Reviewerly/exHarmony huggingface dataset:
Edmodo-based blended learning on mathematical proving capability

This study aims to learn the differences in terms of mathematical proving capability achievement of students using Edmodo-based blended learning model according to the levels of prior mathematical knowledge (high, medium, low), compared to those with conventional model, and the students' attitude towards mathematics learning using Edmodo-based blended learning model. This study uses quasi-experimental method. The subjects of this study are the second graders of junior high school. The research instrument consists of tests and non-test. The results of this study show that: there is a difference in terms of the improvement of mathematical proving ability between students using Edmodo-based blended learning model and those using conventional model, there is also a difference in terms of the achievement of mathematical proving ability between students using Edmodo-based blended learning and those using conventional model based on the levels of prior mathematical knowledge (high, medium, low), and students show a positive attitude towards mathematics learning using Edmodo-based blended learning model. Edmodo-based blended learning has implications for teacher activeness by using technology for education and student self-reliance in the learning process.
Evaluation of Configurations of Modular Motor for Power-Assist Wheelchair

The paper deals with expandable motors for power assist wheelchairs. Significant part of the users of such wheelchairs are the persons, who are highly motivated to use their wheelchairs applying as much of their own muscle power as possible, so that the rate of assistance may vary from zero to maximal power. On the other hand, the maximal power itself may be different. In the case of wheelchair use for rehabilitation purpose, it decreases, requiring less powerful motor of smaller weight and size. On the other hand, in the case of progressive disability it may increase. In both cases, it would be practical to use the same wheelchair with alternative motor. These two tendencies indicate the need in a motor, which maximal power and torque could be corrected, providing less weighty and bulky solutions for less powerful occasions. In the considered research, the above stated problem is solved with a help of arc shaped motor, varying the number of arc shaped modules for variable maximal power. There are several possible configurations of such motors. These configurations are evaluated in the given report from the point of view of their weight, size, cost and requirements for the corresponding electronic drivers.
ADA: Adversarial Data Augmentation for Object Detection

The use of random perturbations of ground truth data, such as random translation or scaling of bounding boxes, is a common heuristic used for data augmentation that has been shown to prevent overfitting and improve generalization. Since the design of data augmentation is largely guided by reported best practices, it is difficult to understand if those design choices are optimal. To provide a more principled perspective, we develop a game-theoretic interpretation of data augmentation in the context of object detection. We aim to find an optimal adversarial perturbations of the ground truth data (i.e., the worst case perturbations) that forces the object bounding box predictor to learn from the hardest distribution of perturbed examples for better test-time performance. We establish that the game-theoretic solution (Nash equilibrium) provides both an optimal predictor and optimal data augmentation distribution. We show that our adversarial method of training a predictor can significantly improve test-time performance for the task of object detection. On the ImageNet, Pascal VOC and MS-COCO object detection tasks, our adversarial approach improves performance by about 16%, 5%, and 2% respectively compared to the best performing data augmentation methods.
Estimation of solar radiation using support vector regression

AbstractAbstractThe solar radiation estimation is important parameter of interest in the Photovoltaic system installation, maintenance and performance evaluation. In the present work support vector regression (SVR) is used for the estimation of global horizontal solar radiation (GHSR). The model evaluates the performance of SVR model with different combinations of metrological parameters such as ambient temperature, humidity, atmospheric pressure etc. The data of three years from 2009-2011 is acquired from the National Institute of Solar Energy (NISE) Gurugram, India. The performance metric for estimation is the root means square error (RMSE). The results of SVR model were compared and found superior with other state of the art models like Hidden Markov Model and Artificial Neural Networks. After the analysis of results, it was found that temperature is the most important parameter along with atmospheric pressure, relative humidity, day number and wind speed.Keywords: Support vector regressionMeteorological parametersGlobal horizontal solar radiation
Intelligent Detection Using Convolutional Neural Network (ID-CNN)

In this paper, we will study the basic flow and the principle of state-of-the-art object detection technique (i.e. Faster R-CNN) and improve it further with the inclusion of two strategies into it. Firstly, we propose a multi-layer features merging strategy by using a concatenation layer. Secondly, we introduce a contextual learning scheme for Faster R-CNN. Previously, Faster R-CNN just uses regional features. Contextual features are added with the regional features for the classification and detection task. Our improvement on Faster R-CNN shows promising results. We call our improved Faster R-CNN network as ID-CNN (Intelligent Detection Using Convolutional Neural Network) as its detection accuracy is better. Therefore, we call it as an intelligent detector. We use a deep VGG-16 model as our base model, as Faster R-CNN did. We evaluated our ID-CNN on Pascal VOC public datasets. Experimental results show that ID-CNN can effectively improve the object detection average precision to some extent. On VOC 2007 and 2012, we achieved a mean average precision (mAP) of 74.7% and 71.9%, respectively. ID-CNN is also end-to-end trainable with the same alternating fine-tuning optimization scheme of Faster R-CNN. Finally, we compared ID-CNN with Faster R-CNN on ImageNet object detection dataset and we achieved mAP of 48.1% compared with 46.2% for Faster R-CNN.
Trust enforcement through self-adapting cloud workflow orchestration

