Xu Xiaoli^1,∗, Li Dong¹, Jiang Zhanglei¹, Li Ning¹, Wu Guoxin¹, Wang Hongjun¹, Zhang Xu², Bai Feng²

Computer Systems Science and Engineering, Vol.34, No.4, pp. 191-199, 2019, DOI:10.32604/csse.2019.34.191

Abstract With the aim of protecting, bequeathing, and sharing globally the Dongba manuscripts of the Chinese Naxi minority, the memory and heritage of which is under threat, this paper proposes ideas and plans for building a digital sharing platform to fulfil this aim using computer technology, information processing, online dissemination, multimedia display and other technologies to build an international digital platform for the sharing of Dongba manuscripts. This platform provides digital resources comprising Dongba manuscripts and related literature, tools for deciphering Dongba manuscripts, an environment for undertaking and sharing research, and dynamic information on the research findings and inheritance. The platform… More >

Abayomi-Alli A.¹, Misra S.^2,3, Fernández-Sanz L.⁴, Abayomi-Alli O.^2,*, Edun A. R.¹

Intelligent Automation & Soft Computing, Vol.26, No.3, pp. 385-396, 2020, DOI:10.32604/iasc.2020.013915

Abstract University timetable scheduling is a complicated constraint problem because educational institutions use timetables to maximize and optimize scarce resources, such as time and space. In this paper, an examination timetable system using Genetic Algorithm and Tabu Search memory with course sandwiching (GAT_CS), was developed for a large public University. The concept of Genetic Algorithm with Selection and Evaluation was implemented while the memory properties of Tabu Search and course sandwiching replaced Crossover and Mutation. The result showed that GAT_CS had hall allocation accuracies of 96.07% and 99.02%, unallocated score of 3.93% and 0.98% for first and second semesters, respectively. It… More >

Rizwan Ali Naqvi¹, Muhammad Adnan Khan^{2, *}, Nauman Malik², Shazia Saqib², Tahir Alyas², Dildar Hussain³

CMC-Computers, Materials & Continua, Vol.65, No.2, pp. 1221-1236, 2020, DOI:10.32604/cmc.2020.011686

Abstract Roman Urdu has been used for text messaging over the Internet for years especially in Indo-Pak Subcontinent. Persons from the subcontinent may speak the same Urdu language but they might be using different scripts for writing. The communication using the Roman characters, which are used in the script of Urdu language on social media, is now considered the most typical standard of communication in an Indian landmass that makes it an expensive information supply. English Text classification is a solved problem but there have been only a few efforts to examine the rich information supply of Roman Urdu in the… More >

Joo-Chan Lee¹, Hyun-Pyo Choi¹, Jang-Hoon Kim¹, Jun-Won Kim¹, Da-Un Jung¹, Ji-Ho Shin¹, Jung-Taek Seo^{1, *}

CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 53-67, 2020, DOI:10.32604/cmc.2020.011251

Abstract Cyberattacks on the Industrial Control System (ICS) have recently been increasing, made more intelligent by advancing technologies. As such, cybersecurity for such systems is attracting attention. As a core element of control devices, the Programmable Logic Controller (PLC) in an ICS carries out on-site control over the ICS. A cyberattack on the PLC will cause damages on the overall ICS, with Stuxnet and Duqu as the most representative cases. Thus, cybersecurity for PLCs is considered essential, and many researchers carry out a variety of analyses on the vulnerabilities of PLCs as part of preemptive efforts against attacks. In this study,… More >

Hui Li¹, Cailin Shi^{2, 3}, Xin Liu^{2, 3}, Aziguli Wulamu^{2, 3, *}, Alan Yang⁴

CMC-Computers, Materials & Continua, Vol.64, No.2, pp. 987-1004, 2020, DOI:10.32604/cmc.2020.09812

Abstract The difference in electricity and power usage time leads to an unbalanced current among the three phases in the power grid. The three-phase unbalanced is closely related to power planning and load distribution. When the unbalance occurs, the safe operation of the electrical equipment will be seriously jeopardized. This paper proposes a Hierarchical Temporal Memory (HTM)-based three-phase unbalance prediction model consisted by the encoder for binary coding, the spatial pooler for frequency pattern learning, the temporal pooler for pattern sequence learning, and the sparse distributed representations classifier for unbalance prediction. Following the feasibility of spatialtemporal streaming data analysis, we adopted… More >

