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Articles 3661 - 3690 of 8479
Full-Text Articles in Computer Sciences
Biker: A Tool For Bi-Information Source Based Api Method Recommendation, Liang Cai, Haoye Wang, Qiao Huang, Xin Xia, Zhenchang Xing, David Lo
Biker: A Tool For Bi-Information Source Based Api Method Recommendation, Liang Cai, Haoye Wang, Qiao Huang, Xin Xia, Zhenchang Xing, David Lo
Research Collection School Of Computing and Information Systems
No abstract provided.
Inspect: Iterated Local Search For Solving Path Conditions, Fuxiang Chen, Aldy Gunawan, David Lo, Sunghun Kim
Inspect: Iterated Local Search For Solving Path Conditions, Fuxiang Chen, Aldy Gunawan, David Lo, Sunghun Kim
Research Collection School Of Computing and Information Systems
Automated test case generation is attractive as it can reduce developer workload. To generate test cases, many Symbolic Execution approaches first produce Path Conditions (PCs), a set of constraints, and pass them to a Satisfiability Modulo Theories (SMT) solver. Despite numerous prior studies, automated test case generation by Symbolic Execution is still slow, partly due to SMT solvers’ high computationally complexity. We introduce InSPeCT, a Path Condition solver, that leverages elements of ILS (Iterated Local Search) and Tabu List. ILS is not computational intensive and focuses on generating solutions in search spaces while Tabu List prevents the use of previously …
How Does Machine Learning Change Software Development Practices?, Zhiyuan Wan, Xin Xia, David Lo, Gail C. Murphy
How Does Machine Learning Change Software Development Practices?, Zhiyuan Wan, Xin Xia, David Lo, Gail C. Murphy
Research Collection School Of Computing and Information Systems
Adding an ability for a system to learn inherently adds uncertainty into the system. Given the rising popularity of incorporating machine learning into systems, we wondered how the addition alters software development practices. We performed a mixture of qualitative and quantitative studies with 14 interviewees and 342 survey respondents from 26 countries across four continents to elicit significant differences between the development of machine learning systems and the development of non-machine-learning systems. Our study uncovers significant differences in various aspects of software engineering (e.g., requirements, design, testing, and process) and work characteristics (e.g., skill variety, problem solving and task identity). …
Evaluating Vulnerability To Fake News In Social Networks: A Community Health Assessment Model, Bhavtosh Rath, Wei Gao, Jaideep Srivastava
Evaluating Vulnerability To Fake News In Social Networks: A Community Health Assessment Model, Bhavtosh Rath, Wei Gao, Jaideep Srivastava
Research Collection School Of Computing and Information Systems
Understanding the spread of false information in social networks has gained a lot of recent attention. In this paper, we explore the role community structures play in determining how people get exposed to fake news. Inspired by approaches in epidemiology, we propose a novel Community Health Assessment model, whose goal is to understand the vulnerability of communities to fake news spread. We define the concepts of neighbor, boundary and core nodes of a community and propose appropriate metrics to quantify the vulnerability of nodes (individual-level) and communities (group-level) to spreading fake news. We evaluate our model on communities identified using …
A Survey On Bluetooth 5.0 And Mesh: New Milestones Of Iot, Juenjie Yin, Zheng Yang, Hao Cao, Tongtong Liu, Zimu Zhou, Chenshu Wu
A Survey On Bluetooth 5.0 And Mesh: New Milestones Of Iot, Juenjie Yin, Zheng Yang, Hao Cao, Tongtong Liu, Zimu Zhou, Chenshu Wu
Research Collection School Of Computing and Information Systems
No abstract provided.
