Reviewerly/exHarmony huggingface dataset:
Users Acceptance Online Attendance Application Using Barcode Scanner: The Case Study in al Buraimi University College (BUC) Students

Marking students' attendance within a class session is the prime task of the subject requirements since marking the attendance will regulate the students to attend the session. Moreover, it verifies how many students are attending the class. Previously, attendance was recorded manually by pen and paper. This method takes a long time, especially if the number of students is significant and adds workload to the teacher, and sometimes the data may be prone to error. This paper presents a mobile application for student attendance to avoid these issues. This application will ease the process of attending to the students in the registration instead of the traditional ones where the teacher spends time calling students to know who is present or absent. The students will use their mobile to log in to the application and select the current class to open the camera to scan the code provided by the teacher, and automatically their attendance will be registered. This application is mainly designed for Buraimi University College lecturers and students.
Developing Artificial Intelligence via Organizational Learning Capability to Improve Innovation Performance: A Case Study of Aluminium Bahrain (Alba)

organizational learning is a subset of artificial intelligence (AI) focused on creating systems that learn—or improve performance. This research aims to analyze the relationship between knowledge sharing and organizational learning capability in Aluminium Bahrain B.S.C. (Alba). A quantitative approach was adopted in this study, where a crosssectional survey was employed. The sample consisted of 477 employees (15%) of the population. A self-administrated questionnaire was used to collect data from the sample. The questionnaire was adapted from existing measurements. The total number of completed and usable questionnaires was 114, indicating a response rate of 23.9%. This study used Partial Least Square (PLS) to test the hypotheses with Structural Equation Model (SEM), this study used Partial Least Square (PLS) as a statistical analysis technique. The collected data was analyzed using EXCEL, SPSS, and SmartPLS software. The findings revealed that organizational learning capability significantly affects innovation performance. The study presents significant implications to empower and occur more advancement in the linkage between the studied variables.
The Use of Artificial Intelligence Capabilities in Logistics and Supply Chain Management: Major Challenges and Key Benefits

This study seeks to contribute to identifying the use of artificial intelligence (AI) capabilities in logistics and supply chain management (SCM) through a systematic classification of its notable capabilities, major challenges, and key benefits. To examine the existing technical gap of AI in logistics and SCM, this research intended to demonstrate the current AI capabilities, challenges, and benefits that can help to enhance the use of AI in logistics and SCM at both the theoretical and practical levels. Using simple random sampling technique, the required data will be gathered via a survey instrument from 60 firms that adopted and effectively using AI in managing logistics and supply chain.
A Random Forest Based Approach to Classify Spam URLs Data

Networking plays leading role to meet day-to-day activities. Huge usage of networking in education systems, financial transactions, businesses, and social gatherings across the globe peaked to the level of spam URL's. In addition to this, the quench for finding most relevant and up-to-date data in any sector lead to severe threat of spam URL's. It's quite difficult to identify the real website from fake one because of spam URL's and thus leads the users to fall under hacker's trick which further threatens the system safety. Therefore, it is worth to classify spam URL's to identify the true websites from fake ones. This led to the emergence of using machine learning techniques to classify Spam URL'S data. In this research, spam URL's data was taken from Kaggle database and machine learning classifiers were used to classify the URL's into spam or non-spam URL's. 10-fold cross validation and hold out method was used. It was observed that random forest yielded high classification accuracy of 97% in classifying the URL data when 10-fold cross validation was performed when compared to hold-out method. Support vector machine yielded 92% classification accuracy followed by naive bayes with a classification accuracy of 91%. The outcomes were assessed on few parameters like accuracy, true positive rate, false positive rate, precision and recall.
Forming Software Development Team: Machine-Learning Approach

Software development team formation is a task done by skilled persons who have enough experience in mapping crew members to project tasks. Choosing software development team members according to their experience and within the amount of available budget/time is a vital task. However, the availability of a tool to suggest the best team members to the different tasks in some projects will definitely help project managers in their selections. This work is related to suggesting the most suitable skilled software development professionals to projects tasks based on some machine learning technique (Random Forest Classifier). The project manager just feeds the tool with the required tasks, and the latter suggests a ranked list of the most suitable professionals that fit each task which reflects positively on the team formation process. The experimental results conducted at the end of the work reflect the improvement of software development team formation gained comparable with the ordinary, self-experience-based one.
Computational Methods for Forgery Detection in Printed Official Documents

