Reviewerly/exHarmony huggingface dataset:
Release Planning Process Model in Agile Global Software Development

Global Software Development (GSD) refers to the practice of geographically distributing software development teams with intended goals, including reducing labor costs, increasing the software development capacity, and productivity over 24 h. The Software Release Planning process addresses decisions associated with selecting and assigning features to create a sequence of consecutive product releases that fulfill significant technical, resource, budget, and risk constraints. In the literature, numerous are release planning processes proposed for distributed settings; however, release planning processes suited to the Global Software Development context and support the agile way of working and planning remains rare. In this paper, we present an Agile Release Planning Process Model for GSD projects to assist the agile release planning process adopted in the distributed environments. The proposed model contains two main activities: High-Level Design and architecture, and Feature Development Process, two repositories: Design and Knowledge repositories, and some supporting practices.
Student Attention Estimation Based on Body Gesture

Human beings receive and communicate various information through their gestures. Gestures are symbolic actions that use parts of the body to convey specific messages. The importance of gestures and body language can capture the listener’s attention. Indeed, the analysis of these information during a recognition or learning operation, presents a worthwhile resource for monitoring classes. In this paper, we investigate the problem of student body estimation to extract information on the attention of students in the class during the course. We propose an approach capable of analyzing the posture of the student and detecting his attention. We choose four postures for estimating the student attention. Writing note and leaning forward which express a state of commitment and concentration. However, leaning back and hand supporting the head poses can be considered a state of boredom, disinterest, or student fatigue. We use the openpose library to extract these features. Then, we proceed to the classification task using an algorithm based on the rules carries out the classification of the postures. The generated model makes it possible to automatically predict the student’s posture from an input image. Finally, the student’s attention is deduced from the predicted posture.
Towards an E-commerce Personalized Recommendation System with KNN Classification Method

Nowadays, the increase in online transactions is pushing various areas to take an interest in recommendation systems. These systems support customer decision-making by recommending the items most likely to be preferred. The main advantages of this type of systems are, on the one hand, the increase in sales without increasing marketing effort, the increase of user satisfaction by reducing their sale’s path and increase of loyalty by encouraging them to spend more on the website, and on the other hand, the reduction of the churn by re-engaging them. To arrive to an accurate recommendation, several studies in the field of recommendation systems based on machine learning algorithms have attempted to capture the complex interactions between user and items features. In this article, we propose three recommendation systems, the first is based on popularity, while the others are based on a collaborative filtering model, the most common technique used when it comes to building intelligent recommender systems, that uses the KNN method (user-based and article-based), to recommend products to customers based on their previous ratings and similar customer ratings for other products. In the end, optimization, and cross-validation of hyperparameters via GridSearchCV was applied to avoid overfitting and increase the accuracy of the best model.
FUZZY C-MEANS Based Extended Isolation Forest for Anomaly Detection

Anomaly detection is a mechanism that identifies different observations from regular instances. Also, it is often applied to unlabeled data, which is known as unsupervised anomaly detection. In the last decade, many research studies have provided techniques that enhance and improve the quality of anomaly detection. Unfortunately, they are not very accurate for high-dimensional data. Also, the exponential growth in data volume and complexity generated by modern applications affects existing techniques in terms of both performance and accuracy. However, a modified version of the Isolation Forest (IF), Extended Isolation Forest (EIF), has improved the robustness of the anomaly score map. The EIF can build scores with less variance and obtain better accuracy. This study presents an enhanced version of the Extended Isolation Forest algorithm. This is a Fuzzy C-Means-Based Extended Isolation Forest (FCMBEIF), which allows building the search trees and nodes based on Fuzzy C-Means (FCM) clustering. Experimental results demonstrate the performance of the proposed algorithm and show that FCM clustering contributes to the algorithm’s efficiency.
Artificial Neural Networks Cryptanalysis of Merkle-Hellman Knapsack Cryptosystem

Machine learning in general, and artificial neural networks in particular have been used in many fields, and especially in cryptanalysis, showing good results for the recovery attacks on lightweight ciphers especially, and as good distinguishers. In this paper, show how we applied artificial neural networks based cryptanalysis to perform a known-plaintext attack to the Merkle–Hellman knapsack public key cryptosystem. After revisiting some of the related work already done in the literature, and showing how the Merkle Hellman knapsack public key cryptosystem works, and a brief introduction of neural networks, we show how the model used, the data used and present the obtained results.
Blockchain-Based Cloud Computing: Model-Driven Engineering Approach

