Anil Kumar Gulivindala¹, M. V. A. Raju Bahubalendruni¹, R. Chandrasekar^1,2, Ejaz Ahmed², Mustufa Haider Abidi^3,*, Abdulrahman Al-Ahmari⁴

CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 2531-2548, 2021, DOI:10.32604/cmc.2021.018014

Abstract The evolution of Industry 4.0 made it essential to adopt the Internet of Things (IoT) and Cloud Computing (CC) technologies to perform activities in the new age of manufacturing. These technologies enable collecting, storing, and retrieving essential information from the manufacturing stage. Data collected at sites are shared with others where execution automatedly occurs. The obtained information must be validated at manufacturing to avoid undesirable data losses during the de-manufacturing process. However, information sharing from the assembly level at the manufacturing stage to disassembly at the product end-of-life state is a major concern. The current research validates the information optimally… More >

Zunaira Sher¹, Muhammad Umair Majid¹, Sameera Hassan¹, Fatima Batool¹, Beenish Aftab^1,2, Bushra Rashid^1,*

Phyton-International Journal of Experimental Botany, Vol.90, No.6, pp. 1613-1632, 2021, DOI:10.32604/phyton.2021.016154

Abstract Pakistan is facing the threat of Cotton Leaf Curl Virus (CLCuV) which is transmitted through whitefly to cotton crop. Molecular mechanism of leaf epicuticular wax protects the plants from different pathogens including insect attack and disease transmission. Objective of current study is the isolation and characterization of a wax related gene GaCyPI from Gossypium arboreum under CLCuV infection. A fragment of 475 bp was isolated from the total RNA and 3’ and 5’ RACE-PCR products were arranged by overlapping the extended sequences at both the ends. Deduced protein sequence of GaCyPI showed homology with Cyclophilin cis-trans isomerase gene of Gossypium… More >

Huizhong Sun¹, Guosheng Xu^1,*, Hewei Yu², Minyan Ma³, Yanhui Guo¹, Ruijie Quan⁴

Journal of Quantum Computing, Vol.3, No.2, pp. 55-63, 2021, DOI:10.32604/jqc.2021.017365

Abstract Language detection models based on system calls suffer from certain false negatives and detection blind spots. Hence, the normal behavior sequences of some malware applications for a short period can become malicious behavior within a certain time window. To detect such behaviors, we extract a multidimensional time distribution feature matrix on the basis of statistical analysis. This matrix mainly includes multidimensional time distribution features, multidimensional word pair correlation features, and multidimensional word frequency distribution features. A multidimensional time distribution model based on neural networks is built to detect the overall abnormal behavior within a given time window. Experimental evaluation is… More >

Zhichao Hu¹, Xiangzhan Yu^1,*, Jiantao Shi¹, Lin Ye^1,2

CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 695-711, 2021, DOI:10.32604/cmc.2021.017574

Abstract With the continuous development of network technology, various large-scale cyber-attacks continue to emerge. These attacks pose a severe threat to the security of systems, networks, and data. Therefore, how to mine attack patterns from massive data and detect attacks are urgent problems. In this paper, an approach for attack mining and detection is proposed that performs tasks of alarm correlation, false-positive elimination, attack mining, and attack prediction. Based on the idea of CluStream, the proposed approach implements a flow clustering method and a two-step algorithm that guarantees efficient streaming and clustering. The context of an alarm in the attack chain… More >

Yuan Sun^1,2,*, Chaofan Chen^1,2, Andong Chen³, Xiaobing Zhao^1,2

CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3203-3213, 2021, DOI:10.32604/cmc.2021.016517

Abstract As the dual task of question answering, question generation (QG) is a significant and challenging task that aims to generate valid and fluent questions from a given paragraph. The QG task is of great significance to question answering systems, conversational systems, and machine reading comprehension systems. Recent sequence to sequence neural models have achieved outstanding performance in English and Chinese QG tasks. However, the task of Tibetan QG is rarely mentioned. The key factor impeding its development is the lack of a public Tibetan QG dataset. Faced with this challenge, the present paper first collects 425 articles from the Tibetan… More >

