Clustering composite SaaS components in Cloud computing using a Grouping Genetic Algorithm

Recently, Software as a Service (SaaS) in Cloud computing, has become more and more significant among software users and providers. To offer a SaaS with flexible functions at a low cost, SaaS providers have focused on the decomposition of the SaaS functionalities, or known as composite SaaS. This approach has introduced new challenges in SaaS resource management in data centres. One of the challenges is managing the resources allocated to the composite SaaS. Due to the dynamic environment of a Cloud data centre, resources that have been initially allocated to SaaS components may be overloaded or wasted. As such, reconfiguration for the components’ placement is triggered to maintain the performance of the composite SaaS. However, existing approaches often ignore the communication or dependencies between SaaS components in their implementation. In a composite SaaS, it is important to include these elements, as they will directly affect the performance of the SaaS. This paper will propose a Grouping Genetic Algorithm (GGA) for multiple composite SaaS application component clustering in Cloud computing that will address this gap. To the best of our knowledge, this is the first attempt to handle multiple composite SaaS reconfiguration placement in a dynamic Cloud environment. The experimental results demonstrate the feasibility and the scalability of the GGA

Staff scheduling for a courier distribution centre using evolutionary algorithm

Staff scheduling is a combinatorics optimization problem and companies face this complex task on daily basis in constructing a schedule fitting all conditions. In a courier distribution center, staffs are assigned to work in processes of a continuous workflow. Staffs have varying work ability for each process. Instead of generating staff schedule instinctively, it is an advantage to optimize staff’s schedule by measuring the performance of each staff. An optimized schedule improves the operation’s efficiency and fully utilize staffs’ work ability, hence, minimizing the cost. This paper proposed evolutionary algorithm, namely genetic algorithm as the solution to courier center staff scheduling. Based on the result, the produced schedule can reduce up to 30% of the staff in schedule while not affecting operation workflow. The cut down on number of working staffs could amount to a substantial reduction of operation cost every month. The generated schedule is significantly customized and take less time to complete an operation. Although the proposed solution is specific to the use case of a courier distribution center, it is however, potentially a generalize model for the logistics industry, introducing a more effective staff scheduling system to cope with the industry’s ever-rising demands

BERT based named entity recognition for automated hadith narrator identification

Hadith serves as a second source of Islamic law for Muslims worldwide, especially in Indonesia, which has the world's most significant Muslim population of 228.68 million people. However, not all Hadith texts have been certified and approved for use, and several falsified Hadiths make it challenging to distinguish between authentic and fabricated Hadiths. In terms of Hadith science, determining the authenticity of a Hadith can be accomplished by examining its Sanad and Matn. Sanad is an essential aspect of the Hadith because it indicates the chain of the Narrator who transmits the Hadith. The research reported in this paper provides an advanced Natural Language Processing (NLP) technique for identifying and authenticating the Narrator of Hadith as a part of Sanad, utilizing Named Entity Recognition (NER) to address the necessity of authenticating the Hadith. The NER technique described in the research adds an extra feed-forward classifier to the last layer of the pre-trained BERT model. In the testing process using Cahya/bert-base-indonesian-1.5G, the proposed solution received an overall F1-score of 99.63 percent. On the Hadith Narrator Identification using other Hadith passages, the final examination yielded a 98.27 percent F1-score

Internet Of Things-Proactive Security Approach

The proposed solution in this study is to use a proactive WPA/WPA2 approach in order to secure the access link side of the IoT. The proactive approach is controlled by a DDWRT router which changes the password proactively after a specific time interval after instructing the connected devices to do so as well. The solution uses an IPsec security on the end routers to ensure the data security on the public internet side of the connection. This simple solution allows using a simple Wi-Fi setup or even better to use the current Wi-Fi infrastructure which is available in almost every enterprise or home environment where the IoT is needed. A separate Wi-Fi network will be created for the IoT devices, so that, the current normal users experience will not change. The solution proved to be secure by evaluating the three security pillars: confidentiality, integrity and availability

The Role Of Access Control And Device Authentication In The Internet Of Things

The new generation of wireless sensor networks that is known as the internet of things enables the direct connection of physical objects to the internet using microcontrollers. In most cases, these microcontrollers have very limited computational resources. In this study, we investigate the access control solution for the IETF standard draft constrained application protocol using the datagram transport layer security protocol for transport security. We use the centralized approach to save access control information in the framework. Since, the public key cryptography operations might be computationally too expensive for constrained devices we build our solution based on symmetric cryptography

Knowledge Acquisition in GraPE (Grant Proposal Electronic Reviewing Assistant)