Blockchain has attracted attention as a next generation financial technology because of its security which is suitable for the era of computerization and storing information electronically in digital format. In particular, it provides security through authentication of peers who share Cryptocurrency without the need of a centralized third party, encryption and hash value generation. In addition, Blockchain can be applied beyond the Cryptocurrency environment to include Cloud Computing, which has been widely adopted in all IT environments for its efficiency and availability. Blockchain is a representative technology to guarantee anonymity, if combined with the cloud computing and can be upgraded to a convenient service that provides enhanced security for Cloud Computing environments. However, studies on the protection of privacy and anonymity in this area are not sufficient. In this paper, we present a systematic study that covers the security and privacy aspects of Blockchain, highlight its potential fusion with cloud computing. We provide an overview of Blockchain technology, followed by a detailed explanation of use cases of Blockchain with Cloud Computing. Also, we highlight the various challenges to blockchain-based data in the cloud computing environment. Finally, we compare recently published literatures with our studies to draw insights for future research directions.
Combining Descriptors for Efficient Retrieval in Databases Images

Content based image retrieval is one of the import search area. Its principle consists to search the images similar to image request by using a set of descriptors. The multimedia content description interface, MPEG-7, provides normative descriptors, such as texture, color, region and shape descriptors for effective visual content retrieval. These descriptors represent visual contents. with numerical feature values from which the similarity could be measured quantitatively. One of the important manners of indexing images consists on extracting several visual features and afterward, using a weighted linear combination, where a weight values are assigned to the visual features. In this work, we propose to develop an efficient application based on combination of several descriptors adapted by mpeg-7. Each image in database are described by six descriptors Angular Radial Transform, Fourier Descriptor, Curvature Scale Space, Color Structure Descriptor, Scalable Color Descriptor and EHD. Each descriptor generates a specific feature vector where the generated vector matches with a specific distance to compute the similarity between two or more images in order to found the nearest image. The application developed was tested on Mpeg7 database and Corel Database using a combination of metric to measure similarity between images and recall/precision to measure performance of search. The obtained results prove the importance and the performance of developed application.
An MCDM-Based Methodology for Influential Nodes Detection in a Social Network. Facebook as a Case Study

The identification of influential nodes in a social network is very important for many uses like the control of information spreading. This paper studies how a multi- criteria decision method (MCDM) can find influential persons in a social network. The empirical study performed on a Facebook dataset shows how we can visualize links between profiles and who are the top-k influential ones.
Path-enhanced graph convolutional networks for node classification without features

Most current graph neural networks (GNNs) are designed from the view of methodology and rarely consider the inherent characters of graph. Although the inherent characters may impact the performance of GNNs, very few methods are proposed to resolve the issue. In this work, we mainly focus on improving the performance of graph convolutional networks (GCNs) on the graphs without node features. In order to resolve the issue, we propose a method called t-hopGCN to describe t-hop neighbors by the shortest path between two nodes, then the adjacency matrix of t-hop neighbors as features to perform node classification. Experimental results show that t-hopGCN can significantly improve the performance of node classification in the graphs without node features. More importantly, adding the adjacency matrix of t-hop neighbors can improve the performance of existing popular GNNs on node classification.
Generating Artworks Using One Class SVM with RBF Kernel

This work presents a new use of a supervised machine learning model to generate automatically artworks after a learning step. The forms of art that will be discussed in this paper are painting and drawing. The proposed model is based on the decision function of One Class Kernel Support Vector Machine (OCKSVM) with a Radial Basis Function (RBF). Experimental results using a large training dataset composed of portrait paintings and drawings belonging to six significant art movements in art history, including impressionism, post-impressionism, high renaissance, northern renaissance, rococo, and ukiyo showed that the proposed model is able to generate automatically and successfully new digital paintings and drawings. This model can be extended easily to generate other kinds of arts like literature, music, etc.
Traffic Congestion and Road Anomalies Detection Using CCTVs Images Processing, Challenges and Opportunities

