N-opcode analysis for android malware classification and categorization

Malware detection is a growing problem particularly on the Android mobile platform due to its increasing popularity and accessibility to numerous third party app markets. This has also been made worse by the increasingly sophisticated detection avoidance techniques employed by emerging malware families. This calls for more effective techniques for detection and classification of Android malware. Hence, in this paper we present an n-opcode analysis based approach that utilizes machine learning to classify and categorize Android malware. This approach enables automated feature discovery that eliminates the need for applying expert or domain knowledge to define the needed features. Our experiments on 2520 samples that were performed using up to 10-gram opcode features showed that an f-measure of 98% is achievable using this approach.

Discovery of Spatial Periodicities in a Coronal Loop Using Automated Edge-Tracking Algorithms

A new method for automated coronal loop tracking, in both spatial and temporal domains, is presented. Applying this technique to TRACE data, obtained using the 171 angstrom filter on 1998 July 14, we detect a coronal loop undergoing a 270 s kink-mode oscillation, as previously found by Aschwanden et al. However, we also detect flare-induced, and previously unnoticed, spatial periodicities on a scale of 3500 km, which occur along the coronal loop edge. Furthermore, we establish a reduction in oscillatory power for these spatial periodicities of 45% over a 222 s interval. We relate the reduction in detected oscillatory power to the physical damping of these loop-top oscillations.

Towards an automated evaluation process for software architectures

Optimizing and editing enterprise software systems, after the implementation process has started, is widely recognized to be an expensive process. This has led to increasing emphasis on locating mistakes within software systems at the design stage, to help minimize development costs. There is increasing interest in the field of architecture evaluation techniques that can identify problems at the design stage, either within complete, or partially complete architectures. Most current techniques rely on manual review-based evaluation methods that require advanced skills from architects and evaluators. We are currently considering what a formal Architecture Description Language (ADL) can contribute to the process of architecture evaluation and validation. Our investigation is considering the inter-relationships between the activities performed during the architecture evaluation process, the characteristics an ADL should possess to support these activities, and the tools needed to provide convenient access to, and presentation of architectural information.

Adaptive automated construction of hybrid heuristics for exam timetabling and graph colouring problems

In this paper, we present a random iterative graph based hyper-heuristic to produce a collection of heuristic sequences to construct solutions of different quality. These heuristic sequences can be seen as dynamic hybridisations of different graph colouring heuristics that construct solutions step by step. Based on these sequences, we statistically analyse the way in which graph colouring heuristics are automatically hybridised. This, to our knowledge, represents a new direction in hyper-heuristic research. It is observed that spending the search effort on hybridising Largest Weighted Degree with Saturation Degree at the early stage of solution construction tends to generate high quality solutions. Based on these observations, an iterative hybrid approach is developed to adaptively hybridise these two graph colouring heuristics at different stages of solution construction. The overall aim here is to automate the heuristic design process, which draws upon an emerging research theme on developing computer methods to design and adapt heuristics automatically. Experimental results on benchmark exam timetabling and graph colouring problems demonstrate the effectiveness and generality of this adaptive hybrid approach compared with previous methods on automatically generating and adapting heuristics. Indeed, we also show that the approach is competitive with the state of the art human produced methods.

Systematic Usage of Embedded Modelling Languages in Automated Model Transformation Chains

Annotation of programs using embedded Domain-Specific Languages (embedded DSLs), such as the program annotation facility for the Java programming language, is a well-known practice in computer science. In this paper we argue for and propose a specialized approach for the usage of embedded Domain-Specific Modelling Languages (embedded DSMLs) in Model-Driven Engineering (MDE) processes that in particular supports automated many-step model transformation chains. It can happen that information defined at some point, using an embedded DSML, is not required in the next immediate transformation step, but in a later one. We propose a new approach of model annotation enabling flexible many-step transformation chains. The approach utilizes a combination of embedded DSMLs, trace models and a megamodel. We demonstrate our approach based on an example MDE process and an industrial case study.

