Integrity protection for code-on-demand mobile agents in e-commerce

The mobile agent paradigm has been proposed as a promising solution to facilitate distributed computing over open and heterogeneous networks. Mobility, autonomy, and intelligence are identified as key features of mobile agent systems and enabling characteristics for the next-generation smart electronic commerce on the Internet. However, security-related issues, especially integrity protection in mobile agent technology, still hinder the widespread use of software agents: from the agent’s perspective, mobile agent integrity should be protected against attacks from malicious hosts and other agents. In this paper, we present Code-on-Demand(CoD) mobile agents and a corresponding agent integrity protection scheme. Compared to the traditional assumption that mobile agents consist of invariant code parts, we propose the use of dynamically upgradeable agent code, in which new agent function modules can be added and redundant ones can be deleted at runtime. This approach will reduce the weight of agent programs, equip mobile agents with more flexibility, enhance code privacy and help the recoverability of agents after attack. In order to meet the security challenges for agent integrity protection, we propose agent code change authorization protocols and a double integrity verification scheme. Finally, we discuss the Java implementation of CoD mobile agents and integrity protection

Cadaveric Study of Zone 2 Flexor Hallucis Longus Tendon Sheath

Purpose: The purpose of this study was to investigate the anatomy of the zone 2 flexor hallucis longus (FHL) tendon sheath. Methods: Dissection of the zone 2 FHL tendon sheath was performed in 12 feet of 6 cadavers. The tendon sheath was subdivided into proximal fibrous (zone 2A) and distal fascial (zone 2B) parts. The lengths of the zone 2A and 2B FHL tendon were measured and represented the length of the corresponding tendon sheaths, and the relation of the medial plantar nerve to each part of the zone 2 FHL tendon sheath was studied. Results: In all specimens there were fibrous and fascial components of the zone 2 FHL tendon sheath. The medial plantar nerve crossed the zone 2B tendon sheaths and then became plantar lateral to the sheath in 7 specimens. The distance between the medial plantar nerve and the orifice of the zone 2A tendon sheath averaged 7.6 mm. The distance between the medial plantar nerve and the junction between zones 2A and 2B averaged 3.2 mm. The distance between the medial plantar nerve and the distal end of the zone 2B tendon sheath averaged 4.2 mm. The mean length of the zone 2A tendon sheath was 35.9 mm, and the mean length of the zone 2B tendon sheath was 30.5 mm. Conclusions: The zone 2 FHL tendon sheath can be subdivided into a proximal fibrous zone (2A) and a distal fascial zone (2B). Because of the close proximity of the medial plantar nerve to the tendon sheath, there is a significant risk of iatrogenic nerve injury when surgical procedures are performed in zone 2B. Clinical Relevance: An understanding of the anatomy of the zone 2 FHL tendon sheath is useful for the safe practice of zone 2 FHL tendoscopy. © 2010 Arthroscopy Association of North America.postprin

A randomised controlled trial of a health education intervention provided by nurses to mothers of sick children

Health Services Research Fund & Health Care and Promotion Fund: Research Dissemination Reports (Series 7)published_or_final_versio

Hardcore smoking after comprehensive smoke-free legislation and health warnings on cigarette packets in Hong Kong

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A Hybrid Differential Evolution Approach to Designing Deep Convolutional Neural Networks for Image Classification

Convolutional Neural Networks (CNNs) have demonstrated their superiority in image classification, and evolutionary computation (EC) methods have recently been surging to automatically design the architectures of CNNs to save the tedious work of manually designing CNNs. In this paper, a new hybrid differential evolution (DE) algorithm with a newly added crossover operator is proposed to evolve the architectures of CNNs of any lengths, which is named DECNN. There are three new ideas in the proposed DECNN method. Firstly, an existing effective encoding scheme is refined to cater for variable-length CNN architectures; Secondly, the new mutation and crossover operators are developed for variable-length DE to optimise the hyperparameters of CNNs; Finally, the new second crossover is introduced to evolve the depth of the CNN architectures. The proposed algorithm is tested on six widely-used benchmark datasets and the results are compared to 12 state-of-the-art methods, which shows the proposed method is vigorously competitive to the state-of-the-art algorithms. Furthermore, the proposed method is also compared with a method using particle swarm optimisation with a similar encoding strategy named IPPSO, and the proposed DECNN outperforms IPPSO in terms of the accuracy.Comment: Accepted by The Australasian Joint Conference on Artificial Intelligence 201

