Reviewerly/exHarmony huggingface dataset:
Comparison of Super Resolution Methods in Magnetic Resonance Images for Small Animals

Super resolution (SR) is an array of methods that utilize different approaches to enhance the quality and resolution of an image. This is particularly useful for images that are very small and have low quality. Small images are usually obtained due to the limitation of imaging capture systems or the subject captured is small. For small animals such as rats, imaging can be difficult and expensive to produce high-resolution images. Therefore, SR is a very relevant field of study for small animals. The study of small animals involve many impactful fields such as testing of harmful chemicals on the biological processes. The objective of this study is to compare 11 SR methods for rat magnetic resonance images (MRI). This study is a pilot study and the beginning of the research to see the effect of kratom that is a hallucinogen misused in south east Asia. This study used chosen images from six rats. These MRI images were captured at UM MRI Research Centre. This study compared the quality of SR methods using several measures including Peak Sound to Noise Ratio (PSNR), Sound to Noise Ratio (SNR), Mean Square Error (MSE), Structural Similarity Index (SSI). This study compared these methods on two different size factors of resolution which were two and four. The results show promising results for the next stage of research.
WITHDRAWN: An NLP-powered human rights monitoring platform

This article has been withdrawn at the request of the author(s). The Publisher apologizes for any inconvenience this may cause. The full Elsevier Policy on Article Withdrawal can be found at https://www.elsevier.com/about/our-business/policies/article-withdrawal.
Application development of website content filling in the Information Technology Department of State Polytechnic of Malang

Abstract The website is a communication tool that is used as an intermediary to share the latest news. The website of the Information Technology Department - Malang State Polytechnic (JTI-Polinema) is also a means of communication to share the latest activities and information about JTI. From January 2018 to November 2018, the news uploaded is only six content. This is not proportional to the number of activities available at the JTI. This is because there is only one type of user, the admin who can fill the website content. And the admin does not always know the activities in the information technology department. This study aims to develop an application to fill the website content of the Information Technology Department in state polytechnic Malang’s website by adding one more type of user, namely students and also make the interface look better and easy to use. This application allows students to upload articles about departmental activities, competitions, workshops and other information they know. This application development uses Kanban which is one of the lean software development methods (lean method). Based on validation testing this application is able to meet all the main functional requirements. while the usability test results based on usefulness, ease of use, ease to learn and satisfaction shows a high average value of 86.95%, it can be concluded that this application is acceptable and easy to use.
Oil and Gas Production Prediction Based on SVM and Improved Particle Swarm Optimization

Cognitive information is widely used in the field of oil and gas, where production forecasts are of great importance to companies. In this chapter, combining support vector machine and improved particle swarm optimization algorithm, a gas field production prediction model is established, and the model is validated by the actual production data of an enterprise over the years. The results show that the model has good convergence, high prediction accuracy, and training speed and can predict its output more accurately. The method adopted in this chapter is the development of cognitive information technology. The authors have reason to believe that with the continuous development of cognitive information technology, it will have a far-reaching impact on social progress.
Conceptual Model of Risk Assessment for Insider Threats Detection

This study proposes a conceptual model of risk assessment for insider threats detection in Cyber-Physical system (CPs). The objectives of this research are two folds: a) finding the gap of study and b) produce a conceptual model of risk assessment for insider threats. This study has been conducted since an increasing number of cyber-attacks cases reported in CPs. In fact, the attack occurs unintentionally or intentionally from inside and outside of the organization due to the growth of new devices, sensors and mobile phones that connected to the network. However, to charge that the attack is coming from inside is more difficult since lack of evidence and involved cyber laws. Therefore, to detect insider threats demand new approach for better decision making. Current methods used for detecting insider threats are OCTAVE, FRAP, CRAMM, NIST, Monte Carlo and Markov Chain. Based on our findings, Monte Carlo and Markov Chain is a simulation-based method and applicable for organizations in terms of effective cost and system maintenance. The impact of the new model brings a better solution for assessing insider threats in organizations.
Self-Healing Anonymous Routing in Unstable Sensor Networks

A lightweight decentralized adaptive anonymous routing scheme is presented that combines onion routing for the initial global route request and threshold-based secret sharing for the subsequent local route tuning/healing. The encrypted propagation of the partial routes information allows to handle minor network topology changes locally, without new route requests and with a limited deanonymization of participants. The intermediate node can discover/decrypt the local routing data only together with its designated neigbour (threshold-based secret sharing is used) and only in the event of a topology change.
(Fr)agile objects: Thinking Scrum through Post‐It Notes

