It represents a universally applicable attitude and skill set everyone, not just computer scientists, would be eager to learn and use.

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Computational Thinking 計算論的思考

Anomaly detection for time-series data has been an important research field for a long time. Seminal work on anomaly detection methods has been focussing on statistical approaches. In recent years an increasing number of machine learning algorithms have been developed to detect anomalies on time-series. Subsequently, researchers tried to improve these techniques using (deep) neural networks. In the light of the increasing number of anomaly detection methods, the body of research lacks a broad comparative evaluation of statistical, machine learning and deep learning methods. This paper studies 20 univariate anomaly detection methods from the all three categories. The evaluation is conducted on publicly available datasets, which serve as benchmarks for time-series anomaly detection. By analyzing the accuracy of each method as well as the computation time of the algorithms, we provide a thorough insight about the performance of these anomaly detection approaches, alongside some general notion of which method is suited for a certain type of data.

Anomaly Detection in Univariate Time-series: A Survey on the State-of-the-Art

Many distributed workloads in today's data centers are written in managed languages such as Java or Ruby. Examples include big data frameworks such as Hadoop, data stores such as Cassandra or applications such as the SOLR search engine. These workloads typically run across many independent language runtime systems on different nodes. This setup represents a source of inefficiency, as these language runtime systems are unaware of each other. For example, they may perform Garbage Collection at times that are locally reasonable but not in a distributed setting. We address these problems by introducing the concept of a Holistic Runtime System that makes runtime-level decisions for the entire distributed application rather than locally. We then present Taurus, a Holistic Runtime System prototype. Taurus is a JVM drop-in replacement, requires almost no configuration and can run unmodified off-the-shelf Java applications. Taurus enforces user-defined coordination policies and provides a DSL for writing these policies. By applying Taurus to Garbage Collection, we demonstrate the potential of such a system and use it to explore coordination strategies for the runtime systems of real-world distributed applications, to improve application performance and address tail-latencies in latency-sensitive workloads.

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Taurus: A Holistic Language Runtime System for Coordinating Distributed Managed-Language Applications

Automating medical diagnosis and training medical students with real-life situations requires the accumulation of large dataset variants covering all aspects of a patient's condition. For preventing the misuse of patient's private information, datasets are not always publicly available. There is a need to generate synthetic data that can be trained for the advancement of public healthcare without intruding on patient's confidentiality. Currently, rules for generating synthetic data are predefined and they require expert intervention, which limits the types and amount of synthetic data. In this paper, we propose a novel generative adversarial networks (GAN) model, named SynSigGAN, for automating the generation of any kind of synthetic biomedical signals. We have used bidirectional grid long short-term memory for the generator network and convolutional neural network for the discriminator network of the GAN model. Our model can be applied in order to create new biomedical synthetic signals while using a small size of the original signal dataset. We have experimented with our model for generating synthetic signals for four kinds of biomedical signals (electrocardiogram (ECG), electroencephalogram (EEG), electromyography (EMG), photoplethysmography (PPG)). The performance of our model is superior wheen compared to other traditional models and GAN models, as depicted by the evaluation metric. Synthetic biomedical signals generated by our approach have been tested while using other models that could classify each signal significantly with high accuracy.

https://www.mdpi.com/2079-7737/9/12/441/pdf

SynSigGAN: Generative Adversarial Networks for Synthetic Biomedical Signal Generation

http://hub.hku.hk/bitstream/10722/220463/1/content.pdf

5W+1H pattern

The advent of the Internet of Things (IoT) has led to a major change in the way we interact with increasingly ubiquitous connected devices such as smart objects and cyber-physical systems. It has also led to an exponential increase in the number of such Internet-connected devices over the last few years. Conducting extensive functional and performance testing is critical to assess the robustness and efficiency of IoT systems in order to validate them before their deployment in real life. However, creating an IoT test environment is a difficult and expensive task, usually requiring a significant amount of physical hardware and human effort to build it. This paper proposes a method to emulate an IoT environment using the Network Emulator for Mobile Universes (NEMU), itself built on the popular QEMU system emulator, in order to construct a testbed of inter-connected, emulated Raspberry Pi devices. Additionally, we experimentally demonstrate how our method can be successfully applied to IoT by showing how such an emulated environment can be used to detect anomalies in an IoT system.