Printed documents continue to be the most commonly used media for information transfer in official context. However, such documents may be subject to illegitimate modification or malicious purposes. Therefore, agencies must be able to verify their authenticity before accepting them. This work aims to identify and classify the current computational methods that are used to identify and detect a forgery in printed documents. We focus on methods that detect forger passively, i.e., no requirement of active information to be included in the document during or shortly after printing. Hence, we classify the methods into four general categories. First, detection based on printer and ink properties. The second is based on character-level features analysis that is based on detecting imperfections produced by scan, edit, and print forgery techniques. It is also possible to detect forgery through analysis of paper properties. Finally, forgery detection can be achieved by analyzing the pixel-level features.
Proposed Model for Detection and Classification of Vehicles in Real-Time Video Based on Deep Learning

Many people die every year due to fatal accidents that occur all over the world as a result of the increase in the number of vehicles on the roads, the most common reasons being driver inattention or poor vision in bad weather conditions. Therefore, human detection and classification of vehicles is complex. Due to the development of the automobile industry due to technological advancement, it has become critical to employ self-driving cars since the computer system in them assumes all driving tasks in order to avoid collisions and accidents. However, there are numerous issues with an autonomous car's capacity to recognize and track vehicles. One of these issues is the wide variety of vehicle sizes and shapes. Our paper will focus on resolving this issue, as this paper proposed a hybrid deep learning model for vehicle classification and determined the location of vehicles based on the principles of the YOLO technique. This study uses two datasets in training and testing: The Stanford car dataset and three classes from the open image dataset v6 (bus, truck, and motorbike). The proposed model achieved high results in vehicle detection and classification, with an accuracy rate of 98.19%. The proposed model has been a success as it was run for the purpose of detecting and classifying vehicles in many videos in real time and under different environmental conditions.
Leukemia Detection Algorithms: A Comparative Study

Leukemia is a group of blood cancers that usually begins in the bone marrow and is caused by a high number of abnormal blood cells. Yearly, the number of new cases of Leukemia increases with a high mortality rate due to the late detection of the disease. However, computerizing disease detection decreases the number of mortal cases by implementing several methodologies and algorithms. Image processing techniques are one of the major disciplines in detecting the disease early. A set of related algorithms were developed to help detect Leukemia depending on several blood components features like cells' properties (shape, color, size), cytoplasm features, and others. This work compares two approaches to Leukemia detection: Color K-means Clustering (CKC) and Acute Lymphoblastic Leukemia Subtypes (ALLS) detection algorithms. CKC algorithm depends on morphological filtering and segmentation using color k-means clustering and is tested with Nearest Neighbor (KNN) classifier. ALLS is based on detecting Leukemia subtypes by going through several image processing stages that lead to classifying the image as infected or not. In order to test the amount of accuracy difference between the two approaches, the extracted features of the testing set of images are processed using Weka software. The precision, recall, f-score, accuracy, and time measures were computed for comparison purposes.
Uncertainty Estimation based Intrinsic Reward For Efficient Reinforcement Learning

For reinforcement learning, the extrinsic reward is a core factor for the learning process which however can be very sparse or completely missing. In response, researchers have proposed the idea of intrinsic reward, such as encouraging the agent to visit novel states through prediction error. However, the deep prediction model can provide over-confident and miscalibrated predictions. To mitigate the impact of inaccurate prediction, previous research applied deep ensembles and achieved superior results, despite the increased computation and storage space. In this paper, inspired by the uncertainty estimation, we leverage Monte Carlo Dropout to generate intrinsic reward from the perspective of uncertainty estimation with the goal to decrease the demands for computing resources while retaining superior performance. Utilizing the simple yet effective approach, we conduct extensive experiments across a variety of benchmark environments. The experimental results suggest that our method provides a competitive performance in final score and is faster in running speed, while requiring much fewer computing resources and storage space.
Mapping Arabic Text Studying Material With The Most Suitable Student Grade

Students' poor comprehension is one of the major difficulties that struggle with fully understanding passages. Several factors are related to this problem such as huge material cramming, the absence of creativity, and unsuitable vocabularies. Reading is one of the vital methods to increase students' vocabularies which reflects positively on their comprehension. The suitable mapping between reading materials and students' levels of comprehension is very important in enhancing their reading practice as well as preventing them from feeling frustrated because of the lack of suitable reading materials. In this work, we implemented an Arabic classifier tool based on a Language Modeling Approach used in English and French documents to match reading materials with students' comprehension so that the tool can suggest the lowest level of student grade that fits the tested material. Moreover, the tool can be used to study the comprehension level of students by analyzing their answers to some textual writing assignments as a method to better tune the teaching skills and techniques to increase student's comprehension levels. As an initial effort, 3 subjects were chosen to test the tool (Arabic, Science, and Islamic Studies). A set of students from different schools were chosen for the experimental tests in which good results were achieved.
King Fahad Causeway Quick Response Code Pass

