor-based algorithm's implementation is tested and compared with a straight-forward recursive algorithm and with SciKit-learn library implementation in Python programming language.
Notes on Espenson Method for Determining Catalyzed Reaction Fitting-Constants

Abstract Espenson states that many catalyzed reactions can be represented in a unified equation. Based on the basic assumption that the catalyzed reaction occurs in 2 reaction mechanisms ( zeroth-order and first-order ), he also introduced a time-lag -based equation for determining the fitting-constants, q and κ , using a combination of outset and near-end data. A new method, compiled based on the outset and near-end data separately, has been introduced. This study aims to demonstrate the inaccuracy of Espenson Method. The study was carried out theoretically, and the inaccuracies were tested by applying both methods to the literature data. Assessment is based on the k obs and κ values. The results showed that Espenson Method does not always use basic assumptions. The average percentage difference in the mean value of k obs , relative to the new method was 14.24% and for κ was 52.23%. The q value is not constant; q is not the maximum velocity. Doubling [ Cat .] will double q , but k obs and κ are the same. Therefore, q is simply a maximum relative velocity. The real constant is k obs as the observed catalyst constant.
The effect of multilingual suggested tags on cross-language information tagging behaviour

Purpose Suggested tag was considered as one of the critical factors affecting a user’s tagging behaviour. However, compared to the findings on the suggested tags for the monolingual environment, it still lacks focused studies on the tag suggestions for cross-language information. Therefore, this paper aims to concern with annotation behaviour and psychological cognition in the cross-language environment when suggested tags are provided. Design/methodology/approach A cross-language tagging experiment was conducted to explore the impact of suggested tags on the tagging results and process. The descriptive statistics of tags, the sources and semantic relations of tags, as well as the user’s psychological cognition were all measured in the test. Findings The experimental results demonstrated that the multilingual suggested tags could bring some costs to a user’s tagging perception. Furthermore, the language factor of suggested tags led to different paths of tagging imitation (reflected by longer semantic mapping and imitation at the visual level) and different cognitive processes (topic extraction and inference process). Originality/value To the best of the authors’ knowledge, this study is one of the first to emphasize the effect of suggested tags during multilingual tagging. The findings will enrich the theories of user-information interaction in the cross-language environment and, in turn, provide practical implications for tag-based information system design.
Semantic Modeling and Rendering

3D objects get more attention in various applications including computer games, movies, urban planning, training and so on. To model a large-scale 3D scene, semantic modeling method is getting more attention to efficiently handle the complexity of modeling process. Semantic modeling method could be utilized to model a large-scale scene, through interactive editing of the scene, and automatically generating complex environments. Semantic model could be also used to support intelligent behaviour on virtual scene, semantic rendering and adaptive visualization of complex 3D objects. In this chapter, methods to create semantic models from real objects are discussed. In addition, application examples of semantic models are illustrated with large-scale urban scene. Semantic modeling and rendering would provide ability to create interactive and responsive virtual world by considering its semantics in addition to shape appearances.
The Relationship Between Human Resource Practices and Organizational Performance and Their Operation in Light of the Development of Using Big Data Technology

This study aims to analyses how Jordanian public universities take advantage of the relationship between HRM practices and organizational performance with their combined dimensions and their operation in light of the development of using big data technology. This study is so important for different sectors to choose appropriate HRM practices which help to organizational performance in the organization. As well as, the organizational performance in the Jordanian universities is low comparing with expected results. However, HRM practices integration formulate strong and influential instruments to put a figure on ways Organizational performance.The literature review outlined a wide range of relations between HRM practices and it is a positive relationship. The results display gaps in the plane and the implementation of researches. The researchers observed the dominance of positive results, in the studies that seek to evaluate the impact of HRM on performance.
SegPoseNet: Segmentation-Guided 3D Hand Pose Estimation