Introduction to Domain-Driven Data Mining Special Section

In the last decade, data mining has emerged as one of the most dynamic and lively areas in information technology. Although many algorithms and techniques for data mining have been proposed, they either focus on domain independent techniques or on very specific domain problems. A general requirement in bridging the gap between academia and business is to cater to general domain-related issues surrounding real-life applications, such as constraints, organizational factors, domain expert knowledge, domain adaption, and operational knowledge. Unfortunately, these either have not been addressed, or have not been sufficiently addressed, in current data mining research and development.Domain-Driven Data Mining (D3M) aims to develop general principles, methodologies, and techniques for modeling and merging comprehensive domain-related factors and synthesized ubiquitous intelligence surrounding problem domains with the data mining process, and discovering knowledge to support business decision-making. This paper aims to report original, cutting-edge, and state-of-the-art progress in D3M. It covers theoretical and applied contributions aiming to: 1) propose next-generation data mining frameworks and processes for actionable knowledge discovery, 2) investigate effective (automated, human and machine-centered and/or human-machined-co-operated) principles and approaches for acquiring, representing, modelling, and engaging ubiquitous intelligence in real-world data mining, and 3) develop workable and operational systems balancing technical significance and applications concerns, and converting and delivering actionable knowledge into operational applications rules to seamlessly engage application processes and systems.

The appraisal of an automated multi-immunoaffinity chromatography system to detect anabolic agents in bile and urine

Screening for residues of anabolic steroids frequently requires extraction from tissues and fluids before analysis. Chemical procedures for these extractions can be complicated, expensive to perform and not ideal for the simultaneous extraction of analytes with different solubilities. Extraction by multi-immunoaffinity chromatography (MIAC) may be used as an alternative. Samples are passed through a column containing a range of antibodies immobilized on an inert support. The desired analytes are bound to their respective antibodies, washed and then eluted by a suitable solvent. The purified extracts can then be incorporated into the analytical tests, The analytes that can be extracted presently are alpha-nortestosterone, zeranol, trenbolone, diethylstilboestrol, boldenone and dexamethasone. Manually, the MIAC procedure is limited to about six columns per operator but bq automating the process using a robotic sample processor (RSP), 48 columns can be run simultaneously during the day or night. The RSP has also been adapted to transfer extracts and reagents on to ELISA plates. The automated system has proved to be a robust and reliable means of screening large numbers of samples for anabolic agents with minimal manual input

Automated nonlinear system modelling with multiple neural networks

This article discusses the identification of nonlinear dynamic systems using multi-layer perceptrons (MLPs). It focuses on both structure uncertainty and parameter uncertainty, which have been widely explored in the literature of nonlinear system identification. The main contribution is that an integrated analytic framework is proposed for automated neural network structure selection, parameter identification and hysteresis network switching with guaranteed neural identification performance. First, an automated network structure selection procedure is proposed within a fixed time interval for a given network construction criterion. Then, the network parameter updating algorithm is proposed with guaranteed bounded identification error. To cope with structure uncertainty, a hysteresis strategy is proposed to enable neural identifier switching with guaranteed network performance along the switching process. Both theoretic analysis and a simulation example show the efficacy of the proposed method.

Robust automated tumour segmentation on histological and immunohistochemical tissue images

Tissue microarray (TMA) is a high throughput analysis tool to identify new diagnostic and prognostic markers in human cancers. However, standard automated method in tumour detection on both routine histochemical and immunohistochemistry (IHC) images is under developed. This paper presents a robust automated tumour cell segmentation model which can be applied to both routine histochemical tissue slides and IHC slides and deal with finer pixel-based segmentation in comparison with blob or area based segmentation by existing approaches. The presented technique greatly improves the process of TMA construction and plays an important role in automated IHC quantification in biomarker analysis where excluding stroma areas is critical. With the finest pixel-based evaluation (instead of area-based or object-based), the experimental results show that the proposed method is able to achieve 80% accuracy and 78% accuracy in two different types of pathological virtual slides, i.e., routine histochemical H&E and IHC images, respectively. The presented technique greatly reduces labor-intensive workloads for pathologists and highly speeds up the process of TMA construction and provides a possibility for fully automated IHC quantification.

Discovery of Main-Belt Comet P/2006 VW139 by Pan-STARRS 1

The main-belt asteroid (300163) 2006 VW139 (later designated P/2006 VW139) was discovered to exhibit comet-like activity by the Pan-STARRS1 (PS1) survey telescope using automated point-spread-function analyses performed by PS1's Moving Object Processing System. Deep follow-up observations show both a short (~10'') antisolar dust tail and a longer (~60'') dust trail aligned with the object's orbit plane, similar to the morphology observed for another main-belt comet (MBC), P/2010 R2 (La Sagra), and other well-established comets, implying the action of a long-lived, sublimation-driven emission event. Photometry showing the brightness of the near-nucleus coma remaining constant over ~30 days provides further evidence for this object's cometary nature, suggesting it is in fact an MBC, and not a disrupted asteroid. A spectroscopic search for CN emission was unsuccessful, though we find an upper limit CN production rate of Q CN 100 Myr, while a search for a potential asteroid family around the object reveals a cluster of 24 asteroids within a cutoff distance of 68 m s-1. At 70 m s-1, this cluster merges with the Themis family, suggesting that it could be similar to the Beagle family to which another MBC, 133P/Elst-Pizarro, belongs.

