Reviewerly/exHarmony huggingface dataset:
Blockchain enabled Ambient Assisted living for Critically ill Patients

The blockchain technology of the 4 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> industrial revolution was introduced to fundamentally change the online world by moving the entire system to a decentralized platform instead of the prevailing centralized system. It is an equitable system where the data is open to everyone thus any technology that makes use of blockchain framework retains lucidity thus making it hassle-free for its customers. The most popular implementation of blockchain technology, bitcoin is famous for its security and privacy, attributed to its proof of work mining algorithm. On the other hand, its enormous energy consumption hampers its implementation in resource-restrained IoT applications. While the traditional blockchain operation demands a solely dedicated mining machine, contrarily, IoT devices seems not capable enough to entertain the high cost, value, and validity of the blockchain domain. Therefore, some simplification in the maximum energy-consuming part of the blockchain which is the mining process is required to be done. This work implements a simplified energy-efficient blockchain for ambient assisted living applications notably for critically ill patients. It collects data from five digital sensors on a single board computer for a medical facility, which computes PoW for up to a particular level of difficulty as allowed by available computation power. The emulation results obtained on comparison of the recommended blockchain with respect to traditional blockchain exhibit that simplified blockchain implementation is energy efficient in the terms of computation time and more pertinent to the resource-constrained IoT domain.
Automatic construction of academic profile: A case of information science domain

To provide junior researchers with domain-specific concepts efficiently, an automatic approach for academic profiling is needed. First, to obtain personal records of a given scholar, typical supervised approaches often utilise structured data like infobox in Wikipedia as training dataset, but it may lead to a severe mis-labelling problem when they are utilised to train a model directly. To address this problem, a new relation embedding method is proposed for fine-grained entity typing, in which the initial vector of entities and a new penalty scheme are considered, based on the semantic distance of entities and relations. Also, to highlight critical concepts relevant to renowned scholars, scholars’ selective bibliographies which contain massive academic terms are analysed by a newly proposed extraction method based on logistic regression, AdaBoost algorithm and learning-to-rank techniques. It bridges the gap that conventional supervised methods only return binary classification results and fail to help researchers understand the relative importance of selected concepts. Categories of experiments on academic profiling and corresponding benchmark datasets demonstrate that proposed approaches outperform existing methods notably. The proposed techniques provide an automatic way for junior researchers to obtain organised knowledge in a specific domain, including scholars’ background information and domain-specific concepts.
On Privileged Information Driven Robust Face Verification: A Siamese Convolutional Neural Network Approach

Face verification is an important task to verify people's identities, which has sample pairs and labels with only side information about whether the two images in a pair are from the same subject or not instead of a specific label for each sample. This paper is motivated by the limitations of previous efforts that the deep neural networks with only the RGB feature are not robust to intra-class noise (e.g., illumination), and the deep networks with both the RGB and depth feature are not robust to data availability of the depth images in applications. To overcome these limitations, we focus on developing a "double robust" face verification architecture using the Siamese convolutional neural network (SCNN). In particular, our goal is to propose an SCNN with privileged information (SCNN+), which is inspired by Learning Using Privileged Information (LUPI), through incorporating additional depth feature along with the RGB feature in the training stage to provide privileged information to restrain the RGB feature's prediction error. We conduct experiments to evaluate the performance of the proposed SCNN+ architecture and compare it with different categories of state-of-the-art baselines on two real-world RGB-D face datasets. The evaluation results demonstrate that SCNN+ significantly outperforms all types of baselines.
Linked Vocabularies for Mobility and Transport Research

The paper describes the creation of a vocabulary for a domain-specific information service platform (SIS move) by vocabulary re-use and linking. Source vocabularies differ with respect to several factors (domain-specificity, accessibility, data model). We address why vocabularies should be considered for a domain-specific vocabulary and how they are brought under a common modelling paradigm with standards for knowledge organization systems and alignment of schemata. We also discuss the creation and validation of alignments. Eventually, we give an outlook on the vocabulary’s further evolution and application.
Incorporating curriculum mapping as a method to increase approval plan precision

This study incorporates curriculum mapping as a method to assess the effectiveness of approval plans supporting undergraduate psychology courses offered at a large research university. Usage data is aggregated over a nine-year period for 2,917 monographs acquired between 2011 and 2017. Results show that curriculum mapping is effective for examining usage at a more granular level than using broad Library of Congress classification breakdowns. Analysis by content level and publisher provided insights into unused or low-usage monographs allowing for more precise profile adjustments. Though psychology profile adjustment decisions are discussed, this study can serve as a model for other subject areas.
A Model for Identifying Frequent Errors in Incorrect Solutions

