Reviewerly/exHarmony huggingface dataset:
Building a Graph Representation of LOINC® to Facilitate its Alignment to French Terminologies.

The aim of our study was to create a graph model for the description of LOINC® concepts. The main objective of the constructed structure is to facilitate the alignment of French local terminologies to LOINC. The process consisted of automatically incorporating the naming rules of LOINC labels, based on punctuation. We implemented these rules and applied them to the French variants of LOINC and then created attributes and concepts described with synonymous labels. When comparing the created attributes to the stated ones, the multiple mappings led to the identification of errors that must be corrected for improving the translation quality. These mappings are consecutive to semantic errors generated during the translation process. They mainly corresponded to misinterpretations of LOINC concepts and/or LOINC attributes.
Discovering topics in Twitter about the COVID-19 outbreak in Spain

In this work, we apply topic modeling to study what users have been discussing in Twitter during the beginning of the COVID-19 pandemic More particularly, we explore the period of time that includes three differentiated phases of the COVID-19 crisis in Spain: the pre-crisis time, the outbreak, and the beginning of the lockdown To do so, we first collect a large corpus of Spanish tweets and clean them Then, we cluster the tweets into topics using a Latent Dirichlet Allocation model, and define generative and discriminative routes to later extract the most relevant keywords and sentences for each topic Finally, we provide an exhaustive qualitative analysis about how such topics correspond to the situation in Spain at different stages of the crisis © 2021 Sociedad Espanola para el Procesamiento del Lenguaje Natural
Video-Based Detection, Counting and Classification of Vehicles Using OpenCV

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A Task Scheduling Approach for Cloud Environments Employing Evolutionary Algorithms

One of the key challenges in the domain of cloud computing is task scheduling and estimation of cloud workloads for time critical applications pertaining to constrained cloud resources. While effective task scheduling is necessary for balancing the load, workload forecasting is necessary to plan in advance the requirements of cloud platforms based on previous data so as to effectively utilize cloud resources. Often it is challenging to gather sufficient information about the tasks and hence allocating the tasks to virtual machines (VMs) in the most optimal way is non-trivial. In this paper, a hybrid task scheduling approach is proposed based on evolutionary algorithms. The first approach is the amalgamation of bat and particle swarm optimization (PSO) techniques. The scheduling approach also combines the processing time preemption (PTP) approach to schedule the source intensive tasks which allows to reduce the response time of the proposed system. The second approach is a machine learning based approach employing gradient descent with momentum (GDM). The evaluation of the proposed system has been done based on the response time and mean square error of the system.
Rapid non-contact visual measurement method for key dimensions of revolving workpieces

Aimed at the rapid non-contact measurement problem of a revolving workpiece's radial and axial dimensions, a fast and high-precision visual inspection method has been presented in this paper. For the workpiece with large axial size, the proposed method established the measurement transformation chain using the object-image and object-object transformations, thus realizing the rapid axial dimensional measurements. For the workpiece with large radial size, this method determined the measurement transformation model based on two-dimensional target and measurement correspondence relationship, and further achieved rapid radial dimensional measurements. The experimental results have shown that the method is effective and can be applied to in situ dimensional measurement of revolving workpieces on high quality production lines.
Scrum anti-patterns, team performance and responsibility

Currently, the most widely used agile method is Scrum. Scrum works on the principle of a self-organised team, and it is in such teams that so-called anti-patterns appear. These represent a bad pattern of behaviour or bad habit, which can have a negative impact on team performance. The aim of this paper is, through the use of several data collection methods in three Scrum teams, to determine which anti-patterns exist within them that impact team performance results and to set the responsibility for these anti-patterns. We establish a set of methods for the analysis of anti-patterns in Scrum teams and their influence on the performance of these teams. The results indicate four critical issues and problems looming. It is somewhat surprising to find that in terms of responsibility, the Scrum Master is not directly to blame for any of these problems, and in fact, cannot influence these problems from its role.
Recognition of Ancient stone Inscription Characters Using Histogram of Oriented Gradients

The transliteration of ancient stone inscription characters into present characters is required in the area of epigraphy. Optical Character Recognition is not adopted as the rock surface becomes texture of an image posing several challenges like degradation due to weather conditions and minimal distinction between foreground and background. In this paper, a new feature using Histogram of Oriented Gradients is proposed for ancient stone inscribed character recognition that is independent to any language script. This feature counts occurrences of gradient orientation in localized portions of an image. Here 11th century stone inscription digital images are used for creating database. Once the features are computed, the system is trained up with Support Vector Machine for classifying them into modern characters. Experiments are performed on 10 characters of 300 database size and showed significant recognition rate.
THE CRISIS SITUATION CREATED BY COVID-19 AND BULGARIAN UNIVERSITIES