The development and expansion in technologies nowadays is very rapid in comparison with the past years, which makes the life much easier and faster if we improve and employ its usages properly and correctly. We decided to develop a mobile application with Quick Response (QR) Code that can help and reduce the congestion on King Fahad Causeway. It is a genuine idea that has not been implemented and presented until the time of building this application. “King Fahad Causeway QR Code Pass” application provides the ability to let the King Fahad Causeway travelers register all their information that are required from them. By downloading and using this application; the travelers can pass and travel in comfortable way. The application was built by xCode 9 using Swift 4 programming language. The application screens designed with code written to meet the requirements by linking each element in the screen (button, image, text box, etc.) with the required code. The application was tested on more than one device to make sure it will work properly.
High Speed Encrypted Computing: Stochastic Confusion and Lies in a Secret Computer

A signal-level open-source hardware definition for a superscalar processor delivering high-speed ‘encrypted computing’ has been tested. This ‘KPU’ processor provides general purpose Turing-complete computation with encrypted inputs, outputs and intermediate results in registers and memory, and its objective is mathematically provable security for the user against eavesdropping and tampering by the administrator, at near contemporaneous computing speeds. User code runs encrypted while administrator code runs unencrypted. The administrator can programmatically see and modify user data, but it is in encrypted form and the key is not available to the administrator. No barrier other than encryption is intended in this system, simplifying analysis. This paper summarizes the current architecture and performance and outlines the stochastic theory that provides a form of the classic semantic security property.
A Low Cost Blockchain-Based Framework for Preserving Critical Data in Health-Care IoT Systems Using Classification

Security and privacy of Internet of Things (IoT) data are highly essential for a wide spectrum of applications, on top of which are medical systems. In medical systems, we need to guarantee the confidentiality, integrity, and availability (CIA triad) of the data generated from IoT devices in order to investigate medical malpractices. Blockchain provides an immutable storage without relying on an external third party to build trust and transparency. Therefore, blockchain is a suitable option to securely store such data. However, the excessive reliance on IoT devices in medical applications results in producing a large amount of data periodically, and thus, makes its solely storage on blockchain extremely expensive. In this paper, we propose a cost-efficient comprehensive framework to store IoT medical devices data on the blockchain by using a data-driven classifier. The classifier in the proposed framework is driven by a range of normal data for each sensor type. All data whether normal or abnormal is stored on a typical cloud. Only data that is classified as abnormal (critical) is stored on the blockchain for cost saving purposes. Furthermore, a distributed file system (IPFS) is utilized for extra cost reduction. Experimental results show that our proposed framework reduces the cost of storing healthcare IoT data by an average of 84% if compared with solely storing all data on the blockchain.
Towards Secure Multi-Agent Deep Reinforcement Learning: Adversarial Attacks and Countermeasures

Reinforcement Learning (RL) is one of the most popular methods for solving complex sequential decision-making problems. Deep RL needs careful sensing of the environment, selecting algorithms as well as hyper-parameters via soft agents, and simultaneously predicting which best actions should be. The RL computing paradigm is progressively becoming a popular solution in numerous fields. However, many deployment decisions, such as security of distributed computing, the defence system of network communication and algorithms details such as frequency of batch updating and the number of time steps, are typically not treated as an integrated system. This makes it difficult to have appropriate vulnerability management when applying deep RL in real life problems. For these reasons, we propose a framework that allows users to focus on the algorithm of reasoning, trust, and explainability in accordance with human perception, followed by exploring potential threats, especially adversarial attacks and countermeasures.
Optical Flow-Hermite and Fuzzy Q-Learning Based Robotic Navigation Approach

The present paper presents a bio-inspired optical flow approach to autonomous robotics navigation. It uses a Fuzzy Q-Learning (FQL) method to take decisions in an unknown environment through a reinforcement signal. The proposed method was implemented in a virtual robotics system using the V-REP software and in communication con MATLAB. The preliminary results show that the robot was able to navigate successfully in unknown environments.
Cache Locking and Encryption to Prevent Memory Snooping in Embedded Systems

