Reviewerly/exHarmony huggingface dataset:
Evaluating M-Learning System Adoption by Faculty Members in Saudi Arabia Using Concern Based Adoption Model (CBAM) Stages of Concern

This study assesses the use of an m-learning system by faculty members in Saudi Arabia using a new approach and methodology. Optimum use of educational technology requires consideration of requirements, obstacles and opportunities expected from user interaction with such systems and tools. While the use of m-learning in Saudi Arabia is relatively new, different research studies have investigated the use of m-learning in Saudi Arabia using different models. Most of the presented models investigated the acceptance and use from student perspectives, with little consideration of adoption by faculty members, their use of m-learning systems and their concerns (i.e. facilitators and barriers) as users. Some of the used models managed to provide significant results in relation to m-learning use, while others were found to lack a systematic and appropriate methodology. Concern Based Adoption Model (CBAM), which is widely used in the USA, Canada and (more recently) the Middle East (particularly Jordan), was used in this study to investigate m-learning adoption as an educational technology in Saudi Arabia. This framework provides tools to evaluate the use of educational technology within educational settings. This framework has not previously been used in Saudi Arabian educational research literature, and it is believed that the output will be valuable for enhancing the level of concern, adoption and use of m-learning in the future.
Renyi Differentially Private ERM for Smooth Objectives

In this paper, we present a Renyi Differentially Private stochastic gradient descent (SGD) algorithm for convex empirical risk minimization. The algorithm uses output perturbation and leverages randomness inside SGD, which creates a randomized sensitivity, in order to reduce the amount of noise that is added.One of the benefits of output perturbation is that we can incorporate a periodic averaging step that serves to further reduce sensitivity while improving accuracy (reducing the well-known oscillating behavior of SGD near the optimum). Renyi Differential Privacy can be used to provide (epsilon, delta)-differential privacy guarantees and hence provide a comparison with prior work. An empirical evaluation demonstrates that the proposed method outperforms prior methods on differentially private ERM.
Ultrasound prostate segmentation based on 3D V-Net with deep supervision

We propose a method to automatically segment prostate from TRUS image based on multi-derivate deeply supervised network and multi-directional contour refinement. 3D multi-derivate V-Net is introduced to enable end-to-end segmentation. Deep supervision mechanism is integrated into the hidden layers to cope with the optimization difficulties when training such a network with limited training data. The probability map of new prostate contour is generated by the well-trained network and fused to reconstruct the prostate contour by multi-directional contour refinement. This proposed algorithm was evaluated using 30 patients’ data with TRUS image and manual contours. The mean Dice similarity coefficient (DSC) and mean surface distance (MSD) were 0.92 and 0.60 mm, which demonstrate the high accuracy of the proposed segmentation method. We have developed a novel deep learning-based method demonstrated that this method could significantly improve contour accuracy especially around the apex and base region. This segmentation technique could be a useful tool in ultrasound-guided interventions for prostate-cancer diagnosis and treatment.
Hybrid Multiview Correlation for Measuring and Monitoring Thermomechanical Fatigue Test

This paper focuses on the development of fully-coupled 3D thermomechanical field measurement techniques applied for monitoring thermal fatigue tests. First, an original hybrid and multiview system composed of one infrared (IR) camera and two visible light cameras is introduced. The spatial registration of multimodal imaging devices is solved successfully using global Hybrid Multiview Correlation (HMC) based on the NURBS representation of the 3D calibration target and the surface of interest. The measurement uncertainties are estimated with an initial heating up phase prior to the fatigue test. Then, HMC is performed to measure the 3D surface displacement and temperature fields during laser shocks onto an austenitic stainless steel plate. Last, the HMC measurements are validated in comparison with finite element simulations of the test.
Unsupervised Learning of Accurate Camera Pose and Depth From Video Sequences With Kalman Filter

Unsupervised learning-based scene perception has recently become an important research direction. Most unsupervised methods for scene perception tasks (e.g., dense depth recovery and ego-motion estimation) train the convolutional network via minimizing the photometric error of images, achieving very impressive results. Since the supervision signal is weaker than ground truth data, the existing unsupervised methods generally do poorly inaccurate pose estimation and high-resolution depth map generation. To this end, we present an architecture based on the convolutional neural network and the Kalman filter, which is used for unsupervised learning of accurate ego-motion and high-resolution single view depth, with as fewer parameters as possible. Specifically, we first present pose network (P-CNN) with the decoupled (multi-stream) convolutional network for learning accurate camera pose (translation and rotation vector) to avoid the fault aliasing of multiple camera pose, with a relatively light-weighted network. Then, for the sake of improving the relationship of features between consecutive image pairs, we introduce the Kalman filter into the learning framework to improve the smoothness of the estimated camera pose. Finally, in order to decode a high-resolution depth map with a less uneven and unsmooth area, we adopt a new upsampling module in the encoder-decoder architecture of depth network (D-CNN). The extensive experiments are implemented on the KITTI driving dataset, proving that our method obviously predicts a more accurate camera pose and a clear depth map.
Multi-Modality Empowered Network for Facial Action Unit Detection

