A heuristic search approach is developed for service restoration of a distribution system. The purpose is to devise a proper restoration plan after the location of a fault has been identified and the faulted zone has been isolated. Since service restoration is an urgent matter in distribution system operation, the restoration plan must be reached in a very short time. In addition, the out-fservice area must be minimized to enhance system reliability. To reach a restoration plan which satisfies all practical requirements, a set of heuristic rules are compiled through interviews with experienced operators at Taiwan Power Company. A heuristic search algorithm is developed for service restoration following a fault on distribution feeders. The effectiveness of the proposed heuristic search approach is demonstrated by the restoration of the electricity service following a fault in a underground distribution system within the service area of Taipei City District Office of Taiwan Power Company. It is found that the restoration plan can be reached very efficiently. Therefore, it can serve as a valuable tool to help distribution system dispatchers reach a proper restoration plan.

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Abstract-Security is a crucial issue for wireless sensor networks due to the deployment nature and the resources limitations of tiny sensor devices used in such networks. Sensor networks are used sometime in very sensitive applications such as healthcare and military. With this in mind we must address the security concerns from the beginning of network design. Owing to limited resources and computing constraints security in sensor networks poses more severe challenges as compare to the traditional networks. There are currently enormous approaches in the field of wireless sensor networks security. No comprehensive study lists the security issues and the threat models which pose unique threats to the wireless sensor networks. In this paper we have corroborated well known security issues and have provided the direction of research towards effective countermeasures against the threats posed by these issues.Index Terms-Countermeasures, network attacks, security issues, threat models, wireless sensor networks. I. INTRODUCTIONWe envision in near future that hundreds to thousands of sensor devices will be used in self-organizing Wireless Sensor Networks (WSNs). Indeed wireless sensor networks gaining rapid popularity because of their potentially low cost solutions to a variety of real-world challenges [1]. Security in WSNs is not easy; compared with conventional desktop computers; severe challenges meet these sensor nodes, such as limitation in processing power, storage, channel bandwidth and energy. We attempt to overcome these challenges, due to importance of security. Sensor networks have the potential to be deployed in applications in all aspects of our lives. Some typical applications are energy management, logistics and inventory management, battlefield and emergency response information. Sensor networks pose unique security challenges because of their inherent limitations in communication and computing abilities.Deployment of sensor networks in an unattended environment makes them vulnerable to potential attacks. Attackers can compromise the network to accept malicious nodes as legitimate nodes. Hardware and software improvements will address these issues at some extend but comprehensive security requires development of countermeasures such as secure key management, lightweight encryption techniques; secure routing protocols and malicious node detection mechanism. This paper provides an overview of security issues and threat models in WSNs and provides direction for research in developing the countermeasures.The rest of the paper is organized as follows. In section II we summarize the obstacles for the sensor network security. In section III the requirements of a wireless sensor network security are listed. The major attacks in sensor network are categorized in section IV, and we outline the corresponding defensive measures in section V. Finally, section VI points out our future observation and concludes the paper. II. OBSTACLES IN WIRELESS SENSOR SECURITYA wireless sensor network has many constraints compared to...

Cardiovascular disease (CVD) is the leading cause of death worldwide. A Machine Learning (ML) system can predict CVD in the early stages to mitigate mortality rates based on clinical data. Recently, many research works utilized different machine learning approaches to detect CVD or identify the patient's severity level. Although these works obtained promising results, none focused on employing optimization methods to improve the ML model performance for CVD detection and severity-level classification. This study provides an effective method based on the Synthetic Minority Oversampling Technique (SMOTE) to handle imbalance distribution issue, six different ML classifiers to detect the patient status, and Hyperparameter Optimization (HPO) to find the best hyperparameter for ML classifier together with SMOTE. Two public datasets were used to build and test the model using all features. The results show that SMOTE and Extra Trees (ET) optimized using hyperband achieved higher results than other models and outperformed the state-of-the-art works by achieving 99.2% and 98.52% in CVD detection, respectively. Also, the developed model converged to 95.73% severity classification using the Cleveland dataset. The proposed model can help doctors determine a patient's current heart disease status. As a result, it is possible to prevent heart disease-related mortality by implementing early therapy.

Abstract-In recent years, traditional cybersecurity safeguards have proven ineffective against insider threats. Famous cases of sensitive information leaks caused by insiders, including the WikiLeaks release of diplomatic cables and the Edward Snowden incident, have greatly harmed the U.S. government's relationship with other governments and with its own citizens. Data Leak Prevention (DLP) is a solution for detecting and preventing information leaks from within an organization's network. However, state-of-art DLP detection models are only able to detect very limited types of sensitive information, and research in the field has been hindered due to the lack of available sensitive texts. Many researchers have focused on document-based detection with artificially labeled "confidential documents" for which security labels are assigned to the entire document, when in reality only a portion of the document is sensitive. This type of whole-document based security labeling increases the chances of preventing authorized users from accessing non-sensitive information within sensitive documents. In this paper, we introduce Automated Classification Enabled by Security Similarity (ACESS), a new and innovative detection model that penetrates the complexity of big text security classification/detection. To analyze the ACESS system, we constructed a novel dataset, containing formerly classified paragraphs from diplomatic cables made public by the WikiLeaks organization. To our knowledge this paper is the first to analyze a dataset that contains actual formerly sensitive information annotated at paragraph granularity.