Testing and evaluating virus detectors for handheld devices

The widespread use of personal digital assistants and smartphones gives securing these devices a high priority. Yet little attention has been placed on protecting handheld devices against viruses. Currently available antivirus software for handhelds is few in number. At this stage, the opportunity exists for the evaluation and improvement of current solutions. By pinpointing weaknesses in the current antivirus software, improvements can be made to properly protect these devices from a future tidal wave of viruses. This research evaluates four currently available antivirus solutions for handheld devices. A formal model of virus transformation that provides transformation traceability is presented. Two sets of ten tests each were administered; nine tests from each set involved the modification of source code of two known viruses for handheld devices. The testing techniques used are well established in PC testing; thus the focus of this research is solely on handheld devices. Statistical analysis of the test results show high false negative production rates for the antivirus software and an overall false negative production rate of 47.5% with a 95% confidence interval between 36.6% and 58.4%. This high rate shows that current solutions poorly identify modified versions of a virus. The virus is left undetected and capable of spreading, infecting and causing damage.     

Silhouette-based human action recognition using sequences of key poses

In this paper, a human action recognition method is presented in which pose representation is based on the contour points of the human silhouette and actions are learned by making use of sequences of multi-view key poses. Our contribution is twofold. Firstly, our approach achieves state-of-the-art success rates without compromising the speed of the recognition process and therefore showing suitability for online recognition and real-time scenarios. Secondly, dissimilarities among different actors performing the same action are handled by taking into account variations in shape (shifting the test data to the known domain of key poses) and speed (considering inconsistent time scales in the classification). Experimental results on the publicly available Weizmann, MuHAVi and IXMAS datasets return high and stable success rates, achieving, to the best of our knowledge, the best rate so far on the MuHAVi Novel Actor test.     

Virtualized Web server cluster self-configuration to optimize resource and power use

This work proposes a reusable architecture that enables the self-configuration of a supporting infrastructure for Web server clusters using virtual machines. The goal of the architecture is to ensure service quality, evaluating how broadly it complies with the application's operating restrictions and proportionally acting on the configuration of physical servers (hosts) or virtual machines. In addition, through the rational use of resources, the proposal aims at saving energy. A prototype of the architecture was developed and a performance evaluation carried out with two different resource management approaches. This evaluation shows how fully functional and advantageous the proposal is in terms of using resources, avoiding waste, yet maintaining the application's quality of service within acceptable levels. The architecture also shows to be flexible enough to accept, with a reasonable amount of effort, different resource self-configuration policies.     

Impact resistance and tolerance of interleaved tape laminates

This paper presents and discusses the results of low-velocity impact and compression-after-impact (CAI) tests conducted on interleaved and non-interleaved carbon/epoxy tape laminates. Olefin film interleaves provided a strong interface bond, resulting in a reduction in projected damage area. These interleaves changed the stress distribution under impact and restricted delamination formation at the ply interface. An investigation into the compression behaviour of these laminates revealed a reduction in undamaged strength using olefin interleaves. This was attributed to the lack of lateral support for fibres at the fibre/interleaf interface, allowing fibre microbuckling to occur at a low load. Low modulus copolyamide web interleaves resulted in an increase in damage area and minor changes to CAI strength. Examination of laminate cross-sections revealed that this was due to both the open structure of the interleaf and poor resin/interleaf adhesion. High shear modulus polyethylene interleaves resulted in a significant decrease in damage area at various impact energies, with CAI strength improved compared to the non-interleaved laminates.     

Automated design of error-resilient and hardware-efficient deep neural networks

Applying deep neural networks (DNNs) in mobile and safety-critical systems, such as autonomous vehicles, demands a reliable and efficient execution on hardware. The design of the neural architecture has a large influence on the achievable efficiency and bit error resilience of the network on hardware. Since there are numerous design choices for the architecture of DNNs, with partially opposing effects on the preferred characteristics (such as small error rates at low latency), multi-objective optimization strategies are necessary. In this paper, we develop an evolutionary optimization technique for the automated design of hardware-optimized DNN architectures. For this purpose, we derive a set of inexpensively computable objective functions, which enable the fast evaluation of DNN architectures with respect to their hardware efficiency and error resilience. We observe a strong correlation between predicted error resilience and actual measurements obtained from fault injection simulations. Furthermore, we analyze two different quantization schemes for efficient DNN computation and find one providing a significantly higher error resilience compared to the other. Finally, a comparison of the architectures provided by our algorithm with the popular MobileNetV2 and NASNet-A models reveals an up to seven times improved bit error resilience of our models. We are the first to combine error resilience, efficiency, and performance optimization in a neural architecture search framework.

     

Free-living inferential modeling of blood glucose level using only noninvasive inputs

The goal of this work is to present a causation modeling methodology with the ability to accurately infer blood glucose levels using a large set of highly correlated noninvasive input variables over an extended period of time. These models can provide insight to improve glucose monitoring, and glucose regulation through advanced model-based control technologies. The efficacy of this approach is demonstrated using real data from a type 2 diabetic (T2D) subject collected under free-living conditions over a period of 25 consecutive days. The model was identified and tested using eleven variables that included three food variables as well as several activity and stress variables. The model was trained using 20 days of data and validated using 5 days of data. This gave a fitted correlation coefficient of 0.70 and an average absolute error (AAE) (i.e., the average of the absolute values for the measured glucose concentration minus modeled glucose concentration) of 13.3 mg/dL for the validation data. This AAE result was significantly better than the subject’s personal glucose meter AAE of 15.3 mg/dL for replicated measurements.     

Specifying Real-Time Finite-State Systems in Linear Logic (Extended Abstract)

Abstract

Real-time finite-state systems may be specified in linear logic by means of linear implications between conjunctions of fixed finite length. In this setting, where time is treated as a dense linear ordering, safety properties may be expressed as certain provability problems. These provability problems are shown to be in pspace. They are solvable, with some guidance, by finite proof search in concurrent logic programming environments based on linear logic and acting as sort of model-checkers. One advantage of our approach is that either it provides unsafe runs or it actually establishes safety.     

Faster to Sound Parts by Advanced Forming Process Simulation ‐ Advanced Forming Process Model Including the Elastic Effects of the Forming Press and Tool

This article addresses process, stamping, and manufacturing engineers, as well as tool designers (prototype and series production tools), and press shop planners in the range of metal forming. The paper deals with methods of modelling and simulating the metal forming process and their application in product design, production, and forming process planning. In models usually applied major effects on the forming process are neglected. For instance, the elastic behaviour of presses and die tools is not considered in process and tool planning. Thus, reworking of tools is a consequence of this model oversimplification. The paper illustrates how interactions between forming press, die tool and metal forming processes can be modelled by enhancing conventional FE models. Several examples demonstrate the information value of the Advanced Forming Process Model (AFPM).     

Challenges in Securing Networked J2ME Applications

An increasing number of smart phones support Java 2, Micro Edition. Mobile application developers must deal with J2ME's inherent security weaknesses as well as bugs in implementations on real devices. The new Security and Trust Services API for J2ME addresses some of these challenges, although it too has shortcomings