An extension to the Brun-Titchmarsh theorem

The Siegel-Walfisz theorem states that for any B > 0, we have ∑/p≤x/p≡a(mod k) 1 ∼ x/φ(k) lox x for k ≤ log B x and (k, a) = 1. This only gives an asymptotic formula for the number of primes over an arithmetic progression for quite small moduli k compared with x. However, if we are only concerned about upper bound, we have the Brun-Titchmarsh theorem, namely for any 1 ≤ k 0, s ≥ 1 and 1 ≤ k < x.In particular, for s ≤ log log (x/k), we have ∑/y<n≤x+y ≡ a (mod k)ω (n) < s 1 ≪ x/φ (k) log (x/k) (log log (x/k) + K)s-1/(s-1)! √ log log (x/k) + K and for any ε∈(0, 1) and s ≤ (1-ε) log log (x/k), we have. ∑/y<n≤x+y ≡ a (mod k)ω (n) < s 1 ≪ ε-1x/φ (k) log (x/k) (log log (x/k) +K)s-1/(s-1) !. © 2010. Published by Oxford University Press. All rights reserved.postprin

Decision support and data mining for direct consumer-to-consumer trading

This paper describes a decision support system that integrates a hybrid neighborhood search algorithm for determining the price of sale item when it is placed for trading in the Internet. The seller would provide the condition and number of years of usage of the used item, and the intelligent system would provide real-time search on related items in the marketplace and suggest a price for trading. Data mining techniques are explored for efficient processing of a vast amount of information in the database tables. In addition, the trading system would also have the intelligence of recommending items or products to a potential buyer given the previous purchase patterns. Related items to a recently purchased item would also be suggested with an aim of providing friendly reminders and recommendations so that the user of the website would obtain a pleasant trading experience. © 2014 Infonomics Society.published_or_final_versio

Fault localization based only on failed runs

Fault localization commonly relies on both passed and failed runs, but passed runs are generally susceptible to coincidental correctness and modern software automatically produces a huge number of bug reports on failed runs. FOnly is an effective new technique that relies only on failed runs to locate faults statistically. © 2012 IEEE.published_or_final_versio

EClass: An execution classification approach to improving the energy-efficiency of software via machine learning

Energy efficiency at the software level has gained much attention in the past decade. This paper presents a performance-aware frequency assignment algorithm for reducing processor energy consumption using Dynamic Voltage and Frequency Scaling (DVFS). Existing energy-saving techniques often rely on simplified predictions or domain knowledge to extract energy savings for specialized software (such as multimedia or mobile applications) or hardware (such as NPU or sensor nodes). We present an innovative framework, known as EClass, for general-purpose DVFS processors by recognizing short and repetitive utilization patterns efficiently using machine learning. Our algorithm is lightweight and can save up to 52.9% of the energy consumption compared with the classical PAST algorithm. It achieves an average savings of 9.1% when compared with an existing online learning algorithm that also utilizes the statistics from the current execution only. We have simulated the algorithms on a cycle-accurate power simulator. Experimental results show that EClass can effectively save energy for real life applications that exhibit mixed CPU utilization patterns during executions. Our research challenges an assumption among previous work in the research community that a simple and efficient heuristic should be used to adjust the processor frequency online. Our empirical result shows that the use of an advanced algorithm such as machine learning can not only compensate for the energy needed to run such an algorithm, but also outperforms prior techniques based on the above assumption. © 2011 Elsevier Inc. All rights reserved.postprin

Will mothers of sick children help their husbands to stop smoking after receiving a brief intervention from nurses? Secondary analysis of a randomised controlled trial

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