This analysis makes a research on the real situation as a result of the pandemic crisis caused by COVID-19 and looks for answers of the lack of appropriate training of the Bulgarian students for action in times of crisis. Past periods are analysed when there was such specialized training and possible correlation is searched for in this emergency situation as well as the steps taken by the government and those of the whole population.The change we are moving towards requires a certain fairness and accuracy, but we are unlikely to achieve it only by these adjustments in the legislation. It should rather be linked to changes in system management. Very often we talk about mandate, creativity, strategies, etc., and it is generally related to shaping the attitudes of general meetings in a certain way, which in some cases correlates with the norms of morality and law. However, the procedures are legal and not very moral. There is a lot of evidence in this direction all the time.Not always the democratic rules allow choosing the most capable and competent candidates and sometimes they do not even allow some scientists to express themselves for a number of reasons. This is a general issue mostly applicable to the academic community. The personalities here are numerous and are specialized in different fields. Consolidation is sometimes at the expense of a choice that is related to other circumstances rather than the qualities of the individual. The change in this direction is possible if this choice includes competition based on the most objective criteria.In the current development of society, science cannot develop on its own, and the existence of science alone is impossible and unthinkable. This requires work in wider teams and cooperation in various areas. It also requires the development of an interdisciplinary approach and the development of scholars in different directions and fields. This does not make the person/scientist a painkiller, but makes them far more able to meet the dynamics of the requirements of a rapidly changing environment.Finding such a system of social criteria that would describe and value this social process to the highest extent and as failrly as possible is quite a challenging task, which requires the analysis of a large amount of information and conducting various research to allow the development of such mechanism that on the one hand, meets expectations and, on the other hand, achieves a relatively accurate and valid assessment of these processes.
Stemtech Model in ASEAN Universities: An Empirical Research at Can Tho University

In order to train human resources to adapt to the digital era, universities in ASEAN region have invested and developed programs related to Science, Technology, Engineering and Mathematics (STEM) for many years. In that trend, under the support of Arizona State University (from the United States), Can Tho University, a member of the ASEAN University Network, has built a model to promote creativity, in which Technology elements are particularly emphasized and it is called STEMTech. An experiment on STEMTech model at Can Tho University was conducted in 2018 with three steps: (1) Training for students about STEM education, STEMTech model and some new technologies; (2) Each group of students make STEM products based on STEMTech model; (3) Student surveys. A statistical analysis was used to evaluate and improve STEMTech model. The results show that learners perceive STEMTech model quite positively, nevertheless, the access to the Technology factor in the model is quite difficult. This shows the necessary to develop STEMTech models to overcome limitations for learners and replicate this model to several other universities in ASEAN region.
Learning Macromanagement in Starcraft by Deep Reinforcement Learning

StarCraft is a real-time strategy game that provides a complex environment for AI research. Macromanagement, i.e., selecting appropriate units to build depending on the current state, is one of the most important problems in this game. To reduce the requirements for expert knowledge and enhance the coordination of the systematic bot, we select reinforcement learning (RL) to tackle the problem of macromanagement. We propose a novel deep RL method, Mean Asynchronous Advantage Actor-Critic (MA3C), which computes the approximate expected policy gradient instead of the gradient of sampled action to reduce the variance of the gradient, and encode the history queue with recurrent neural network to tackle the problem of imperfect information. The experimental results show that MA3C achieves a very high rate of winning, approximately 90%, against the weaker opponents and it improves the win rate about 30% against the stronger opponents. We also propose a novel method to visualize and interpret the policy learned by MA3C. Combined with the visualized results and the snapshots of games, we find that the learned macromanagement not only adapts to the game rules and the policy of the opponent bot, but also cooperates well with the other modules of MA3C-Bot.
An Energy-aware Real-time Task Scheduling Approach in a Cloud Computing Environment