Identifying frequent errors in student solutions is one of the most important but also most laborious tasks in education, regardless of the subject matter. However, when the topic is computer programming, dealing with errors in program codes is a particularly challenging task for both students and instructors. In this paper, we propose a model for identifying frequent error patterns in solution codes in which source code pairs (wrong-corrected) from an e-learning system are selected and converted into token sequences. Then, differences between the paired codes are clustered to discover the most frequent errors by using the K-mean clustering algorithm. To test our model, we conducted an experiment involving large-scale solution codes collected from the Aizu online judge (AOJ) system in which more than five million solutions have been accumulated. Our experimental results show that frequent error patterns can provide useful hints for both learners and instructors, which can then be used to resolve errors in solutions quickly.
Cross-Sectional Evaluation of Grammatical Error Correction Models

文法誤り訂正の既存研究の多くはこれまで主に CoNLL-2014 評価データセットを用いた単一コーパス上で文法誤り訂正モデルを評価してきた．しかし，書き手の習熟度やエッセイのトピックなど様々なバリエーションのある入力文が想定される文法誤り訂正タスクにおいて，タスクの難易度は各条件下によって異なるため，単一コーパスによる評価では不十分であると考えられる．そこで本研究では，文法誤り訂正の評価の方法論として，単一コーパス評価は不十分であるという仮説に基づきコーパス横断評価の必要性について調査を行う．具体的には，4 種類の手法 (LSTM, CNN, Transformer, SMT) を 6 種類のコーパス (CoNLL-2014, CoNLL-2013, FCE, JFLEG, KJ, BEA-2019) で評価し，各コーパス間でモデル順位にばらつきが生じるかについて検証を行った．評価実験の結果，モデル順位は各コーパスによって大きく変動したため，既存の単一コーパス評価では不十分であることがわかった．また，横断評価はメタ評価方法としてだけではなく，実応用を見据えた場合においても有用であると考えられる．そこで，横断評価の有用性のケーススタディとして，文法誤り訂正の入力に想定される代表的な条件の一つである，書き手の習熟度を評価セグメントとした場合の横断評価について調査を行った．その結果，書き手の習熟度が初中級レベルと上級レベル間ではモデルの性能評価に関して大きな乖離があることがわかった．
Blockchain, Smart Contracts and ADR

Blockchain and smart (legal) contracts occupy arbitration's theoretical and practical discussions. It is hardly surprising, given the number of platforms that emerged over the last years, using new concepts to provide dispute resolution services. A portion of the discourse focuses on the benefits and setbacks of relying on game theory and decentralized justice in dispute resolution design. Scholars also began to look at automation of commercial relationships through data-oriented and computable contracts, bringing to light the potential of new disputes which may need specialized community and institutions. Given the nature of the topic, the fact that the technologies are continuously improving, and the community's increased engagement in the sphere, the paper aims primarily to contribute to the debate, rather than to offer any conclusive remarks. A starting point will be an overview of the blockchain technology, its operation, and core features before discussing the impacts on the arbitration field (Parts 2 &3). The discussion will then focus on examining the dispute resolution platforms, and their key features, as the basis for broader practical, legal, and social implications (Part 4). The interplay of the two worlds – technology and law – brings to light not only the potential for an increase in efficiency of the existing system but also an opportunity to consider the disruption the technology will bring (Part 5).
A Performance Comparisons of Machine Learning Classification Techniques for Job Titles Using Job Descriptions

An inappropriate candidate shortlisted and a potential one missed simply means an inappropriate resume linked to the incorrect keyword. Document classification is being excessively researched upon these days, due to growing interest in text classification which has become a major contributor to the online texts and documents. The repetitive tasks of a person categorizing the details can be handled by the machinery using an expert system that correctly captures and identifies the text and then classifies it into different categories defined. After the preprocessing of the data, the classification is done as a comparative analysis of Bernoulli’s Naïve Bayes, Multinomial Naïve Bayes, Random Forest, Linear SVM and LSVM with elastic penalty classification on the Top 30 Job listing dataset with different parameters and thus we are able to analyze the dependencies between different terms in classes with varying densities and accounts. The accuracy was evaluated and LSVM provides the best accuracy in classifying job entitlements based on the queries submitted and was able to achieve 96.25% accuracy for 55000 samples.
Gender domain adaptation for automatic speech recognition