Lin Zhou^{1, *}, Siyuan Lu¹, Qiuyue Zhong¹, Ying Chen^{1, 2}, Yibin Tang³, Yan Zhou³

CMC-Computers, Materials & Continua, Vol.63, No.3, pp. 1373-1386, 2020, DOI:10.32604/cmc.2020.010182

Abstract Speaker separation in complex acoustic environment is one of challenging tasks in speech separation. In practice, speakers are very often unmoving or moving slowly in normal communication. In this case, the spatial features among the consecutive speech frames become highly correlated such that it is helpful for speaker separation by providing additional spatial information. To fully exploit this information, we design a separation system on Recurrent Neural Network (RNN) with long short-term memory (LSTM) which effectively learns the temporal dynamics of spatial features. In detail, a LSTM-based speaker separation algorithm is proposed to extract the spatial features in each time-frequency… More >

Ming Wang¹, Jing Liu^2,*, Qiwu Sun^3,4, Wenzhen Zhu⁴

International Journal of Mental Health Promotion, Vol.21, No.3, pp. 81-97, 2019, DOI:10.32604/IJMHP.2019.011010

Abstract Trauma-related flashback is one of the typical symptoms of patients suffered from posttraumatic stress disorder (PTSD), which intrudes into the body and mind of patients uncontrobaly and repeatedly. Psychodynamic theories of mechanisms of the formation and involuntary repetition of trauma-related flashback establish a foundation for most cognitive theories of PTSD. Cognitive behavioral theories of PTSD, from the initial use of behavioristic principles (e.g., conditioning, alternative learning, generalization, etc.) to explain fear conditioning to the emphasis on the roles of cognitive and behavioral factors (e.g., cognitive model or schema, completion tendency, associative network, data-driven processing, analogue representation, etc.) in the representation… More >

Oakyoung Han¹, Jaehyoun Kim^{2, *}

CMC-Computers, Materials & Continua, Vol.63, No.2, pp. 553-566, 2020, DOI:10.32604/cmc.2020.09362

Abstract The processing of sound signals is significantly improved recently. Technique for sound signal processing focusing on music beyond speech area is getting attention due to the development of deep learning techniques. This study is for analysis and process of music signals to generate tow-dimensional tabular data and a new music. For analysis and process part, we represented normalized waveforms for each of input data via frequency domain signals. Then we looked into shorted segment to see the difference wave pattern for different singers. Fourier transform is applied to get spectrogram of the music signals. Filterbank is applied to represent the… More >

R. Mythili^{1, *}, P. Kalpana²

CMC-Computers, Materials & Continua, Vol.62, No.3, pp. 1097-1107, 2020, DOI:10.32604/cmc.2020.08396

Abstract NIDS (Network Intrusion Detection Systems) plays a vital role in security threats to computers and networks. With the onset of gigabit networks, hardware-based Intrusion Detection System gains popularity because of its high performance when compared to the software-based NIDS. The software-based system limits parallel execution, which in turn confines the performance of a modern network. This paper presents a signature-based lookup technique using reconfigurable hardware. Content Addressable Memory (CAM) is used as a lookup table architecture to improve the speed instead of search algorithms. To minimize the power and to increase the speed, precomputation based CAM (PBCAM) can be used,… More >

Duyen Le Hien Nguyen¹, Dieu Thi Thanh Do², Jaehong Lee², Timon Rabczuk³, Hung Nguyen-Xuan^1,4,*

CMC-Computers, Materials & Continua, Vol.61, No.3, pp. 951-977, 2019, DOI:10.32604/cmc.2019.08001

Abstract We in this paper exploit time series algorithm based deep learning in forecasting damage mechanics problems. The methodologies that are able to work accurately for less computational and resolving attempts are a significant demand nowadays. Relied on learning an amount of information from given data, the long short-term memory (LSTM) method and multi-layer neural networks (MNN) method are applied to predict solutions. Numerical examples are implemented for predicting fracture growth rates of L-shape concrete specimen under load ratio, single-edge-notched beam forced by 4-point shear and hydraulic fracturing in permeable porous media problems such as storage-toughness fracture regime and fracture-height growth… More >