This paper presents a multi-task learning architecture named SegPoseNet for 3D hand estimation problem. Previous methods usually gather image features from one perspective, which are unable to make full use of information. In this work, we dig into the hand part segmentation task and introduce semantic guidance information to help the hand pose estimation task. The use of segmentation information is novel but effective in hand pose estimation. Since predicting 3D hand pose directly is hard, we design a two-stage network, which consists of the 2D-StageNet and the 3D-StageNet. For an RGB image, 2D-StageNet carries out 2D hand pose estimation and 2D hand part segmentation at the same time under the multi-task learning mechanism. The 3D-StageNet utilizes the fusion of 2D heatmaps and 2D segmentation silhouette to predict 3D heatmaps, converting them into estimated 3D joint coordinates in the end. We conduct experiments on the pubic hand benchmark called the Rendered Hand Dataset (RHD) and achieve state-of-the-art performance. Extended ablation experiments prove the effectiveness brought by segmentation information.
A Model for Predicting n-gram Frequency Distribution in Large Corpora

The statistical extraction of multiwords (n-grams) from natural language corpora is challenged by computationally heavy searching and indexing, which can be improved by low error prediction of the n-gram frequency distributions. For different n-gram sizes ( $$n\!\ge \!1$$ ), we model the sizes of groups of equal-frequency n-grams, for the low frequencies, $$k=1, 2,\ldots $$ , by predicting the influence of the corpus size upon the Zipf’s law exponent and the n-gram group size. The average relative errors of the model predictions, from 1-grams up to 6-grams, are near $$4\%$$ , for English and French corpora from 62 Million to 8.6 Billion words.
Disparity Estimation with Scene Depth Cues

The cost volume plays a pivotal role in stereo matching, usually working as an optimization object. However, we find it also can provide effective scene prior to guide the disparity learning, as it reflects well the depth relationship between scenario objects. Inspired by this new perspective, we propose the CSA module, which consists of a new correlation and selection (CS) layer and a new aggregation layer. The CS layer can regulate the matching costs and re-encode the feature information into the correlation volume. The aggregation layer can preserve better the depth cues of the refined cost volume, through a convolution network and a unimodalization operation. The proposed module can be trained in a supervised manner, making the extraction of scene depth cues more accurate. Extensive experiments on the Sceneflow and KITTI datasets have demonstrated that with our module embedded, SOTA networks can achieve substantially better performance.
Super-Resolution Based on Generative Adversarial Network for HRTEM Images

As an important tool to study the microstructure and properties of materials, High Resolution Transmission Electron Microscope (HRTEM) images can obtain the lattice fringe image (reflecting the crystal plane spacing information), structure image and individual atom image (which reflects the configuration of atoms or atomic groups in crystal structure). Despite the rapid development of HTTEM devices, HRTEM images still have limited achievable resolution for human visual system. With the rapid development of deep learning technology in recent years, researchers are actively exploring the Super-resolution (SR) model based on deep learning, and the model has reached the current best level in various SR benchmarks. Using SR to reconstruct high-resolution HRTEM image is helpful to the material science research. However, there is one core issue that has not been resolved: most of these super-resolution methods require the training data to exist in pairs. In actual scenarios, especially for HRTEM images, there are no corresponding HR images. To reconstruct high quality HRTEM image, a novel Super-Resolution architecture for HRTEM images is proposed in this paper. Borrowing the idea from Dual Regression Networks (DRN), we introduce an additional dual regression structure to ESRGAN, by training the model with unpaired HRTEM images and paired nature images. Results of extensive benchmark experiments demonstrate that the proposed method achieves better performance than the most resent SISR methods with both quantitative and visual results.
Research on water environment and water saving management of swimming places

The swimming gymnasium is a social public water industry with water as the carrier, which integrates fitness, education and entertainment. It is an important part of water saving work to strengthen the water environment and water saving management of swimming places because of its large water consumption, complex water users and relatively concentrated water consumption time. In recent years, with the rapid development of the national economy and the continuous improvement of people’s living standards, the demand and construction of swimming venues have rapid development, consuming a lot of water resources.
Comparative assessment of the effectiveness of the organizational and economic mechanism for managing integration processes in higher education institutions

