Reviewerly/exHarmony huggingface dataset:
Analyzing the Performance of Canny Edge Detection on Interpolated Images

Surveillance cameras are extensively used nowadays in many commercial and domestic places to monitor theft, intrusion and other illegal activities. Typically, the cameras are placed at a high position to monitor a large area. Therefore, the captured images include background area in addition to the target objects. Under such situation, the image can be zoomed to focus on the target objects using various interpolation techniques. For further processing of the image, many techniques like edge detection, image sampling and image thresholding etc. are available. Considering edge detection to be a basic step for many application such as Object detection, Object recognition etc, in this work, we analyze the performance of the Canny Edge Detection algorithm on images interpolated using Nearest Neighbour, Bilinear and Bicubic interpolation methods. Canny Edge Detection is applied to the input image and the resultant image is saved for later comparison. The same image is upscaled using interpolation and the Canny Edge Detection algorithm is used on this upscaled image. This image is then resized to the original image size. Both the images are compared to check for their similarity using the Structural Similarity Index Method (SSIM).
SQuAP-Ont: An ontology of software quality relational factors from financial systems

Quality, architecture, and process are considered the keystones of software engineering. ISO defines them in three separate standards. However, their interaction has been scarcely studied, so far. The SQuAP model (Software Quality, Architecture, Process) describes twenty-eight main factors that impact on software quality in banking systems, and each factor is described as a relation among some characteristics from the three ISO standards. Hence, SQuAP makes such relations emerge rigorously, although informally. In this paper, we present SQuAP-Ont, an OWL ontology designed by following a well-established methodology based on the re-use of Ontology Design Patterns (i.e. ODPs). SQuAP-Ont formalises the relations emerging from SQuAP to represent and reason via Linked Data about software engineering in a three-dimensional model consisting of quality, architecture, and process ISO characteristics.
A SPATIAL DOMAIN BASED SECURE AND ROBUST VIDEO WATERMARKING TECHNIQUE USING MODIFIED LSB AND SECRET IMAGE SHARING

This paper proposes a spatial domain based video watermarking scheme to improve security of the video data using a combination of modified LSB watermarking technique of spatial domain and a (n, n) secret image sharing scheme. In this scheme the secret image sharing scheme is applied during transmission right after the embedding of the watermark in the video frame to overcome the drawback of the single LSB technique. This combination ensures high security and robustness as the watermarked image is being distributed into 3 meaningless shares during transmission thus making it imperceptible to the attacker. This dual scheme improves the extraction capability of the secret message and enhances the information embedding capacity. The average PSNR obtained was 55dB which proves that the quality of reconstruction is high and higher than most of the existing watermarking techniques in similar domain.
A Modified Particle Swarm Optimization Algorithm Used for Feature Selection of UCI Biomedical Data Sets

In biomedical and health care applications, classification examination is extensively used to help diagnose health problems, decision making and enhance standards of patient care. Feature selection is a significant data preprocessing method in classification problems. Training of the data is achieved by using a subset dataset from the UCI biomedical database. If the training dataset comprises inappropriate features, classification analysis resulted in inaccurate and incomprehensible results. In data mining, feature subset selection is data pre-processing phase that is of enormous importance. In this paper, for selecting a minimum number of features K-Nearest Neighbour (KNN) classifier is presented with a modified particle swarm optimization (MPSO) to obtain good classification precision. The proposed method is applied to three UCI medical data sets and is compared with other feature selection approach available in the literature. Results demonstrate that the feature subset recognized by the presented MPSO with KNN neighbor classifiers give better results and accuracy as compared to the other techniques.
A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

With the advent of new technological advances, it is possible to study social interactions at a microlevel with unprecedented accuracy. High frequency sensors, such as eye-trackers, electrodermal activity wristbands, EEG bands, and motion sensors provide observations at the millisecond level. This level of precision allows researchers to collect large datasets on social interactions. In this paper, I discuss how multiple eye-trackers can capture a fundamental construct in social interactions, joint visual attention (JVA). JVA has been studied by developmental psychologists to understand how children acquire language, learning scientists to understand how small groups of learners work together, and social scientists to understand interactions in small teams. This paper describes a methodology for capturing JVA in colocated settings using mobile eye-trackers. It presents some empirical results and discusses implications of capturing microobservations to understand social interactions.
Structured Sparse Fully-Connected Layers in the CNNs and Its GPU Acceleration

