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JournalISSN: 2231-5403

Computer Science and Information Technology 

About: Computer Science and Information Technology is an academic journal. The journal publishes majorly in the area(s): Information technology & The Internet. Over the lifetime, 518 publications have been published receiving 1484 citations.


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Journal ArticleDOI
TL;DR: Key among the issues raised in this paper are the various applications of AR that enhance the user's ability to understand the movement of mobile robot, the movements of a robot arm and the forces applied by a robot.
Abstract: Since the origins of Augmented Reality (AR), industry has always been one of its prominent application domains. The recent advances in both portable and wearable AR devices and the new challenges introduced by the fourth industrial revolution (renowned as industry 4.0) further enlarge the applicability of AR to improve the productiveness and to enhance the user experience. This paper provides an overview on the most important applications of AR regarding the industry domain. Key among the issues raised in this paper are the various applications of AR that enhance the user's ability to understand the movement of mobile robot, the movements of a robot arm and the forces applied by a robot. It is recommended that, in view of the rising need for both users and data privacy, technologies which compose basis for Industry 4.0 will need to change their own way of working to embrace data privacy.

70 citations

Journal Article
TL;DR: This paper presents the result of a detailed analysis of all items being evaluated in each questionnaire to indicate those that can identify users’ perceptions about specific usability problems and confronts each questionnaire item with usability criteria proposed by quality standards and classical quality ergonomic criteria.
Abstract: For the last few decades more than twenty standardized usability questionnaires for evaluating software systems have been proposed. These instruments have been widely used in the assessment of usability of user interfaces. They have their own characteristics, can be generic or address specific kinds of systems and can be composed of one or several items. Some comparison or comparative studies were also conducted to identify the best one in different situations. All these issues should be considered while choosing a questionnaire. In this paper, we present an extensive review of these questionnaires considering their key features, some classifications and main comparison studies already performed. Moreover, we present the result of a detailed analysis of all items being evaluated in each questionnaire to indicate those that can identify users’ perceptions about specific usability problems. This analysis was performed by confronting each questionnaire item (around 475 items) with usability criteria proposed by quality standards (ISO 9421-11 and ISO/WD 9241-112) and classical quality ergonomic criteria.

63 citations

Proceedings ArticleDOI
TL;DR: In this article, the authors study the impact of different pretrained CNN feature extractors on the problem of image set clustering for object classification as well as fine-grained classification and propose a rather straightforward pipeline combining deep-feature extraction using a CNN pretrained on ImageNet and a classic clustering algorithm to classify sets of images.
Abstract: This paper aims at providing insight on the transferability of deep CNN features to unsupervised problems. We study the impact of different pretrained CNN feature extractors on the problem of image set clustering for object classification as well as fine-grained classification. We propose a rather straightforward pipeline combining deep-feature extraction using a CNN pretrained on ImageNet and a classic clustering algorithm to classify sets of images. This approach is compared to state-of-the-art algorithms in image-clustering and provides better results. These results strengthen the belief that supervised training of deep CNN on large datasets, with a large variability of classes, extracts better features than most carefully designed engineering approaches, even for unsupervised tasks. We also validate our approach on a robotic application, consisting in sorting and storing objects smartly based on clustering.

55 citations

Proceedings ArticleDOI
TL;DR: An apache spark based model to classify Amharic Facebook posts and comments into hate and not hate is developed and achieves a promising result with unique feature of spark for big data.
Abstract: The anonymity of social networks makes it attractive for hate speech to mask their criminal activities online posing a challenge to the world and in particular Ethiopia. With this everincreasing volume of social media data, hate speech identification becomes a challenge in aggravating conflict between citizens of nations. The high rate of production, has become difficult to collect, store and analyze such big data using traditional detection methods. This paper proposed the application of apache spark in hate speech detection to reduce the challenges. Authors developed an apache spark based model to classify Amharic Facebook posts and comments into hate and not hate. Authors employed Random forest and Naïve Bayes for learning and Word2Vec and TF-IDF for feature selection. Tested by 10-fold crossvalidation, the model based on word2vec embedding performed best with 79.83%accuracy. The proposed method achieve a promising result with unique feature of spark for big data.

53 citations

Journal ArticleDOI
TL;DR: Three new configurations of level shifters for low power application in 0.35{\mu}m technology have been presented and shows better performance in terms of power consumption with a little conciliation in delay.
Abstract: With scaling of Vt sub-threshold leakage power is increasing and expected to become significant part of total power consumption.In present work three new configurations of level shifters for low power application in 0.35µm technology have been presented. The proposed circuits utilize the merits of stacking technique with smaller leakage current and reduction in leakage power. Conventional level shifter has been improved by addition of three NMOS transistors, which shows total power consumption of 402.2264pW as compared to 0.49833nW with existing circuit. Single supply level shifter has been modified with addition of two NMOS transistors that gives total power consumption of 108.641pW as compared to 31.06nW. Another circuit, contention mitigated level shifter (CMLS) with three additional transistors shows total power consumption of 396.75pW as compared to 0.4937354nW. Three proposed circuit’s shows better performance in terms of power consumption with a little conciliation in delay. Output level of 3.3V has been obtained with input pulse of 1.6V for all proposed circuits.

32 citations

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Performance
Metrics
No. of papers from the Journal in previous years
YearPapers
202120
202070
201926
2018118
201750
201657