INTEGRATED SPECIFICATION OF QUALITY REQUIREMENTS IN SOFTWARE PRODUCT LINE ARTIFACTS

Author :  Mworia Daniel

Affiliation :  Jommo Kenyatta University of Agriculture and Technology

Country :  Kenya

Category :  Computer Science & Information Technology

Volume, Issue, Month, Year :  11, 03, March, 2021

Abstract :

There are many calls from software engineering scholars to incorporate non-functional requirements as first-class citizens in the software development process. In Software Product Line Engineering emphasis is on explicit definition of functional requirements using feature models while non-functional requirements are considered implicit. In this paper we present an integrated requirements specification template for common quality attributes alongside functional requirements at software product line variation points. This approach implemented at analytical description phase increases the visibility of quality requirements obliging developers to consider them in subsequent phases. The approach achieves weaving of quality requirements into associated functional requirements through higher level feature abstraction method. This work therefore promotes achievement of system quality by elevating nonfunctional requirement specification. The approach is illustrated with an exemplar mobile phone family data storage requirements case study.

Keyword :  Software Product Line Engineering, Functional and Non-functional requirements, Quality attributes, feature variability, integration and requirements specification.

For More Details :  https://aircconline.com/csit/papers/vol11/csit110309.pdf

INVESTIGATING DATA SHARING IN SPEECH RECOGNITION FOR AN UNDER-RESOURCED LANGUAGE: THE CASE OF ALGERIAN DIALECT

Author :  Mohamed Amine Menacer

Affiliation :  Université de Lorraine

Country :  France

Category :  Computer Science & Information Technology

Volume, Issue, Month, Year :  11, 03, March, 2021

Abstract :

The Arabic language has many varieties, including its standard form, Modern Standard Arabic (MSA), and its spoken forms, namely the dialects. Those dialects are representative examples of under-resourced languages for which automatic speech recognition is considered as an unresolved issue. To address this issue, we recorded several hours of spoken Algerian dialect and used them to train a baseline model. This model was boosted afterwards by taking advantage of other languages that impact this dialect by integrating their data in one large corpus and by investigating three approaches: multilingual training, multitask learning and transfer learning. The best performance was achieved using a limited and balanced amount of acoustic data from each additional language, as compared to the data size of the studied dialect. This approach led to an improvement of 3.8% in terms of word error rate in comparison to the baseline system trained only on the dialect data.

Keyword :  Automatic speech recognition, Algerian dialect, MSA, multilingual training, multitask learning, transfer learning.

For More Details :  https://aircconline.com/csit/papers/vol11/csit110308.pdf

BEST PRACTICES IN DESIGNING AND IMPLEMENTING CLOUD AUTHENTICATION SCHEMES

Author :  Zhihao Zheng

Affiliation :  Northern Kentucky University

Country :  USA

Category :  Computer Science & Information Technology

Volume, Issue, Month, Year :  11, 03, March, 2021

Abstract :

At present, the development and innovation in any business/engineering field are inseparable from the computer and network infrastructure that supports the core business. The world has been turning into an era of rapid development of information technology. Every year, there are more individuals and companies that start using cloud storages and other cloud services for computing and information storage. Therefore, the security of sensitive information in cloud becomes a very important challenge that needs to be addressed. The cloud authentication is a special form of authentication for today’s enterprise IT infrastructure. Cloud applications communicate with the LDAP server which could be an on-premises directory server or an identity management service running on cloud. Due to the complex nature of cloud authentication, an effective and fast authentication scheme is required for successful cloud applications. In this study, we designed several cloud authorization schemes to integrate an onpremises or cloud-based directory service with a cloud application. We also discussed the pros and cons of different approaches to illustrate the best practices on this topic.

Keywords :  Cloud Application Authentication, Identity Management in Cloud, IAM.