Convolutional Neural Networks (CNNs) have become one of the most important and popular artificial intelligent techniques recently. While capabilities of CNNs have improved dramatically, enlargement of the network has become a problem. Thus far, several studies about network model compression for CNNs have been devoted. However, most of the compressed networks are unsuitable for parallel computing platforms since the compressed networks are unstructured. To address this problem, we propose a new sparse network architecture for CNNs. We compress the fully-connected layers (FLs) in the CNNs that occupying most of the weights. By considering the characteristic of the output of convolutional layers, we can highly compress the network structurally with almost the same accuracy. In addition, using the structure of the compressed network, we propose an efficient implementation of the inference computation on the GPU. We have applied the proposed approach to AlexNet for the classification problem for ILSVRC-2012 dataset. The experimental results show that the number of weights in FLs is reduced from 58.6 million to 6.8 million with 0.68% and 0.19% loss of top-1 and top-5 accuracy, respectively. Also, the GPU implementation for the compressed FLs can run 5.86 times faster than that for non-compressed FLs.
Research on an Infrared Multi-Target Saliency Detection Algorithm under Sky Background Conditions

Aiming at solving the problem of incomplete saliency detection and unclear boundaries in infrared multi-target images with different target sizes and low signal-to-noise ratio under sky background conditions, this paper proposes a saliency detection method for multiple targets based on multi-saliency detection. The multiple target areas of the infrared image are mainly bright and the background areas are dark. Combining with the multi-scale top hat (Top-hat) transformation, the image is firstly corroded and expanded to extract the subtraction of light and shade parts and reconstruct the image to reduce the interference of sky blurred background noise. Then the image obtained by a multi-scale Top-hat transformation is transformed from the time domain to the frequency domain, and the spectral residuals and phase spectrum are extracted directly to obtain two kinds of image saliency maps by multi-scale Gauss filtering reconstruction, respectively. On the other hand, the quaternion features are extracted directly to transform the phase spectrum, and then the phase spectrum is reconstructed to obtain one kind of image saliency map by the Gauss filtering. Finally, the above three saliency maps are fused to complete the saliency detection of infrared images. The test results show that after the experimental analysis of infrared video photographs and the comparative analysis of Receiver Operating Characteristic (ROC) curve and Area Under the Curve (AUC) index, the infrared image saliency map generated by this method has clear target details and good background suppression effect, and the AUC index performance is good, reaching over 99%. It effectively improves the multi-target saliency detection effect of the infrared image under the sky background and is beneficial to subsequent detection and tracking of image targets.
K-TIPS: KNOWLEDGE EXTENSION BASED ON TAILOR-MADE INFORMATION PROVISION SYSTEM

Thanks to an increase in the amount of information on the Internet and the spread of ICT-supported educational environments, much attention has been paid to learning support based on "smart" recommendation technologies.In this study, we propose an education improvement model based on the recommender system using the human-in-the-loop design strategy.Our proposed model enhances not only learners via recommendation, but also teachers and the system itself through the interaction between teachers and the system.In this paper, we introduce the details of the proposed model and implementation strategy followed by a report of preliminary experimental results.
THE INTEGRATION OF DIGITAL FORENSICS SCIENCE AND ISLAMIC EVIDENCE LAWS

Evidence is anything that tends to prove or disprove a fact at issue in legal action. It involves the offering of alleged proof through testimony or objects at court proceedings to persuade the trier of fact about an issue in dispute. Islamic Evidence Law is a body of rules that helps to govern conduct and determines what will admissible in certain legal proceedings and trials. In the proceeding that involves digital evidence, the court will consider whether the digital evidence is admissible or inadmissible depends on the requirements of admissibility stated in law statutes in force and the existence of any Standard Operating Procedure (SOP). Under section 33 of the Syariah Court (Federal Territories) Evidence Act or other Syariah Evidence Enactments, digital evidence is subjected to be authenticated by the digital forensics experts. In digital forensics, the process of identification, preservation, collection, analysis, and presentation is the main procedures contained in any Standard Operating Procedure (SOP) of any digital forensics services. The court will ensure that this procedure can maintain the authenticity and the originality of the evidence especially on the issue of expert qualification, a chain of custody and analysis part. Thus, digital forensics is integrated with the Islamic law of evidence to maintain justice in delivering judgment. Therefore, this article examines the standard requirement of the admissibility of digital evidence by digital forensic methodology by using the qualitative approach on the analysis of articles, books, law statutes documents and law cases. The results show that the need for amendment of Syariah Court Evidence and Procedure statutes and the necessity of the existence of Standard Operating Procedure (SOP) on digital evidence in the Syariah courts as a guideline for judges, lawyers and parties involved.
CLUSTERING OF LEARNERS BASED ON KNOWLEDGE MAPS