Cold-Start Aware Deep Memory Networks For Multi-Entity Aspect-Based Sentiment Analysis, Kaisong Song, Wei Gao, Lujun Zhao, Changlong Sun, Xiaozhong Liu
Cold-Start Aware Deep Memory Networks For Multi-Entity Aspect-Based Sentiment Analysis, Kaisong Song, Wei Gao, Lujun Zhao, Changlong Sun, Xiaozhong Liu
Research Collection School Of Computing and Information Systems
Various types of target information have been considered in aspect-based sentiment analysis, such as entities and aspects. Existing research has realized the importance of targets and developed methods with the goal of precisely modeling their contexts via generating target-specific representations. However, all these methods ignore that these representations cannot be learned well due to the lack of sufficient human-annotated target-related reviews, which leads to the data sparsity challenge, a.k.a. cold-start problem here. In this paper, we focus on a more general multiple entity aspect-based sentiment analysis (ME-ABSA) task which aims at identifying the sentiment polarity of different aspects of multiple …
Enhancing Multi-Hop Sensor Calibration With Uncertainty Estimates, Balz Maag, Zimu Zhou, Lothar Thiele
Enhancing Multi-Hop Sensor Calibration With Uncertainty Estimates, Balz Maag, Zimu Zhou, Lothar Thiele
Research Collection School Of Computing and Information Systems
Low-cost sensors, installed on mobile vehicles, provide a cost-effective way for fine-grained urban air pollution monitoring. However, frequent calibration is crucial for lowcost sensors to consistently deliver accurate measurements. Multi-hop calibration is a common practice to calibrate mobile sensor deployments, but is prone to severe error accumulation over hops. Prior research mitigates error accumulation by designing special calibration models, which only apply to linear models. In this paper, we propose an orthogonal approach by selecting reliable measurements for calibration at each hop. We analyze the impact of different data-induced uncertainties on calibration errors and devise a scheme to estimate these …
Faster First-Order Methods For Stochastic Non-Convex Optimization On Riemannian Manifolds, Pan Zhou, Xiao-Tong Yuan, Shuicheng Yan, Jiashi Feng
Faster First-Order Methods For Stochastic Non-Convex Optimization On Riemannian Manifolds, Pan Zhou, Xiao-Tong Yuan, Shuicheng Yan, Jiashi Feng
Research Collection School Of Computing and Information Systems
First-order non-convex Riemannian optimization algorithms have gained recent popularity in structured machine learning problems including principal component analysis and low-rank matrix completion. The current paper presents an efficient Riemannian Stochastic Path Integrated Differential EstimatoR (R-SPIDER) algorithm to solve the finite-sum and online Riemannian non-convex minimization problems. At the core of R-SPIDER is a recursive semi-stochastic gradient estimator that can accurately estimate Riemannian gradient under not only exponential mapping and parallel transport, but also general retraction and vector transport operations. Compared with prior Riemannian algorithms, such a recursive gradient estimation mechanism endows R-SPIDER with higher computational efficiency in first-order oracle complexity. …
Generalized Majorization-Minimization For Non-Convex Optimization, Hu Zhang, Pan Zhou, Yi Yang, Jiashi Feng
Generalized Majorization-Minimization For Non-Convex Optimization, Hu Zhang, Pan Zhou, Yi Yang, Jiashi Feng
Research Collection School Of Computing and Information Systems
Majorization-Minimization (MM) algorithms optimize an objective function by iteratively minimizing its majorizing surrogate and offer attractively fast convergence rate for convex problems. However, their convergence behaviors for non-convex problems remain unclear. In this paper, we propose a novel MM surrogate function from strictly upper bounding the objective to bounding the objective in expectation. With this generalized surrogate conception, we develop a new optimization algorithm, termed SPI-MM, that leverages the recent proposed SPIDER for more efficient non-convex optimization. We prove that for finite-sum problems, the SPI-MM algorithm converges to an stationary point within deterministic and lower stochastic gradient complexity. To our …
Bidding Mechanisms In Graph Games, Guy Avni, Thomas A. Henzinger, Dorde Zikelic
Bidding Mechanisms In Graph Games, Guy Avni, Thomas A. Henzinger, Dorde Zikelic
Research Collection School Of Computing and Information Systems
