Universal codes from switching strategies

We discuss algorithms for combining sequential prediction strategies, a task which can be viewed as a natural generalisation of the concept of universal coding. We describe a graphical language based on Hidden Markov Models for defining prediction strategies, and we provide both existing and new models as examples. The models include efficient, parameterless models for switching between the input strategies over time, including a model for the case where switches tend to occur in clusters, and finally a new model for the scenario where the prediction strategies have a known relationship, and where jumps are typically between strongly related ones. This last model is relevant for coding time series data where parameter drift is expected. As theoretical contributions we introduce an interpolation construction that is useful in the development and analysis of new algorithms, and we establish a new sophisticated lemma for analysing the individual sequence regret of parameterised models.

Social engineering in social networking sites : the art of impersonation

Social networking sites (SNSs), with their large number of users and large information base, seem to be the perfect breeding ground for exploiting the vulnerabilities of people, who are considered the weakest link in security. Deceiving, persuading, or influencing people to provide information or to perform an action that will benefit the attacker is known as “social engineering.” Fraudulent and deceptive people use social engineering traps and tactics through SNSs to trick users into obeying them, accepting threats, and falling victim to various crimes such as phishing, sexual abuse, financial abuse, identity theft, and physical crime. Although organizations, researchers, and practitioners recognize the serious risks of social engineering, there is a severe lack of understanding and control of such threats. This may be partly due to the complexity of human behaviors in approaching, accepting, and failing to recognize social engineering tricks. This research aims to investigate the impact of source characteristics on users’ susceptibility to social engineering victimization in SNSs, particularly Facebook. Using grounded theory method, we develop a model that explains what and how source characteristics influence Facebook users to judge the attacker as credible.

Creating flexible mappings between building information models and cost information

During the early design stages of construction projects, accurate and timely cost feedback is critical to design decision making. This is particularly challenging for cost estimators, as they must quickly and accurately estimate the cost of the building when the design is still incomplete and evolving. State-of-the-art software tools typically use a rule-based approach to generate detailed quantities from the design details present in a building model and relate them to the cost items in a cost estimating database. In this paper, we propose a generic approach for creating and maintaining a cost estimate using flexible mappings between a building model and a cost estimate. The approach uses queries on the building design that are used to populate views, and each view is then associated with one or more cost items. The benefit of this approach is that the flexibility of modern query languages allows the estimator to encode a broad variety of relationships between the design and estimate. It also avoids the use of a common standard to which both designers and estimators must conform, allowing the estimator added flexibility and functionality to their work.

Human single-stranded DNA binding protein 1 (hSSB1/NABP2) is required for the stability and repair of stalled replication forks

Aberrant DNA replication is a primary cause of mutations that are associated with pathological disorders including cancer. During DNA metabolism, the primary causes of replication fork stalling include secondary DNA structures, highly transcribed regions and damaged DNA. The restart of stalled replication forks is critical for the timely progression of the cell cycle and ultimately for the maintenance of genomic stability. Our previous work has implicated the single-stranded DNA binding protein, hSSB1/NABP2, in the repair of DNA double-strand breaks via homologous recombination. Here, we demonstrate that hSSB1 relocates to hydroxyurea (HU)-damaged replication forks where it is required for ATR and Chk1 activation and recruitment of Mre11 and Rad51. Consequently, hSSB1-depleted cells fail to repair and restart stalled replication forks. hSSB1 deficiency causes accumulation of DNA strand breaks and results in chromosome aberrations observed in mitosis, ultimately resulting in hSSB1 being required for survival to HU and camptothecin. Overall, our findings demonstrate the importance of hSSB1 in maintaining and repairing DNA replication forks and for overall genomic stability.

Supporting process model validation through natural language generation

The design and development of process-aware information systems is often supported by specifying requirements as business process models. Although this approach is generally accepted as an effective strategy, it remains a fundamental challenge to adequately validate these models given the diverging skill set of domain experts and system analysts. As domain experts often do not feel confident in judging the correctness and completeness of process models that system analysts create, the validation often has to regress to a discourse using natural language. In order to support such a discourse appropriately, so-called verbalization techniques have been defined for different types of conceptual models. However, there is currently no sophisticated technique available that is capable of generating natural-looking text from process models. In this paper, we address this research gap and propose a technique for generating natural language texts from business process models. A comparison with manually created process descriptions demonstrates that the generated texts are superior in terms of completeness, structure, and linguistic complexity. An evaluation with users further demonstrates that the texts are very understandable and effectively allow the reader to infer the process model semantics. Hence, the generated texts represent a useful input for process model validation.