This study aimed to cluster learners based on the structures of the knowledge maps they created.Learners drew their own knowledge maps to reflect their learning activities.Our system collected individual knowledge maps from many learners and clustered them to generate an integrated version of the knowledge maps of each cluster.We applied the graph analysis method to extract important keywords from the knowledge map.The results of the analysis showed that the utilization of the knowledge map helped to improve lectures and grasp the learners' level of understanding.We conducted surveys asking course managers to evaluate the effectiveness of the integrated knowledge maps of learners included in the cluster and received both positive and negative responses.
The influence of soft multifocal contact lenses with high additions on the eye–hand coordination

The aim of the present study was to assess whether soft multifocal contact lens (SMFCLs) with high addition powers, designed for myopia control, may have an impact on eye movements and eye–hand coordination. A total of 24 young adults used SMFCLs (Relax, SwissLens) with high additions: +2D (Add2) and +4D (Add4). Single vision (Add0) contact lenses were used as controls (Orbis, SwissLens). In the eye–hand coordination task, the study participants were asked to point out the small circle displayed on a touch screen. Reaction time and accuracy of pointing, as well as latency of eye movements were measured. Results showed that SMFCLs had no significant impact on the latency of eye movements (135, 136, 139 ms, for Add0, Add2 and Add4, p = 0.171), reaction time (732, 730, 727 ms for Add0, Add2 and Add4, p = 0.932) or percent error (17%, 13%, 18% for Add0, Add2 and Add4, p = 0.386).
First Demonstration of a Two-Layer All-Optical Neural Network by Using Photonic Integrated Chips and SOAs

We demonstrate the first all-optical feed-forward neural networks constituted by two full photonic layers of neurons based on SOAs with Broadcast and Weighting protocol. The impact of optical nonlinear function is studied and signal processing with 10 Gbit/s binary sequences is presented which results in 0.21 error.
Integer factoring problem and elliptic curves over the ring $\mathbb Z_n$

We investigate the reduction of factoring of squarefree positive integers coprime to 6 to computing exponents of points on an elliptic curve $E(\mathbb Z _n)$ over the ring&nbsp;$\mathbb Z _n$. We contribute to an accurate estimate for the error proba
Salient Time Slice Pruning and Boosting for Person-Scene Instance Search in TV Series

It is common that TV audiences want to quickly browse scenes with certain actors in TV series. Since 2016, the TREC Video Retrieval Evaluation (TRECVID) Instance Search (INS) task has started to focus on identifying a target person in a target scene simultaneously. In this paper, we name this kind of task as P-S INS (Person-Scene Instance Search). To find out P-S instances, most approaches search person and scene separately, and then directly combine the results together by addition or multiplication. However, we find that person and scene INS modules are not always effective at the same time, or they may suppress each other in some situations. Aggregating the results shot after shot is not a good choice. Luckily, for the TV series, video shots are arranged in chronological order. We extend our focus from time point (single video shot) to time slice (multiple consecutive video shots) in the time-line. Through detecting salient time slices, we prune the data. Through evaluating the importance of salient time slices, we boost the aggregation results. Extensive experiments on the large-scale TRECVID INS dataset demonstrate the effectiveness of the proposed method.
Smartphone Mode Recognition During Stairs Motion

Smartphone mode classification is essential to many applications, such as daily life monitoring, healthcare, and indoor positioning. In the latter, it was shown that knowledge of the smartphone location on pedestrians can improve the positioning accuracy. Most of the research conducted in this field is focused on pedestrian motion in a horizontal plane. In this research, we use supervised machine learning techniques to recognize and classify the smartphone mode (text, talk, pocket and swing) while accounting for the movement up and downstairs. We distinguish between the going up and the down motion, each with four different smartphone modes, making eight states in total. This classification is based on the use of an optimal set of sensors that varies according to battery life and the energy consumption of each sensor. The classifier was trained and tested on a dataset constructed from multiple user measurements (total of 94 min) to achieve robustness. This provided an accuracy of more than 90% in the cross validation method and 91.5% if the texting mode is excluded. When considering only stairs motion, regardless of the direction, the accuracy improves to 97%. These results may assist many algorithms, mainly in pedestrian dead reckoning, in improving a variety of challenges such as speed and step length estimation and cumulative error reduction.
Generating a True Color Image with Data from Scanning White-Light Interferometry by Using a Fourier Transform

In this paper we propose a method to generate a true color image in scanning white-light interferometry (SWLI). Previously, a true color image was obtained by using a color camera, or an RGB multichannel light source. Here we focused on acquiring a true color image without any hardware changes in basic SWLI, in which a monochrome camera is utilized. A Fourier transform method was used to obtain the spectral intensity distributions of the light reflected from the sample. RGB filtering was applied to the intensity distributions, to determine RGB values from the spectral intensity. Through color corrections, a true color image was generated from the RGB values. The image generated by the proposed method was verified on the basis of the RGB distance and peak signal-to-noise ratio analysis for its effectiveness.
SIEM Implementation for Small and Mid-Sized Business Environments