In two-player games on graphs, the players move a token through a graph to produce a finite or infinite path, which determines the qualitative winner or quantitative payoff of the game. We study bidding games in which the players bid for the right to move the token. Several bidding rules were studied previously. In Richman bidding, in each round, the players simultaneously submit bids, and the higher bidder moves the token and pays the other player. Poorman bidding is similar except that the winner of the bidding pays the “bank” rather than the other player. Taxman bidding spans the spectrum …
Who Should Pay The Cost: A Game-Theoretic Model For Government Subsidized Investments To Improve National Cybersecurity, Xinrun Wang, Bo An, Hau Chan
Who Should Pay The Cost: A Game-Theoretic Model For Government Subsidized Investments To Improve National Cybersecurity, Xinrun Wang, Bo An, Hau Chan
Research Collection School Of Computing and Information Systems
Due to the recent cyber attacks, cybersecurity is becoming more critical in modern society. A single attack (e.g., WannaCry ransomware attack) can cause as much as $4 billion in damage. However, the cybersecurity investment by companies is far from satisfactory. Therefore, governments (e.g., in the UK) launch grants and subsidies to help companies to boost their cybersecurity to create a safer national cyber environment. The allocation problem is hard due to limited subsidies and the interdependence between self-interested companies and the presence of a strategic cyber attacker. To tackle the government's allocation problem, we introduce a Stackelberg game-theoretic model where …
Constructing Strong Designated Verifier Signatures From Key Encapsulation Mechanisms, Borui Gong, Ho Man Au, Haiyang Xue
Constructing Strong Designated Verifier Signatures From Key Encapsulation Mechanisms, Borui Gong, Ho Man Au, Haiyang Xue
Research Collection School Of Computing and Information Systems
A designated verifier signature (DVS) allows a signer to convince a verifier that a message has been endorsed in a way that the conviction cannot be transferred to any third party. This is achieved by the property that the signature can be generated by one of them. Since DVS is publicly verifiable, a valid DVS implies that the signature must be created by either the signer or the verifier. To enhance privacy of signers' identity, a strong DVS (SDVS) disallows public verification. In this paper, we investigate various aspects of SDVS with making two contributions. Firstly, we consider SDVS in …
Fintech Empowerment: Data Science, Ai, And Machine Learning, Keng Siau, Michael Hilgers, Langtao Chen, Steve Liu, Fiona Fui-Hoon Nah, Richard Hall, Barry Flachsbart
Fintech Empowerment: Data Science, Ai, And Machine Learning, Keng Siau, Michael Hilgers, Langtao Chen, Steve Liu, Fiona Fui-Hoon Nah, Richard Hall, Barry Flachsbart
Research Collection School Of Computing and Information Systems
The article discusses how data science, artificial intelligence and machine learning are affecting the evolution of “fintech,” the technologies used to deliver financial services. After presenting fintech’s competitive advantages in combination with these other advanced technologies, the article posits that financial institutions that don’t move forward with the innovations will be eliminated from the marketplace.
Shared Dynamic Data Audit Supporting Anonymous User Revocation In Cloud Storage, Yinghui Zhang, Chen Chen, Dong Zheng, Rui Guo, Shengmin Xu
Shared Dynamic Data Audit Supporting Anonymous User Revocation In Cloud Storage, Yinghui Zhang, Chen Chen, Dong Zheng, Rui Guo, Shengmin Xu
Research Collection School Of Computing and Information Systems
Collusion between revoked users and cloud service providers can pose a threat to the security of cloud storage data. If the original legitimate users cannot be revoked securely, it will lead to the leakage of shared data, thus affecting the security of cloud storage. In this paper, we combine vector commitment and anonymous revocation of group signature to propose an integrity audit scheme for cloud storage data that can support data modification. The anonymity of the group signature ensures that users’ privacy information will not be snooped by the server. The proposed scheme supports the dynamic operation of stored data …
Definitions And Mathematical Models Of Single Vehicle Routing Problems With Profits, Pieter Vansteenwegen, Aldy Gunawan
Definitions And Mathematical Models Of Single Vehicle Routing Problems With Profits, Pieter Vansteenwegen, Aldy Gunawan
