Reviewerly/exHarmony huggingface dataset:
Component-based Development Using Moodle As Alternative for E-learning Software Development

This study used component-based development approach as alternative to develop software quickly and flexible. Moodle LMS as open source system was used as alternative to build e-learning software with traffic impact analysis (Andalalin) material as the case study. This study had done comparison design between system development from scratch and system development using existing component to prove that component-based development could be used as alternative for software development. The comparison started from development steps, strength and weakness of each methods, effort, and time. The effort and time estimation calculation were done by using adjusted story points (ASF) methods for Scrum methodology and COCOMO for CBSE methodology. To discover the flexibility of component-based development using Moodle, this study built additional component that related to the case study needs. After that, functional and nonfunctional testing was done towards the whole system which was developed using Moodle. This study also raised component addition model which could be used to integrate web-based additional component. The comparison result which was done in this study showed the development steps with component-based development were simpler than from scratch development. However, the effort estimation result for each methodology could not be compared, with the ASP value to calculate effort estimation was 1.1832 for Scrum methodology and 1.89 ManMonth for CBSE methodology from COCOMO calculation. Meanwhile, the time estimation result did not show the advantages of CBSE methodology because the calculation result was 12 weeks or 3 months for Scrum methodology and 3.18 months for CBSE methodology. The component additional model which was used to integrate web-based additional component into Moodle was successfully implemented. The evaluation result of Andalalin e-learning system showed the needs of Andalalin material learning could be fulfilled. Furthermore, the performance testing, which was done using Moodle Benchmark, found that the environment, where Moodle system was implemented, affected the system performance. From the usability testing and questionnaire result, the Moodle system was considered not difficult for respondents to use. Other than that, using the knowledge management system was considered helping respondent to understand the Andalalin material.
An Architecture-Based Approach for Modeling Dynamically Adaptive IoT Systems

This paper proposes an approach for modeling dynamically adaptive IoT systems with the help of architectural models and aspect-oriented software development techniques. The modeling is carried out in two distinct segments. First, a declarative model is developed that comprises the functional and deployment perspectives of the system as well as reflects various deployment strategies. Second, a reconfiguration model is specified that constitutes the adaptive behavior. The runtime adaptation is implemented by using an aspect-oriented mechanism that injects the pre-determined functionality when required. Both the declarative and reconfiguration models are put to work in an integrated way. The approach is validated by performing latency analysis for an end-to-end flow within the system architecture.
Towards Designing Conceptual Data Models for Big Data Warehouses: The Genomics Case

Data Warehousing applied in Big Data contexts has been an emergent topic of research, as traditional Data Warehousing technologies are unable to deal with Big Data characteristics and challenges. The methods used in this field are already well systematized and adopted by practitioners, while research in Big Data Warehousing is only starting to provide some guidance on how to model such complex systems. This work contributes to the process of designing conceptual data models for Big Data Warehouses proposing a method based on rules and design patterns, which aims to gather the information of a certain application domain mapped in a relational conceptual model. A complex domain that can benefit from this work is Genomics, characterized by an increasing heterogeneity, both in terms of content and data structure. Moreover, the challenges for collecting and analyzing genome data under a unified perspective have become a bottleneck for the scientific community, reason why standardized analytical repositories such as a Big Genome Warehouse can be of high value to the community. In the demonstration case presented here, a genomics relational model is merged with the proposed Big Data Warehouse Conceptual Metamodel to obtain the Big Genome Warehouse Conceptual Model, showing that the design rules and patterns can be applied having a relational conceptual model as starting point.
Quantifying Unlinkability in Multi-hop Wireless Networks

Consider a multi-hop wireless network in which devices act as anonymizing routers. Even if devices anonymize their link transmissions, an adversary may still be able to infer key information by observing the traffic patterns in the network. In this work, we quantify what impacts how well a Kalman-filter based adversary can infer unlinkability, that is, the probability that different pairs of devices are communicating, from anonymized link transmissions. We assume that devices do not reorder packets to mix traffic and thereby increase unlinkability. Instead, we show that traffic mixing is still possible due to the use of multi-hop routing and broadcast transmissions, with the amount of mixing dependent on the network characteristics. In simulation, we find that i) for unicast links, as network connectivity increases, unlinkability decreases, while for broadcast links as connectivity increases unlinkability increases, ii) link dynamics increase unlinkability in poorly connected topologies, iii) well-connected topologies achieve the same level of unlinkability with fewer transmissions per packet delivered, and (iv) a lattice topology has consistently good unlinkability in different scenarios.
Augmented Reality as an Academic Training Experience in Higher Education