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Towards an emulated IoT test environment for anomaly detection using NEMU

Conducting extensive testing of anonymization techniques is critical to assess their robustness and identify the scenarios where they are most suitable. However, the access to real microdata is highly restricted and the one that is publicly-available is usually anonymized or aggregated; hence, reducing its value for testing purposes. In this paper, we present a framework (COCOA) for the generation of realistic synthetic microdata that allows to define multi-attribute relationships in order to preserve the functional dependencies of the data. We prove how COCOA is useful to strengthen the testing of anonymization techniques by broadening the number and diversity of the test scenarios. Results also show how COCOA is practical to generate large datasets.

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COCOA: A Synthetic Data Generator for Testing Anonymization Techniques

Nowadays, clustered environments are commonly used in high-performance computing and enterprise-level applications to achieve faster response time and higher throughput than single machine environments. Nevertheless, how to effectively manage the workloads in these clusters has become a new challenge. As a load balancer is typically used to distribute the workload among the cluster's nodes, multiple research efforts have concentrated on enhancing the capabilities of load balancers. Our previous work presented a novel adaptive load balancing strategy TRINI that improves the performance of a clustered Java system by avoiding the performance impacts of major garbage collection, which is an important cause of performance degradation in Java. The aim of this paper is to strengthen the validation of TRINI by extending its experimental evaluation in terms of generality, scalability and reliability. Our results have shown that TRINI can achieve significant performance improvements, as well as a consistent behaviour, when it is applied to a set of commonly used load balancing algorithms, demonstrating its generality. TRINI also proved to be scalable across different cluster sizes, as its performance improvements did not noticeably degrade when increasing the cluster size. Finally, TRINI exhibited reliable behaviour over extended time periods, introducing only a small overhead to the cluster in such conditions. These results offer practitioners a valuable reference regarding the benefits that a load balancing strategy, based on garbage collection, can bring to a clustered Java system. Copyright © 2016 John Wiley & Sons, Ltd.

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TRINI: an adaptive load balancing strategy based on garbage collection for clustered Java systems

Cloud computing is causing a paradigm shift in the provision and use of software. This has changed the way of obtaining, managing and delivering computing services and solutions. Similarly, it has brought new challenges to software testing. A particular area of concern is the performance of cloud-based applications. This is because the increased complexity of the applications has exposed new areas of potential failure points, complicating all performance-related activities. This situation makes the performance testing of cloud environments very challenging. Similarly, the identification of performance issues and the diagnosis of their root causes are time-consuming and complex, usually require multiple tools and heavily rely on expertise. To simplify these tasks, hence increasing the productivity and reducing the dependency on human experts, this paper presents a lightweight approach to automate the usage of expert tools in the performance testing of cloud-based applications. In this paper, we use a tool named Whole-system Analysis of Idle Time to demonstrate how our research work solves this problem. The validation involved two experiments, which assessed the overhead of the approach and the time savings that it can bring to the analysis of performance issues. The results proved the benefits of the approach by achieving a significant decrease in the time invested in performance analysis while introducing a low overhead in the tested system.

/pdf/automated-wait-for-cloud-based-application-testing-1paa9f2h7d.pdf

Automated WAIT for Cloud-Based Application Testing

Modern computer applications, especially at enterprise-level, are commonly deployed with a big number of clustered instances to achieve a higher system performance, in which case single machine based solutions are less cost-effective. However, how to effectively manage these clustered applications has become a new challenge. A common approach is to deploy a front-end load balancer to optimise the workload distribution between each clustered application. Since then, many research efforts have been carried out to study effective load balancing algorithms which can control the workload based on various resource usages such as CPU and memory. The aim of this paper is to propose a new load balancing approach to improve the overall distributed system performance by avoiding potential performance impacts caused by Major Java Garbage Collection. The experimental results have shown that the proposed load balancing algorithm can achieve a significant higher throughput and lower response time compared to the round-robin approach. In addition, the proposed solution only has a small overhead introduced to the distributed system, where unused resources are available to enable other load balancing algorithms together to achieve a better system performance.

/pdf/load-balancing-of-java-applications-by-forecasting-garbage-53m449uw4p.pdf

A. Omar Portillo-Dominguez

Papers

Towards an emulated IoT test environment for anomaly detection using NEMU

COCOA: A Synthetic Data Generator for Testing Anonymization Techniques

TRINI: an adaptive load balancing strategy based on garbage collection for clustered Java systems

Automated WAIT for Cloud-Based Application Testing

Load Balancing of Java Applications by Forecasting Garbage Collections