Research Collection School Of Computing and Information Systems
In this chapter, single vehicle routing problems with profits are introduced anddefined. Three variants are considered: the profitable tour problem, the prizecollecting traveling salesperson problem, and the orienteering problem. The difference between these variants is the way in which the profit and the travel cost, mostlydistance or time, are modeled. Profit and travel cost can be modeled as (part of) theobjective or as a constraint. All three problems differ from the well-known travelingsalesperson problem, for which the only objective is to find the shortest route to visitall customers in a given set. In vehicle routing problems with profits, some customerswill …
Topic Enhanced Word Embedding For Toxic Content Detection In Q&A Sites, Do Yeon Kim, Xiaohang Li, Sheng Wang, Yunying Zhuo, Ka Wei, Roy Lee
Topic Enhanced Word Embedding For Toxic Content Detection In Q&A Sites, Do Yeon Kim, Xiaohang Li, Sheng Wang, Yunying Zhuo, Ka Wei, Roy Lee
Research Collection School Of Computing and Information Systems
Increasingly, users are adopting community question-and-answer (Q&A) sites to exchange information. Detecting and eliminating toxic and divisive content in these Q&A sites are paramount tasks to ensure a safe and constructive environment for the users. Insincere question, which is founded upon false premises, is one type of toxic content in Q&A sites. In this paper, we proposed a novel deep learning framework enhanced pre-trained word embeddings with topical information for insincere question classification. We evaluated our proposed framework on a large real-world dataset from Quora Q&A site and showed that the topically enhanced word embedding is able to achieve better …
State-Of-The-Art Solution Techniques For Optw And Toptw, Pieter Vansteenwegen, Aldy Gunawan
State-Of-The-Art Solution Techniques For Optw And Toptw, Pieter Vansteenwegen, Aldy Gunawan
Research Collection School Of Computing and Information Systems
In Chaps. 2 and 3, different orienteering problems (or routing problems with profits) were introduced. The single vehicle problems were discussed in Chap. 2: the profitable tour problem (PTP), the prize-collecting traveling salesperson problem (PCTSP), and the orienteering problem (OP). The multi vehicle problems were discussed in Chap. 3: the team orienteering problem (TOP) and the team orienteering problem with time windows (TOPTW). For discussing the state-of-the-art solution techniques for these different orienteering problems in Chaps. 4, 5, and 6, the problems will be classified differently, based on the similarities between the solution techniques. Therefore, the PTP and PCTSP are …
Itaa: An Intelligent Trajectory-Driven Outdoor Advertising Deployment Assistant, Yipeng Zhang, Zhifeng Bao, Songsong Mo, Yuchen Li, Yanghao Zhou
Itaa: An Intelligent Trajectory-Driven Outdoor Advertising Deployment Assistant, Yipeng Zhang, Zhifeng Bao, Songsong Mo, Yuchen Li, Yanghao Zhou
Research Collection School Of Computing and Information Systems
In this paper, we demonstrate an Intelligent Trajectory-driven outdoor Advertising deployment Assistant (ITAA), which assists users to find an optimal strategy for outdoor advertising (ad) deployment. The challenge is how to measure the influence to the moving trajectories of ads, and how to optimize the placement of ads among billboards that maximize the influence has been proven NP-hard. Therefore, we develop a framework based on two trajectory-driven influence models. ITAA is built upon this framework with a user-friendly UI. It serves both ad companies and their customers. We enhance the interpretability to improve the user's understanding of the influence of …
State-Of-The-Art Solution Techniques For Op And Top, Pieter Vansteenwegen, Aldy Gunawan
State-Of-The-Art Solution Techniques For Op And Top, Pieter Vansteenwegen, Aldy Gunawan
Research Collection School Of Computing and Information Systems
Definitions and mathematical models of the OP and the TOP were introduced in Chaps. 2 and 3. In this chapter, we will discuss the benchmark instances and state-of-the-art solution techniques for both OP and TOP. Some illustrations of benchmark instances and solutions are included in order to increase the understanding in the difficulty of solving this problem and to provide additional insights. The solution techniques are classified into two different categories: exact approaches and (meta)heuristic techniques.
