Reviewerly/exHarmony huggingface dataset:
The Method of League Analysis and Its Application in Comparing Global University Rankings and Russia’s University Performance Monitoring

A new technique called Method of Analysis of Leagues (MethALeague) is proposed for comparing performance of higher education institutions measured by different assessment methods. The MethALeague uses the convolution operations from the theory of small-group decision making to create aggregate charts of university leagues based on the performance indicators obtained with different assessment techniques. Specifically, researchers are given the opportunity to bring widely divergent university performance indicators into unified assessment charts and carry out comparative analysis of different assessment approaches. The MethALeague was applied successfully to compare the performance indicators of the Project 5–100 universities reflected in three major global rankings, Academic Ranking of World Universities, QS World University Rankings, and Times Higher Education World University Rankings. A formalized concept of “world ranking” proposed in the article makes it possible to visualize the performance dynamics of Russia’s top universities and compare it to that of the top universities in other countries (United States, Great Britain, Australia, Germany, and China). Suggestions are made on using a modified version of the MethALeague at the national level to analyze the results of university performance monitoring and compare them to the universities’ global ranking positions. The method described in the article could be applied by educational authorities, researchers and higher education institutions to determine the frameworks of strategic development, both for specific universities and for Russia’s higher education system as a whole.
BIA: <u>B</u>ehavior <u>I</u>dentification <u>A</u>lgorithm Using Unsupervised Learning Based on Sensor Data for Home Elderly

Behavior identification plays an important role in supporting homecare for the elderly living alone. In literature, plenty of algorithms have been designed to identify behaviors of the elderly by learning features or extracting patterns from sensor data. However, most of them adopted probabilistic models or supervised learning to identify behaviors based on labeled sensor data. This paper proposes a behavior identification algorithm (BIA) using unsupervised learning based on unlabeled sensor data for the elderly living alone in smart home. This paper presents the observation of elder behaviors with three features: Event Order, Time Length Similarity and Time Interval Similarity features. Based on these features of behavior observations, two properties of behaviors, including the Event Shift and Histogram Shape Similarity properties, are presented. According to these properties, the proposed BIA is developed. Finally, performance results show that the proposed BIA outperforms the existing unsupervised machine learning mechanisms in terms of the behavior identification precision and recall.
Blockchain based e-Cheque Clearing Framework

This research work proposes a novel and comprehensive electronic cheque transactions framework. The proposed e-cheque system is free from the various security attacks such as alteration of the e-cheque, double spending of e-cheque, counterfeits e-cheques. The e-cheque generated in the proposed system can be deposited electronically or physically via teller machines. This facility provides greater flexibility to the customers of the banking system. The proposed system also provides space for professional miners to participate in the e-cheque transaction system by performing mining's and earn incentives. As the customer's perspective of security, the proposed system uses digital signature and cryptographic hash in each transaction hence it is a completely secure system. The existing CTS based cheque clearance request requires at least one day to clear a cheque which could extend to two or three days but the proposed system requires only 1.65 seconds for clearing any e-cheque.
Usability and Engagement Study for a Serious Virtual Reality Game of Lunar Exploration Missions

Virtual reality (VR) technologies have opened new possibilities for creating engaging educational games. This paper presents a serious VR game that immerses players into the activities of lunar exploration missions in a virtual environment. We designed and implemented the VR game with the goal of increasing players’ interest in space science. The game motivates players to learn more about historical facts of space missions that astronauts performed on the Moon in the 1970s. We studied usability and engagement of the game through user experience in both VR and non-VR versions of the game. The experimental results show that the VR version improved their engagement and enhanced the interest of players in learning more about the events of lunar exploration.
Evaluating Added Value of Augmented Reality to Assist Aeronautical Maintenance Workers—Experimentation on On-field Use Case

Augmented Reality (AR) technology facilitates interactions with information and understanding of complex situations. Aeronautical Maintenance combines complexity induced by the variety of products and constraints associated to aeronautic sector and the environment of maintenance. AR tools seem well indicated to solve constraints of productivity and quality on the aeronautical maintenance activities by simplifying data interactions for the workers. However, few evaluations of AR have been done in real processes due to the difficulty of integrating the technology without proper tools for deployment and assessing the results. This paper proposes a method to select suitable criteria for AR evaluation in industrial environment and to deploy AR solutions suited to assist maintenance workers. These are used to set up on-field experiments that demonstrate benefits of AR on process and user point of view for different profiles of workers. Further work will consist on using these elements to extend results to AR evaluation on the whole aeronautical maintenance process. A classification of maintenance activities linked to workers specific needs will lead to prediction of the value that augmented reality would bring to each activity.
A Client/Server based Online Environment for the Calculation of Medical Segmentation Scores