Interest in cloud computing has grown considerably over recent years, primarily due to scalable virtualized resources. So, cloud computing has contributed to the advancement of real-time applications such as signal processing, environment surveillance and weather forecast where time and energy considerations to perform the tasks are critical. In real-time applications, missing the deadlines for the tasks will cause catastrophic consequences; thus, real-time task scheduling in cloud computing environment is an important and essential issue. Furthermore, energy-saving in cloud data center, regarding the benefits such as reduction of system operating costs and environmental protection is an important concern that is considered during recent years and is reducible with appropriate task scheduling. In this paper, we present an energy-aware task scheduling approach, namely EaRTs for real-time applications. We employ the virtualization and consolidation technique subject to minimizing the energy consumptions, improve resource utilization and meeting the deadlines of tasks. In the consolidation technique, scale up and scale down of virtualized resources could improve the performance of task execution. The proposed approach comprises four algorithms, namely Energy-aware Task Scheduling in Cloud Computing(ETC), Vertical VM Scale Up(V2S), Horizontal VM Scale up(HVS) and Physical Machine Scale Down(PSD). We present the formal model of the proposed approach using Timed Automata to prove precisely the schedulability feature and correctness of EaRTs. We show that our proposed approach is more efficient in terms of deadline hit ratio, resource utilization and energy consumption compared to other energy-aware real-time tasks scheduling algorithms.
A Cognitive Approach in Software Automation Testing

Number of updates in the test scripts are directly proportional to the updates a developer makes in the application. If any attribute like ‘id’, ‘name’ or ‘class’ of an element of a web page is changed by the developer then automated test scripts meant to test that area of code need also be changed. Debugging these changes are easy and sometimes difficult but if there are multiple such changes in the application under test, this becomes a tedious task for automation testers and consumes a lot of time and effort to update test scripts and run them.This where the idea of self-update or self-healing comes into picture. The AI detects the anomalies in the current test scripts and automatically fix them without human intervention to prevent tests from false failure.
Virtual Machines Placement Using Predicted Utilization of Physical Machine in Cloud Datacenter

Cloud computing has enabled Information Technology to provide computing resources to the end user. Thousands of Physical Machines (PMs) or hosts are executing in datacenter for provisioning on demand delivery to the user. These datacenter requires power supply in large scale and produces the huge amount of heat to deliver the best quality of services. Virtualization plays the key role to obtain the objectives of quality of services, so management of Virtual Machines (VMs) is the major focused research area in this field. Management of VMs in datacenter is mainly done in three parts, VM selection from overloaded PM or host, migration of selected VMs and the placement of VMs into suitable PMs, while maintaining the consumption of energy and service quality. In the current article, a technique is used in which predicted value of CPU utilization is used for the selection of suitable PM for hosting the migrated VM from overloaded PM. The focus of our research is to minimize consumption of power and enhance the service level agreement.
Undirected Graphs: Is the Shift-Enabled Condition Trivial or Necessary?

With the growing application of undirected graphs for signal/image processing on graphs and distributed machine learning, we demonstrate that the shift-enabled condition is as necessary for undirected graphs as it is for directed graphs. It has recently been shown that, contrary to the widespread belief that a shift-enabled condition (necessary for any shift-invariant filter to be representable by a graph shift matrix) can be ignored because any non-shift-enabled matrix can be converted to a shift-enabled matrix, such a conversion in general may not hold for a directed graph with non-symmetric shift matrix. This paper extends this prior work, focusing on undirected graphs where the shift matrix is generally symmetric. We show that while, in this case, the shift matrix can be converted to satisfy the original shift-enabled condition, the converted matrix is not associated with the original graph, that is, it does not capture anymore the structure of the graph signal. We show via examples, that a non-shift-enabled matrix cannot be converted to a shift-enabled one and still maintain the topological structure of the underlying graph, which is necessary to facilitate localized signal processing.
Dataset to Study Indirectly Dependent Documentation in GitHub Repositories

In the research work, we have highlighted the importance of regularly updating the software documentation. For this purpose, we analyzed the function documentations indirectly dependent on other functions. This artifact provides scripts to extract the data and the final dataset containing observations obtained on manually annotating the extracted data. The details of this work may be found in the paper appearing in the technical track, titled 'On Indirectly Dependent Documentation in the Context of Code Evolution: A Study'.
Masked Feature Generation Network for Few-Shot Learning

