Automatic Speech Recognition Errors Detection Using Supervised Learning Techniques

Over the last years, many advances have been made in the field of Automatic Speech Recognition (ASR). However, the persistent presence of ASR errors is limiting the widespread adoption of speech technology in real life applications. This motivates the attempts to find alternative techniques to automatically detect and correct ASR errors, which can be very effective and especially when the user does not have access to tune the features, the models or the decoder of the ASR system or when the transcription serves as input to downstream systems like machine translation, information retrieval, and question answering. In this paper, we present an ASR errors detection system targeted towards substitution and insertion errors. The proposed system is based on supervised learning techniques and uses input features deducted only from the ASR output words and hence should be usable with any ASR system. Applying this system on TV program transcription data leads to identify 40.30% of the recognition errors generated by the ASR system

Vertical intent prediction approach based on Doc2vec and convolutional neural networks for improving vertical selection in aggregated search

Vertical selection is the task of selecting the most relevant verticals to a given query in order to improve the diversity and quality of web search results. This task requires not only predicting relevant verticals but also these verticals must be those the user expects to be relevant for his particular information need. Most existing works focused on using traditional machine learning techniques to combine multiple types of features for selecting several relevant verticals. Although these techniques are very efficient, handling vertical selection with high accuracy is still a challenging research task. In this paper, we propose an approach for improving vertical selection in order to satisfy the user vertical intent and reduce user’s browsing time and efforts. First, it generates query embeddings vectors using the doc2vec algorithm that preserves syntactic and semantic information within each query. Secondly, this vector will be used as input to a convolutional neural network model for increasing the representation of the query with multiple levels of abstraction including rich semantic information and then creating a global summarization of the query features. We demonstrate the effectiveness of our approach through comprehensive experimentation using various datasets. Our experimental findings show that our system achieves significant accuracy. Further, it realizes accurate predictions on new unseen data

A survey on author profiling, deception, and irony detection for the Arabic language

"This is the peer reviewed version of the following article: [FULL CITE], which has been published in final form at [Link to final article using the DOI]. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving."[EN] The possibility of knowing people traits on the basis of what they write is a field of growing interest named author profiling. To infer a user's gender, age, native language, language variety, or even when the user lies, simply by analyzing her texts, opens a wide range of possibilities from the point of view of security. In this paper, we review the state of the art about some of the main author profiling problems, as well as deception and irony detection, especially focusing on the Arabic language.Qatar National Research Fund, Grant/Award Number: NPRP 9-175-1-033Rosso, P.; Rangel-Pardo, FM.; Hernandez-Farias, DI.; Cagnina, L.; Zaghouani, W.; Charfi, A. (2018). 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Survey on IoT: Security Threats and Applications

the rapid growth of the internet of things (IoT) in the world in recent years is due to its wide range of usability, adaptability, and smartness. Most of the IoT applications are performing jobs an automatic manner without interactions of human or physical objects. It’s required that the current and upcoming devices will be smart, efficient and able to provide the services to the users to implement such a new technology with a secure manner. Thus the security issues are exploring day by day by the researchers. IoT devices are most portable and light in nature so it has several issues such as battery consumption, memory, and as these devices are working open range so the most important is security. In this survey paper, we have elaborated on the security attacks with reference to the different kinds of IoT layers. In the last, we have presented some of the applications of the IoT. This study will provide assistance to the researchers and manufacturers to evaluate and decrease the attacks range on IoT devices

Smart Learning

Artificial intelligence applied to the educational field has a vast potential, especially after the e ects worldwide of the COVID-19 pandemic. Online or blended educational modes are needed to respond to the health situation we are living in. The tutorial e ort is higher than in the traditional face-to-face approach. Thus, educational systems are claiming smarter learning technologies that do not pretend to substitute the faculty but make their teaching activities easy. This Special Issue is oriented to present a collection of papers of original advances in educational applications and services propelled by artificial intelligence, big data, machine learning, and deep learning

A study on IoT-related security issues, challenges, and solutions.

The Internet of Things is now being developed to be the most cutting-edge and user-centric technology in the works. Raising both an individual\u27s and society\u27s level of life is the goal of this endeavour. When a technology advances, it always acquires certain flaws, which are always open to being attacked and taken advantage of in some manner. In this work, the problems posed by the Internet of Things (IoT) based on the fundamental security principles of confidentiality, integrity, and availability are discussed. It has also been discussed how an overview of the security restrictions, requirements, processes, and solutions implemented for the challenges generated in secured communication inside the IoT ecosystem. In this paper, the vulnerabilities of the underlying Internet of Things network are brought to light, and many security concerns on multiple tiers of the Internet of Things ecosystem have been examined. Based on the findings of our research into the vulnerabilities that are now present, a variety of potential solutions have been proposed in order to solve the ongoing problems that are plaguing the IoT ecosystem. In addition to that, it provides an overview of the various protocols that are used for security in IoT

A novel approach for partial shape matching and similarity based on data envelopment analysis

Due to the growing number of 3D objects in digital libraries, the task of searching and browsing models in an extensive 3D database has been the focus of considerable research in the area. In the last decade, several approaches to retrieve 3D models based on shape similarity have been proposed. The majority of the existing methods addresses the problem of similarity between objects as a global matching problem. Consequently, most of these techniques do not support a part of the object as a query, in addition to their poor performance for classes with globally non-similar shape models and also for articulated objects. The partial matching technique seems to be a suitable solution to these problems. In this paper, we address the problem of shape matching and retrieval. We propose a new approach based on partial matching in which each 3D object is segmented into its constituent parts, and shape descriptors are computed from these elements to compare similarities. Several experiments investigated that our technique enables fast computing for content-based 3D shape retrieval and significantly improves the results of our method based on Data Envelopment Analysis descriptor for global matching

Blockchain Challenges and Security Schemes: A Survey

International audienceWith the increasing number of connected devices and the number of online transactions today, managing all these transactions and devices and maintaining network security is a research issue. Current solutions are mainly based on cloud computing infrastructures, which require servers high-end and broadband networks to provide data storage and computing services. These solutions have a number of significant disadvantages, such as high maintenance costs of centralized servers, critical weakness of Internet Of Things applications, security and trust issues, etc. The blockchain is seen as a promising technique for addressing the mentioned security issues and design new decentralization frameworks. However, this new technology has a great potential in the most diverse technological fields. In this paper, we focus on presenting an overview of blockchain technology, highlighting its advantages, limitations and areas of application. The originality of this work resides in the comparison between the different blockchain systems and their security schemes and the perspective of integrating this technology into secured systems models for our comfort and our private life