The review of grant proposals is an important task in every academic field, but there exist no exact methodologies to help referees in doing this. The objective of this project is to build an expert system to assist beginner referees in this process. The system will be based on ERA (Electronic Referee Assistant), a knowledge-based advisor for Informatics research papers, and named GraPE (Grant Proposal ERA). GraPE aims to provide guidance to beginner referees to help them make better reviews and become better reviewers. As a fact, an expert system’s problem solving strategy relies on its knowledge. The knowledge has to be well defined, modelled, and represented in order to allow successful inference processes. This paper will describe the process of knowledge acquisition in GraPE, from the identification of knowledge sources to the knowledge modelling process

Person authentication using electroencephalogram (EEG) brainwaves signals

This chapter starts with the introduction to various types of authentication modalities, before discussing on the implementation of electroencephalogram (EEG) signals for person authentication task in more details. In general, the EEG signals are unique but highly uncertain, noisy, and difficult to analyze. Event-related potentials, such as visual-evoked potentials, are commonly used in the person authentication literature work. The occipital area of the brain anatomy shows good response to the visual stimulus. Hence, a set of eight selected EEG channels located at the occipital area were used for model training. Besides, feature extraction methods, i.e., the WPD, Hjorth parameter, coherence, cross-correlation, mutual information, and mean of amplitude have been proven to be good in extracting relevant information from the EEG signals. Nevertheless, different features demonstrate varied performance on distinct subjects. Thus, the Correlation-based Feature Selection method was used to select the significant features subset to enhance the authentication performance. Finally, the Fuzzy-Rough Nearest Neighbor classifier was proposed for authentication model building. The experimental results showed that the proposed solution is able to discriminate imposter from target subjects in the person authentication task

EEG-based biometric authentication modelling using incremental fuzzy-rough nearest neighbour technique

This paper proposes an Incremental Fuzzy-Rough Nearest Neighbour (IncFRNN) technique for biometricauthentication modelling using feature extracted visual evoked. Only small training set is needed for model initialisation. Theembedded heuristic update method adjusts the knowledge granules incrementally to maintain all representativeelectroencephalogram (EEG) signal patterns and eliminate those rarely used. It reshapes the personalized knowledge granulesthrough insertion and deletion of a test object, based on similarity measures. A predefined window size can be used to reducethe overall processing time. This proposed algorithm was verified with test data from 37 healthy subjects. Signal pre-processingsteps on segmentation, filtering and artefact rejection were carried out to improve the data quality before model building. Theexperimental paradigm was designed in three different conditions to evaluate the authentication performance of the IncFRNNtechnique against the benchmarked incremental K-Nearest Neighbour (KNN) technique. The performance was measured interms of accuracy, area under the Receiver Operating Characteristic (ROC) curve (AUC) and Cohen's Kappa coefficient. Theproposed IncFRNN technique is proven to be statistically better than the KNN technique in the controlled window sizeenvironment. Future work will focus on the use of dynamic data features to improve the robustness of the proposed model

EEG-Based Biometric Authentication Modelling Using Incremental Fuzzy-Rough Nearest Neighbour Technique

This paper proposes an Incremental Fuzzy-Rough Nearest Neighbour (IncFRNN) technique for biometric authentication modelling using feature extracted visual evoked. Only small training set is needed for model initialisation. The embedded heuristic update method adjusts the knowledge granules incrementally to maintain all representative electroencephalogram (EEG) signal patterns and eliminate those rarely used. It reshapes the personalized knowledge granules through insertion and deletion of a test object, based on similarity measures. A predefined window size can be used to reduce the overall processing time. This proposed algorithm was verified with test data from 37 healthy subjects. Signal pre-processing steps on segmentation, filtering and artefact rejection were carried out to improve the data quality before model building. The experimental paradigm was designed in three different conditions to evaluate the authentication performance of the IncFRNN technique against the benchmarked incremental K-Nearest Neighbour (KNN) technique. The performance was measured in terms of accuracy, area under the Receiver Operating Characteristic (ROC) curve (AUC) and Cohen's Kappa coefficient. The proposed IncFRNN technique is proven to be statistically better than the KNN technique in the controlled window size environment. Future work will focus on the use of dynamic data features to improve the robustness of the proposed model

Evaluation Process for GraPE (A Web-Based Expert System for Reviewing Grant Proposal)

A reviewing process of grant proposals is of paramount importance and tiresome for a referee. It excessively consumes time in reading and analysing the content. However, there is no exact methodology to help and assist referees in the evaluation process. The objective of this vroject is to build a web-based expert system to assist beginner referees in this process The system is based on ERA (Electronic Referee Assistant), la knowledge-based advisor for Informatics research papers, and named GraPE (Grant Proposal ERA ). GraPE aims to provide guidance to beginner referees to help them make a better review and become better reviewers. This paper will describe GraPE and the system evaluation that has been carried out in order to confirm the hypothesis proposed in this project