This paper is focused on the finetuning of acoustic models for speaker adaptation goals on a given gender. We pretrained the Transformer baseline model on Librispeech-960 and conducted experiments with finetuning on the gender-specific test subsets. The obtained word error rate (WER) relatively to the baseline is up to 5% and 3% lower on male and female subsets, respectively, if the layers in the encoder and decoder are not frozen, and the tuning is started from the last checkpoints. Moreover, we adapted our base model on the complete L2 Arctic dataset of accented speech and finetuned it for particular speakers and male and female genders separately. The models trained on the gender subsets obtained 1-2% lower WER when compared to the model tuned on the whole L2 Arctic dataset. Finally, it was experimentally confirmed that the concatenation of the pretrained voice embeddings (x-vector) and embeddings from a conventional encoder cannot significantly improve the speech recognition accuracy.
The Development of Science Student Worksheet for Elementary Student Grade IV Based on Scientific

Abstract The research is a type of research and development that aims to determine the validity and response of students and teachers to the science student worksheet constructed on the science approach. Science student worksheet development applies the 4-D model development procedure consist of 4 stages specifically: define, design, develop, and disseminate. The subjects of this research were IV A &amp; IV B class at SD IT Deli Insani consist of 55 students. The data collection instruments were in the formula of assessment sheets for material authorities, learning design authorities, layout, linguists, and the response questionnaire sheets for students and teachers. This development research produces the material, learning design, layout, and language with an average percentage level 93,5% which is characterized as legitimate and suitable for usage, and get a decent response from the students reached 96,2% and with decent criteria and response teacher getting 94,9% with decent criteria. Based on expert validation, the developed student worksheets are declared suitable for use by students, especially class four. Based on the responses of students and teachers, the students’ worksheets developed received a good response so that it was stated that the worksheets were easy to understand and interesting to learn. The benefits of developing science student worksheets based on the developed scientific approach are expected to be reference materials for educators in developing student worksheets according to the needs of students and in accordance with the demands of the 2013 curriculum.
A Cluster Based Classification for Imbalanced Data Using SMOTE

Abstract There is tremendous upturn in data repositories because of data generation by various organizations like government, cooperates, health caring in large amounts. Large amount of data is being produced, processed, collected, and analysed online. So there comes a requirement to transform this data into valuable information. This process of extracting the knowledge from large amount of data is referred as data mining. The proposed hybrid approach can be checked on different classifiers like Naïve Bayes, Random forest classifier etc. In proposed methodology we find that SMOTE algorithm which used K-nearest neighbour algorithm is limited to some minority class instances for creating synthetic samples, which sometimes leads to over fitting, so an effective oversampling approach can be developed.
Adaptive Enterprise Architecture

Nowadays, enterprises are facing many changes due to digital transformation. So, they have to deal with new requirements that impact various parts of their Enterprise Architecture. To address the needs created by this new digital era, agility and adaptation are compulsory. Though Enterprise Architecture (EA) was designed as a catalyst for change management, traditional Enterprise Architecture approaches do not rely on well-defined concepts, lack agility and are constrained by layered architecture (business layer, functional layer, deployment layer, etc.). Thus, a new generation of EA, allowing continuous self-improvement and proactive adaptation to better match the new conditions, is required. In this paper, we highlighted some criteria, regarding the adaptation of EA and exploited them to show the limitations of the EA approaches analyzed. Then, we presented agile methods principles and discussed the mapping between them and the criteria that we proposed before. Finally, we proposed a simplified model based on Scrum for adaptive enterprise architecture.
Novel Formulation for Line of Sight Guidance and Obstacle Avoidance for Under-actuated Ships

Automatic control of under-actuated ships is a challenging task due to the external factors and limited actuators onboard a ship. It is even more so when the controller needs to seamlessly integrate with a guidance system and obstacle avoidance for the purpose of autonimity. In this paper, line of sight guidance system for marine surface vessels is augmented to include obstacle avoidance. The process of directing the ship movement to avoid a stationary and moving obstacles is tackled by introducing an iterative mathematical formulation for the circle of avoidance algorithm. Unlike learning based guidance system, the proposed formulation has an explicit solution that is updated at each instant in time. Three simulations are conducted to assess the performance of the overall guidance and avoidance system. The developed algorithm is validated through simulation results of a 6-degree of freedom model of a ship. The simulation results prove the effectiveness of the developed technique to converge the ship to the desired trajectory autonomously while avoiding obstacles along the path.
Level and Ways of Educating Future Teachers in the Use of Digital Technologies: Basic Results of a Questionnaire Survey

The paper describes partial results and findings of an extensive questionnaire survey, which took place among students of teaching disciplines of the Faculty of Education, University of Ostrava. The aim of this questionnaire survey was to find out what knowledge, skills and overview students of various teaching disciplines have in the use of digital technologies in the educational process. We conducted the questionnaire survey at a time of several key changes in education. These changes affect all subjects and levels of education making the use of digital technologies in the Czech school environment inevitable for all those involved - teachers, pupils and students. For this reason, we consider it necessary to map whether future graduates of teaching disciplines are sufficiently prepared for these changes.
Web Application for Graph Visualization Purposes