This paper presents a new thermal empowered multi-task network (TEMT-Net) to improve facial action unit detection. Our primary goal is to leverage the situation that the training set has multi-modality data while the application scenario only has one modality. Thermal images are robust to illumination and face color. In the proposed multi-task framework, we utilize both modality data. Action unit detection and facial landmark detection are correlated tasks. To utilize the advantage and the correlation of different modalities and different tasks, we propose a novel thermal empowered multi-task deep neural network learning approach for action unit detection, facial landmark detection and thermal image reconstruction simultaneously. The thermal image generator and facial landmark detection provide regularization on the learned features with shared factors as the input color images. Extensive experiments are conducted on the BP4D and MMSE databases, with the comparison to the state of the art methods. The experiments show that the multi-modality framework improves the AU detection significantly.
Stippling of 2D Scalar Fields

We propose a technique to represent two-dimensional data using stipples. While stippling is often regarded as an illustrative method, we argue that it is worth investigating its suitability for the visualization domain. For this purpose, we generalize the Linde-Buzo-Gray stippling algorithm for information visualization purposes to encode continuous and discrete 2D data. Our proposed modifications provide more control over the resulting distribution of stipples for encoding additional information into the representation, such as contours. We show different approaches to depict contours in stipple drawings based on locally adjusting the stipple distribution. Combining stipple-based gradients and contours allows for simultaneous assessment of the overall structure of the data while preserving important local details. We discuss the applicability of our technique using datasets from different domains and conduct observation-validating studies to assess the perception of stippled representations.
The Contract Education as a Goal-oriented System of Activity

The aim of the work is an application of the theory of Goal-oriented System of Activity to analysis and organization of effective contract targeted training at higher education institutions. The organization of targeted training of specialists is considered as one of the components of public administration to address staff problems in various regions and sectors of the economy in the context of rapidly changing demands of society, to implement promising youth policy and mitigate demographic problems. An algorithmic chain of meaningful actions is con structed on the basis of the methodology of the Goal-oriented System of Activity, which takes into account the real state of human, intellectual, material and economic resources and enables to minimize existing contradictions and, accordingly, ensure the establishment of partnerships between all interested participants in the process of targeted training (government agencies, employers, higher education institutions and students). As an example, four stages of contract teacher training have been considered.
Teaching and Learning Microbiology for Engineers in a Digital World: The Case of the FIT Courses at the Tecnologico de Monterrey, Mexico

The availability of online courses has increased dramatically; however, the effectiveness of online teaching remains uncertain. The Tecnologico de Monterrey has implemented the Tec21 educational model that includes online teaching with the modality of the FIT courses (Flexibility, Interaction and Technology), a type of well-designed courses using the CANVAS web-based platform and using ZOOM as a tool for communication with students. To compare the objective learning outcomes between a FIT course and a face-to-face course (Microbiology for Engineering), an analysis was made between these two types of courses, with the same or different instructor. Our results clearly indicate that the grades of the students enrolled in the FIT groups were higher than those of the classroom courses. These results were confirmed when the historical record files of both types of teaching were examined. Satisfaction surveys however revealed that students prefer face-to-face courses to online courses. Our data implies that it is necessary to work more on the tools of the distance-learning courses to make them extremely attractive and interesting for students.
An Empirical Study for Enhanced Software Defect Prediction Using a Learning-Based Framework