Educational processes in general demand the use of digital resources in the classroom. Experimentation with real objects allows academic training to be more interactive and dynamic. In this sense, the incorporation of tools such as Augmented Reality in the classroom is reflecting a new way of teaching and learning. The objective of this article was to apply the OARA methodology for the design of learning objects with Augmented Reality (AR) and through its use to determine the level of technological acceptance (TAM), validate the design of the final product and assess the academic performance of third semesters students of the Basic Education program of the Faculty of Human Sciences and Education of the Technical University of Ambato. In this university teaching experience, the results showed that students value highly the use of content enriched with AR using their mobile devices, as well as the design of the final product was highly satisfactory and evidenced an improvement in student’s academic performance.
A lightweight 2D Pose Machine with attention enhancement

Pose estimation is a challenging task in computer vision that has many applications, as for example: in motion capture, in medical analysis, in human posture monitoring, and in robotics. In other words, it is a main tool to enable machines do understand human patterns in videos or images. Performing this task in real-time while maintaining accuracy and precision is critical for many of these applications. Several papers propose real time approaches considering deep neural networks for pose estimation. However, in most cases they fail when considering run-time performance or do not achieve the precision needed. In this work, we propose a new model for real-time pose estimation considering attention modules for convolutional neural networks (CNNs). We introduce a two-dimensional relative attention mechanism for feature extraction in pose machines leading to improvements in accuracy. We create a single shot architecture where both operations to infer key points and part affinity fields share the information. Also, for each stage, we use tensor decompositions to not only reduce dimensionality, but also to improve performance. This allows us to factorize each convolution and drastically reduce the number of parameters in our network. Our experiments show that, with this factorized approach, it is possible to achieve state-of-art performance in terms of run-time while we have a small reduction in accuracy.
Appearance Monitoring of the Transmission Lines based on Hough Transform

The significance of appearance condition monitoring of transmission line is self-evident. In this paper, Hough transform is used to detect the number of curves in the transmission line image. In the monitoring image of transmission line, the transmission line may be displayed as straight line or catenary line according to different shooting angles, so it is necessary to identify these two typical curves. By changing the threshold, the interference of other lines can be effectively removed. The simulation results show that the proposed method can effectively identify the transmission lines in the monitoring photos.
An Automated System to Detect Phishing URL by Using Machine Learning Algorithm

Malicious URLs play a very important role in today’s critical scam and attacks. They are harmful to every aspect of the usage of computers. Identification and detection of these malicious URL are very crucial. Malignant codes are synchronized with malicious software by invaders or hackers. Malicious content can be like Trojan horses, worms, backdoors, etc.; detection of these URLs is done previously by the usage of blacklists and whitelists. Blacklist itself cannot be sufficient to check the malicious URLs because they suffer from a shortage in the capacity in terms of newly created malicious URLs. These conventional approaches shortfalls by effectively dealing with evolving technologies and web searching mechanisms. In recent years, systems have been explored and evolved with the increasing research attention on enhancing the ability to detect malicious URLs. In this research paper, an innovative classification method was proposed to solve the difficulties encountered in malicious URL detection by using the existing mechanisms. The proposed classification model is based on high-performance machine learning methods which not only takes the syntactic essence of the URL into consideration but also the semantic and lexical meaning of these dynamically changing URLs. It is expected that the proposed approach will overcome the drawbacks of the existing techniques. A comparative analysis of Logistic regression, Support Vector Machine, and Naïve Bayes classification has also been performed. The tests of computer simulation have developed SVM with greater accuracy than logistic regression and Naive Bayes. Support Vector Machine has been obtained with an accuracy of 85.35%.
The Application of Artificial Intelligence Video Feedback System in Tennis Teaching in Colleges and Universities

Objective: To test the advantages and disadvantages of the system through the intervention of artificial intelligence video feedback system in college tennis teaching. Methods: 64 male and female students of school tennis team in XX University were randomly divided into two groups: the experimental group (n=32) and the control group (n=32). The experimental group and the control group had eight weeks of tennis learning (The same learning content), during which the experimental group had video feedback system intervention. Results: 1) The scores of the forehand and backhand batting techniques and the movement technique evaluation in the experimental group were significantly better than those before the experiment (p<; 0.05). 2) The control group was significantly better than the control group (p<; 0.05). 3) After the experiment, the test group was significantly better than the control group (p<; 0.05).Conclusion: The application of artificial intelligence video feedback system in college physical education is helpful to: 1) Students are richer and more intuitive to acquire learning knowledge. 2) Help students to establish action model and spatial thinking concept faster. 3) The correction and consolidation of technical actions are more personalized and timely.
Teach Me What You Want to Play: Learning Variants of Connect Four Through Human-Robot Interaction