Image segmentation plays a major role in medical imaging. Especially in radiology, the detection and development of tumors and other diseases can be supported by image segmentation applications. Tools that provide image segmentation and calculation of segmentation scores are not available at any time for every device due to the size and scope of functionalities they offer. These tools need huge periodic updates and do not properly work on old or weak systems. However, medical use-cases often require fast and accurate results. A complex and slow software can lead to additional stress and thus unnecessary errors. The aim of this contribution is the development of a cross-platform tool for medical segmentation use-cases. The goal is a device-independent and always available possibility for medical imaging including manual segmentation and metric calculation. The result is Studierfenster (studierfenster.at), a web-tool for manual segmentation and segmentation metric calculation. In this contribution, the focus lies on the segmentation metric calculation part of the tool. It provides the functionalities of calculating directed and undirected Hausdorff Distance (HD) and Dice Similarity Coefficient (DSC) scores for two uploaded volumes, filtering for specific values, searching for specific values in the calculated metrics and exporting filtered metric lists in different file formats.
Static analysis with demand-driven value refinement

Static analysis tools for JavaScript must strike a delicate balance, achieving the level of precision required by the most complex features of target programs without incurring prohibitively high analysis time. For example, reasoning about dynamic property accesses sometimes requires precise relational information connecting the object, the dynamically-computed property name, and the property value. Even a minor precision loss at such critical program locations can result in a proliferation of spurious dataflow that renders the analysis results useless. We present a technique by which a conventional non-relational static dataflow analysis can be combined soundly with a value refinement mechanism to increase precision on demand at critical locations. Crucially, our technique is able to incorporate relational information from the value refinement mechanism into the non-relational domain of the dataflow analysis. We demonstrate the feasibility of this approach by extending an existing JavaScript static analysis with a demand-driven value refinement mechanism that relies on backwards abstract interpretation. Our evaluation finds that precise analysis of widely used JavaScript utility libraries depends heavily on the precision at a small number of critical locations that can be identified heuristically, and that backwards abstract interpretation is an effective mechanism to provide that precision on demand.
A novel and synergistic geostatistical approach to identify sources and cores of Potentially Toxic Elements in soils: An application in the region of Cantabria (Northern Spain)

The distribution of Potentially Toxic Elements is a major concern in environmental science since an exposure to high concentrations may affect human, animal or plant health. In this regard, soils are highly exposed to the entrance of hazardous elements due to diffuse pollution. Thus giving rise to areas where the concentrations of certain pollutants may be concerning. However, many of these enrichments exist due to geogenic factors, especially in regions with historical mining legacy. Here it is proposed an innovative and synergistic methodology that combines the use of raw data, enrichment factors and compositional data, through statistical and geostatistical approaches (Ordinary Kriging), to detect elements of concern and clusters where each contaminant may be individually hazardous. This methodology was successfully applied in this work to soils of the region of Cantabria where local high concentrations of As, Cd, Cu, Hg, Ni, Pb, Sb and Zn were found, and specifically, notable enrichments for Hg, Pb and Zn were revealed in areas of intense human activity.
Facial Expression Recognition Based on Auxiliary Models

In recent years, with the development of artificial intelligence and human–computer interaction, more attention has been paid to the recognition and analysis of facial expressions. Despite much great success, there are a lot of unsatisfying problems, because facial expressions are subtle and complex. Hence, facial expression recognition is still a challenging problem. In most papers, the entire face image is often chosen as the input information. In our daily life, people can perceive other’s current emotions only by several facial components (such as eye, mouth and nose), and other areas of the face (such as hair, skin tone, ears, etc.) play a smaller role in determining one’s emotion. If the entire face image is used as the only input information, the system will produce some unnecessary information and miss some important information in the process of feature extraction. To solve the above problem, this paper proposes a method that combines multiple sub-regions and the entire face image by weighting, which can capture more important feature information that is conducive to improving the recognition accuracy. Our proposed method was evaluated based on four well-known publicly available facial expression databases: JAFFE, CK+, FER2013 and SFEW. The new method showed better performance than most state-of-the-art methods.
Saarland at MRP 2019: Compositional parsing across all graphbanks

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Blockchain based auditable medical transaction scheme for organ transplant services

