Recent Advances in Computer Science and Communications - Volume 13, Issue 4, 2020

Background: With the explosion of communication technologies and the accompanying pervasive use of social media, we notice an outstanding proliferation of posts, reviews, comments, and other forms of expressions in different languages. This content attracted researchers from different fields; economics, political sciences, social sciences, psychology and particularly language processing. One of the prominent subjects is the discrimination between similar languages and dialects using natural language processing and machine learning techniques. The problem is usually addressed by formulating the identification as a classification task. Methods: The approach is based on machine learning classification methods to discriminate between Modern Standard Arabic (MSA) and four regional Arabic dialects: Egyptian, Levantine, Gulf and North-African. Several models were trained to discriminate between the studied dialects in large corpora mined from online Arabic newspapers and manually annotated. Results: Experimental results showed that n-gram features could substantially improve performance. Logistic regression based on character and word n-gram model using Count Vectors identified the handled dialects with an overall accuracy of 95%. Best results were achieved with Linear Support vector classifier using TF-IDF Vectors trained by character-based uni-gram, bi-gram, trigram, and word-based uni-gram, bi-gram with an overall accuracy of 95.1%. Conclusion: The results showed that n-gram features could substantially improve performance. Additionally, we noticed that the kind of data representation could provide a significant performance boost compared to simple representation.

Background: Road accidents have become a major social and health problem for being dramatically increasing day after day worldwide. Scientists are conducting their studies to define the main attributes that share the severity of road accidents. Finding a new approach to analyze road accidents is of great urgency. Data mining techniques are best fitting to discover useful information out of enormous data which are used to make proactive decisions. Methods: This paper tempts a rule-based machine learning method known as association rule mining, which can identify strong rules discovered in databases using interesting measures. Given a data- set from the Lebanese territory for the years 2016-2017, the application of association rule mining, the Apriori method takes its place. However, its implementation leads to a very large number of rules. The task that is the most difficult is extracting meaningful and non-redundant rules. In order to find out the most interesting and relevant rules out of fatal rules such, ELECTRE TRI and PROMETHEE methods, the most significant methods of decision making, Multi-Criteria Decision Analysis (MCDA) are integrated to resolve the outranking problem. The integration is presented by the use of the same set of weights and the same constant values of Indifference and Preference thresholds used in ELECTRE-TRI method to define the linear preference function needed by PROMETHEE method. Realizing the sensitivity of the final output of alternatives ranking to the changes of the input parameters of the decision-making tool, this proposed integration helps the decision makers to overcome their ambivalence between preference and indifference thresholds and to cope adequately with the issue of the uncertainty of MCDA procedures; it comes up with the complete ranking of rules. Results: The obtained ranked rules declare the most significant attributes or combinations of attributes that influence the severity of road accidents. Four main factors of fatal road accidents are pinpointed: over-speeding mainly leading up to rollover crashes, pedestrians encountering in the context, distracted driving leading to fatal road vehicle collisions with Pedestrian victims; and wet roads particularly in the case of single car accidents. Meanwhile, the importance of ELECRE-TRI and PROMETHEE and their integration in dealing with such complex phenomena and corresponding database with a large number of involved attributes have been validated. Conclusion: This paper studies the phenomenon of road accidents. Association rule mining has been applied to discover all possible relations between the various attributes. The integration of ELECTRE- TRI and PROMETHEE MCDA techniques aims at extracting meaningful information from the big dataset. The obtained results have shown how influencing the behavior of the driver is on the occurrence of fatal road accidents. These findings contribute to supporting decision makers to draw new design conceptions for road infrastructure and develop preventive measures that improve road safety in Lebanon.