Graphs are one of several abstract topics presented in teaching of computer science at university. Graphs are part of various subjects such as optimization, compilers, networks, etc. Main objective of this paper is to present online application aimed at graph visualization. Implemented tool can be also used as a translator between various formats of graph description - adjacency matrix, graph6 format and diagram of graph. The greatest emphasis was placed on functionalities which are lacking in other online tools for similar purposes - mainly method of graphical representation of graph, possibility of various forms of input for graphical representation and possibility of multiple graphs on the input for simultaneous visualization of set of graphs. Presented application is first part of Moodle course project which is focused on graphs and parallel computing.
DNN-based Human Activity Recognition by Learning Initial Motion Data for Virtual Multi-Sports

This paper proposes a new deep neural network-based approach of human activity recognition for virtual multi-sports. The proposed approach acquires initial motion data of sports balls, not full motion data of human activities, with a single highspeed camera. Then, a deep neural network model is trained to recognize corresponding human activities, such as baseball batting, soccer kicking, golf swing, and so on, by learning those initial motion data. The proposed approach is very efficient and effective for practical applications of virtual multi-sports. In practice, the proposed approach was successfully applied to a unified virtual multi-sport platform. The effectiveness and efficiency of proposed approach was verified with real experimental results.
API for Communication between Motion Sensors and Raspberry Pi

Motion sensors have been used in a number of diverse applications recently. They can be found in aviation, virtual reality, robotics, medicine, advanced scientific research or even on our bodies. With this growing presence of motion sensors in the field of information technology, problems related to working with them seamlessly have appeared. One of major issues stems from technological differences between sensors and requires their users to implement sensorspecific software logic independently for every sensor used. This paper aims at solving the problem described by proposing a solution designed to integrate this logic into an application programming interface for straightforward work with motion sensors.
Foreign Aid Complementarities and Inclusive Human Development in Africa

This study complements existing literature by assessing how various types of foreign aid complement each other in boosting inclusive human development in Africa. (a) When ‘aid to social infrastructure’ is moderated with other aid types, ‘action on debts’ is substitutive whereas ‘aid to the production sector’, ‘aid for program assistance’ and humanitarian assistance are complementary. (b) ‘Aid to the production sector’ (‘action on debt’) is complementary (substitutive) to ‘aid for economic infrastructure’. (c) Whereas ‘action on debt’ is a substitute to ‘aid to the production sector’, ‘aid for social infrastructure’ and ‘aid for economic infrastructure’ are complementary. (d) ‘Action on debt’ is a substitute for ‘aid to the multi-sector’. (e) While ‘aid for social infrastructure’ and ‘action on debt’ are substitutive to ‘aid for program assistance’; humanitarian assistance is complementary. (f) The following are substitutes to ‘action on debt’: ‘aid for economic infrastructure’, ‘aid to the production sector’, ‘aid to the multi-sector’ and ‘programme assistance’. (g) ‘Aid for social infrastructure’ and ‘programme assistance’ are complementary to humanitarian assistance. The findings reveal various patterns that inform policy makers on the relevance of sequencing aid types to enhance inclusive development. Future research should focus on country-specific studies.
MLAS: Metric Learning on Attributed Sequences

Distance metric learning has attracted much attention in recent years, where the goal is to learn a distance metric based on user feedback. Conventional approaches to metric learning mainly focus on learning the Mahalanobis distance metric on data attributes. Recent research on metric learning has been extended to sequential data, where we only have structural information in the sequences, but no attribute is available. However, real-world applications often involve attributed sequence data (e.g., clickstreams), where each instance consists of not only a set of attributes (e.g., user session context) but also a sequence of categorical items (e.g., user actions). In this paper, we study the problem of metric learning on attributed sequences. Unlike previous work on metric learning, we now need to go beyond the Mahalanobis distance metric in the attribute feature space while also incorporating the structural information in sequences. We propose a deep learning framework, called MLAS (Metric Learning on Attributed Sequences), to learn a distance metric that effectively measures dissimilarities between attributed sequences. Empirical results on real-world datasets demonstrate that the proposed MLAS framework significantly i mproves the performance of metric learning compared to state-of-the-art methods on attributed sequences.
Image Reconstruction Based on Shape Analysis

In recent years, image reconstruction based on graph theoryRamanathan, Shalini has aroused growing interest. Here a novel MRS framework was introduced. It works to reconstruct the brain image based on shape analysis. The shape was recognized from the input image. Its descriptionRamasundaram, Mohan and count were collected in a hypergraph structure for the reconstruction process. The MRS framework reconstructs an image based on the identified shapes. The reconstructed images help to locate the missing data and to identify the disorder in the brain.