This paper investigates the use of game theoretic representations to represent and learn how to play interactive games such as Connect Four. We combine aspects of learning by demonstration, active learning, and game theory allowing a robot to leverage its developing representation of the game to conduct question/answer sessions with a person, thus filling in gaps in its knowledge. The paper demonstrates a method for teaching a robot the win conditions of the game Connect Four and its variants using a single demonstration and a few trial examples with a question and answer session led by the robot. Our results show that the robot can learn arbitrary win conditions for the game with little prior knowledge of the win conditions and then play the game with a human utilizing the learned win conditions. Our experiments also show that some questions are more important for learning the game's win conditions. We believe that this method could be broadly applied to a variety of interactive learning scenarios. (A preliminary version of this paper was accepted at [5]).
Hybrid Fourier-Wavelet Heart Magnetic Field Signal Denoising Based on Sparse Representation

In order to effectively remove the magnetocardiography (MCG) signal noise and improve the accuracy of heart disease diagnosis, a cardiac Fourier-wavelet denoising method based on sparse representation is used to process the cardiac magnetic signal. The noise reduction method combines the advantages of Fourier and wavelet. The initial denoising process is performed in Fourier domain, and then the residual noise is removed in the wavelet domain. Based on the theory of sparse representation, the wavelet denoising is transformed into an optimization problem, and a denoising model based on sparse representation is established. By eliminating the sparsity of wavelet coefficients, the purpose of noise removal is achieved. The experimental results verify that the method of the paper can effectively improve the denoising effect of the cardiac magnetic signal.
Design and Fabrication of Sensing System for Rehabilitation of Finger

Assessment of hand kinematics is essential in the rehabilitation field. This paper presents a sensing system embedded with (6-axis) inertial measurement unit (IMU) sensors. The suggested system can use the finger's accelerations, angular velocities, and range of motion (ROM) which is calculated by a sensor fusion algorithm, to help physicians evaluate the rehabilitation process. In order to survey the reliability and accuracy of the proposed system, the behavior of the sensors is evaluated statically and dynamically. The Root Mean Square Error (RMSE) obtained in static and dynamic states are 2° and 3°, respectively, which represent the optimal function of the fusion algorithm.
Comparison of Naïve Bayes Algorithm with Genetic Algorithm and Particle Swarm Optimization as Feature Selection for Sentiment Analysis Review of Digital Learning Application

Abstract The problem examined in this study is about the user’s trust in using digital learning applications that are downloaded on playstore. Many reviews are given by the public about the application that has been downloaded on playstore. This review is very influential on their trust in using the application. The purpose of this study is to classify data according to labels and find out the best choice between the classification method and the proposed selection feature as a consideration in determining the use of digital learning applications. This study compares the classification method, the Naïve Bayes algorithm and the genetic algorithm (GA) as feature selection with the Naïve Bayes algorithm classification method and the particle swarm optimization (PSO) as feature selection to categorize the reviews in the playstore. The experimental results show that the Naïve Bayes algorithm and PSO as feature selection is the best model between the two models proposed in this study. Reviews can be classified into positive and negative labels well. The accuracy is 98.00%. The results of the classification are expected to help in making decisions when going to use digital learning application.
An Efficient Hola Filter for Saliency Detection

Currently, saliency detection plays an important role in a wide range of applications, such as image segmentation, image recognition, image retrieval, target detection, and so on. These applications require not only the precise saliency localization but also the precise saliency shape. However, most existing approaches did not focus on the precise saliency shape. Therefore, this paper proposes an efficient approach for obtaining the more precise saliency shape. The key contribution of this work is designing a set of Hola filters for more precise localization and sharp edge of detected saliency. Based on a challenging dataset divided into seven categories with different characteristics, experimental results showed that our proposed method outperformed the baselines in almost categories in terms of AUC performance.
Localin Reshuffle Net: Toward Naturally and Efficiently Facial Image Blending

