Restrictive Voting Technique for Faces Spoofing Attack

Face anti-spoofing has become widely used due to the increasing use of biometric authentication systems that rely on facial recognition. It is a critical issue in biometric authentication systems that aim to prevent unauthorized access. In this paper, we propose a modified version of majority voting that ensembles the votes of six classifiers for multiple video chunks to improve the accuracy of face anti-spoofing. Our approach involves sampling sub-videos of 2 seconds each with a one-second overlap and classifying each sub-video using multiple classifiers. We then ensemble the classifications for each sub-video across all classifiers to decide the complete video classification. We focus on the False Acceptance Rate (FAR) metric to highlight the importance of preventing unauthorized access. We evaluated our method using the Replay Attack dataset and achieved a zero FAR. We also reported the Half Total Error Rate (HTER) and Equal Error Rate (EER) and gained a better result than most state-of-the-art methods. Our experimental results show that our proposed method significantly reduces the FAR, which is crucial for real-world face anti-spoofing applications

Negation and Speculation in NLP: A Survey, Corpora, Methods, and Applications

Negation and speculation are universal linguistic phenomena that affect the performance of Natural Language Processing (NLP) applications, such as those for opinion mining and information retrieval, especially in biomedical data. In this article, we review the corpora annotated with negation and speculation in various natural languages and domains. Furthermore, we discuss the ongoing research into recent rule-based, supervised, and transfer learning techniques for the detection of negating and speculative content. Many English corpora for various domains are now annotated with negation and speculation; moreover, the availability of annotated corpora in other languages has started to increase. However, this growth is insufficient to address these important phenomena in languages with limited resources. The use of cross-lingual models and translation of the well-known languages are acceptable alternatives. We also highlight the lack of consistent annotation guidelines and the shortcomings of the existing techniques, and suggest alternatives that may speed up progress in this research direction. Adding more syntactic features may alleviate the limitations of the existing techniques, such as cue ambiguity and detecting the discontinuous scopes. In some NLP applications, inclusion of a system that is negation- and speculation-aware improves performance, yet this aspect is still not addressed or considered an essential step

The Development of QMMS: A Case Study for Reliable Online Quiz Maker and Management System

The e-learning and assessment systems became a dominant technology nowadays and distribute across the globe. With severe consequences of COVID19-like crises, the key importance of such technology appeared in which courses, quizzes and questionnaires have to be conducted remotely. Moreover, the use of Learning Management Systems (LMSs), such as blackboard, eCollege, and Moodle, has been sanctioned in all respects of education. This paper presents an open-source interactive Quiz Maker and Management System (QMMS) that suits the research, education (under-grad, grad, or post-grad), and industrial organizations to perform distant quizzes, training and questionnaires with an integration facility with other LMS tools such as Moodle. The proposed system supports three basic levels: 1) administration, 2) instructors, and 3) learners at the micro-level teaching. The proposed system is adopted using .Net framework integrated with SQL-Server database engine that compromise between performance, security and stability. The proposed QMMS is described through different phases of Software Development Life Cycle (SDLC) including detailed analysis, design, implementation, testing, verification, and maintenance in order to exploit the importance of the analysis and design of LMS from the software engineering point of view. A comparative analysis, among the proposed system and a recent list of challenging ones, is presented in different aspects that shows the effectiveness, reliability and validity of proposed tool. Moreover, the proposed QMMS shows an enhancement ratio of up to 42.19% in response time perspective as compared to Moodle system in the case of massive concurrent transactions

CRITICAL PATH ROUTING (CPR) PROTOCOL FOR MOBILE AD HOC NETWORKS

A mobile ad hoc network (MANET) is a network consisting of a set of wireless nodes capable of moving around freely while still being connected without any fixed infrastructure. In this paper we present a hybrid adaptive ad hoc routing protocol, named Critical Path Routing (CPR) that strives to incorporate the merits of both reactive and proactive ad hoc routing algorithms. The genuine aspect of CPR is that it initially starts-off as a conventional reactive Dynamic Source Routing (DSR) protocol. Then, the network traffic is monitored in attempt to gradually discover information that identifies pairs of highly interactive nodes that engage in sending data to each other more often than other nodes in the MANET. CPR then constructs Critical Paths (CPs) between these pairs of nodes and proactively safe guards these CPs. The aim of our work is to achieve low latency between highly active pairs of nodes, thus increasing the overall performance of the network. Simulation results showed that CPR outclassed DSR with a decrease of 23.7 % in end-to-end delay when nodes were in a high degree of mobility, but with a relatively high cost as overhead increased by 36%. We enhanced our CPR protocol in attempt to decrease the relatively high overhead by decreasing the engagement of intermediate nodes in the proactive monitoring of CPs. The total overhead decreased to only 14.74%

Annotated Corpus with Negation and Speculation in Arabic Review Domain: NSAR

Negation and speculation detection are critical for Natural Language Processing (NLP) tasks, such as sentiment analysis, information retrieval, and machine translation. This paper presents the first Arabic corpus in the review domain annotated with negation and speculation. The Negation and Speculation Arabic Review (NSAR) corpus consists of 3K randomly selected review sentences from three well-known and benchmarked Arabic corpora. It contains reviews from different categories, including books, hotels, restaurants, and other products written in various Arabic dialects. The negation and speculation keywords have been annotated along with their linguistic scope based on the annotation guidelines reviewed by an expert linguist. The inter-annotator agreement between two independent annotators, Arabic native speakers, is measured using the Cohen’s Kappa coefficients with values of 95 and 80 for negation and speculation, respectively. Furthermore, 29% of this corpus includes at least one negation instance, while only 4% of this corpus contains speculative content. Therefore, the Arabic reviews focus more on negation structures rather than speculation. This corpus will be available for the Arabic research community to handle these critical phenomena