Corporate sustainability reporting for water : water footprint, global reporting initiative and the water accounting framework

Three initiatives with respect to water reporting in the mining sector are compared in this paper to understand the quantities that are asked for by each initiative and the guidelines of those initiatives through means of a case study. The Global Reporting Initiative (GRI) was chosen because it has achieved widespread acceptance amongst mining companies and its water-related indicators are widely reported in corporate sustainability reporting. In contrast, the Water Footprint Network, which has been an important initiative in food and agricultural industries, has had low acceptance in the mining industry. The third initiative is the Water Accounting Framework, a collaboration between The Minerals Council of Australia and the Sustainable Minerals Institute of the University of Queensland. A water account had previously been created according to the Water Accounting Framework for the case study site, an open pit coal mine in the Bowen Basin. The resulting account provided consistent data for the Global Reporting Initiative (GRI) and the Water Footprint attributable to mining but in particular, a deficiency in the GRI indicator of EN10 reuse and recycling efficiency was illustrated quantitatively. This has far-reaching significance due to the widespread use of GRI indicators in mining corporate reports.

Effect of plasma environment on synthesis of vertically aligned carbon nanofibers in plasma-enhanced chemical vapor deposition

We present a theoretical model describing a plasma-assisted growth of carbon nanofibers (CNFs), which involves two competing channels of carbon incorporation into stacked graphene sheets: via surface diffusion and through the bulk of the catalyst particle (on the top of the nanofiber), accounting for a range of ion- and radical-assisted processes on the catalyst surface. Using this model, it is found that at low surface temperatures, Ts, the CNF growth is indeed controlled by surface diffusion, thus quantifying the semiempirical conclusions of earlier experiments. On the other hand, both the surface and bulk diffusion channels provide a comparable supply of carbon atoms to the stacked graphene sheets at elevated synthesis temperatures. It is also shown that at low Ts, insufficient for effective catalytic precursor decomposition, the plasma ions play a key role in the production of carbon atoms on the catalyst surface. The model is used to compute the growth rates for the two extreme cases of thermal and plasma-enhanced chemical vapor deposition of CNFs. More importantly, these results quantify and explain a number of observations and semiempirical conclusions of earlier experiments.

Effect of ion current density on the properties of vacuum arc-deposited TiN coatings

The influence of ion current density on the thickness of coatings deposited in a vacuum arc setup has been investigated to optimize the coating porosity. A planar probe was used to measure the ion current density distribution across plasma flux. A current density from 20 to 50 A/m2 was obtained, depending on the probe position relative to the substrate center. TiN coatings were deposited onto the cutting inserts placed at different locations on the substrate, and SEM was used to characterize the surfaces of the coatings. It was found that lowdensity coatings were formed at the decreased ion current density. A quantitative dependence of the coating thickness on the ion current density in the range of 20-50 A/m2 were obtained for the films deposited at substrate bias of 200 V and nitrogen pressure 0.1 Pa, and the coating porosity was calculated. The coated cutting inserts were tested by lathe machining of the martensitic stainless steel AISI 431. The results may be useful for controlling ion flux distribution over large industrial-scale substrates.

Predicting movie ratings from audience behaviors

We propose a method of representing audience behavior through facial and body motions from a single video stream, and use these features to predict the rating for feature-length movies. This is a very challenging problem as: i) the movie viewing environment is dark and contains views of people at different scales and viewpoints; ii) the duration of feature-length movies is long (80-120 mins) so tracking people uninterrupted for this length of time is still an unsolved problem, and; iii) expressions and motions of audience members are subtle, short and sparse making labeling of activities unreliable. To circumvent these issues, we use an infrared illuminated test-bed to obtain a visually uniform input. We then utilize motion-history features which capture the subtle movements of a person within a pre-defined volume, and then form a group representation of the audience by a histogram of pair-wise correlations over a small-window of time. Using this group representation, we learn our movie rating classifier from crowd-sourced ratings collected by rottentomatoes.com and show our prediction capability on audiences from 30 movies across 250 subjects (> 50 hrs).

A unique battery/supercapacitor direct integration scheme for hybrid electric vehicles

Battery/supercapacitor hybrid energy storage systems have been gaining popularity in electric vehicles due to their excellent power and energy performances. Conventional designs of such systems require interfacing dc-dc converters. These additional dc-dc converters increase power loss, complexity, weight and cost. Therefore, this paper proposes a new direct integration scheme for battery/supercapacitor hybrid energy storage systems using a double ended inverter system. This unique approach eliminates the need for interfacing converters and thus it is free from aforementioned drawbacks. Furthermore, the proposed system offers seven operating modes to improve the effective use of available energy in a typical drive cycle of a hybrid electric vehicle. Simulation results are presented to verify the efficacy of the proposed system and control techniques.

Battery clamped three-level inverter for renewable energy systems

This paper presents a novel battery direct integration scheme for renewable energy systems. The idea is to replace ordinary capacitors of a three-level flying-capacitor inverter by three battery banks to alleviate power fluctuations in renewable generation. This approach eliminates the need for interfacing dc-dc converters and thus considerably improves the overall efficiency. However, the major problem with this approach is the uneven distribution of space vectors which is due to unavoidable unbalance in clamping voltages. A detailed analysis on the effects of this issue and a novel carrier based pulse width modulation method, which can generate undistorted currents even in the presence of unevenly distributed space vectors, are presented in this paper. A charge/discharge controller is also proposed for power sharing and state of charge balancing of battery banks. Simulation results are presented to verify the efficacy of the proposed system, modulation method and power sharing controller.