Providing runtime intelligence of a workflow in a highly dynamic cloud execution environment is a challenging task due the continuously changing cloud resources. Guaranteeing a certain level of workflow Quality of Service (QoS) during the execution will require continuous monitoring to detect any performance violation due to resource shortage or even cloud service interruption. Most of orchestration schemes are either configuration, or deployment dependent and they do not cope with dynamically changing environment resources. In this paper, we propose a workflow orchestration, monitoring, and adaptation model that relies on trust evaluation to detect QoS performance degradation and perform an automatic reconfiguration to guarantee QoS of the workflow. The monitoring and adaptation schemes are able to detect and repair different types of real time errors and trigger different adaptation actions including workflow reconfiguration, migration, and resource scaling. We formalize the cloud resource orchestration using state machine that efficiently captures different dynamic properties of the cloud execution environment. In addition, we use validation model checker to validate our model in terms of reachability, liveness, and safety properties. Extensive experimentation is performed using a health monitoring workflow we have developed to handle dataset from Intelligent Monitoring in Intensive Care III (MIMICIII) and deployed over Docker swarm cluster. A set of scenarios were carefully chosen to evaluate workflow monitoring and the different adaptation schemes we have implemented. The results prove that our automated workflow orchestration model is self-adapting, self-configuring, react efficiently to changes and adapt accordingly while supporting high level of Workflow QoS.
Support Vector Machine Optimized Using the Improved Fish Swarm Optimization Algorithm and Its Application to Face Recognition

Support vector machine (SVM) is always used for face recognition. However, kernel function selection is a key problem for SVM. This paper tries to make some contributions to this problem with focus on optimizing the parameters in the selected kernel function to improve the accuracy of classification and recognition of SVM. Firstly, an improved artificial fish swarm optimization algorithm (IAFSA) is proposed to optimize the parameters in SVM. In the improved version of artificial fish swarm optimization algorithm, the visual distance and the step size of artificial fish are adjusted adaptively. In the early stage of convergence, artificial fish are widely distributed, and the visual distance and step size take larger values to accelerate the convergence of the algorithm. In the later stage of convergence, artificial fish gathered gradually, and the visual distance and the step size were given small values to prevent oscillation. Then the optimized SVM is used to recognize face images. Simultaneously, in order to improve the accuracy rate of face recognition, an improved local binary pattern (ILBP) is proposed to extract features of face images. Numerical results show the advantage of our new algorithm over a range of existing algorithms.
Infimal convolution type regularization of TGV and shearlet transform for image restoration

We propose a novel infimal convolution type functional based on total generalized variation (TGV) and shearlet transform, which can be easily incorporated into image restoration problems. We employ the TGV functional to represent the piecewise smooth cartoon part of an image and the L1 norm of shearlet transform for the edge-like texture part. The proposed model recovers both fine details and edge features of images satisfactorily. The existence of solutions to our proposed model is proved. We also design a general algorithm using the classical first-order Primal–Dual method for solving our imaging problems. Numerical experiments are carried out to illustrate the ability of the new regularizer in preserving edge-like textures better than some existing variational and sparsity-based methods.
A novel algorithm for parameter estimation of Hidden Markov Model inspired by Ant Colony Optimization

HMM is a powerful method to model data in various fields. Estimation of Hidden Markov Model parameters is an NP-Hard problem. We propose a heuristic algorithm called "AntMarkov" to improve the efficiency of estimating HMM parameters. We compared our method with four algorithms. The comparison was conducted on 5 different simulated datasets with different features. For further evaluation, we analyzed the performance of algorithms on the prediction of protein secondary structures problem. The results demonstrate that our algorithm obtains better results with respect to the results of the other algorithms in terms of time efficiency and the amount of similarity of estimated parameters to the original parameters and log-likelihood. The source code of our algorithm is available in https://github.com/emdadi/HMMPE.
Source code plagiarism detection with low-level structural representation and information retrieval

