Presents a novel metrics-based approach for detecting design problems in object-oriented software. Introduces an important suite of detection strategies for the identification of different well-known ...design flaws as well as some rarely mentioned ones.
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Distance measures are core building blocks in time-series analysis and the subject of active research for decades. Unfortunately, the most detailed experimental study in this area is outdated (over a ...decade old) and, naturally, does not reflect recent progress. Importantly, this study (i) omitted multiple distance measures, including a classic measure in the time-series literature; (ii) considered only a single time-series normalization method; and (iii) reported only raw classification error rates without statistically validating the findings, resulting in or fueling four misconceptions in the time-series literature. Motivated by the aforementioned drawbacks and our curiosity to shed some light on these misconceptions, we comprehensively evaluate 71 time-series distance measures. Specifically, our study includes (i) 8 normalization methods; (ii) 52 lock-step measures; (iii) 4 sliding measures; (iv) 7 elastic measures; (v) 4 kernel functions; and (vi) 4 embedding measures. We extensively evaluate these measures across 128 time-series datasets using rigorous statistical analysis. Our findings debunk four long-standing misconceptions that significantly alter the landscape of what is known about existing distance measures. With the new foundations in place, we discuss open challenges and promising directions.
Detecting Ponzi Schemes on Ethereum Chen, Weili; Zheng, Zibin; Cui, Jiahui ...
Proceedings of the 2018 World Wide Web Conference,
04/2018
Conference Proceeding
Open access
Blockchain technology becomes increasingly popular. It also attracts scams, for example, Ponzi scheme, a classic fraud, has been found making a notable amount of money on Blockchain, which has a very ...negative impact. To help dealing with this issue, this paper proposes an approach to detect Ponzi schemes on blockchain by using data mining and machine learning methods. By verifying smart contracts on Ethereum, we first extract features from user accounts and operation codes of the smart contracts and then build a classification model to detect latent Ponzi schemes implemented as smart contracts. The experimental results show that the proposed approach can achieve high accuracy for practical use. More importantly, the approach can be used to detect Ponzi schemes even at the moment of its creation. By using the proposed approach, we estimate that there are more than 400 Ponzi schemes running on Ethereum. Based on these results, we propose to build a uniform platform to evaluate and monitor every created smart contract for early warning of scams.
Nowadays large-scale data-centric systems have become an essential element for companies to store, manipulate and derive value from large volumes of data. Capturing this value depends on the ability ...of these systems in managing large-scale workloads including complex analytical queries. One of the main characteristics of these queries is that they share computations in terms of selections and joins. Materialized views (MV) have shown their force in speeding up queries by exploiting these redundant computations. MV selection problem (VSP) is one of the most studied problems in the database field. A large majority of the existing solutions follow workload-driven approaches since they facilitate the identification of shared computations. Interesting algorithms have been proposed and implemented in commercial DBMSs. But they fail in managing large-scale workloads. In this paper, we presented a comprehensive framework to select the most beneficial materialized views based on the detection of the common subexpressions shared between queries. This framework gives the right place of the problem of selection of common subexpressions representing the causes of the redundancy. The utility of final MV depends strongly on the selected subexpressions. Once selected, a heuristic is given to select the most beneficial materialized views by considering different query ordering. Finally, experiments have been conducted to evaluate the effectiveness and efficiency of our proposal by considering large workloads.
The rapid development of Massive Open Online Courses (MOOCs) platforms has created an urgent need for an efficient personalized course recommender system that can assist learners of all backgrounds ...and levels of knowledge in selecting appropriate courses. Currently, most existing methods utilize a sequential recommendation paradigm that captures the user’s learning interests from their learning history, typically through recurrent or graph neural networks. However, fewer studies have explored how to incorporate principles of human learning at both the course and category levels to enhance course recommendations. In this article, we aim at addressing this gap by introducing a novel model, named Prerequisite-Enhanced Catory-Aware Graph Neural Network (PCGNN), for course recommendation. Specifically, we first construct a course prerequisite graph that reflects the human learning principles and further pre-train the course prerequisite relationships as the base embeddings for courses and categories. Then, to capture the user’s complex learning patterns, we build an item graph and a category graph from the user’s historical learning records, respectively: (1) the item graph reflects the course-level local learning transition patterns and (2) the category graph provides insight into the user’s long-term learning interest. Correspondingly, we propose a user interest encoder that employs a gated graph neural network to learn the course-level user interest embedding and design a category transition pattern encoder that utilizes GRU to yield the category-level user interest embedding. Finally, the two fine-grained user interest embeddings are fused to achieve precise course prediction. Extensive experiments on two real-world datasets demonstrate the effectiveness of PCGNN compared with other state-of-the-art methods.
Software has become omnipresent and vital in our information-based society, so all software producers should assume responsibility for its reliability. While 'reliable' originally assumed ...implementations that were effective and mainly error-free, additional issues like adaptability and maintainability have gained equal importance recently. For example, the 2004 ACM/IEEE Software Engineering Curriculum Guidelines list software evolution as one of ten key areas of software engineering education. Mens and Demeyer, both international authorities in the field of software evolution, together with the invited contributors, focus on novel trends in software evolution research and its relations with other emerging disciplines such as model-driven software engineering, service-oriented software development, and aspect-oriented software development. They do not restrict themselves to the evolution of source code but also address the evolution of other, equally important software artifacts such as databases and database schemas, design models, software architectures, and process management. The contributing authors provide broad overviews of related work, and they also contribute to a comprehensive glossary, a list of acronyms, and a list of books, journals, websites, standards and conferences that together represent the community’s body of knowledge. Combining all these features, this book is the indispensable source for researchers and professionals looking for an introduction and comprehensive overview of the state of the art. In addition, it is an ideal basis for an advanced course on software evolution.
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Evaluation of design artefacts and design theories is a key activity in Design Science Research (DSR), as it provides feedback for further development and (if done correctly) assures the rigour of ...the research. However, the extant DSR literature provides insufficient guidance on evaluation to enable Design Science Researchers to effectively design and incorporate evaluation activities into a DSR project that can achieve DSR goals and objectives. To address this research gap, this research paper develops, explicates, and provides evidence for the utility of a Framework for Evaluation in Design Science (FEDS) together with a process to guide design science researchers in developing a strategy for evaluating the artefacts they develop within a DSR project. A FEDS strategy considers why, when, how, and what to evaluate. FEDS includes a two-dimensional characterisation of DSR evaluation episodes (particular evaluations), with one dimension being the functional purpose of the evaluation (formative or summative) and the other dimension being the paradigm of the evaluation (artificial or naturalistic). The FEDS evaluation design process is comprised of four steps: (1) explicate the goals of the evaluation, (2) choose the evaluation strategy or strategies, (3) determine the properties to evaluate, and (4) design the individual evaluation episode(s). The paper illustrates the framework with two examples and provides evidence of its utility via a naturalistic, summative evaluation through its use on an actual DSR project.
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Apache Hive Camacho-Rodríguez, Jesús; Chauhan, Ashutosh; Gates, Alan ...
Proceedings of the 2019 International Conference on Management of Data,
06/2019
Conference Proceeding
Apache Hive is an open-source relational database system for analytic big-data workloads. In this paper we describe the key innovations on the journey from batch tool to fully fledged enterprise data ...warehousing system. We present a hybrid architecture that combines traditional MPP techniques with more recent big data and cloud concepts to achieve the scale and performance required by today's analytic applications. We explore the system by detailing enhancements along four main axis: Transactions, optimizer, runtime, and federation. We then provide experimental results to demonstrate the performance of the system for typical workloads and conclude with a look at the community roadmap.
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