The blending of facial images is an effective way to fuse attributes such that the synthesis is robust to the finer details (e.g., periocular-region, nostrils, hairlines). Specifically, facial blending aims to transfer the style of a source image to a target such that violations in the natural appearance are minimized. Despite the many practical applications, facial image blending remains mostly unexplored with the reasons being two-fold: 1) the lack of quality paired data for supervision and 2) facial synthesizers (i.e., the models) are sensitive to small variations in lighting, texture, resolution and age. We address the reasons for the bottleneck by first building Facial Pairs to Blend (FPB) dataset, which was generated through our facial attribute optimization algorithm. Then, we propose an effective normalization scheme to capture local statistical information during blending: namely, Local Instance Normalization (LAN). Lastly, a novel local-reshuffle-layer is designed to map local patches in the feature space, which can be learned in an end-to-end fashion with dedicated loss. This new layer is essential for the proposed Localin Reshuffle Network (LRNet). Extensive experiments, and both quantitative and qualitative results, demonstrate that our approach outperforms existing methods.
THE MODAL LOGIC OF -CENTERED FORCING AND RELATED FORCING CLASSES

Abstract We consider the modality “ $\varphi $ is true in every $\sigma $ -centered forcing extension,” denoted $\square \varphi $ , and its dual “ $\varphi $ is true in some $\sigma $ -centered forcing extension,” denoted $\lozenge \varphi $ (where $\varphi $ is a statement in set theory), which give rise to the notion of a principle of $\sigma $ - centered forcing . We prove that if ZFC is consistent, then the modal logic of $\sigma $ -centered forcing, i.e., the ZFC-provable principles of $\sigma $ -centered forcing, is exactly $\mathsf {S4.2}$ . We also generalize this result to other related classes of forcing.
PRISME: Toward a model linking tangible interaction and domain-specific tasks in mixed reality environments

The PRISME model introduced in this article is part of ongoing research in ergonomics on the design and manufacture of industrial operator platforms. The underlying research objective is based on new advances in Mixed Reality when used as an intermediate stage within the simulation process. While using Virtual Environments as such a way holds great potential in cutting down production costs, it also requires user interactions to be fit-to-use to each specific line of work. PRISME is introducing an innovative solution to this issue by proving an automatic link (removing the need for domain-specific adaptations) between operator tasks and interactions with their platform. This research will be presented in two stages: a generic typology of interactors allowing to represent the majority of tangible and MR devices will be followed by an interaction model based on MASCARET's activity description meta-model syntax. Finally, an aeronautical use case will validate the model by simulating the standard operations performed by an airplane controller.
Body Pose and Deep Hand-shape Feature Based American Sign Language Recognition

This work presents an approach for American Sign Language (ASL) gesture recognition from videos. Gestures are comprised of various upper body motions involving hand shapes, motion of both hands with facial expression and head movements. Previous approaches tackled this problem by directly learning 3D convolutional spatio-temporal models from video in a simplified settings with uniform backgrounds. To handle more complex variation in appearance and backgrounds we propose to exploit recent advances in estimation of 2D body pose using Deep Convolutional Neural Networks trained on large corpus of human pose annotations. We use the trajectories of 2D skeletal data estimated from video to train a baseline recursive neural network gesture recognition model. The basic model is further extended using embeddings of hand images obtained from another hand shape recognition model [15] with dynamics modeled by another recursive neural network. The final model learns how to fuse two Long Short Term Model (LSTM) recursive neural network models for skeletal and hand image data. We train and evaluate this model on the GMU-ASL51 dataset of 12 users and 51 ASL gestures [8] demonstrating its superior performance compared to several baseline models.
Decoupled Feature-Temporal CNN: Explaining Deep Learning-Based Machine Health Monitoring

Machine learning, especially deep learning, have been extensively applied and studied in the area of machine health monitoring in the recent "big data" era. For machine health monitoring systems (MHMS), the major efforts have been put in designing and deploying more and more complex machine learning models which are also called as black-box models considering they are non-transparent towards their working mechanism. However, this research trend brings huge potential risk in real life. Since machine health monitoring itself is a high-stake decision scenario, the decision of the autonomous monitoring system should be trustworthy and reliable, which refers to obtain explainability. Then, it comes to the following key question: Why the deployed MHMS predict what they predict. In this paper, we shed some lights on this meaningful research direction: explainable machine health monitoring systems (EMHMS). In EMHMS, the machine doctor could act like a real doctor who can only make diagnosis but also describe the patient's symptoms. First, we propose a specific convolutional neural network (CNN) structure, named as DecouplEd Feature-Temporal CNN (DEFT-CNN), balances precision-explainability trade-off. Specifically, features and temporal information will be encoded in different stages of our model. Second, to explain the decision of the model, we adopt the gradient-based methods to generate features and temporal saliency maps highlighting which kind of features and time steps are key for the model's predictions. At last, we conduct the experimental studies in the CWRU bearing fault diagnosis dataset to verify the effectiveness of our proposed framework.