Semantically Enhanced Software Traceability Using Deep Learning Techniques

In most safety-critical domains the need for traceability is prescribed by certifying bodies. Trace links are generally created among requirements, design, source code, test cases and other artifacts, however, creating such links manually is time consuming and error prone. Automated solutions use information retrieval and machine learning techniques to generate trace links, however, current techniques fail to understand semantics of the software artifacts or to integrate domain knowledge into the tracing process and therefore tend to deliver imprecise and inaccurate results. In this paper, we present a solution that uses deep learning to incorporate requirements artifact semantics and domain knowledge into the tracing solution. We propose a tracing network architecture that utilizes Word Embedding and Recurrent Neural Network (RNN) models to generate trace links. Word embedding learns word vectors that represent knowledge of the domain corpus and RNN uses these word vectors to learn the sentence semantics of requirements artifacts. We trained 360 different configurations of the tracing network using existing trace links in the Positive Train Control domain and identified the Bidirectional Gated Recurrent Unit (BI-GRU) as the best model for the tracing task. BI-GRU significantly out-performed state-of-the-art tracing methods including the Vector Space Model and Latent Semantic Indexing.Comment: 2017 IEEE/ACM 39th International Conference on Software Engineering (ICSE

Modeling Financial Time Series with Artificial Neural Networks

Financial time series convey the decisions and actions of a population of human actors over time. Econometric and regressive models have been developed in the past decades for analyzing these time series. More recently, biologically inspired artificial neural network models have been shown to overcome some of the main challenges of traditional techniques by better exploiting the non-linear, non-stationary, and oscillatory nature of noisy, chaotic human interactions. This review paper explores the options, benefits, and weaknesses of the various forms of artificial neural networks as compared with regression techniques in the field of financial time series analysis.CELEST, a National Science Foundation Science of Learning Center (SBE-0354378); SyNAPSE program of the Defense Advanced Research Project Agency (HR001109-03-0001

Electronic Cooling via Interlayer Coulomb Coupling in Multilayer Epitaxial Graphene

In van der Waals bonded or rotationally disordered multilayer stacks of two-dimensional (2D) materials, the electronic states remain tightly confined within individual 2D layers. As a result, electron-phonon interactions occur primarily within layers and interlayer electrical conductivities are low. In addition, strong covalent in-plane intralayer bonding combined with weak van der Waals interlayer bonding results in weak phonon-mediated thermal coupling between the layers. We demonstrate here, however, that Coulomb interactions between electrons in different layers of multilayer epitaxial graphene provide an important mechanism for interlayer thermal transport even though all electronic states are strongly confined within individual 2D layers. This effect is manifested in the relaxation dynamics of hot carriers in ultrafast time-resolved terahertz spectroscopy. We develop a theory of interlayer Coulomb coupling containing no free parameters that accounts for the experimentally observed trends in hot-carrier dynamics as temperature and the number of layers is varied.Comment: 54 pages, 15 figures, uses documentclass{achemso}, M.T.M. and J.R.T. contributed equally to this wor

Intelligent component selection

Component-based software engineering (CBSE) provides solutions to the development of complex and evolving systems. As these systems are created and maintained, the task of selecting components is repeated. The context-driven component evaluation (CdCE) project is developing strategies and techniques for automating a repeatable process for assessing software components. This paper describes our work using artificial intelligence (AI) techniques to classify components based on an ideal component specification. Using AI we are able to represent dependencies between attributes, overcoming some of the limitations of existing aggregation-based approaches to component selection

Symbiotic deep learning for medical image analysis with applications in real-time diagnosis for fetal ultrasound screening

The last hundred years have seen a monumental rise in the power and capability of machines to perform intelligent tasks in the stead of previously human operators. This rise is not expected to slow down any time soon and what this means for society and humanity as a whole remains to be seen. The overwhelming notion is that with the right goals in mind, the growing influence of machines on our every day tasks will enable humanity to give more attention to the truly groundbreaking challenges that we all face together. This will usher in a new age of human machine collaboration in which humans and machines may work side by side to achieve greater heights for all of humanity. Intelligent systems are useful in isolation, but the true benefits of intelligent systems come to the fore in complex systems where the interaction between humans and machines can be made seamless, and it is this goal of symbiosis between human and machine that may democratise complex knowledge, which motivates this thesis. In the recent past, datadriven methods have come to the fore and now represent the state-of-the-art in many different fields. Alongside the shift from rule-based towards data-driven methods we have also seen a shift in how humans interact with these technologies. Human computer interaction is changing in response to data-driven methods and new techniques must be developed to enable the same symbiosis between man and machine for data-driven methods as for previous formula-driven technology. We address five key challenges which need to be overcome for data-driven human-in-the-loop computing to reach maturity. These are (1) the ’Categorisation Challenge’ where we examine existing work and form a taxonomy of the different methods being utilised for data-driven human-in-the-loop computing; (2) the ’Confidence Challenge’, where data-driven methods must communicate interpretable beliefs in how confident their predictions are; (3) the ’Complexity Challenge’ where the aim of reasoned communication becomes increasingly important as the complexity of tasks and methods to solve also increases; (4) the ’Classification Challenge’ in which we look at how complex methods can be separated in order to provide greater reasoning in complex classification tasks; and finally (5) the ’Curation Challenge’ where we challenge the assumptions around bottleneck creation for the development of supervised learning methods.Open Acces