Agile methodologies have taken hold as a model to be followed in software industry. Among them, Scrum is one of the most used frameworks and has a high level of acceptance among a large range of organizations. The underlying premise of Scrum is that by implementing an iterative and incremental process of development, an organization can become more efficient in coping with unpredictability, thus, increasing the chances of delivering business value. In this paper we use the context of SIDIA, an R&amp;D center based in Manaus (Brazil), to look at how Scrum is practiced, by following Post‐its notes, which are commonly used in agile landscapes. Following previous work on the idea of thinking through things (instead of thinking about things) as an analytic method to account for the ethnographic experience (Henare, 2006), the purpose here is to draw out the capacity of these objects to re‐conceive the workplace. We argue that somehow the extensive use of post‐its in this specific context helps to reify the core values of scrum and the agile mindset, at the same time that it shapes much of its practices and discourses. Although we use a specific context as a case‐study to articulate the argument, we are less interested in bringing the specifics of the case, than in throwing light on the current perception of agile methodologies as a site of organizational promise, through an object‐oriented approach.
Visual Question Answering Using Video Clips

Visual Question Answering (VQA) is a technique by which humans can ask simple questions about an image and get answers. This technique can be extended on video clips to answer simple questions about the things happening in the video. The system will take a video and a natural language question as an input, and it will output a natural language answer. It is a multi-discipline research problem by nature. In this work, we limit our work to answering binary questions, i.e. questions having only yes or no as their answers. It could be further designed to answer complex questions.
A Study for Semi-supervised Learning with Random Erasing

Due to the recent popularization of various data classified by computer, machine learning is attracting great attention. A common method of machine learning is supervised learning, which classifies data using a large number of class labeled training data called labeled data. To improve the processing performance of supervised learning, it is effective to use Random Erasing in data augmentation. However, since supervised learning requires much labeled data, the cost of manually adding label information to an unclassified training case (unlabeled data) is very high. In this paper, we propose a method for achieving high classification accuracy using Random Erasing for semi-supervised learning using few labeled data and unlabeled data. In our evaluation, we confirm the availability of the proposed method compared with conventional methods.
Salient Object Detection via Graph-Based Flexible Manifold Ranking

The task of saliency detection is to segment salient objects in natural scenes. Simple and effective saliency detection model has always been a challenging problem. We explore a graph-based flexible manifold ranking approach for single image saliency detection. An input image is represented as an undirected graph. Feature vectors are extracted covering regional color and texture. An optimal function is used to infer the labels based on linear classification projection and manifold ranking in our work. The optimal function further ensures the reliability of the prediction results. Extensive experiments on four benchmark datasets show that our method is better than the other eight classic methods. So the proposed method is a competitive method.
Modality-Dependent Cross-Modal Retrieval Based on Graph Regularization

Nowadays, the heterogeneity gap of different modalities is the key problem for cross-modal retrieval. In order to overcome heterogeneity gaps, potential correlations of different modalities need to be mined. At the same time, the semantic information of class labels is used to reduce the semantic gaps between different modalities data and realize the interdependence and interoperability of heterogeneous data. In order to fully exploit the potential correlation of different modalities, we propose a cross-modal retrieval framework based on graph regularization and modality dependence (GRMD). Firstly, considering the potential feature correlation and semantic correlation, different projection matrices are learned for different retrieval tasks, such as image query text (I2T) or text query image (T2I). Secondly, utilizing the internal structure of original feature space constructs an adjacent graph with semantic information constraints which can make different labels of heterogeneous data closer to the corresponding semantic information. The experimental results on three widely used datasets demonstrate the effectiveness of our method.
Obtaining Reference's Topic Congruity in Indonesian Publications using Machine Learning Approach

There are some criteria on how an article is categorized as a good article for publications. It could depend on some aspect like formatting and clarity, but mainly it depends on how the content of the article is constructed. The consistency of the topic that the article was written could show us how the authors construct the main idea in the article content. One indication that shows this consistency is congruity in the article's topic and the topic of literature or reference cited in the document listed in the bibliography. This works attempting to automate the topic detection on the article's references then obtain the congruity to the article title's topic through metadata extraction and text classification. This is done by extracting metadata of an article file to obtain all possible reference title using GROBID than classify the topic using a supervised classification model. We found that some refinements in the whole approach should be considered in the next step of this work.
Stabilization of the Underactuated Robot on the Visual Trajectory