Applications Of The Op, Pieter Vansteenwegen, Aldy Gunawan
Applications Of The Op, Pieter Vansteenwegen, Aldy Gunawan
Research Collection School Of Computing and Information Systems
In recent years, we observe from literature that the VRP and OP, including their variants, have been used to model many different planning and scheduling problems from practice, such as the routing of technicians, athlete recruitment, or military applications. Recently, other practical applications, such as the tourist trip design problem, the mobile crowdsourcing problem, the smuggler search problem, the wildfire routing problem, and the integration of vehicle routing, inventory management, and customer selection problems, have been studied and use the OP as a basic model. In this chapter, various practical applications will be discussed in more detail. We will describe …
Towards Robust Resnet: A Small Step But A Giant Leap, Jingfeng Zhang, Bo Han, Laura Wynter, Bryan Kian Hsiang Low, Mohan Kankanhalli
Towards Robust Resnet: A Small Step But A Giant Leap, Jingfeng Zhang, Bo Han, Laura Wynter, Bryan Kian Hsiang Low, Mohan Kankanhalli
Research Collection School Of Computing and Information Systems
This paper presents a simple yet principled approach to boosting the robustness of the residual network (ResNet) that is motivated by a dynamical systems perspective. Namely, a deep neural network can be interpreted using a partial differential equation, which naturally inspires us to characterize ResNet based on an explicit Euler method. This consequently allows us to exploit the step factor h in the Euler method to control the robustness of ResNet in both its training and generalization. In particular, we prove that a small step factor h can benefit its training and generalization robustness during backpropagation and forward propagation, respectively. …
How Can Ai Help To Enhance Diversity And Inclusion?, Keng Siau
How Can Ai Help To Enhance Diversity And Inclusion?, Keng Siau
Research Collection School Of Computing and Information Systems
In many organizations, promoting diversity and enhancing inclusion are still major concerns. Unconscious biases and stereotyping cause us to have preconceived ideas about what an ideal employee or leader should look like. Unconscious biases are also a major roadblock to an inclusive environment and business culture. Organizations have been investing heavily in training programs for their employees attempting to changes these patterns. Human habits, especially unconscious ones, are not easy to overcome. This research looks at the use of AI to enhance diversity and inclusion in organizations. Literature has shown that a more diverse and inclusive workforce has a competitive …
Higher Education In The Ai Age, Yizhi Ma, Keng Siau
Higher Education In The Ai Age, Yizhi Ma, Keng Siau
Research Collection School Of Computing and Information Systems
A perfect storm is hitting higher education. Decrease funding from traditional funding sources such as State Governments and transformative changes caused by artificial intelligence (AI) will revolutionize higher education (Siau 2018). Higher education needs to change and evolve quickly and continuously to prepare students for the upheavals in the job market caused by AI, machine learning, and automation. Further, continuous organizational and curriculum changes will be necessary for a higher education institution to stay relevant and to stay afloat. This qualitative research looks at higher education in the AI age. Stakeholders (i.e., administrators, faculty, students, industry recruiters) in higher education …
Industry 4.0: Challenges And Opportunities In Different Countries, Keng Siau, Yingrui Xi, Cui Zou
Industry 4.0: Challenges And Opportunities In Different Countries, Keng Siau, Yingrui Xi, Cui Zou
Research Collection School Of Computing and Information Systems
Along with the rapid development of artificial intelligence (AI), cyber-physical systems (CPSs), big data analytics, and cloud computing, Industry 4.0 — a subset of the fourth Industrial Revolution — has started to emerge and take root in many countries. Many expect that Industry 4.0 will be transformative and revolutionary for multiple industries and countries. Its impact will be much more significant than those of Industry 1.0, 2.0, and 3.0. Most studies and papers on Industry 4.0 have examined its impact on various industries, jobs, and organizations. In this article, we investigate the impact of Industry 4.0 on countries and groups …
Industry 4.0: Ethical And Moral Predicaments, W. Wang, Keng Siau
Industry 4.0: Ethical And Moral Predicaments, W. Wang, Keng Siau
Research Collection School Of Computing and Information Systems
