Computer and Information Science

Journal Information
ISSN / EISSN : 1913-8989 / 1913-8997
Current Publisher: Canadian Center of Science and Education (10.5539)
Total articles ≅ 867
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Latest articles in this journal

Chris Lee
Computer and Information Science, Volume 14; doi:10.5539/cis.v14n2p109

Reviewer Acknowledgements for Computer and Information Science, Vol. 14, No. 2, 2021
Linqing Yang, Bo Liu, Youpei Huang, Xiaozhuo Li
Computer and Information Science, Volume 14; doi:10.5539/cis.v14n2p63

The lack of entity label values is one of the problems faced by the application of Knowledge Graph. The method of automatically assigning entity label values still has shortcomings, such as costing more resources during training, leading to inaccurate label value assignment because of lacking entity semantics. In this paper, oriented to domain-specific Knowledge Graph, based on the situation that the initial entity label values of all triples are completely unknown, an Entity Label Value Assignment Method (ELVAM) based on external resources and entropy is proposed. ELVAM first constructs a Relationship Triples Cluster according to the relationship type, and randomly extracts the triples data from each cluster to form a Relationship Triples Subset; then collects the extended semantic text of the entities in the subset from the external resources to obtain nouns. Information Entropy and Conditional Entropy of the nouns are calculated through Ontology Category Hierarchy Graph, so as to obtain the entity label value with moderate granularity. Finally, the Label Triples Pattern of each Relationship Triples Cluster is summarized, and the corresponding entity is assigned the label value according to the pattern. The experimental results verify the effectiveness of ELVAM in assigning entity label values in Knowledge Graph.
Caifeng Lv, Xiujie Huang, Shancheng Zhao
Computer and Information Science, Volume 14; doi:10.5539/cis.v14n2p75

In Multi-Level-Cell (MLC) NAND flash memory, cell-to-cell interference (CCI) and retention time have become the main noise that degrades the data storage reliability. To mitigate such noise, a relative precision loss (RPL) nonuniform reference voltage sensing strategy is proposed in this paper. First, based on the NAND flash channel model with CCI and retention noise, we simulate the data storage process of MLC NAND flash by Monte Carlo method, and find that the threshold-voltage of each disturbed storage state shows approximately to be Gaussian distributed. Then, by Gaussian approximation, the distribution of threshold voltage can be estimated easily in mathematics with a little loss. Second, we introduce a concept of log-likelihood ratio (LLR)-based RPL ratio to determine the dominating overlap regions, and then propose a new nonuniform reference voltage sensing strategy. This strategy does not only reduce the memory sensing precision (i.e., the number of reference voltages), but also maintains the reliability of the soft information of NAND flash memory channel output for soft decoding. Third, we implement extensive simulations to verify the performance of the new nonuniform sensing strategy. The BER performances of LDPC codes for different sensing strategies are provided to show that the proposed LLR-based RPL-nonuniform sensing strategy can make a good compromise between memory sensing latency and error-correction performance.
Hussain Mohammad Abu-Dalbouh, Fahad Almansour, Nehal Aldowighri
Computer and Information Science, Volume 14; doi:10.5539/cis.v14n2p87

In recent decades, massive improvements in graphic sophistication have begun to produce declining returns. The creative focus in game development has shifted to artificial intelligence. The queens’ task game is part of a sequence of popular games. It is the challenge of putting n chess queens on a game board such that no two queens are threatening each other. The plan does not involve two queens sharing the same row, column or diagonal. Each column contains exactly one queen, each row contains exactly one queen, and each diagonal contains exactly one queen. For every level in the game, there are many ways to solve it. For example, there are 92 solutions to the 8×8 problem. There are many levels in the literature, but each level should be downloaded separately. Thus, it causes a lot of difficulties for players, and they should download each level to complete the challenge. This will lead to more time and effort being spent by the players, and the cost of each level will cost the players more and more. As a result, the number of players who want to play this game will decrease. The aim of this paper is to incorporate a number of levels in order to save time, money and effort by downloading each level separately. This paper also aims to develop the proposed prototype and display all the solutions while playing a puzzle game at any level. The proposed game was tested by a questionnaire-based empirical study. Descriptive statistics on the questions revealed that the players had achieved the objectives of the game by applying their skills and knowledge and that the players had positive emotions about the effectiveness of the proposed game.
Iyas Alodat
Computer and Information Science, Volume 14; doi:10.5539/cis.v14n2p42

In this paper will discuss and examine message transmission from the attacker process within the scope of Delay Tolerance Networks (DTNs). DTNs are a new area of research that can be developed in networking. Delay-tolerant networks are those networks that may not have a complete path between networks end-to-end via direct links and may be under development for a long time. As part of the improvement, we will compare a survey of DTN routing protocols with a real region area, and then taking into account the possibilities of detecting the presence of areas of weakness that lead to penetration, which will occur in the nodes while on the move. In this study, we will use the ONE simulator to track messages within nodes.
Asma Alsayyar, Riyad Almakki
Computer and Information Science, Volume 14; doi:10.5539/cis.v14n2p50