The article considers the problem of moving and stabilizing underactuated wheeled robot on the visual curve in the motion plane. The curve is detected by the video camera mounted on the robot. The task is solved with the help of the approach named visual servoing which uses the visual information in the feedback. The aim of the visual serving is to minimize the difference between desired and the actual position of the set of the points on the image. The main feature of the proposed solution is considering a number of requirements to the robot’s dynamic including the reaction on the presence of the external disturbances. Thereby the regulators of special kind called multipurpose structure are used which makes possible to decompose the basic set of requirements into smaller independent tasks. Additional complication is the underactuated nature of the considered robot. Efficiency of the derived regulator is shown through the experiments with computer model.
Bring Light to the Night: Classifying Thermal Image Via Convolutional Neural Network Based on Visible Domain Transformation

Most existing vision systems target at processing images captured during the day time. However, it is also essential to enable cameras to see the scenes during the night, such as in outdoor places where no light exists and power outage in indoor environments. We capture thermal images to observe objects in the dark environment. Based on the captured thermal images, we develop a convolutional neural network to classify the images. As thermal images require to invest a substantial amount of time to create clear images, we also rely on color images to enrich the training samples and apply transfer learning to refine the CNN classification models. The visible source domain network is learned together with a decoding network to enforce the source domain learning outcome resembling the target thermal domain properties.
ICT, Inclusion and Pedagogical Diferention: Exploratory Study

The present work intends to address the importance of Information and Communication Technologies (ICT) in the teaching and learning process of a child with educational special needs in a pre-school context. The child in our study has a structural chromosomal anomaly, which makes the overall development atypical for her 6 years of age. Her difficulties lie at the level of attention, concentration, motricity and language. After an evaluation of the difficulties and possibilities of the child, the main objective of the educational intervention was to motivate the child and to optimize the learning of the basic contents essential for her entrance into the 1st CEB school, as well as to promote her active participation and involvement in a task. The family was involved to achieve continuous and systematic intervention through playfulness. The results of the intervention were obtained through direct and participatory observation with the child, and it is possible to observe some progress in its overall development.
Strategies and gamified teaching tools to reduce english learning difficulties in children with down syndrome

The Universidad Tecnica Particular de Loja through its academic vice-rectorate, as an incentive for the improvement of the teaching activities, has raised the figure of good teaching practice, in which one or several professors plan innovative strategies Adaptable to their classrooms. In addition, the UN approved Agenda 2030 on sustainable development, whose objectives include quality education, which should also be inclusive. From this context is presented this practice of teaching innovation, which has been implemented with 35 students of the English degree in the classroom of learning difficulties, organized in 13 groups, using the methodology of Problem-Based Learning (PBL). The objective is to design and apply didactic resources in physical format (n = 13), Digital with app Jclic (n = 2) and mixed (n = 1), for the teaching of the English language (level A1) to school-age children who have Down syndrome and who are included in education contexts Regular. The intervention has been planned in four phases, including implication, information, practice and evaluation. A satisfaction survey was erected, according to which, 80% of students believe that collaborative work fosters the development of better teaching and design strategies in physical and digital didactic resources.
Radio Cellular Forensics Analysis: Where is the Adversary?

The evolution of mobile cellular communication has been growing rapidly from 1G to 4G. Unfortunately, with this technology comes an increasing risk of cellular cybercrime. This research proposed a novel methodology to reveal the cybercrime adversary's location by using radio cellular forensics. Several tests were conducted to validate the proposed methodology. This research also presented the digital evidence in a forensically sound manner to support the forensics investigators. Finally, it is concluded that the proposed methodology can be used as a reference for investigators, analysts, and police in cybercrime cases.
Are Armageddon chess games implemented fairly?