The advancements in software technology and data science are enabling Industry 4.0, aka the Fourth Industrial Revolution or the Industrial Internet of Things (IIoT). While the first three industrial revolutions have brought about immense change, the impact of Industry 4.0 will be much wider and far greater, especially with regard to the easily overlooked ethical and moral aspects. Widening wealth gaps between countries and among classes of people within countries, a potential growing unemployment rate, data privacy and accessibility issues, and the treatment of intelligent agents (e.g., military robots) present new and complex ethical and moral dilemmas. In this article, …
Digital Marketing In The Artificial Intelligence And Machine Learning Age, Z. Ruan, Keng Siau
Digital Marketing In The Artificial Intelligence And Machine Learning Age, Z. Ruan, Keng Siau
Research Collection School Of Computing and Information Systems
We are living in a period of profound change driven by digitization, information and communication technology, artificial intelligence, machine learning, and robotics (Gupta, Keen, Shah, and Verdier, 2017; Wang and Siau, 2019). Traditional marketing is shifting to digital marketing enabled by AI and machine learning. Customer consumption behavior has changed from traditional in-store shopping to online shopping (Thiraviyam, 2018). The large volume of transaction and demographic data enables business analytics, AI, and machine learning to analyze and predict customer behavior to improve customer satisfaction and enhance sales (Siau and Wang, 2018). For example, predictive analytics uses different algorithms to predict …
Potential Impact Of Artificial Intelligence On Mental Well-Being, Weiyu Wang, Keng Siau
Potential Impact Of Artificial Intelligence On Mental Well-Being, Weiyu Wang, Keng Siau
Research Collection School Of Computing and Information Systems
Artificial Intelligence (AI) will result in job replacement and job elimination. Some AI technologies, such as self-driving vehicles, have the potential to disrupt existing industries. Self-driving trucks may replace the 3.5 million truck drivers in the US. Scholars at Oxford University estimated that no less than 47% of American jobs and 54% of those in Europe are at a high risk of being taken over by machines. Routine, repetitive, and predictable jobs are expected to be automated (Siau, 2018). Although new jobs will be created, unemployment rate may go up in the short term and the emergence of a “useless …
Ai-Fashion: Collaborative Ai In The Fashion Industry, Y. Luo, Keng Siau
Ai-Fashion: Collaborative Ai In The Fashion Industry, Y. Luo, Keng Siau
Research Collection School Of Computing and Information Systems
Abstract The word vintage is generally accepted to mean clothing produced in the period between 1920s and 1980s (Cervellon et al., 2012). According to Fischer (2015), fashion usually means rapid changes and up-to-date trendiness. Vintage dressing, however, has been a fashionable trend for over 40 years. Can AI be used to predict the next fashion trend? Fashion industry is currently exploring the use of AI to analyze customer behavior and predict next year’s fashion trends. Predicting the correct next trend is vital to the competitiveness and survivability of fashion brands. Research in this area is not new. For example, research …
Gradient Boosting With Piece-Wise Linear Regression Trees, Yu Shi, Jian Li, Zhize Li
Gradient Boosting With Piece-Wise Linear Regression Trees, Yu Shi, Jian Li, Zhize Li
Research Collection School Of Computing and Information Systems
Gradient Boosted Decision Trees (GBDT) is a very successful ensemble learning algorithm widely used across a variety of applications. Recently, several variants of GBDT training algorithms and implementations have been designed and heavily optimized in some very popular open sourced toolkits including XGBoost, LightGBM and CatBoost. In this paper, we show that both the accuracy and efficiency of GBDT can be further enhanced by using more complex base learners. Specifically, we extend gradient boosting to use piecewise linear regression trees (PL Trees), instead of piecewise constant regression trees, as base learners. We show that PL Trees can accelerate convergence of …
Let Me In: Guidelines For The Successful Onboarding Of Newcomers To Open Source Projects, Igor Steinmacher, Christoph Treude, Marco Aurélio Gerosa
Let Me In: Guidelines For The Successful Onboarding Of Newcomers To Open Source Projects, Igor Steinmacher, Christoph Treude, Marco Aurélio Gerosa
Research Collection School Of Computing and Information Systems
Many community-based open source software (OSS) projects depend on a continuous influx of newcomers for their survival and continuity, yet newcomers face many barriers to contributing to a project. We provide guidelines based on our previous work for both OSS communities and newcomers to OSS projects.