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Articles 1 - 8 of 8
Full-Text Articles in Physical Sciences and Mathematics
Codehow: Effective Code Search Based On Api Understanding And Extended Boolean Model (E), Fei Lv, Jian-Guang Lou, Shaowei Wang, Dongmei Zhang, Jainjun Zhao
Codehow: Effective Code Search Based On Api Understanding And Extended Boolean Model (E), Fei Lv, Jian-Guang Lou, Shaowei Wang, Dongmei Zhang, Jainjun Zhao
Research Collection School Of Computing and Information Systems
Over the years of software development, a vast amount of source code has been accumulated. Many code search tools were proposed to help programmers reuse previously-written code by performing free-text queries over a large-scale codebase. Our experience shows that the accuracy of these code search tools are often unsatisfactory. One major reason is that existing tools lack of query understanding ability. In this paper, we propose CodeHow, a code search technique that can recognize potential APIs a user query refers to. Having understood the potentially relevant APIs, CodeHow expands the query with the APIs and performs code retrieval by applying …
Contract-Based General-Purpose Gpu Programming, Alexey Kolesnichenko, Christopher M. Poskitt, Sebastian Nanz, Bertrand Meyer
Contract-Based General-Purpose Gpu Programming, Alexey Kolesnichenko, Christopher M. Poskitt, Sebastian Nanz, Bertrand Meyer
Research Collection School Of Computing and Information Systems
Using GPUs as general-purpose processors has revolutionized parallel computing by offering, for a large and growing set of algorithms, massive data-parallelization on desktop machines. An obstacle to widespread adoption, however, is the difficulty of programming them and the low-level control of the hardware required to achieve good performance. This paper suggests a programming library, SafeGPU, that aims at striking a balance between programmer productivity and performance, by making GPU data-parallel operations accessible from within a classical object-oriented programming language. The solution is integrated with the design-by-contract approach, which increases confidence in functional program correctness by embedding executable program specifications into …
Memes As Building Blocks: A Case Study On Evolutionary Optimization + Transfer Learning For Routing Problems, Liang Feng, Yew-Soon Ong, Ah-Hwee Tan, Ivor W. Tsang
Memes As Building Blocks: A Case Study On Evolutionary Optimization + Transfer Learning For Routing Problems, Liang Feng, Yew-Soon Ong, Ah-Hwee Tan, Ivor W. Tsang
Research Collection School Of Computing and Information Systems
A significantly under-explored area of evolutionary optimization in the literature is the study of optimization methodologies that can evolve along with the problems solved. Particularly, present evolutionary optimization approaches generally start their search from scratch or the ground-zero state of knowledge, independent of how similar the given new problem of interest is to those optimized previously. There has thus been the apparent lack of automated knowledge transfers and reuse across problems. Taking this cue, this paper presents a Memetic Computational Paradigm based on Evolutionary Optimization + Transfer Learning for search, one that models how human solves problems, and embarks on …
Neural Modeling Of Sequential Inferences And Learning Over Episodic Memory, Budhitama Subagdja, Ah-Hwee Tan
Neural Modeling Of Sequential Inferences And Learning Over Episodic Memory, Budhitama Subagdja, Ah-Hwee Tan
Research Collection School Of Computing and Information Systems
Episodic memory is a significant part of cognition for reasoning and decision making. Retrieval in episodic memory depends on the order relationships of memory items which provides flexibility in reasoning and inferences regarding sequential relations for spatio-temporal domain. However, it is still unclear how they are encoded and how they differ from representations in other types of memory like semantic or procedural memory. This paper presents a neural model of sequential representation and inferences on episodic memory. It contrasts with the common views on sequential representation in neural networks that instead of maintaining transitions between events to represent sequences, they …
Detection And Classification Of Malicious Javascript Via Attack Behavior Modelling, Yinxing Xue, Junjie Wang, Yang Liu, Hao Xiao, Jun Sun, Mahinthan Chandramohan
Detection And Classification Of Malicious Javascript Via Attack Behavior Modelling, Yinxing Xue, Junjie Wang, Yang Liu, Hao Xiao, Jun Sun, Mahinthan Chandramohan
Research Collection School Of Computing and Information Systems
Existing malicious JavaScript (JS) detection tools and commercial anti-virus tools mostly use feature-based or signature-based approaches to detect JS malware. These tools are weak in resistance to obfuscation and JS malware variants, not mentioning about providing detailed information of attack behaviors. Such limitations root in the incapability of capturing attack behaviors in these approches. In this paper, we propose to use Deterministic Finite Automaton (DFA) to abstract and summarize common behaviors of malicious JS of the same attack type. We propose an automatic behavior learning framework, named JS∗ , to learn DFA from dynamic execution traces of JS malware, where …
Verifying Parameterized Timed Security Protocols, Li Li, Jun Sun, Yang Liu, Jin Song Dong
Verifying Parameterized Timed Security Protocols, Li Li, Jun Sun, Yang Liu, Jin Song Dong
Research Collection School Of Computing and Information Systems
Quantitative timing is often explicitly used in systems for better security, e.g., the credentials for automatic website logon often has limited lifetime. Verifying timing relevant security protocols in these systems is very challenging as timing adds another dimension of complexity compared with the untimed protocol verification. In our previous work, we proposed an approach to check the correctness of the timed authentication in security protocols with fixed timing constraints. However, a more difficult question persists, i.e., given a particular protocol design, whether the protocol has security flaws in its design or it can be configured secure with proper parameter values? …
Heuristic Collective Learning For Efficient And Robust Emergence Of Social Norms, Jianye Hao, Jun Sun, Dongping Huang, Yi Cai, Chao Yu
Heuristic Collective Learning For Efficient And Robust Emergence Of Social Norms, Jianye Hao, Jun Sun, Dongping Huang, Yi Cai, Chao Yu
Research Collection School Of Computing and Information Systems
In multiagent systems, social norms is a useful technique in regulating agents’ behaviors to achieve coordination or cooperation among agents. One important research question is to investigate how a desirable social norm can be evolved in a bottom-up manner through local interactions. In this paper, we propose two novel learning strategies under the collective learning framework: collective learning EV-l and collective learning EV-g, to efficiently facilitate the emergence of social norms. Experimental results show that both learning strategies can support the emergence of desirable social norms more efficiently in a much broader range of multiagent interaction scenarios than previous work, …
Privacycanary: Privacy-Aware Recommenders With Adaptive Input Obfuscation, Thivya Kandappu, Arik Friedman, Roksan Borelli, Vijay Sivaraman
Privacycanary: Privacy-Aware Recommenders With Adaptive Input Obfuscation, Thivya Kandappu, Arik Friedman, Roksan Borelli, Vijay Sivaraman
Research Collection School Of Computing and Information Systems
Recommender systems are widely used by online retailers to promote products and content that are most likely to be of interest to a specific customer. In such systems, users often implicitly or explicitly rate products they have consumed, and some form of collaborative filtering is used to find other users with similar tastes to whom the products can be recommended. While users can benefit from more targeted and relevant recommendations, they are also exposed to greater risks of privacy loss, which can lead to undesirable financial and social consequences. The use of obfuscation techniques to preserve the privacy of user …