Embedded systems are designed to have security measures in place that protect users' data from software and network attacks, but these measures can prove useless when the attacker gains physical access to the system. Research has shown that dynamic random access memory (DRAM) is vulnerable to attacks that take advantage of its remanence property where data remains in DRAM shortly after the system is powered off. In this paper, we propose a method utilizing both cache locking and encryption to secure the DRAM on any embedded system by modifying the cache architecture of the CPU. We demonstrate an implementation using a MicroBlaze CPU, but the design can be used with any FPGA soft-core CPU, even if it does not have pre-existing cache locking capabilities. The cache modifications introduce almost no impact on performance and minimal extra hardware utilization.
Symbolon: Enabling Flexible Multi-device-based User Authentication

Hardware tokens are increasingly used to support second-factor and passwordless authentication schemes. While these devices improve security over weaker factors like passwords, they suffer from a number of security and practical issues. We present the design and implementation of Symbolon, a system that allows users to authenticate to an online service in a secure and flexible manner by using multiple personal devices (e.g., their smartphone and smart watch) together, in place of a password. The core idea behind Symbolon is to let users authenticate only if they carry a sufficient number of their personal devices and give explicit consent. We use threshold cryptography at the client side to protect against strong adversaries while overcoming the limitations of multi-factor authentication in terms of flexibility. Symbolon is compatible with FIDO servers, but improves the client-side experience compared to FIDO in terms of security, privacy, and user control. We design Symbolon such that the user can (i) authenticate using a flexible selection of devices, which we call “authenticators”; (ii) define fine-grained threshold policies that enforce user consent without involving or modifying online services; and (iii) add or revoke authenticators without needing to generate new cryptographic keys or manually (un)register them with online services. Finally, we present a detailed design and analyse the security, privacy and practical properties of Symbolon; this includes a formal proof using ProVerif to show the required security properties are satisfied.
Object classification in urban environments for autonomous navigation

Depending on the environment, urban, office or home, different objects can be identified in the scene during machine-human interaction. An essential task to facilitate object recognition in natural scenes is image segmentation, due to segmented regions can be analyzed separately. In this work, we propose the classification of objects in urban context images using image segmentation and convolutional neural networks (CNNs). A training set was created by extracting patches in color images of the Kitti-360 database, operating as a reference the semantic images for only encircling the object of interest. Eight categories of everyday objects found commonly in urban places were selected. Different CNN models were trained using our gathered set. During experimental tests, a segmentation approach based on CIELab and complex-space was used to provide the most representative regions of the image. Selected bounding boxes are classified per image instead of the whole image. Our classification results show high-accuracy values using images of small size but strategically selected based on the segmented image.
The Implications of Linguistic Characteristics in the Definition of a Learning Model

In recent years, powerful deep learning models have emerged yielding state-of-the-art results in fields such as image recognition and natural language processing. In the particular case of text processing, various neural architectures have been defined to cope with the necessity of memory to process previous elements in a sequence text. This article makes use of quantitative methods and complexity indicators to provide empirical evidence for the adequacy of recurrent neural models and their corresponding variants. In other words, in this article we show how to determine the linguistic characteristics from text is fundamental to define the deep learning model to be used.
Impact of tropical convective conditions on solar irradiance forecasting based on cloud motion vectors

Abstract Intra-day forecasts of global horizontal solar irradiance (GHI) are widely produced by displacing existing clouds on a geo-stationary satellite image to their future locations with cloud motion vectors (CMVs) derived from preceding images. The CMV estimation methods assume rigid cloud bodies with advective motion, which performs reasonably well in mid-latitudes but can be strained for tropical and sub-tropical climatic zones during prolonged periods of seasonal convection. We study the impact of the South Asian monsoon time convection on the accuracy of CMV based forecasts by analysing 2 years of forecasts from three commonly used CMV methods—Block-match, Farnebäck (Optical flow) and TV-L 1 (Optical flow). Forecasted cloud index (CI) maps of the entire image section are validated against analysis CI maps for the period 2018–2019 for forecast lead times from 0 to 5.5 h. Site-level GHI forecasts are validated against ground measured data from two Baseline Surface Radiation Network stations—Gurgaon (GUR) and Tiruvallur (TIR), located in hot semi-arid and tropical savanna climatic zones respectively. The inter-seasonal variation of forecast accuracy is prominent and a clear link is found between the increase in convection, represented by a decrease in outgoing longwave radiation (OLR), and the decrease in forecast accuracy. The GUR site shows the highest forecast error in the southwest monsoon period and exhibits a steep rise of forecast error with the increase in convection. The highest forecast error occurs in the northeast monsoon period of December in TIR. The impact of convection on the number of erroneous time blocks of predicted photovoltaic production is also studied. Our results provide insights into the contribution of convection to errors in CMV based forecasts and shows that OLR can be used as a feature in future forecasting methods to consider the impact of convection on forecast accuracy.