For More Details :  https://aircconline.com/csit/papers/vol11/csit110307.pdf

CLASSIFYING AUTISM SPECTRUM DISORDER USING MACHINE LEARNING MODELS

Author :  Tingyan Deng

Affiliation :  Vanderbilt University

Country :  USA

Category :  Computer Science & Information Technology

Volume, Issue, Month, Year :  11, 03, March, 2021

Abstract :

Autistic Spectrum Disorder (ASD) is a very common and serious developmental disability, which impairs the ability to communicate and interact, causing significant social, communication, and behavior challenges. From a rare childhood disorder, ASD has evolved into a disorder that is found, according to the National Institute of Health, in 1% to 2% of the population in high income countries. A potential early and accurate diagnosis can not only help doctors to find the disease early, leading to a more on time treatment to the patient, but also can save significant healthcare costs for the patients. With the rapid growth of ASD cases, many open-source ASD related datasets were created for scientists and doctors to investigate this disease. Autistic Spectrum Disorder Screening Data for Adult is a well-known dataset, which contains 20 features to be utilized for further analysis on the potential cause and prediction of ASD. In this paper, we developed an Autism classification algorithm based on logistic regression model. Our model starts with featuring engineering to extract deep information from the dataset and then applied a modified logistic regression classifier to the data. The model predicts the ASD well in an average F1 score of 0.92.

Keyword :  ASD, Classification, Machine Learning, Neurodiversity.

For More Details :  https://aircconline.com/csit/papers/vol11/csit110306.pdf

ROLLING BEARING FAULT DIAGNOSIS AND PREDICTION BASED ON VMD-CWT AND MOBILENET

Author :  Jing Zhu

Affiliation :  southeast university

Country :  China

Category :  Computer Science & Information Technology

Volume, Issue, Month, Year :  11, 03, March, 2021

Abstract :

When deep learning is used for rolling bearing fault diagnosis, there are problems of high model complexity, time-consuming, and large memory. In order to solve this problem. This paper presents an intelligent diagnosis method of rolling bearings based on VMD-CWT feature extraction and MobileNet, VMD is used to extract the signal features, and then wavelet transform is used to extract the timefrequency features. After the image is enhanced, the MobileNet network is trained. In order to accelerate the convergence speed, this paper adds transfer learning in the network training process, and migrates the weights of the first several layers pretrained to the corresponding network. Experimental results based on bearing fault data sets show that after adopting VMD-CWT, the accuracy of mobilenet increased from 68.7% to 94%, and its network parameters were reduced by an order of magnitude compared with CNN.

Keyword :  Mobilenet, Variational modal decomposition, Continuous wavelet transform, Rolling bearing.

For More Details :  https://aircconline.com/csit/papers/vol11/csit110305.pdf

TOWARDS COMPARING MACHINE LEARNING MODELS TO FORESEE THE STAGES FOR HEART DISEASE

Author :  Khalid Amen

Affiliation :  Oakland University

Country :  USA

Category :  Computer Science & Information Technology

Volume, Issue, Month, Year :  11, 03, March, 2021

Abstract :

With the increase in heart disease rates at advanced ages, we need to put a high quality algorithm in place to be able to predict the presence of heart disease at an early stage and thus, prevent it. Previous Machine Learning approaches were used to predict whether patients have heart disease. The purpose of this work is to compare two more algorithms (NB, KNN) to our previous work [1] to predict the five stages of heart disease starting from no disease, stage 1, stage 2, stage 3 and advanced condition, or severe heart disease. We found that the LR algorithm performs better compared to the other two algorithms. The experiment results show that LR performs the best with an accuracy of 82%, followed by NB with an accuracy of 79% when all three classifiers are compared and evaluated for performance based on accuracy, precision, recall and F measure.

Keyword :  Machine Learning (ML), Logistic Regression (LR), Naïve Bayes (NB), K-Nearest Neighbors (KNN).

For More Details :  https://aircconline.com/csit/papers/vol11/csit110304.pdf

DEEP LEARNING SELF-ORGANIZING MAP OF CONVOLUTIONAL LAYERS

Author :  Christos Ferles

Affiliation :  University of West Attica, Aegaleo

Country :  Greece

Category :  Computer Science & Information Technology

Volume, Issue, Month, Year :  11, 03, March, 2021

Abstract :

The Self-Organizing Convolutional Map (SOCOM) combines convolutional neural networks, clustering via self-organizing maps, and learning through gradient backpropagation into a novel unified unsupervised deep architecture. The proposed clustering and training procedures reflect the model’s degree of integration and synergy between its constituting modules. The SOCOM prototype is in position to carry out unsupervised classification and clustering tasks based upon the distributed higher level representations that are produced by its underlying convolutional deep architecture, without necessitating target or label information at any stage of its training and inference operations. Due to its convolutional component SOCOM has the intrinsic capability to model signals consisting of one or more channels like grayscale and colored images.