The article discusses the methodology for assessing the effectiveness of the organizational and economic mechanism for managing integration processes in universities. In modern conditions of the development of higher education, more and more attention of world science is riveted to the processes of integration in this area. The seprocesses have so strongly influenced the field of higher education that terms such as “integration”, “internationalization”, “globalization” are becoming more synonyms than concepts that differentiate essentially different processes observed in the field of higher education. In the context of globalization and internationalization of higher education, the process of implementation, measures and methods of introducing transformations that contribute to the high-quality and effective development of the university's activities not only in the field of education, but also in science are becoming significant. In this regard, it is the mechanism for the implementation of integration processes, or more precisely, the organizational and economic mechanism of management, which contributes to increasing the efficiency of higher education, the quality of education, which becomes the object of close and thorough research. The development of integration processes creates a competitive environment for universities, which allows us to consider the organizational and economic mechanism of management at a university as a business process, however, taking into account the relevant specific features that are characteristic only of a university as an educational organization.
Neuroevolution for Sentiment Analysis in Tweets Written in Mexican Spanish

In this article we propose a special kind of Neuroevolution, called NeuroEvolution of Augmenting Topologies (NEAT), which is based on a genetic algorithm, that is then used to generate an artificial neural network to analyze tweets written in Mexican Spanish, and then labeling them as positive, negative and neutral. Classification performance of neural networks generated through neuroevolution is compared to other Machine Learning approaches, such as Support Vector Machines, Naïve-Bayes, and a handcrafted neural network. This is made through a 10-fold cross validation. As it will be explained in this paper, statistical results suggest that the Neuroevolution neural network generated is simpler than the generated by hand, and gets a competitive performance with respect to the other compared methods.
Automated Method for Evaluating Neural Network’s Attention Focus

Rapid progress in machine learning and artificial intelligence (AI) has brought increased attention to the potential security and reliability of AI technologies. This paper identifies the threat of network incorrectly relying on counterfactual features that can stay undetectable during validation but cause serious issues in life application. Furthermore, we propose a method to counter this hazard. It combines well-known techniques: object detection tool and saliency map obtaining formula to compute metric indicating potentially faulty learning. We prove the effectiveness of the method, as well as discuss its shortcomings.
Fusion Dynamic Time Warping Algorithm for Hand Gesture Recognition in FMCW Radar System

In this paper, we propose a fusion dynamic time warping (FDTW) method for hand gesture recognition using a frequency modulated continuous wave (FMCW) radar. First, we use two-dimensional Fast Fourier Transform (2D-FFT) to estimate the parameters of the radar raw data, and construct the range-time spectrogram (RTM) and Doppler-time spectrogram (DTM). Second, a central time–frequency trajectory is clustered for each hand gesture spectrogram using the k-means algorithm, and then the hand gesture is recognized by the proposed Fusion Dynamic Time Warping (FDTW) algorithm. Experiments with radar data show that the proposed algorithm can significantly improve the recognition accuracy with the time complexity reduced by more than 50%.
Parenthetical Units and Structures in Italian and German spoken language: Prosodic and textual analysis

&#x0D; This work presents the prosodic analysis of Parenthetical Units in spoken Italian and German within the theoretical framework of the Language into Act Theory (L-AcT), which defines such textual units as Parentheses (PARs), i.e. devices used by the speaker to add information to the utterance on a secondary level of the text. Data were extracted from the Italian Minicorpus of the DB-IPIC resource and from the German FOLK corpus. Linguistic content and distribution of Parentheses were analyzed. Furthermore, a subset of approximately 100 PARs was selected for each corpus and these were prosodically analyzed in relation to the mandatory textual units called Comment within the L-AcT framework, which express the illocutionary force of the utterance. The results show similar characteristics for the two languages, such as an overall decrease of f0 values of the PARs and the correlation between the length of the PARs, as well as the presence of additional units separating it from the COM, and frequency values. The authors also noted the presence of Parenthetical Structures that go beyond the level of the simple textual unit, which could suggest that Parentheses are a wider textual strategy operating as inter-utterance.&#x0D;