A strategy to find fair time controls for Armageddon chess games is presented and exemplified with 2750+ ELO players. The research was divided in two stages. The first one consisted in finding a time control for Stockfish 9 so that it played with an effective strength equal to the average 2750+ ELO players of the 2017 World Blitz Chess Championship. Analysis of this stage showed that a fair time control for an Armageddon game depends on the players’ rating. In the second stage, another instance of Stockfish 9 with a different time control was paired as the black player to the engine with the time control found in the first stage as the white player. The new engine’s time control was adjusted so that the expected result of Armageddon games between these two engines would be a draw. This game generation was done because there is a very small amount of Armageddon games in order to draw statistical conclusions out of them. The resulting time controls were found to be 180±35 seconds for white and 110±12.27 seconds for black plus a 2±0.39 second increment per move, starting from move 1. Therefore, the ratio between the initial times for the black and white players is approximately 3 to 5, which is different from the normally implemented 4 to 5 ratio.
Agile Software Integration in Telecommunications — a Case Study

Agile methodologies changed the way of software development in the last decades. However, there is still struggling with the implementation of agile methods in environments such as the telecommunications industry with its long lasting projects in complex and large system set-ups. In this paper, we present a survey on agile software projects in telecommunications and comparable organizations and a case study of a multi-year software integration project and its transition from traditional project execution to agile methods at an Austrian mobile network operator. Including all pitfalls and failures.
Automatic Threshold Selection Method for SAR Edge Detection

In order to reduce the manual intervention and calculate amount in edge detection processing, an automatic threshold selection method for edge detection of synthetic aperture radar (SAR) images is proposed. The proposed automatic threshold selection (ATS) method determines the optimal thresholds by establishing a relationship between different thresholds and corresponding detection results. The thresholds chosen by the proposed ATS method are very close to the thresholds determined by manual intervention. Comparing with conventional method, ratio-based detectors with ATS obviously reduce the calculate amount and ensure the accuracy of edge detection at the same time, benefiting from less manual intervention and almost the same thresholds. The experimental results on SAR images show that ratio-based detectors combined with the proposed ATS method not only achieve the accurate detection results, but also effectively reduce the time consumption during thresholds selection.
Theory and Practice of Teaching Information Technology in After-School Education Institutions

The article deals with the theory and practice of teaching information technology in after-school educational establishments. Theoretical foundations of information technology development are revealed, the main chronological stages of information technology formation and development are established. The current state of the information technology system is analyzed. The scientific substantiation of the methodological system of teaching information technologies in scientific and technical circles of after-school educational institutions is carried out. The purpose and content of teaching information technology students in after-school educational institutions are presented, organizational forms, methods and means are presented.
A Generating Method of Long-time-scale Sequential PV Output Facing Power System Planning

A generating method of long-time-scale photovoltaic (PV) output vector facing power system planning was proposed in the paper. The major feature of the method is that only several kinds of accessible data are needed, while four main functions could be realized. In the created long-time-scale PV output vector, the cyclical changes of the PV output caused by sunrise and sunset are considered, the overall trend change related to monthly or seasonal differences are considered, the changes in magnitude leaded by weather changing which usually last a few days are considered, and the adjustment of PV output to make the vector reflecting the utilization hours of local PV facility are able to realize. The effectiveness of the proposed method was verified by the simulation of PV output in Beijing region.
Development of An Efficient and Practical Control System in Autonomous Vehicle Competition

In this paper, we develop a self-driving car called Clothoid towards Hyundai autonomous vehicle competition 2019. This work presents a development of the Control System which composed low and high-level control. Global Path Planning based on an HD-map generates the shortest path for the vehicle to reach the destination. Mission Planning and Local Path Planning use Finite State Machine(FSM) with behavior, which to deal with missions and control state of the vehicle, respectively. The lower level, the Local Path Planning uses a real-time hybrid A* algorithm based on probabilistic occupancy grid map to avoid obstacles and generates smooth trajectory while minimizing zerk in change lane scenario. And the Lowest-level Control System implements the Purse Pursuit for lateral control and PID controller for a longitude control. Our algorithms are implemented in the ROS framework; which written by C++and Python in Linux Operating System. The results show that our technology works effectively in both simulation tool and practical proving ground in K-city, South Korea.
Learn&amp;safe

Digital security is one of the most important topics in Latin America and also one of the less attended, specially in our country. As an interdisciplinary team, we found out this is a main concern at the teacher's community. The most worrying thing about this is the lack of tools for learning about security and other digital competences, making difficult to reduce digital gap between educational community. To face this context, we propose Learn&Safe, the first of an educational tools series to learn about digital security.