Low-level approach is a recent way for source code plagiarism detection. Instead of relying on source code tokens, it relies on low-level structural representation resulted from compiling given source codes. However, to date, existing low-level approaches are unsuitable for handling large-sized source codes; their comparison takes either quadratic or cubic time complexity. This paper presents a low-level approach with a more-efficient comparison; the comparison only takes linear time complexity with the help of Cosine Correlation in Vector Space Model. According to our evaluation, three findings can be deducted. First, proposed approach can be more efficient and effective than the state-of-the-art approach. Second, low-level structural representation can be more effective and efficient than source code token sequence as a medium for source code plagiarism detection. Third, subsequence length (which is related to n in n-gram) can be inversely proportional to effectiveness without significant impact on efficiency.
Thai finger spelling localization and classification under complex background using a YOLO-based deep learning

Sign language recognition has been actively studied and remains a challenge in computer vision. The finger spelling is an integral part of a sign language. This study focuses on Thai finger spelling(TFS), especially TFS single hand schema under complex background condition. We proposed a YOLO-based Thai finger spelling(Y-TFS) that used the convolution neural network architecture to localize and classify 25 TFS signs. The experiment on the training dataset of 15,000 images and test dataset of 15,000 images shows that our system has performed well and is robust against various background conditions. For the Thai fingerspelling recognition, our Y-TFS achieved the mAPs of 82.06% under a complex background and 84.99 % under a plain background.
Collecting Virtual and Augmented Reality in the Twenty-First Century Library

In this paper, we discuss possible pedagogical applications for virtual and augmented reality (VR and AR), within a humanities/social sciences curriculum, articulating a critical need for academic libraries to collect and curate 3D objects. We contend that building infrastructure is critical to keep pace with innovative pedagogies and scholarship. We offer theoretical avenues for libraries to build a repository 3D object files to be used in VR and AR tools and sketch some anticipated challenges. To build an infrastructure to support VR/AR collections, we have collaborated with College of Liberal Arts to pilot a program in which Libraries and CLA faculty work together to bring VR/AR into liberal arts curricula.
SAR Image Invariant Feature Extraction by Anisotropic Diffusion and Multi-Gray Level Simplified PCNN

Besides the target in synthetic aperture radar (SAR) image, the structural information remained in speckle and shadow is also very important when we extract the invariant feature of SAR image. However, the output of the classical pulse coupled neural network (PCNN) is a binary value. All the information remained in speckle and shadow is lost, when the classical PCNN is used to process SAR image directly. To overcome this problem, a multi-gray level simplified PCNN (MSPCNN) is proposed in this paper. All the useful information in speckle, shadow, and target can be considered when we use MSPCNN to compute the invariant feature of the SAR target. In order to suppress the negative influence of speckle noise and keep the useful structured information, the improved speckle reducing algorithm (SRAD) is used first. By an adaptive threshold delt0, we can keep the remained speckle in different SAR images to an ideal same level. The negative influence of the remained speckle to different targets' signature becomes basically the same and can be ignored. Combining with SRAD and MSPCNN, the SAR image invariant feature extraction scheme AD-MSPCNN is put forward. After analyzing the performance of different signature computing methods, we splice the time signature and the entropy signature together and use the new spliced vector as the invariant feature of the SAR target. The validity and robustness of AD-MSPCNN are proved by the experimental results on MSTAR database.
Adaptive Visual Interaction Based Multi-Target Future State Prediction For Autonomous Driving Vehicles

Predicting the state of dynamic objects in a real traffic environment is a key issue in autonomous driving vehicles. Various approaches have been proposed to learn the dynamics from visual observations with static background. However, minimal research has been conducted in a real traffic environment due to the complicated and changeable scenes. This paper proposes an adaptive multi-target future state prediction (position/velocity) method under autonomous driving conditions. In particular, an adaptive visual interaction method and control mechanism are introduced to overcome the change in the number of objects in continuous driving frames. In addition, a two-stream architecture with stage-wise learning is utilized for accurate object state prediction by simultaneously complementing spatial and temporal information. Experiments on two public challenging datasets, namely Udacity (CrowdAI) and Udacity (Autti), demonstrate the effectiveness of the proposed method on multi-target dynamic state prediction in a real traffic environment.
LICID: a lightweight image cryptosystem for IoT devices