Traffic congestion and road accidents are some of the most common problems that urban residents suffer from. Because they have a direct impact on their security, physical and psychological health, and even on the economic side etc. this is due to several reasons including the huge number of vehicles on roads, irresponsibility of drivers, and also the state and the architecture of the infrastructures. In order to solve these problems or at least reduce them, several studies have addressed the issue and proposed different solutions from different sides. In this paper, we would like to study the possibility to use Computer Vision techniques and Big Data Analytics subfields, such as machine learning and deep learning algorithms, to build reliable systems which will allow us to detect, identify, and track vehicles and pedestrians in real-time. This will enable us to detect traffic congestion or any other anomaly. These systems will be installed in the network of the Closed-Circuit Televisions (CCTVs) fixed on roads and make them connected. Moreover, this study provides comparative analysis progress of the methods used to detect objects, especially vehicles and pedestrians to choose the most accurate model capable of tracking road users and analysing their behaviours, detecting frauds and saving useful numerical datasets.
Designing Hybrid Storage Architectures with RDBMS and NoSQL Systems: A Survey

The main goal of a decision support system is to provide decision makers, in an appropriate form, with information that will help them make relevant decisions, with the main aim to improve the efficiency of the company. The traditional approach adopted by these systems, are based on common architectures represented in most of the time by the data warehouses. These works with relational database systems supporting data of structured type as well. However, the decision-making process is often not limited to structured data. Indeed, with the emergence of Big Data, the value of data can also be extracted from massive data sources containing data of diverse types, structured, semi-structured and unstructured. As a result, NoSQL systems have been integrated into the architecture of decision support system, in parallel with data warehouses, in order to overcome their limitations regarding Big Data characteristics. In this sense, this article presents a synthetic literature review on the work done in recent years, treating the hybridization of traditional systems with NoSQL databases, including relational databases and traditional data warehouses. We aim by this work to scheme the limits of the existing approaches and to determine the most fitting characteristics for the design of a hybrid architecture suitable for the migration of companies to Big Data architectures. Furthermore, we discussed the utility of implementing a data lake in decision support system architecture: On one hand as a way to solve the problems related to information silos that current architectures encounter and on the other hand, as an upgrading technique for traditional decision support system architecture.
AI-Based Adaptive Learning - State of the Art

In recent decades, the area of education has seen numerous transformations, owing to the rapid advancement of Information and communication technologies (ICTs), the democratization of the internet, the rise of web technologies, and, most recently, the rapid advancement of artificial intelligence (AI) techniques, especially those who fall under the banner of the AI-subset entitled Machine learning. In this respect, the goal of this paper is to present a state of the art about AI applications in education while highlighting the most requested artificial intelligence in education (AIEd) approach called adaptive learning. This approach that has created a new opportunities in terms of adapting the different elements of the learning process (content, pedagogy, learning path, presentation etc.) to the needs of the learner (learning style, level, prior knowledge, preference, performance etc.), through the potential of AI represented by different systems and applications, in order to increase learning outcomes. In this present paper, we will also show real-case AI-based adaptive learning system implementations and explore their objectives, mechanisms, factors employed during adaptation, AI algorithms adopted and impact on learning. This study will provide insight into the topic of adaptive learning through a frame of reference and descriptive analysis, which can be used as a springboard for further investigation.
Geoparsing Recognition and Extraction from Amazigh Corpus Using the NooJ Complex Annotation Structures

This paper deals with a novel toponym recognition approach for various geoparsing purposes, and presents a method for classifying and extraction toponyms as named Entities (NE) from Amazigh corpora, the research starts with the implementation of the identifying rules (local grammar) and the annotation of toponyms recognizer tool (TR) that is able to find references and semantic relations to places, organizations, persons nd historical meaning use. As targets to extract the correct Amazigh names entities, we use a complex annotation system as a text annotation structure (TAS) with two tasks divided into four phases: Clustering (files), Segmentation (POS tagging for each form), Classification (categories & types) and Extraction (identification). We decided to model various texts constraints of toponyms as ambiguous senses and each phases plays a crucial rule in the overall performance. We use the NooJ platform and it structural annotation engine; which requires a separate analysis of these NEs structures and represents more than one annotation at a given location in the text. The appropriate samples used in the study show the importance of the complex annotation and the useful technique of classification processes to improve Amazigh text analysis from a historical and anthropological meaning based approach. Experimental results show that the proposed method has higher toponym recognition ratio in comparison to the previous studies.
Improving Arabic to English Machine Translation