An acoustically enhanced gold film Raman sensor on a lithium niobate substrate

The surface enhanced Raman scattering effect has shown immense potential for detecting trace amounts of explosive vapor molecules. To date, efforts to produce a commercially available, reliable SERS sensor have been impeded by an inability to separate the electromagnetic enhancement produced by the metallic nanostructure from other signal enhancing effects. Here, we show a new Raman sensor that uses surface acoustic waves (SAWs) to produce controllable surface structures on gold films deposited on LiNbO3 substrates that modulate the Raman signal of a target compound (thiophenol) adsorbed on the films. We demonstrate that this sensor can dynamically control the Raman signal simply by changing the SAW’s amplitude, allowing the Raman signal enhancement factor to be directly measured with no variation in the concentration of the target compound. The physically adsorbed molecules can be removed from the sensor without physical cleaning or damage, making it possible to reuse it for real-time Raman detection.

A runtime integrity monitoring framework for real-time relative positioning systems based on GPS and DSRC

This paper provides a three-layered framework to monitor the positioning performance requirements of Real-time Relative Positioning (RRP) systems of the Cooperative Intelligent Transport Systems (C-ITS) that support Cooperative Collision Warning (CCW) applications. These applications exploit state data of surrounding vehicles obtained solely from the Global Positioning System (GPS) and Dedicated Short-Range Communications (DSRC) units without using other sensors. To this end, the paper argues the need for the GPS/DSRC-based RRP systems to have an autonomous monitoring mechanism, since the operation of CCW applications is meant to augment safety on roads. The advantages of autonomous integrity monitoring are essential and integral to any safety-of-life system. The autonomous integrity monitoring framework proposed necessitates the RRP systems to detect/predict the unavailability of their sub-systems and of the integrity monitoring module itself, and, if available, to account for effects of data link delays and breakages of DSRC links, as well as of faulty measurement sources of GPS and/or integrated augmentation positioning systems, before the information used for safety warnings/alarms becomes unavailable, unreliable, inaccurate or misleading. Hence, a monitoring framework using a tight integration and correlation approach is proposed for instantaneous reliability assessment of the RRP systems. Ultimately, using the proposed framework, the RRP systems will provide timely alerts to users when the RRP solutions cannot be trusted or used for the intended operation.

Overview of the ShARe/CLEF eHealth Evaluation Lab 2014

This paper reports on the 2nd ShARe/CLEFeHealth evaluation lab which continues our evaluation resource building activities for the medical domain. In this lab we focus on patients' information needs as opposed to the more common campaign focus of the specialised information needs of physicians and other healthcare workers. The usage scenario of the lab is to ease patients and next-of-kins' ease in understanding eHealth information, in particular clinical reports. The 1st ShARe/CLEFeHealth evaluation lab was held in 2013. This lab consisted of three tasks. Task 1 focused on named entity recognition and normalization of disorders; Task 2 on normalization of acronyms/abbreviations; and Task 3 on information retrieval to address questions patients may have when reading clinical reports. This year's lab introduces a new challenge in Task 1 on visual-interactive search and exploration of eHealth data. Its aim is to help patients (or their next-of-kin) in readability issues related to their hospital discharge documents and related information search on the Internet. Task 2 then continues the information extraction work of the 2013 lab, specifically focusing on disorder attribute identification and normalization from clinical text. Finally, this year's Task 3 further extends the 2013 information retrieval task, by cleaning the 2013 document collection and introducing a new query generation method and multilingual queries. De-identified clinical reports used by the three tasks were from US intensive care and originated from the MIMIC II database. Other text documents for Tasks 1 and 3 were from the Internet and originated from the Khresmoi project. Task 2 annotations originated from the ShARe annotations. For Tasks 1 and 3, new annotations, queries, and relevance assessments were created. 50, 79, and 91 people registered their interest in Tasks 1, 2, and 3, respectively. 24 unique teams participated with 1, 10, and 14 teams in Tasks 1, 2 and 3, respectively. The teams were from Africa, Asia, Canada, Europe, and North America. The Task 1 submission, reviewed by 5 expert peers, related to the task evaluation category of Effective use of interaction and targeted the needs of both expert and novice users. The best system had an Accuracy of 0.868 in Task 2a, an F1-score of 0.576 in Task 2b, and Precision at 10 (P@10) of 0.756 in Task 3. The results demonstrate the substantial community interest and capabilities of these systems in making clinical reports easier to understand for patients. The organisers have made data and tools available for future research and development.

Predicting sense convergence with distributional semantics : an application to the CogALex-IV 2014 shared task

This paper presents our system to address the CogALex-IV 2014 shared task of identifying a single word most semantically related to a group of 5 words (queries). Our system uses an implementation of a neural language model and identifies the answer word by finding the most semantically similar word representation to the sum of the query representations. It is a fully unsupervised system which learns on around 20% of the UkWaC corpus. It correctly identifies 85 exact correct targets out of 2,000 queries, 285 approximate targets in lists of 5 suggestions.