Internet of Things have brought exciting changes in the social norms, work environments and the prospects for future generations.These devices (Things) have already changed the way our networks are used for communication.With the introduction of machine to machine communication (M2M), where devices communicate without human involvement to perform routine day to day tasks.These tasks on one hand, include services that provide convenience to the device owners such as setting off alarms, acting as personal assistant for reminders, keeping track of daily activities etc.On the other hand, there are certain tasks where these devices perform Transactions on behalf of the owners i.e. financial transactions/ online ordering etc.These transactional tasks have significant legal implications if some problem / dispute arises due to such action performed by these devices on behalf of the owners.To ensure that these interactions take place under observation of the owners and to keep track of their occurrence, there is a need to keep record of all such communication.We propose use of Blockchain for tracking all these transactions without compromising secrecy of data by keeping its integrity intact for medico-legal requirements and prevent risk of fraud.
A New Open Information Extraction System Using Sentence Difficulty Estimation

The World Wide Web has a considerable amount of information expressed using natural language. While unstructured text is often difficult for machines to understand, Open Information Extraction (OIE) is a relation-independent extraction paradigm designed to extract assertions directly from massive and heterogeneous corpora. Allocation of low-cost computational resources is a main demand for Open Relation Extraction (ORE) systems. A large number of ORE methods have been proposed recently, covering a wide range of NLP tools, from ``shallow'' (e.g., part-of-speech tagging) to ``deep'' (e.g., semantic role labeling). There is a trade-off between NLP tools depth versus efficiency (computational cost) of ORE systems. This paper describes a novel approach called Sentence Difficulty Estimator for Open Information Extraction (SDE-OIE) for automatic estimation of relation extraction difficulty by developing some difficulty classifiers. These classifiers dedicate the input sentence to an appropriate OIE extractor in order to decrease the overall computational cost. Our evaluations show that an intelligent selection of a proper depth of ORE systems has a significant improvement on the effectiveness and scalability of SDE-OIE. It avoids wasting resources and achieves almost the same performance as its constituent deep extractor in a more reasonable time.
A Novel Method for Image Encryption Based on Chaos and Transcendental Numbers

In this paper, a method for image encryption is proposed based on a novel approach using chaos and transcendental numbers for generating the substitution box and the ciphering keys.The Chaotic Pi Ciphering (CPC) is a robust symmetric cryptosystem of the substitution-permutation-network type, that ciphers images in ten rounds.The objective of CPC is to avoid some general weakness points in cryptosystems, such as the vulnerability to certain types of attacks or the encryption with data loss.The number of rounds can be increased up to 14, with an even further increment of the ciphering security.An 8×8 substitution box is constructed using chaos, and its value of the Differential Power Analysis is lower than the corresponding to the Advanced Encryption Standard (AES).It makes CPC more resistant to differential and linear attacks than AES.CPC has approximately 2 512 keys, presenting a high resistance to brute-force attacks.Chaos is produced by a nonlinear differential equation, and the Walsh function is applied to measure the nonlinearity of the box.The transcendental number pi and a positive integer are employed to generate the schedule keys, and the key length is equal to the image size.The randomness of the encrypted images is evaluated with four tests: entropy, correlation, Discrete Fourier Transform, and a goodness-of-fit test using the chi square distribution χ 2 .CPC was tested using well-known images from the related literature, and its performance was compared to the corresponding to AES.The results show a good entropy and a high randomness, indicating a high-quality ciphering.
Energy-Inspired Models: Learning with Sampler-Induced Distributions

Energy-based models (EBMs) are powerful probabilistic models, but suffer from intractable sampling and density evaluation due to the partition function. As a result, inference in EBMs relies on approximate sampling algorithms, leading to a mismatch between the model and inference. Motivated by this, we consider the sampler-induced distribution as the model of interest and maximize the likelihood of this model. This yields a class of energy-inspired models (EIMs) that incorporate learned energy functions while still providing exact samples and tractable log-likelihood lower bounds. We describe and evaluate three instantiations of such models based on truncated rejection sampling, self-normalized importance sampling, and Hamiltonian importance sampling. These models out-perform or perform comparably to the recently proposed Learned Accept/RejectSampling algorithm and provide new insights on ranking Noise Contrastive Estimation and Contrastive Predictive Coding. Moreover, EIMs allow us to generalize a recent connection between multi-sample variational lower bounds and auxiliary variable variational inference. We show how recent variational bounds can be unified with EIMs as the variational family.
ONLINE GRAMMAR CHECKERS AND THEIR USE FOR EFL WRITING