Replacing static word embeddings with contextualized word representations has brought substantial improvements to a wide range of NLP tasks including Question Answering (QA), Machine Translation (MT) and Sentiment Analysis (SA). In this work, we propose a novel technique called Attention GloVe Vectors (AttGloVs) generated using GloVe model and the attention mechanism. These AttGloVs are then used to train and test a Deep Learning (DL) architecture for Arabic to English MT. Moreover, we show the importance of preprocessing and tokenization and how they contribute to a better translation. The experimental results show that the proposed approach gives the best results, compared to the state-of-the-art Arabic MT systems.
Tifinagh Handwritten Character Recognition Using Machine Learning Algorithms

The Amazigh people are an indigenous ethnic group of North Africa. They are distributed from morocco, to Siwa Oasis in Egypt passing by Algeria, Tunisia, Libya, Niger, Burkina Faso, Mali, Mauritania. Historically, they spoke Amazigh languages, classified under the Amazigh branch of the Afro-Asiatic family. The Tifinagh is the alphabet of this language, it’s normalized in morocco since 2001. In our work we will propose a model to treat the Handwritten Tifinagh Character Recognition and then apply different algorithms of machine learning to identify which one achieve the better accuracy.
A Comparative Review of Tweets Automatic Sarcasm Detection in Arabic and English

Sentiment analysis has become a prevalent issue in the research community, with researchers employing data mining and artificial intelligence approaches to extract insights from textual data. Sentiment analysis has progressed from a simple task of classifying evaluations as positive or negative to a sophisticated work that requires a fine-grained multimodal analysis of emotions, manifestations of aggression, sarcasm, hatred, and racism. Sarcasm occurs when the intended message differs from the literal meaning of the words employed. Generally, the content of the utterance is the opposite of the context. Sentiment analysis tasks are hampered when a sarcastic tone is recognized in user-generated content. Thus, Automatic Sarcasm Detection in textual data dramatically affects the performance of sentiment analysis models. This study aims to explain the basic architecture of a sarcasm detection system and the most effective techniques for extracting sarcasm in the English language. Then, for the Arabic language, determining the gap and challenges.
Response to "The digital pound: a new form of money for households and businesses"

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Cross-Modal Interaction Network for Video Moment Retrieval

The video moment retrieval task aims to fetch a target moment in an untrimmed video, which best matches the semantics of a sentence query. Existing methods mainly focus on utilizing two separate modules: one learns intra-modal relations to understand video and query contents, and the other explores inter-modal interactions to build a semantic bridge between video and language. However, intra-modal relations information can be easily overlooked when capturing inter-modal interactions. In fact, intra-modal relations and inter-modal interactions can be learned simultaneously within a unified module to make video and sentence guide each other. Towards this end, we propose a Cross-Modal Interaction Network (CMIN) for video moment retrieval by jointly exploring the intra-modal relations and inter-modal interactions between video frames and query words. In CMIN, a query-guided channel attention module is designed to suppress query-irrelevant visual features and enhance crucial contents; then a cross-attention module simultaneously considers intra-modal relations within each modality and fine-grained inter-modal interactions between frames and words, to enhance the semantic relevance between video and sentence query. Compared to the state-of-the-art methods, the experiments on two public datasets (Charades-STA and TACoS) demonstrate the superiority of our method.
DOMOPT: A Detection-Based Online Multi-Object Pedestrian Tracking Network for Videos

Due to the problem of low tracking accuracy and weak tracking stability of current multi-object pedestrian tracking algorithms in complex scenes for videos, a Detection-based Online Multi-Object Pedestrian Tracking (DOMOPT) network is proposed. First, a Multi-Level Feature Fusion (MLFF) pedestrian detection network is proposed based on the Center and Scale Prediction (CSP) algorithm. The pyramid convolutional neural network is used as the backbone to enhance the feature extraction capability for small objects. The shallow features and deep features at multiple levels are integrated to fully obtain the position and semantic information to further improve the detection performance for small objects. Then, on the basis of Joint Detection and Embedding (JDE) architecture, a Multi-Branch Pedestrian Appearance (MBPA) feature extraction network is proposed and added into the pedestrian detection network to extract the appearance feature vector corresponding to each pedestrian. The pedestrian appearance feature extraction is treated as a classification task jointly training with the pedestrian detection task, using the multi-task learning strategy. Experimental results show that the proposed network has better tracking accuracy and stability compared with state-of-the-art algorithms.