In this paper, we highlight the limitations of block cipher modes of encryption ECB, CBC, OFB and CTR to cipher multimedia content and when they are implemented in IoT devices. In fact, processing multimedia encryption with classical IoT methods like Present, Simon, Rectangle and others does not provide enough confusion and diffusion even though they are specially designed for IoT devices. Those lightweight cryptosystems are designed for text or binary data and cannot fulfill high security levels when used with multimedia data. We introduce LICID, a new lightweight cipher specially designed for image data that can be implemented in low resource devices, making it the first attempt to resolve multimedia security in IoT context. LICID is based on a new structure "outer–inner structure." This new structure is specially designed to treat high correlation and redundancy of image and to ensure the highest level of confusion and diffusion properties. We also provide a detailed cryptanalysis of LICID by testing its robustness against linear and differential attacks. We show that the linear attack complexity is 2116 for the 14th round of LICID and the differential attack complexity is 2112.66 for just the 8th round. In both cases, the most two powerful known attacks exceed the brute force search, which is 2112, making LICID theoretically secure.
TruZ-View

When OS and hypervisor are compromised, mobile devices currently provide a hardware protected mode called Trusted Execution Environment (TEE) to guarantee the confidentiality and integrity of the User Interface (UI). The present TEE UI solutions adopt a self-contained design model, which provides a fully functional UI stack in the TEE, but they fail to manage one critical design principle of TEE: a small Trusted Computing Base (TCB), which should be more easily verified in comparison to a rich OS. The TCB size of the self-contained model is large as a result of the size of an individual UI stack. To reduce the TCB size of the TEE UI solution, we proposed a novel TEE UI design model called delegation model. To be specific, our design reuses the majority of the rich OS UI stack. Unlike the existing UI solutions protecting 3-dimensional UI processing in the TEE, our design protects the UI solely as a 2-dimensional surface and thus reduces the TCB size. Our system, called TruZ-View, allows application developers to use the rich OS UI development environment to develop TEE UI with consistent UI looks across the TEE and the rich OS. We successfully implemented our design on HiKey board. Moreover, we developed several TEE UI use cases to protect the confidentiality and integrity of UI. We performed a thorough security analysis to prove the security of the delegation UI model. Our real-world application evaluation shows that developers can leverage our TEE UI with few changes to the existing app's UI logic.
A Multi-agent System Framework for Dialogue Games in the Group Decision-Making Context

Dialogue games have been applied to various contexts in computer science and artificial intelligence, particularly to define interactions between autonomous software agents. However, in order to implement dialogue games, the developers need to deal with other important details besides what is presented in the model's definition. This is a complex work, mostly when it is expected that the agents' interactions correctly represent a human group behavior. In this work, we present a multi-agent system framework specifically designed to facilitate the implementation of dialogue games under the context of group decision-making in which agents interact as the humans do in face-to-face meetings. The proposed framework, named MAS4GDM, encapsulates the JADE framework and provides a layer that allows developers to easily implement their dialogue models without being concerned with some complex implementation details, such as: the communication model, the agents' life cycle, among others. We ran an experimental evaluation and verified that the proposed framework allows to implement dialogue models in an easier way and abstract the developers from important implementation details that can compromise the application's success.
Fitness for sale: The value of self-tracking in secondhand exchange

This article uses online advertisements to gain retroactive insights into the use and disuse of wearable self-tracking technologies in everyday life. This is achieved by examining over 2700 listings for devices from manufacturers like Apple, Fitbit, and Garmin. These listings are sourced from an online secondhand marketplace—Gumtree Australia. In this exchange space, sellers often draw on existing retail product descriptions and aim to encourage prospective buyers with positive descriptions. Despite the "sale imperative" of these advertisements, item descriptions often feature disclosures about health goals, bodily capacities, and social expectations. This article identifies a system of for "values"—activity, social, financial, exchange—that inform advertisements for wearable self-tracking devices. Wearable devices are highly transient in the secondhand space. Yet, the apparent importance of undertaking personal self-tracking projects is constantly espoused, even as devices are passed on to new users.
Two-dimensional dam-break wave analysis: particle image velocimetry versus optical flow

Dam-break waves have been the focus of a number of research papers; next to analytical investigations, scaled experimental models and numerical simulations are used to determine dam-break wave propagation. Experimental models and data analysis tools are necessary to generate calibration data for numerical models. Often, particle image velocimetry (PIV) algorithms are used to calculate two-dimensional velocity fields of propagating dam-break waves. This paper deals with a visual analysis method called optical flow (OF). An experimental model was built to identify the initial stage of dam-break waves. A comparison between results using the PIV method and the OF method is given to identify flow velocities for two-dimensional dam-break waves. It can be shown that the OF method is able to predict resulting velocity profiles accurately. The mean relative error for the comparison of manually observed data and OF results is less than 6%, while for the PIV comparison an error of 11% can be calculated. Since velocity vectors are produced for all image pixels, the resulting information density is high compared to results produced by the PIV method.