Background: Design Pattern is regarded as an essential component for enhancement of system design which can further improve the reusability and maintainability whereas antipattern degrades the quality of the program. Antipatterns are sub-optimal implementation choices which initially appears to be a good concept but later on leads to low code maintainability and higher maintenance costs. Objective: The identification of antipatterns which lead to performance degradation plays an important role in the reduction of expensive work and cost involved in maintenance, redesign, reimplementation, and redeployment. The main motivation is to refactor the source code in order to reduce maintenance efforts. Antipatterns impact reliability, testability and maintainability, but they still lack clear identification because of different interpretations and definition of each antipattern. There is a need for a benchmark to analyze the result generated by antipatterns. Methods: Static and dynamic analysis individually do not suffice for antipattern. A hybrid approach is proposed by combining rule based static analysis with dynamic run time analysis. Before applying the hybrid approach a simple manual validation was done to exclude the type of input which had the least probability of containing antipattern. The approach aims at optimizing the results by inclusion of response time metric measure which can be evaluated at runtime execution of the web service. Results and Conclusion: The paper focusses on detection of antipatterns from web services based on code level and interface level static metrics. Only a brief overview of dynamic approach for detection is proposed. The future scope of this paper will focus on detection of antipattern based on more number of dynamic metrics and also a comparative analysis of the results generated from static, dynamic and hybrid approach.

Background: Today's widely used small and portable mobile database technologies have geared toward the mobile distributed real-time database systems (MDRTDBS).Various real time applications like mobile devices, missile systems, navigation control systems, satellites and many others are some examples of MDRTDBS. In the new era of technology, a large domain of applications are based on MDRTDBS, meanwhile different intrinsic limitation like disconnection and mobility typically effect on its correct execution. Mobile distributed real-time systems have different wireless constrained such like energy, processing capacity, memory storage facilities and variable network communication channels. In last few years, different applications run on different mobile nodes needed a suitable transaction mechanism to complete their service without failing its deadline. In recent years researchers focused on MDRTDBS, to develop a suitable concurrency control, commit control method. Replication, check pointing, security, caching and query processing are some other hot research topics in the field of MDRTDBS. Objective: it is needed to maintain data consistency and correct results in mobile distributed real time database system. In our review we have identified key issues which might be considered for development of various transaction executing protocols. We have Introduce a taxonomy of different CC, commit, replication and security issues, which could be advantageous for design, and development of transaction protocols. Method: In the review we have discussed various concurrency, commit, replication methods. Apart from these we have discussed various check pointing, caching and query techniques which is developed in database system. A comparison among various concurrency and commit protocols has been done in the review. The role of different key methods which can affect and help the transaction execution in wireless environment is discussed separately in the paper. Results: Analytical results are not mentioned in the review paper. However the role and affect on the transaction execution are mentioned clearly. Issues and their advantages of different concurrency and commit protocols are mentioned. Conclusion: It is found that transaction processing is still a challenging area of research. A number of issues has been discussed and reviewed various approaches to control concurrency control and atomicity methods. We have presented a detailed survey and classification of various issues based on commit, concurrency, and replication methods for MTDRTDBS. However, in the paper different security, caching and query processing and check pointing issues has been also discussed which should be considered for future work. Database researchers have needed to integrate these issues with their work and develop a suitable protocol.

Background: The properties of the natural bio-sensors as the fuel after treatment, is beneficial and considered as the most environmental friendly alternative. The microbial fuel cell will help in the bio electricity generation. To use them first, it is important to know the stability and the characteristics of such organic compound. The research presents the computational methods of assessment of stability and characteristics analysis of organic herbs, Syzygium and Citrus. Objective: MFC has a very vast research area and many scientists are rigorously working on MFCs. Here, we have explained research work related to what we have presented in the paper. Methods: To compute the stability of these microbial fuel cells, we have used two different methods on each herb, Structural Similarity Index Method (SSIM) and Graph Comparison using their Coordinates (GCC). Results: This research work provides the results of convergence towards the stability of herbs. Further, this section also presents the performance characteristics of the software algorithms and their comparative results to verify the outcomes of the herb characteristics using both methods. Conclusion: The proposed work is efficient in finding stability of MFCs on the selected herbs. The approach should work fine on other herbs as well. Machine Learning could have been much useful for this purpose if the availability of the data would have been much high.