The object of software defect prediction (SDP) is to identify defect-prone modules.This is achieved through constructing prediction models using datasets obtained by mining software historical depositories.However, data mined from these depositories are often associated with high dimensionality, class imbalance, and mislabels which deteriorate classification performance and increase model complexity.In order to mitigate the consequences, this paper proposes an integrated preprocessing framework in which feature selection (FS), data balance (DB), and noise filtering (NF) techniques are fused to deal with the factors that deteriorate learning performance.We apply the proposed framework on three software metrics, namely static code metric (SCM), object oriented metric (OOM), and combined metric (CombM) and build models based on four scenarios (S): (S1) original data; (S2) FS subsets; (S3) FS subsets after DB using random under sampling (RUS) and synthetic minority oversampling technique (SMOTE); (S4) FS subsets after DB (RUS and SMOTE); and NF using iterative partitioning filter (IPF) and iterative noise filtering based on the fusing of classifiers (INFFC).Empirical results show that 1. the integrated preprocessing of FS, DB, and NF improves the performance of all the models built for SDP, 2. for all FS methods, all the models improve performance progressively from S2 through to S4 in all the software metrics, 3. model performance based on S4 is statistically significantly better than the performance based on S3 for all the software metrics, and 4. in order to achieve optimal model performance for SDP, appropriate implementation of the proposed framework is required.The results also validate the effectiveness of our proposal and provide guidelines for achieving quality training data that enhances model performance for SDP.
Light Control Design by Using Social Media Telegram Applications Based on Internet Of Things (IOT)

Internet of things (IoT) is a concept with the aim of expanding the connectivity of internet network connected to global networks. Internet of Things (IoT) is used to control electronic device that utilizes internet technology to perform control processes such as lamp. This study aims to build control device that utilizes internet technology to carry out the light control process by utilizing social media telegram applications. With NodeMCU V3 as a Digital controller that supports TCP / IP technology. The testing method is carried out using 4 lights and 4 channel relays. The results obtained by the testing process by using commands on the telegram have been successful, the delay time is very dependent on internet connectivity or Wifi.
Color-based Segmentation of Batik Using the L*a*b Color Space

Indonesian Batik has a variety of styles. Coloring techniques affect the color quality of batik. Color is very important to show staining breakthroughs. The colors R (Red) G (Green) and B (Blue) have a close color correlation level so that it is difficult to segment. Segmentation is the process of partitioning digital images into several segments. The segmentation process is carried out on 15 batik sample data. The first step, each sample is converted from RGB - XYZ - Lab color. The average ratio of Red, Green and Blue on the RGB color and XYZ color 1:10. Segmentation using L*a*b color, displays the composition of the sample data colors into six clusters: background, yellow, magenta, purple, red and green using the clustering method. This proposed work L*a*b color space is selected which is a homogeneous space for visual perception.
A dual EM algorithm for TV regularized Gaussian mixture model in image segmentation

A dual expectation-maximization (EM) algorithm for total variation (TV) regularized Gaussian mixture model (GMM) is proposed in this paper. The algorithm is built upon the EM algorithm with TV regularization (EM-TV) model which combines the statistical and variational methods together for image segmentation. Inspired by the projection algorithm proposed by Chambolle, we give a dual algorithm for the EM-TV model. The related dual problem is smooth and can be easily solved by a projection gradient method, which is stable and fast. Given the parameters of GMM, the proposed algorithm can be seen as a forward-backward splitting method which converges. This method can be easily extended to many other applications. Numerical results show that our algorithm can provide high quality segmentation results with fast computation speed. Compared with the well-known statistics based methods such as hidden Markov random field with EM method (HMRF-EM), the proposed algorithm has a better performance. The proposed method could also be applied to MRI segmentation such as SPM8 software and improve the segmentation results.
Radar Specific Emitter Recognition Based on DBN Feature Extraction

Deeping learning possesses excellent performance of extracting deep features and processing high-dimensional data, therefore deep belief network is considered to realize radar specific emitter recognition. A radar specific emitter recognition algorithm based on DBN feature extraction is proposed. Firstly, unsupervised extraction of pulse envelope frontier is realized in time-domain by DBN. Then model parameters are supervised fine-tuning to complete the training using labeled data, and radar specific emitters are recognized finally. Compared to traditional algorithm, the advantage of the novel algorithm can adaptively extract deep pulse features and the progress of feature extraction reduce the dependence on human experiences and signal processing technology. The experimental results show that the novel algorithm provides significant performance of pulse envelope feature extraction and higher recognition accuracy for simulation data and measured data. The validity and application value of this algorithm are verified°
A bilevel framework for joint optimization of session compensation and classification for speaker identification