Keyword :  Deep Learning, Unsupervised Learning, Convolutional Neural Network (CNN), Self-Organizing Map (SOM), Clustering.

For More Details :  https://aircconline.com/csit/papers/vol11/csit110303.pdf

ADOPTION OF PRECISION MEDICINE; LIMITATIONS AND CONSIDERATIONS

 Author :  Nasim Sadat Mosavi

Affiliation :  University of Minho

Country :  Portugal

Category :  Computer Science & Information Technology

Volume, Issue, Month, Year :  11, 03, March, 2021

Abstract :

Research is ongoing all over the world for identifying the barriers and finding effective solutions to accelerate the projection of Precision Medicine (PM) in the healthcare industry. Yet there has not been a valid and practical model to tackle the several challenges that have slowed down the widespread of this clinical practice. This study aimed to highlight the major limitations and considerations for implementing Precision Medicine. The two theories Diffusion of Innovation and Socio-Technical are employed to discuss the success indicators of PM adoption. Throughout the theoretical assessment, two key theoretical gaps are identified and related findings are discussed.

Keyword :  Precision Medicine, Adoption, Artificial Intelligence, Healthcare Big Data, Open data exchange, Genomes, Biological indicators, Standards, Internet of Things, Blockchain.

For More Details :  https://aircconline.com/csit/papers/vol11/csit110302.pdf

FINDING SIMILAR ENTITIES ACROSS KNOWLEDGE GRAPHS

Author :  Sareh Aghaei

Affiliation :  University of Innsbruck

Country :  Austria

Category :  Computer Science & Information Technology

Volume, Issue, Month, Year :  11, 03, March, 2021

Abstract :

Finding similar entities among knowledge graphs is an essential research problem for knowledge integration and knowledge graph connection. This paper aims at finding semantically similar entities between two knowledge graphs. This can help end users and search agents more effectively and easily access pertinent information across knowledge graphs. Given a query entity in one knowledge graph, the proposed approach tries to find the most similar entity in another knowledge graph. The main idea is to leverage graph embedding, clustering, regression and sentence embedding. In this approach, RDF2Vec has been employed to generate vector representations of all entities of the second knowledge graph and then the vectors have been clustered based on cosine similarity using K medoids algorithm. Then, an artificial neural network with multilayer perception topology has been used as a regression model to predict the corresponding vector in the second knowledge graph for a given vector from the first knowledge graph. After determining the cluster of the predicated vector, the entities of the detected cluster are ranked through sentence-BERT method and finally the entity with the highest rank is chosen as the most similar one. To evaluate the proposed approach, experiments have been conducted on real-world knowledge graphs.

Keyword :  Knowledge Graph, Similar Entity, Graph Embedding, Clustering, Regression, Sentence Embedding

For More Details :  https://aircconline.com/csit/papers/vol11/csit110301.pdf

CELL SWITCHES MODEL APPLYING MARKOV CHAIN STOCHASTIC MODEL CHECK ON BETWEEN TWO POPULATION WITH REGARDS TO MRNA AND PROTEINS AND NEURONS BOTH CLASSICALLY AND QUANTUM COMPUTATIONALLY

Author :  Qin He

Affiliation :  East China University of Science and Technology

Country :  China

Category :  Computer Science & Information Technology

Volume, Issue, Month, Year :  11, 02, February, 2021

Abstract :