Mary Aksa¹, Junaid Rashid^2,*, Muhammad Wasif Nisar¹, Toqeer Mahmood³, Hyuk-Yoon Kwon⁴, Amir Hussain⁵

CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3931-3946, 2021, DOI:10.32604/cmc.2021.016081

Abstract Advancements in next-generation sequencer (NGS) platforms have improved NGS sequence data production and reduced the cost involved, which has resulted in the production of a large amount of genome data. The downstream analysis of multiple associated sequences has become a bottleneck for the growing genomic data due to storage and space utilization issues in the domain of bioinformatics. The traditional string-matching algorithms are efficient for small sized data sequences and cannot process large amounts of data for downstream analysis. This study proposes a novel bit-parallelism algorithm called BitmapAligner to overcome the issues faced due to a large number of sequences… More >

Peng Wan¹, Ting Wen^2,*, Yunfei Zhang³, Hong Gao¹, Jigan Wang¹

International Journal of Mental Health Promotion, Vol.23, No.2, pp. 201-219, 2021, DOI:10.32604/IJMHP.2021.015759

Abstract Goal self-concordance reflects self-generated personal goals aligning with people’s interests and core values in one’s implicit personality as organic components, which is measured by the “perceived locus of causality” PLOC. Pursuing and achieving self-concordant goals both predict diversified outcomes in need-satisfaction, mental and physical well-being, positive attitude and behavior, etc. Based on expounding and sorting out the concept and measurement about goal self-concordance, the author analyzes the differences among a series of goal self-concordance theories. This paper focuses on the latest research trends and summarizes five influencing aspects of goal self-concordance: mental health, cognition, emotion, personal will, and behavioral outcomes.… More >

HANNA AMOANIMAA-DEDE, JIACHENG ZHANG, CHUNTAO SU, HONGBO ZHU^*

BIOCELL, Vol.45, No.4, pp. 1095-1105, 2021, DOI:10.32604/biocell.2021.015053

Abstract Sweet potato is a multifunctional root crop with many essential nutrients and bioactive compounds. Due to its genetic complexity and lack of genomic resources, efficient genetic studies and cultivar development lag far behind other major crops. Simple sequence repeats (SSRs) offer an effective molecular marker technology for molecular-based breeding and for locating important loci in crop plants, but only a few have previously been developed in sweet potato. To further explore new SSR markers and accelerate their use in sweet potato genetic studies, genome-wide characterization and development of SSR markers were performed using the recently published genome of sweet potato… More >

Javaria Amin¹, Muhammad Almas Anjum², Muhammad Sharif³, Seifedine Kadry⁴, Yunyoung Nam^5,*, ShuiHua Wang⁶

CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 2693-2709, 2021, DOI:10.32604/cmc.2021.016871

Abstract Recognition of human gait is a difficult assignment, particularly for unobtrusive surveillance in a video and human identification from a large distance. Therefore, a method is proposed for the classification and recognition of different types of human gait. The proposed approach is consisting of two phases. In phase I, the new model is proposed named convolutional bidirectional long short-term memory (Conv-BiLSTM) to classify the video frames of human gait. In this model, features are derived through convolutional neural network (CNN) named ResNet-18 and supplied as an input to the LSTM model that provided more distinguishable temporal information. In phase II,… More >

Miguel-Angel Sicilia^1,*, Elena García-Barriocanal¹, Marçal Mora-Cantallops¹, Salvador Sánchez-Alonso¹, Lino González²

CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 1661-1672, 2021, DOI:10.32604/cmc.2021.015874

Abstract Existing studies have challenged the current definition of named bacterial species, especially in the case of highly recombinogenic bacteria. This has led to considering the use of computational procedures to examine potential bacterial clusters that are not identified by species naming. This paper describes the use of sequence data obtained from MLST databases as input for a k-means algorithm extended to deal with housekeeping gene sequences as a metric of similarity for the clustering process. An implementation of the k-means algorithm has been developed based on an existing source code implementation, and it has been evaluated against MLST data. Results… More >