The presence of online grammar checkers as the result of technology advancement is inevitable to be popular among academicians and researchers. With more complete features offered to help improving writing, many utilize these tools to accomplish their work. This paper, thus, aims to present an overview of online grammar checkers and their use for EFL writing. Specifically, this paper explicates online grammar checkers and reviews some research investigating the use of these applications for writing. In addition, brief reviews about a number of online grammar checker tools comprising Grammarly, ProWritingAid, Ginger, and GrammarCheck are provided. Last, some suggestions are also offered for teachers and future researchers regarding the use of online grammar checkers.
Virtual Reality in Army Artillery Observer Training

The practice of observation in Army Artillery has been hampered by the high cost and the limited number of available instruction areas. Using Virtual Reality (VR) becomes a viable alternative for this important training activity. In this context, was developed the first Brazilian Army VR simulator in which an instructor can train an observer in a virtual environment of Artillery employment. This simulator was tested by a sample of 13 Artillery officers with extensive practical experience in observation. The results were compared with reference values obtained from related work. The data show that the simulator reached the reference values for the two parameters evaluated: presence and effectiveness.
A Stream Processing Framework Based on Linked Data for Information Collaborating of Regional Energy Networks

Coordinating of energy networks to form a city-level multidimensional integrated energy system becomes a new trend in Energy Internet (EI). The collaborating in the information layer is a core issue to achieve smart integration. However, the heterogeneity of multiagent data, the volatility of components, and the real-time analysis requirement in EI bring significant challenges. To solve these problems, in this article we propose a stream processing framework based on linked data for information collaboration among multiple energy networks. The framework provides a universal data representation based on linked data and semantic relation discovery approach to model and semantically fuse heterogeneous data. Semantics-based information transmission contracts and channels are automatically generated to adapt to structural changes in EI. A multimodel-based dynamic adjusting stream processing is implemented using data semantics. A real-world case study is implemented to demonstrate the adaptability, feasibility, and flexibility of the proposed framework.
Weight-Variable Scattering Convolution Networks and Its Application in Electromagnetic Signal Classification

Deep learning is an important support for the development of cognitive communication in the cognitive Internet of Things (IoT). Deep convolution neural networks have powerful functional expression and feature extraction capabilities. Mallat et al. proposed a convolutional network model with strict mathematical theory support and excellent feature extraction ability in 2012, i.e., wavelet scattering convolution networks, which is widely used in audio and image classification. In this paper, we improve the wavelet scattering convolution networks and propose construct weight-variable scattering convolution networks by combining the scattering network and the deep convolution neural network organically. We select a variety of wavelet filters for filtering operations and add 1*1 convolution layers to enable the features extracted by different types of wavelet filters to be combined optimally. In order to verify the effectiveness of the proposed network structure, we conduct experiments on classification of typical electromagnetic signals. The experimental results show that the signal classification accuracy obtained by the weight-variable scattering convolution networks is significantly better than that of the traditional wavelet scattering convolution network, and the computational complexity is greatly reduced while the classification accuracy is close to the deep convolution neural network.
Comparison of Results Obtained from Logistic Regression, CHAID Analysis and Decision Tree Methods

In this study, Logistic Regression (LR), CHAID (Chi-squared Automatic Interaction Detection) analysis and data mining methods are used to investigate the variables that predict the mathematics success of the students.Research Methods: In this study, a quantitative research design was employed during the data collection and the analysis phases.Findings: The findings obtained in this study showed that the variables, which had significant effects on the mathematical success of the students in each method, differ from each other.Although the independent variables with a significant effect on the dependent variable were different according to different methods, the findings indicated that the importance order of the variables did not change according to the method used.In this study, the correct classification ratios obtained by the class concerning PISA mathematics literacy differed by different methods.
Neural Generation for Czech: Data and Baselines

We present the first dataset targeted at end-toend NLG in Czech in the restaurant domain, along with several strong baseline models using the sequence-to-sequence approach.While non-English NLG is under-explored in general, Czech, as a morphologically rich language, makes the task even harder: Since Czech requires inflecting named entities, delexicalization or copy mechanisms do not work out-ofthe-box and lexicalizing the generated outputs is non-trivial.In our experiments, we present two different approaches to this this problem: (1) using a neural language model to select the correct inflected form while lexicalizing, (2) a two-step generation setup: our sequence-to-sequence model generates an interleaved sequence of lemmas and morphological tags, which are then inflected by a morphological generator.