Timur Mirzoev, Amy Coleman, Robin Wacha-Bessert, Georgia Southern University: Information Technology, Statesboro, GA USA.
Ben Lai, Douglas Olson, Clayton State University: Information Technology, Morrow, GA USA.
Abstract – As advances in technology occur, mobile cloud computing is able to overcome the issue of resource poverty that occurs in mobile devices. Manufacturers now look at cloud computing as an asset because they can run their products better. The number of businesses and individuals that are investing in cloud computing services is anticipated to grow rapidly. Rapid growth of this nature will have a large impact in the IT industry. This study shows that cloud computing, including mobile computing, is a major contribution to the expansion of output and employment within the IT sector. An improvement in performance is making mobile cloud computing a better choice for consumers. In recent years, changes in the mobile cloud computing framework have enabled engineers to offload computational processing to other systems in the cloud that dramatically improve the mobile device experience.
Keywords- mobile cloud computing employment; advances in cloud computing; mobile cloud computing jobs; mobile cloud
service; mobile cloud job growth.
Fatima T. AL-Khawaldeh, Department of Computer Science, Al-Albayt University, Al-Mafraq, Jordan.
Abstract -Recognizing the entailment relation showed that its influence to extract the semantic inferences in wide-ranging natural language processing domains (text summarization, question answering, etc.) and enhanced the results of their output. For Arabic language, few attempts concerns with Arabic entailment problem. This paper aims to increase the entailment accuracy for Arabic texts by resolving negation of the text-hypothesis pair and determining the polarity of the text-hypothesis pair whether it is Positive, Negative or Neutral. It is noticed that the absence of negation detection feature gives inaccurate results when detecting the entailment relation since the negation revers the truth. The negation words are considered stop words and removed from the text-hypothesis pair which may lead wrong entailment decision. Another case not solved previously, it is impossible that the positive text entails negative text and vice versa. In this paper, in order to classify the text-hypothesis pair polarity, a sentiment analysis tool is used. We show that analyzing the polarity of the text-hypothesis pair increases the entailment accuracy. to evaluate our approach we used a dataset for Arabic textual entailment (ArbTEDS) consisted of 618 text-hypothesis pairs and showed that the Arabic entailment accuracy is increased by resolving negation for entailment relation and analyzing the polarity of the text-hypothesis pair.
Keywords-Arabic NLP; Recognizing Text Entailment (RTE); Sentiment Polarity; Negation; (ArbTEDS) dataset; The Entailment accuracy.
Isao Nishihara, Takayuki Nakata
Department of Information Systems Engineering, Faculty of Engineering, Toyama Prefectural University, Imizu city, Toyama, Japan.
Abstract -In this paper, we propose a simple dynamic image position adjustment method for high-resolution displays in glassless three-dimensional (3D) viewing systems. Glassless stereoscopic displays are currently used in many fields; however, accurately adjusting their physical display position is difficult, especially given their large display area. Our proposed method effects a dynamic adjustment of the positions of images on the display to match various physical conditions in 3D displays. We constructed a simple 3D viewing system using a high-resolution display and a lenticular lens as a prototype. Experimental results confirmed that our method can automatically adjust the positions of images in 3D displays.
In future research, we intend to implement this automatic adjustment system.
Keywords-component; Glassless 3D viewing systems; Autostereoscopy; High-resolution display; Lenticular lens; Software adjustment.