The Security Blanket of the Chat World: An Analytic Evaluation and a User Study of Telegram

The computer security community has advocated widespread adoption of secure communication tools to protect personal privacy. Several popular communication tools have adopted end-to-end encryption (e.g., WhatsApp, iMessage), or promoted security features as selling points (e.g., Telegram, Signal). However, previous studies have shown that users may not understand the security features of the tools they are using, and may not be using them correctly. In this paper, we present a study of Telegram using two complementary methods: (1) a labbased user study (11 novices and 11 Telegram users), and (2) a hybrid analytical approach combining cognitive walk-through and heuristic evaluation to analyse Telegram’s user interface. Participants who use Telegram feel secure because they feel they are using a secure tool, but in reality Telegram offers limited security benefits to most of its users. Most participants develop a habit of using the less secure default chat mode at all times. We also uncover several user interface design issues that impact security, including technical jargon, inconsistent use of terminology, and making some security features clear and others not. For instance, use of the end-to-end-encrypted Secret Chat mode requires both the sender and recipient be online at the same time, and Secret Chat does not support group conversations

The Design Process and Usability Assessment of an Exergame System to Facilitate Strength for Task Training for Lower Limb Stroke Rehabilitation

Successful stroke rehabilitation relies on early, long-term, repetitive and intensive treatment, which is rarely adhered to by patients. Exergames can increase patients’ engagement with their therapy. Marketed exergaming systems for lower limb rehabilitation are hard to find and, none yet, facilitate Strength for Task Training (STT), a novel physiotherapeutic method for stroke rehabilitation. STT involves performing brief but intensive strength training (priming) prior to task-specific training to promote neural plasticity and maximize the gains in locomotor ability. This research investigates how the design of an exergame system (game and game controller) for lower limb stroke rehabilitation can facilitate unsupervised STT and therefore allow stroke patients to care for their own health. The findings suggest that specific elements of STT can be incorporated in an exergame system. Barriers to use can be reduced through considering the diverse physiological and cognitive abilities of patients and aesthetic consideration can help create a meaningful system than promotes its use in the home. The semantics of form and movement play an essential role for stroke patients to be able to carry out their exercises

An Overview about Emerging Technologies of Autonomous Driving

Since DARPA started Grand Challenges in 2004 and Urban Challenges in 2007, autonomous driving has been the most active field of AI applications. This paper gives an overview about technical aspects of autonomous driving technologies and open problems. We investigate the major fields of self-driving systems, such as perception, mapping and localization, prediction, planning and control, simulation, V2X and safety etc. Especially we elaborate on all these issues in a framework of data closed loop, a popular platform to solve the long tailed autonomous driving problems

Development of an integrated genome informatics, data management and workflow infrastructure: a toolbox for the study of complex disease genetics.

The genetic dissection of complex disease remains a significant challenge. Sample-tracking and the recording, processing and storage of high-throughput laboratory data with public domain data, require integration of databases, genome informatics and genetic analyses in an easily updated and scaleable format. To find genes involved in multifactorial diseases such as type 1 diabetes (T1D), chromosome regions are defined based on functional candidate gene content, linkage information from humans and animal model mapping information. For each region, genomic information is extracted from Ensembl, converted and loaded into ACeDB for manual gene annotation. Homology information is examined using ACeDB tools and the gene structure verified. Manually curated genes are extracted from ACeDB and read into the feature database, which holds relevant local genomic feature data and an audit trail of laboratory investigations. Public domain information, manually curated genes, polymorphisms, primers, linkage and association analyses, with links to our genotyping database, are shown in Gbrowse. This system scales to include genetic, statistical, quality control (QC) and biological data such as expression analyses of RNA or protein, all linked from a genomics integrative display. Our system is applicable to any genetic study of complex disease, of either large or small scale.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are