Multiple self-aligned iron nanowires by a dual selective chemical vapor deposition process

We have demonstrated a self-aligned process to fabricate organized iron nanowires on a planarized surface with wire dimensions down to 50 nm. Polishing was used to expose an alternating silicon silicon dioxide edge and a dual selective metal deposition process produced the nanowires. The initial selective deposition produced a tungsten layer on the exposed polysilicon regions. The discovery that selective chemical vapor deposition of iron from Fe(CO)(5) precursor on dielectric surfaces over tungsten surfaces is the key factor that enables the self-alignment of the iron nanowires. Dimensions of the wires are determined by the thickness of the thermal oxide. (c) 2007 The Electrochemical Society.

An Integration Methodology for Automated Recurring Cost Prediction using Digital Manufacturing Technology

The need to account for the effect of design decisions on manufacture and the impact of manufacturing cost on the life cycle cost of any product are well established. In this context, digital design and manufacturing solutions have to be further developed to facilitate and automate the integration of cost as one of the major driver in the product life cycle management. This article is to present an integration methodology for implementing cost estimation capability within a digital manufacturing environment. A digital manufacturing structure of knowledge databases are set out and the ontology of assembly and part costing that is consistent with the structure is provided. Although the methodology is currently used for recurring cost prediction, it can be well applied to other functional developments, such as process planning. A prototype tool is developed to integrate both assembly time cost and parts manufacturing costs within the same digital environment. An industrial example is used to validate this approach.

An automated hybrid procedure for capturing mode-jumping in postbuckling composite stiffened structures

This paper presents a robust finite element procedure for modelling the behaviour of postbuckling structures undergoing mode-jumping. Current non-linear implicit finite element solution schemes, found in most finite element codes, are discussed and their shortcomings highlighted. A more effective strategy is presented which combines a quasi-static and a pseudo-transient routine for modelling this behaviour. The switching between these two schemes is fully automated and therefore eliminates the need for user intervention during the solution process. The quasi-static response is modelled using the are-length constraint while the pseudo-transient routine uses a modified explicit dynamic routine, which is more computationally efficient than standard implicit and explicit dynamic schemes. The strategies for switching between the quasi-static and pseudo-transient routines are presented

Introducing Automated Management Through Iteratively Increased Automation and Indicators

Introducing automation into a managed environment includes significant initial overhead and abstraction, creating a disconnect between the administrator and the system. In order to facilitate the transition to automated management, this paper proposes an approach whereby automation increases gradually, gathering data from the task deployment process. This stored data is analysed to determine the task outcome status and can then be used for comparison against future deployments of the same task and alerting the administrator to deviations from the expected outcome. Using a machinelearning
approach, the automation tool can learn from the administrator's reaction to task failures and eventually react to faults autonomously.

Automated Methods for Virtual Maintainability & Aerospace Maintenance Planning

Abstract. Modern business practices in engineering are increasingly turning to post manufacture service provision in an attempt to generate additional revenue streams and ensure commercial sustainability. Maintainability has always been a consideration during the design process but in the past it has been generally considered to be of tertiary importance behind manufacturability and primary product function in terms of design priorities. The need to draw whole life considerations into concurrent engineering (CE) practice has encouraged companies to address issues such as maintenance, earlier in the design process giving equal importance to all aspects of the product lifecycle. The consideration of design for maintainability (DFM) early in the design process has the potential to significantly reduce maintenance costs, and improve overall running efficiencies as well as safety levels. However a lack of simulation tools still hinders the adaptation of CE to include practical elements of design and therefore further research is required to develop methods by which ‘hands on’ activities such as maintenance can be fully assessed and optimised as concepts develop. Virtual Reality (VR) has the potential to address this issue but the application of these traditionally high cost systems can require complex infrastructure and their use has typically focused on aesthetic aspects of mature designs. This paper examines the application of cost effective VR technology to the rapid assessment of aircraft interior inspection during conceptual design. It focuses on the integration of VR hardware with a typical desktop engineering system and examines the challenges with data transfer, graphics quality and the development of practical user functions within the VR environment. Conclusions drawn to date indicate that the system has the potential to improve maintenance planning through the provision of a usable environment for inspection which is available as soon as preliminary structural models are generated as part of the conceptual design process. Challenges still exist in the efficient transfer of data between the CAD and VR environments as well as the quantification of any benefits that result from the proposed approach. The result of this research will help to improve product maintainability, reduce product development cycle times and lower maintenance costs.