For companies it is also important to protect their assets and know-how.In today's world, this expertise channeled into information systems and resources that can be vulnerable to attackers who may wish to obtain such information or otherwise manipulated.It is necessary to use for protection technologies that enable the prevention, detection and monitoring of these threats.This study deals with the implementation of one of these technologies Security Information and Event Management (SIEM) system in two companies that must protect sensitive data.SIEM provides a comprehensive tool for ensuring the safety management in for cyber security and associates himself into several parts, thus log management, log analysis, reporting and event management.This study describes SIEM architecture, design and implementation of SIEM solutions for the small and mid-sized business environment and complemented by financial analysis solutions.
Trustees’ investment duties and cryptoassets

Abstract This article considers the legal and practical concerns for trustees regarding cryptocurrencies and other related instruments, which will be referred to as “cryptoassets”. It will briefly introduce the various types of cryptoassets and explore the risks involved when trustees decide to (or not to) invest in these instruments. This article provides a framework on how trustees should approach the issue of cryptoassets.
An Efficient Credit Card Fraud Detection Model

online transaction is the most popular mode of payment over the internet. Financial institutes such as banking organization provides various online services for customers such as e-commerce and e-cash. Credit card is one of the supreme conventional methods of online transaction. In recent times, criminal can use illegal ways to carry out fraud transaction by credit card over e-services. Due to the growing volume of electronic payments, the monetary strain of credit-card fraud is turning into a substantial challenge for financial institutions and service providers, thus forcing them to continuously improve their fraud detection systems. Therefore, there is a serious need to develop efficient credit card fraud detection for mortgage companies, financial institutes, credit card enterprises, and banking system.  One of the most common problems in with building credit card detection model is imbalanced data sets. The data set can be imbalanced when the examples of one class significantly outnumber the examples of the other one, i.e., classification becomes very tough as the result may get biased by the dominating class values. In this paper, we will apply two techniques to get rid of this problem for credit card data. Then, the imbalanced dataset used in developing an efficient credit card fraud detection model. In the proposed model, different machine learning algorithms are used such as K-Nearest Neighbor (KNN), Logistic Regression (REG), Latent Dirichlet Allocation (LDA), Classification And Regression Tree (CART), and Naive Bayes (NB). The results show that LDA gives 99.9543 which is best accuracy results while CART algorithm gives 99.9797 which is the higher accuracy in case of up-sampling, and finally, in down-sampling, LDA gives 94.9238 which is the higher accuracy result.
Development of AlphaZero-based Reinforcment Learning Algorithm for Solving Partially Observable Markov Decision Process (POMDP) Problem

In recent years deep reinforcement learning (DRL) methods have advances rapidly, so DRL is applied in many fields. Most of DRL algorithms assume that the information from the environment is perfectly observed. However, in many real problems, the information from the environment is not fully observed. Such a problem is treated as a Partially Observable Markov Decision Processes (POMDPs). So, algorithms that solve POMDPs are important in applying DRL to real-world. In this paper, we apply AlphaZero, a deep reinforcement learning algorithm that achieve great performance in game, to POMDPs and show that the algorithm may be effective for POMDPs using a partially observable maze problem.
Nursing Students' Views and Experiences Concerning the Use of Mobile Applications in Nursing Education: A Qualitative Study

Background: Nursing education is a process of education that consists of both theoretical and skill teaching.Mobile applications developed in accordance with smart phones have started to be used in nursing education, for the students to continue to learn outside school and develop their knowledge and skills.Methods: This study was conducted in the Department of Nursing in the Faculty of Health Sciences at a University located in the western region of Turkey between the dates of between May 7, 2019, and May 30, 2019, by using qualitative research design.Qualitative research was planned in order to examine the opinions and experiences of nursing students who used the mobile application.Purposeful sampling method was used in the study. Results:The findings of the study, the students (45.5%) indicated that the mobile application prepared was appropriate for their level and the mobile application was adequate for them (52.3%).The study also pointed out that nursing students were curious (75%) and thought that it would be useful (31.8%), were excited (22.7%), felt special (20.5%), were relaxed (20.5%) and felt happy (27.3%).The students indicated that they had less stress (68.2%), felt enlightened (90.9%), felt competent on the subject (63.6%) and had more self-confidence (29.5%) after using the mobile application.All of the nursing students (100%) reported that it was necessary to use mobile applications in nursing education. Conclusion:It was seen that using the mobile application in nursing education increased the students' self-confidence and self-competence, and also reduced their stress.Besides, the students belive that it will be useful to use both standard teaching method in which the instructor lectures and relevant mobile applications together.