The i-vector framework based system is one of the most popular systems in speaker identification (SID). In this system, session compensation is usually employed first and then the classifier. For any session-compensated representation of i-vector, there is a corresponding identification result, so that both the stages are related. However, in current SID systems, session compensation and classifier are usually optimized independently. An incomplete knowledge about the session compensation to the identification task may lead to involving uncertainties. In this paper, we propose a bilevel framework to jointly optimize session compensation and classifier to enhance the relationship between the two stages. In this framework, we use the sparse coding (SC) to obtain the session-compensated feature by learning an overcomplete dictionary, and employ the softmax classifier and support vector machine (SVM) in classifying respectively. Moreover, we present a joint optimization of the dictionary and classifier parameters under a discriminative criterion for classifier with conditions for SC. In addition, the proposed methods are evaluated on the King-ASR-010, VoxCeleb and RSR2015 databases. Compared with typical session compensation techniques, such as linear discriminant analysis (LDA) and nonparametric discriminant analysis (NDA), our methods can be more robust to complex session variability. Moreover, compared with the typical classifiers in i-vector framework, i.e. the cosine distance scoring (CDS) and probabilistic linear discriminant analysis (PLDA), our methods can be more suitable for SID (multiclass task).
GOOD PERFORMANCE IMAGES ENCRYPTION USING SELECTIVE BIT T-DES ON INVERTED LSB STEGANOGRAPHY

Transmitting image through the internet needs to be secured because of risk to be stolen. Security techniques that can be used for securing data especially image are cryptography and steganography. Combine these techniques can provide double protection in image security. In this research, we proposed the used of T-DES encryption with a selective bit to improve the time performance because time aspect is one of the important aspects of data transmission process. Four MSB of the secret image will be selected, then it will be encrypted using T-DES. After that, this encrypted results will be combined with other 4 LSB. This encryption scheme result will be embedded into a cover image using inverted LSB because inverted LSB can produce high imperceptible value. From 6 testing images which encrypted using proposed scheme present that proposed encryption scheme is twice faster than classic triple DES and slightly faster than double DES. While the embedding scheme can produce PSNR value above 40 dB with the range between 51 dB to 61 dB as well as SSIM which close to 1. This result denoted that proposed scheme generated good quality of stego images.
Two Cheers and a Pint of Worry: An On-Line Course in Political and Social Philosophy

During the past decade there have been numerous efforts to reinvigorate distance education; some of these have involved video technology, but others have made use of computers and advances in the design of computer software. In 1995, the State University of New York, funded by a major grant from the Sloan Foundation, embarked upon the creation of an "on-line" learning program--a set of asynchronous and distance learning courses delivered solely via computer. In whatfollows, I detail the genesis and structure of a course in political and social philosophy that I designed (and taught) for this project. After some preliminary comments on the "virtual classroom”. I illustrate the design of the course, and then offer a brief qualitative assessment. I close the essay with some cautionary reflections on computer-mediated education. Athough my initial skepticism about such courses has been modified, I still believe that there is something valuable about the direct, face-to-face, encounter of teacher and student that is not easily compensated for in computer-mediated learning.
A methodology for assuring the safety and security of critical infrastructure based on STPA and Event-B

Cyber-physical systems represent a challenge to conventional security and safety analysis techniques due to their complexity and the need to consider both safety and security equally.It is also important that the requirements generated to mitigate against safety and security risks are clear and adequately address the underlying issue.A methodology is presented in this paper to allow for integrated safety and security analysis of cyber-physical systems, particularly in a critical infrastructure context.This methodology uses a modified form of STPA, which has been coupled with our concept of adversarial modelling, to analyse for security and safety hazards which are then mitigated against by the creation of critical requirements.These critical requirements are then validated through their application to an Event-B formal model, allowing for their completeness to be verified.The output of the methodology is a set of critical requirements that guide iteration of and improvements to the system design to ensure its safety and security are maintained.
Multi‐view registration based on weighted LRS matrix decomposition of motions

Recently, the low‐rank and sparse (LRS) matrix decomposition has been introduced as an effective mean to solve the multi‐view registration. It views each available relative motion as a block element to reconstruct one sparse matrix, which then is used to approximate the low‐rank matrix, where global motions can be recovered for multi‐view registration. However, this approach is sensitive to the sparsity of the reconstructed matrix and it treats all block elements equally in spite of their varied reliabilities. Therefore, this study proposes an effective approach for multi‐view registration by weighted LRS matrix decomposition. On the basis of the inverse symmetry property of relative motions, it first proposes a completion method to reduce the sparsity of the reconstructed matrix. The reduced sparsity of the reconstructed matrix can improve the robustness and efficiency of LRS matrix decomposition. Then, it proposes the weighted LRS matrix decomposition, where each block element is assigned with one estimated weight to denote its reliability. By introducing the weight, more accurate registration results can be efficiently recovered from the estimated low‐rank matrix. Experimental results tested on public datasets illustrate the superiority of the proposed approach over the state‐of‐the‐art approaches on robustness, accuracy and efficiency.