Arc, one virus-like gene, crucial for learning and memory, was dis-covered by researchers in neurological disorders fields, Arc mRNA’s single directed path and allowing protein binding regional restric-tively is a potential investigation on helping shuttle toxic proteins responsible for some diseases related to memory deficiency. Mean time to switching (MTS) is calculated explicitly quantifying the switching process in statistical methods combining Hamiltonian Markov Chain(HMC). The model derived from predator and prey with typeII functional response studies the mechanism of normals with intrin-sic rate of increase and the persisters with the instantaneous discovery rate and converting coefficients. During solving the results, since the numeric method is applied for the 2D approximation of Hamiltonion with intrinsic noise induced switching combining geometric minimum action method. In the application of Hamiltonian Markov Chain, the behavior of the convertion (between mRNA and proteins through 6 states from off to on ) is described with probabilistic conditional logic formula and the final concentration is computed with both Continuous and Discret Time Markov Chain(CTMC/DTMC) through Embedding and Switching Diffusion. The MTS, trajectories and Hamiltonian dynamics demonstrate the practical and robust advantages of our model on interpreting the switching process of genes (IGFs, Hax Arcs and etc.) with respects to memory deficiency in aging process which can be useful in further drug efficiency test and disease curing. Coincidentally, the Hamiltonian is also well used in describing quantum mechanics and convenient for computation with time and position information using quantum bits while in the second model we construct, switching between excitatory and inhibitory neurons, similarity of qubit and neuron is an interesting object as well.

Keyword :  switching model, mean time to switching, Hamiltonian Markov Chain, geometric minimum action method

For More Details :  https://aircconline.com/csit/papers/vol11/csit110205.pdf

REVERSIBLE DATA HIDING BASED ON TWO-DIMENSIONAL HISTOGRAM SHIFTING

Author :  Juan Zhao

Affiliation :  Wuhan Polytechnic University

Country :  China

Category :  Computer Science & Information Technology

Volume, Issue, Month, Year :  11, 02, February, 2021

Abstract :

This paper presents a two-dimensional histogram shifting technique for reversible data hiding algorithm. In order to avoid the distortion drift caused by hiding data into stereo H.264 video, we choose arbitrary embeddable blocks from 4×4 quantized discrete cosine transform luminance blocks which will not affect their adjacent blocks. Two coefficients in each embeddable block are chosen as a hiding coefficient pair. The selected coefficient pairs are classified into different sets on the basis of their values. Data could be hidden according to the set which the value of the coefficient pair belongs to. When the value of one coefficient may be changed by adding or subtracting 1, two data bits could be hidden by using the proposed method, whereas only one data bit could be embedded by employing the conventional histogram shifting. Experiments show that this two-dimensional histogram shifting method can be used to improve the hiding performance.

Keyword :  Reversible data hiding, Two-dimensional histogram shifting, H.264, Multi-view coding

For More Details :  https://aircconline.com/csit/papers/vol11/csit110204.pdf

LOSSLESS STEGANOGRAPHY ON ORTHOGONAL VECTOR FOR 3D H.264 WITH LIMITED DISTORTION DIFFUSION

Author :  Juan Zhao

Affiliation :  Wuhan Polytechnic University

Country :  China

Category :  Computer Science & Information Technology

Volume, Issue, Month, Year :  11, 02, February, 2021

Abstract :

In order to improve the undetectability, a lossless algorithm based on orthogonal vectors with limited distortion diffusion for 3D H.264 video is proposed in this paper. Inter-view distortion drift is avoided by embedding data into frames, which do not predict other views. Three conditions and pairs of coefficients are proposed to prevent intra-frame distortion diffusion. Several quantized discrete cosine transform coefficients are chosen from an embeddable luminance 4×4 block to construct a carrier vector, which is modified by an offset vector. When the carrier vector and the offset vector are orthogonal or near to be orthogonal, a data bit can be hidden. Experimental results indicate that the method is effective by enhancing peak signal-to-noise ratio with 7.5dB and reducing the Kullback-Leibler divergence with 0.07 at least. More than 1.7×1015 ways could be utilized for constructing the vectors, so it is more difficult for others to steal data.

Keyword :  Lossless Steganography, Reversible Data Hiding, Orthogonal Vector, 3D H.264, Distortion Drift

For More Details :  https://aircconline.com/csit/papers/vol11/csit110203.pdf