The purpose of this paper was to investigate the impact of Augmented Reality on e-learning systems at colleges in Saudi Arabia. In this research, Augmented Reality could reenact real environment by computerized overlays that learners can interact with and without much of a stretch access. What is more, Augmented Reality helps consumers to explore alternative learning avenues around learning content. Setting that aside, there has not been sufficiently thorough research on the evaluation of Augmented Reality in the context of teaching. The primary objective of this research is to examine possible standard factors identified with the successful use of unparalleled scale. This prototype highlights the essential factors that affect the implementation of AR via the quantitative approach to Augmented Reality knowledge assortment and evaluation. The research finds the principal coefficients for the attainment of Augmented Reality: IT infrastructure, IT agility, interaction stability, self-learning ability, curriculum, student background, ease of use and Usefulness. The after-effects of this analysis includes useful debates to create up a perfect fate of Augmented Reality and help handle the enhancement of instruction and e-learning with competitive societies and frameworks in the Kingdom of Saudi Arabia as well as other countries.
Hussin J. Hejase, Hasan F. Fayyad-Kazan, Ale J. Hejase, Imad A. Moukadem
Computer and Information Science, Volume 14; doi:10.5539/cis.v14n2p10

COVID-19 pandemic obliged thousands of companies pertaining to all economic sectors to undergo the transformation from on-board work to working from home. Along such rush, the probability for companies being hacked incremented many folds. According to VMware cybersecurity strategist Tom Kellermann, quoted in Menn (2020), “There is a digitally historic event occurring in the background of this pandemic, and that is there is a cybercrime pandemic that is occurring” (para 5). In fact, Software and security company VMware Carbon Black declared during April, “that ransomware attacks it monitored jumped 148% in March from the previous month, as governments worldwide curbed movement to slow the spread of the novel corona virus” (Para 4). On the other hand, Anft (2020) reported that “more than 500 educational institutions, including colleges and K-12 schools, faced ransom attacks in 2019” (para 2). This paper uses a descriptive qualitative approach to shed light on the aforementioned subject depending on reported secondary literature about the topic, and offers an analysis to pinpoint weaknesses and barriers, as well as best practices to counterattack the breaches to cybersecurity in organizations. The outcomes serve as an eye opener for security officers in charge of the safety of organizational intellectual properties and stimulates organizations to adopt protection systems and safety practices.
Na Li, Lianguan Huang, Yanling Li, Meng Sun
Computer and Information Science, Volume 14; doi:10.5539/cis.v14n2p26

In recent years, with the development of the Internet, the data on the network presents an outbreak trend. Big data mining aims at obtaining useful information through data processing, such as clustering, clarifying and so on. Clustering is an important branch of big data mining and it is popular because of its simplicity. A new trend for clients who lack of storage and computational resources is to outsource the data and clustering task to the public cloud platforms. However, as datasets used for clustering may contain some sensitive information (e.g., identity information, health information), simply outsourcing them to the cloud platforms can't protect the privacy. So clients tend to encrypt their databases before uploading to the cloud for clustering. In this paper, we focus on privacy protection and efficiency promotion with respect to k-means clustering, and we propose a new privacy-preserving multi-user outsourced k-means clustering algorithm which is based on locality sensitive hashing (LSH). In this algorithm, we use a Paillier cryptosystem encrypting databases, and combine LSH to prune off some unnecessary computations during the clustering. That is, we don't need to compute the Euclidean distances between each data record and each clustering center. Finally, the theoretical and experimental results show that our algorithm is more efficient than most existing privacy-preserving k-means clustering.
Oleg V. Moroz
Computer and Information Science, Volume 14; doi:10.5539/cis.v14n2p1

In this paper, Kant's philosophical doctrine of the categories of the reason is used to substantiate the conceptual model of knowledge representation, based on the collective interaction of a lot of intellectual atomic elements of knowledge (knowledge quanta), which are combined into clusters like neurons in the brain; and also a phenomenological description of the corresponding universal ontology, proceeding from the philosophical premise of Husserl-Heidegger that the meaning of intelligence is not so much in knowing the absolute truth as in survival, is presented. In the process of cognizing the surrounding world, a person uses both a priori knowledge and a posteriori knowledge, but the transcendental content of a priori forms of thinking does not allow them to be used directly in logical judgments. Nevertheless, one can try to use them as "ontological predicates" following the advice of I. Kant, what was done in this article. Heuristic ontological relations that directly follow from the categories of Kant are easy to use and sufficient to describe any ontology. Offered knowledge representation model, the key idea of which is the primacy of knowledge to logical inference and their emergent ability to self-organize, in conjunction with the transcendental logic-based ontology of empirical knowledge can be used to create a universal inference engine.
Chris Lee
Computer and Information Science, Volume 14; doi:10.5539/cis.v14n1p54

Reviewer Acknowledgements for Computer and Information Science, Vol. 14, No. 1, 2021
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