In this paper, we present a feature-augmentation approach called Masked Feature Generation Network (MFGN) for Few-Shot Learning (FSL), a challenging task that attempts to recognize the novel classes with a few visual instances for each class. Most of the feature-augmentation approaches tackle FSL tasks via modeling the intra-class distributions. We extend this idea further to explicitly capture the intra-class variations in a one-to-many manner. Specifically, MFGN consists of an encoder-decoder architecture, with an encoder that performs as a feature extractor and extracts the feature embeddings of the available visual instances (the unavailable instances are seen to be masked), along with a decoder that performs as a feature generator and reconstructs the feature embeddings of the unavailable visual instances from both the available feature embeddings and the masked tokens. Equipped with this generative architecture, MFGN produces nontrivial visual features for the novel classes with limited visual instances. In extensive experiments on four FSL benchmarks, MFGN performs competitively and outperforms the state-of-the-art competitors on most of the few-shot classification tasks.
Information Systems Outcomes based Assessment at Jouf University for ABET Accreditation

The Accreditation Board for Engineering and Technology (ABET) Criteria for Computing Accreditation Commission Programs are based upon knowledge, presentation skills, communication skills, team work and leadership skills that the students gain through the curriculum in a program. The student outcomes (SOs) are the procured skills and knowledge which the student accomplishes through the curriculum. Each program needs to have its own SOs and to evaluate it as per defined assessment cycle, just as to accomplish program educational objectives (PEOs). The continuous improvement plan depended on assessment and evaluation of the program SOs and PEOs. In this paper, an advancement cycle for continuous improvement, for Information systems (IS) program at College of Computer and Information Sciences (CIS), Jouf University, Sakaka, Aljouf, Saudi Arabia, to accomplish the accreditation is discussed. The proposed cycle is appropriate to get the accreditation for bachelor program in information systems discipline and to fulfill the ABET criteria. In result, we will show how the SOs and PEOs were surveyed and assessed through curriculum accordingly. Based on these processes, ABET team has visited CIS college, which had accomplished extraordinary achievement and all of the programs have completely accredited on August 2018 and the next visit to the computing and engineering programs will be on 2026.
Anti-trust and the trust machine

Blockchain is an exciting technology with great potential. For competition and competition policy, it offers a number of promising opportunities, which agencies will wish to explore. However, from the standpoint of antitrust law the concerns remain the same as in traditional markets. Agencies will therefore need to continue to apply their theories of harm, and to focus on understanding rivalry, substitutability and control of competing products.
A modification of artificial bee colony algorithm for solving initial value problems

We would like to acknowledge the support for this project from the Council of Higher Education in Turkey (YOK), Coordination of Academic Member Training Program (OYP) in Adnan Menderes University, under Grant no. ADU-OYP-14011.
The Huya Multi-Speaker and Multi-Style Speech Synthesis System for M2voc Challenge 2020

Text-to-speech systems now can generate speech that is hard to distinguish from human speech. In this paper, we propose the Huya multi-speaker and multi-style speech synthesis system which is based on DurIAN and HiFi-GAN to generate high-fidelity speech even under low-resource condition. We use the fine-grained linguistic representation which leverages the similarity in pronunciation between different languages and promotes the speech quality of code-switch speech synthesis. Our TTS system uses the HiFi-GAN as the neural vocoder which has higher synthesis stability for unseen speakers and can generate higher quality speech with noisy training data than WaveRNN in the challenge tasks. The model is trained on the datasets released by the organizer as well as CMU-ARCTIC, AIShell-1 and THCHS-30 as the external datasets and the results were evaluated by the organizer. We participated in all four tracks and three of them entered high score lists. The evaluation results show that our system outperforms the majority of all participating teams.
The Impact of Implementing Students’ Support Services on Students’ Satisfaction: A Case Study of a Private University in Lebanon

Higher Education in Lebanon has been witnessing various challenges during the past decades. The absence of services in the public sector has caused great competitions and expansion in private universities. This triggered universities to work hard to have their students satisfied and at the center of the learning process. The purpose of this study is to implement and organize a plan for providing services at the fastest growing university in Lebanon in order to improve students’ satisfaction and make it aligned with the university’s mission and strategic plans. This paper discusses how various students’ support services influence the satisfaction of the students. Some forms of offered services are discussed firstly. This includes social security services, health services, counseling services, community services, students’ extracurricular activities, athletic services, and financial aid services. The expected outcome of this study is to be able to assess the influence of the implementation of those services on students’ satisfaction at a multicampus level and to refer to a model to be able to do effective assessment. The methodology used is qualitative and based on case study research.