Background: ViBE (Visual Background Extractor) is an algorithm with a variety of advantages in video moving object detection which utilizes a pixel-level background modeling. However, it is not suitable for distinguishing the scene of drastic change, adapts poorly to the sudden change of the illumination and may lost the object easily, because this algorithm uses a fixed threshold to distinguish the background from the foreground. Methods: In this paper, an improved ViBE algorithm is proposed, which an adaptive dynamic threshold method is introduced for classification of the foreground and the background in the changing scenes. When reconstructing the model it required for drastic change of illumination, Otsu algorithm is used to judge the threshold and select the appropriate frame to complete the reconstruction to achieve quick adapt to the light. Results: Experimental results show that the proposed algorithm has higher recall value, better precision and F value comparing to the original algorithm. The improved algorithm has the highest classification accuracy among other similar algorithms and therefore the improved algorithm significantly improves the detection results. Conclusion: After analyzing the principle of ViBE algorithm, this paper proposed improvements to it from two aspects to aim at its deficiency. Taking into account of the dynamic changes of different environments, the change factor was proposed to measure the dynamic degree of background. According to the value of the factor, adaptive clustering was obtained and clustering threshold was updated to improve the adaptability of the algorithm to the dynamic environments. The improved ViBE algorithm can find the appropriate frame to reconstruct model structure in the case of abrupt light change, which can quickly adapt itself to the light change and be more accurate in the object detection.

Background: In current scenario of internet, large amounts of data are generated and processed. Hadoop framework is widely used to store and process big data in a highly distributed manner. It is argued that Hadoop Framework is not mature enough to deal with the current cyberattacks on the data. Objective: The main objective of the proposed work is to provide a complete security approach comprising of authorisation and authentication for the user and the Hadoop cluster nodes and to secure the data at rest as well as in transit. Methods: The proposed algorithm uses Kerberos network authentication protocol for authorisation and authentication and to validate the users and the cluster nodes. The Ciphertext-Policy Attribute- Based Encryption (CP-ABE) is used for data at rest and data in transit. User encrypts the file with their own set of attributes and stores on Hadoop Distributed File System. Only intended users can decrypt that file with matching parameters. Results: The proposed algorithm was implemented with data sets of different sizes. The data was processed with and without encryption. The results show little difference in processing time. The performance was affected in range of 0.8% to 3.1%, which includes impact of other factors also, like system configuration, the number of parallel jobs running and virtual environment. Conclusion: The solutions available for handling the big data security problems faced in Hadoop framework are inefficient or incomplete. A complete security framework is proposed for Hadoop Environment. The solution is experimentally proven to have little effect on the performance of the system for datasets of different sizes.

Background: In the internet era, there is a prime need to access and manage the huge volume of multimedia data in an effective manner. Shot is a sequence of frames captured by a single camera in an uninterrupted space and time. Shot detection is suitable for various applications such that video browsing, video indexing, content based video retrieval and video summarization. Objective: To detect the shot transitions in the video within a short duration. It compares the visual features of frames like correlation, histogram and texture features only in the candidate region frames instead of comparing the full frames in the video file. Methods: This paper analyses candidate frames by searching the values of frame features which matches with the abrupt detector followed by the correct cut transition frame with in the datacube recursively until it detects the correct transition frame. If they are matched with the gradual detector, then it will give the gradual transition ranges, otherwise the algorithm will compare the frames within the next datacube to detect shot transition. Results: The total average detection rates of all transitions computed in the proposed Data-cube Search Based Shot Boundary Detection technique are 92.06 for precision, 96.92 for recall and 93.94 for f1 measure and the maximum accurate detection rate. Conclusion: Proposed method for shot transitions uses correlation value for searching procedure with less computation time than the existing methods which compares every single frame and uses multi features such as color, edge, motion and texture features in wavelet domain.