A Review of Blockchain-Based E-Voting Systems: Comparative Analysis and Findings

The emergence of blockchain has ushered in a significant transformation in information systems research. Blockchain’s key pillars such as decentralization, immutability, and transparency have paved the path for extensive exploration in various research domains. This particular study is focused on electronic voting, aiming to improve voting procedures by making better use of the benefits offered by blockchain technology. Through a comprehensive review of existing literature, we highlight the potential benefits of blockchain-based electronic voting systems such as transparency, security, and efficiency. However, several challenges, such as scalability, personal data confidentiality, and ensuring robust identity verification, persist. Addressing these issues is necessary to unlock the full potential of blockchain-based electronic voting systems, thereby fostering the development of trustworthy election systems in the future

Formal Specification of QoS Negotiation in ODP System

The future of Open Distributed Processing systems (ODP) will see an increasing of components number, these components are sharing resources. In general, these resources are offering some kind of services. Due to the huge number of components, it is very difficult to offer the optimum Quality of service (QoS). This encourages us to develop a model for QoS negotiation process to optimize the QoS in an ODP system. In such system, there is a High risk of software or hardware failure. To ensure good performance of a system based on our model, we develop it using a formal method. In our case, we will use Event-B to get in the end of our development a system correct by construction

A Graph-based approach for text query expansion using pseudo relevance feedback and association rules mining

Pseudo-relevance feedback is a query expansion approach whose terms are selected from a set of top ranked retrieved documents in response to the original query.  However, the selected terms will not be related to the query if the top retrieved documents are irrelevant. As a result, retrieval performance for the expanded query is not improved, compared to the original one. This paper suggests the use of documents selected using Pseudo Relevance Feedback for generating association rules. Thus, an algorithm based on dominance relations is applied. Then the strong correlations between query and other terms are detected, and an oriented and weighted graph called Pseudo-Graph Feedback is constructed. This graph serves for expanding original queries by terms related semantically and selected by the user. The results of the experiments on Text Retrieval Conference (TREC) collection are very significant, and best results are achieved by the proposed approach compared to both the baseline system and an existing technique

Getting Relational Database from Legacy Data-MDRE Approach

The previous management information systems turning on traditional mainframe environment are often written in COBOL and store their data in files; they are usually large and complex and known as legacy systems. These legacy systems need to be maintained and evolved due to several causes, including correction of anomalies, requirements change, management rules change, new reorganization, etc. But, the maintenance of legacy systems becomes over years extremely complex and highly expensive, In this case, a new or an improved system must replace the previous one. However, replacing those systems completely from scratch is also very expensive and it represents a huge risk. Nevertheless, they should be evolved by profiting from the valuable knowledge embedded in them. This paper proposes a reverse engineering process based on Model Driven engineering that presents a solution to provide a normalized relational database which includes the integrity constraints extracted from legacy data. A CASE tool CETL: (COBOL Extract Transform Load) is developed to support the proposal. Keywords: legacy data, reverse engineering, model driven engineering, COBOL metamodel, domain class diagram, relational database

A methodology for CIM modelling and its transformation to PIM

Developing with Model Driven Architecture is nowadays widely used starting with a CIM that can be transformed to models of low abstraction (PIM, PSM) that can be used to generate the code. The CIM represents the highest level of abstraction of the approach which allowing modeling system’s requirement. However, there is no standard method to build this type of model or how to transform it to lower level of abstraction (PIM) which is considered the final objective of building such model. This paper provides an approach to build the CIM that can be transformed (semi-) automatically later to lower levels of abstraction in PIMs.  Thereby, the proposed architecture represents both the static and dynamic view of the system based on the business process model. Meanwhile, the PIM level is represented by the Domain Diagram class and Sequence Diagram of Systems External behavior. Thus, the proposal helps bridging the gap between those that are experts about the domain and its requirements, and those that are experts of the system design and development. Keywords: CIM to PIM transformation; MDA; software process

Colposcopic image registration using opponentSIFT descriptor

This work presents a colposcopic image registration system able to help physicians for cervical cancer diagnosis. The goal is to make registration between the cervical tissue throughout the whole temporal sequence. Recent digital images processing works, suggested using feature points to compute the tissue displacement. These methods achieve good results, because they are fast and do not need any segmentation, but to date, all methods based on feature points are sensitive to light change and reflections which are frequently current in colposcopic images. To solve this problem, we propose to apply the opponentSIFT descriptor which describes features point in the opponent color space. Experimental results show the robustness of this descriptor in colposcopic images registration in comparison with other descriptors

INDEXATION DES OBJETS 3D BASEE SUR UNE ANALOGIE PARTIELLE DES SEGMENTS

L’indexation 3D est un domaine qui s’impose dans un certain nombre important d'applications liées aux bases de données d’objets 3D. Plusieurs descripteurs ont été définis dont la plupart utilisent la signature géométrique globale des objets 3D et peu d'entre eux sont basés sur une correspondance partielle des segments de ces objets. Dans cet article, nous proposons de raffiner les résultats d’une indexation globale par la prise en compte des signatures des segments composant un objet 3D. L’approche proposée améliore, significativement, les résultats de l’indexation globale et permet de détecter les modèles similaires ayant des poses différentes

From Auto-encoders to Capsule Networks: A Survey

Convolutional Neural Networks are a very powerful Deep Learning algorithm used in image processing, object classification and segmentation. They are very robust in extracting features from data and largely used in several domains. Nonetheless, they require a large number of training datasets and relations between features get lost in the Max-pooling step, which can lead to a wrong classification. Capsule Networks (CapsNets) were introduced to overcome these limitations by extracting features and their pose using capsules instead of neurons. This technique shows an impressive performance in one-dimensional, two-dimensional and three-dimensional datasets as well as in sparse datasets. In this paper, we present an initial understanding of CapsNets, their concept, structure and learning algorithm. We introduce the progress made by CapsNets from their introduction in 2011 until 2020. We compare different CapsNets series to demonstrate strengths and challenges. Finally, we quote different implementations of Capsule Networks and show their robustness in a variety of domains. This survey provides the state-of-the-art of Capsule Networks and allows other researchers to get a clear view of this new field. Besides, we discuss the open issues and the promising directions of future research, which may lead to a new generation of CapsNets

From Auto-encoders to Capsule Networks: A Survey

Convolutional Neural Networks are a very powerful Deep Learning structure used in image processing, object classification and segmentation. They are very robust in extracting features from data and largely used in several domains. Nonetheless, they require a large number of training datasets and relations between features get lost in the Max-pooling step, which can lead to a wrong classification. Capsule Networks(CapsNets) were introduced to overcome these limitations by extracting features and their pose using capsules instead of neurons. This technique shows an impressive performance in one-dimensional, two-dimensional and three-dimensional datasets as well as in sparse datasets. In this paper, we present an initial understanding of CapsNets, their concept, structure and learning algorithm. We introduce the progress made by CapsNets from their introduction in 2011 until 2020. We compare different CapsNets series architectures to demonstrate strengths and challenges. Finally, we quote different implementations of Capsule Networks and show their robustness in a variety of domains. This survey provides the state-of-theartof Capsule Networks and allows other researchers to get a clear view of this new field. Besides, we discuss the open issues and the promising directions of future research, which may lead to a new generation of CapsNets