title: How do testers do it? An exploratory study on manual testing practices

abstract: We present the results of a qualitative observation study on the manual testing practices in four software development companies. Manual testing practices are seldom studied, and based on the literature we conjecture that they have a strong effect on the effectiveness of manual testing. We observed testing sessions of 11 software professionals performing system level functional testing. As a result we identified 22 manual testing practices that we classified into 9 test session strategies and 13 detailed test execution techniques. Many of the identified techniques were based on similar ideas as traditional test case design techniques. However, the subjects applied these techniques during manual testing without separate test design phase. The results indicate that software professionals use a wide set of strategies and techniques when performing manual testing. Testers seem to need and use techniques even if applying exploratory testing.


Early effects of sodium-glucose co-transporter 2 inhibitors in type 2 diabetes: study based on continuous glucose monitoring


title: Preparation and enhanced photocatalytic activity of Co/F codoped tin oxide nanotubes/nanowires: a wall thickness-dependence study

abstract: Aligned Co/F codoped tin oxide (SnO2) nanotubes/nanowires with high photocatalytic activity were successfully prepared via the liquid-phase deposition (LPD) process by immersing the anodic aluminum oxide template in a (NH4)2SnF6 aqueous solution. The mechanism proposed for the formation of Co/F codoped SnO2 nanotubes/nanowires was discussed. The results showed that the morphology can be controlled by the careful manipulation of process parameters. The Co/F codoped SnO2 first nucleated on the alumina membrane walls and then grew inwards, filling the nanoholes until nanotubes were obtained. By prolonging the deposition period up to 240 min, nanowires were also obtained. The photocatalytic behavior of the products was evaluated based on the photodegradation of methylene blue (MB) as a model organic compound under visible-light irradiation. The Co/F codoped SnO2 nanotubes fabricated at 25 °C for 240 min exhibited the highest photocatalytic activity. In addition, the degradation of MB with the Co/F codoped SnO2 nanotubes/nanowires confirmed improved photocatalytic performance compared to that of commercial SnO2 nanoparticles and SnO2 nanotubes.


title: Valletto: A Multimodal Interface for Ubiquitous Visual Analytics

abstract: Modern technologies enable data analysis in scenarios where keyboard and mouse are not available. Research on multimodality in visual analytics is facing this challenge. But existing approaches consider exclusively static environments with large screens. Therefore, we envision Valletto, a prototypical tablet app which allows the user to generate and specify visualizations through a speech-based conversational interface, through multitouch gestures, and through a conventional GUI interface. We conducted an initial expert evaluation to gain information on the modality function mapping and for the integration of different modalities. Our aim is to discuss design and interaction considerations in a mobile context which fits the user's daily life.


title: A Stacking-based Approach to Twitter User Geolocation Prediction

abstract: We implement a city-level geolocation prediction system for Twitter users. The system infers a user’s location based on both tweet text and user-declared metadata using a stacking approach. We demonstrate that the stacking method substantially outperforms benchmark methods, achieving 49% accuracy on a benchmark dataset. We further evaluate our method on a recent crawl of Twitter data to investigate the impact of temporal factors on model generalisation. Our results suggest that user-declared location metadata is more sensitive to temporal change than the text of Twitter messages. We also describe two ways of accessing/demoing our system.


title: Virtual Hand Illusion Induced by Visuomotor Correlations

abstract: BACKGROUND ::: Our body schema gives the subjective impression of being highly stable. However, a number of easily-evoked illusions illustrate its remarkable malleability. In the rubber-hand illusion, illusory ownership of a rubber-hand is evoked by synchronous visual and tactile stimulation on a visible rubber arm and on the hidden real arm. Ownership is concurrent with a proprioceptive illusion of displacement of the arm position towards the fake arm. We have previously shown that this illusion of ownership plus the proprioceptive displacement also occurs towards a virtual 3D projection of an arm when the appropriate synchronous visuotactile stimulation is provided. Our objective here was to explore whether these illusions (ownership and proprioceptive displacement) can be induced by only synchronous visuomotor stimulation, in the absence of tactile stimulation. ::: ::: ::: METHODOLOGY/PRINCIPAL FINDINGS ::: To achieve this we used a data-glove that uses sensors transmitting the positions of fingers to a virtually projected hand in the synchronous but not in the asynchronous condition. The illusion of ownership was measured by means of questionnaires. Questions related to ownership gave significantly larger values for the synchronous than for the asynchronous condition. Proprioceptive displacement provided an objective measure of the illusion and had a median value of 3.5 cm difference between the synchronous and asynchronous conditions. In addition, the correlation between the feeling of ownership of the virtual arm and the size of the drift was significant. ::: ::: ::: CONCLUSIONS/SIGNIFICANCE ::: We conclude that synchrony between visual and proprioceptive information along with motor activity is able to induce an illusion of ownership over a virtual arm. This has implications regarding the brain mechanisms underlying body ownership as well as the use of virtual bodies in therapies and rehabilitation.


title: NITELIGHT: A Graphical Editor for SPARQL Queries

abstract: Query formulation is a key aspect of information retrieval, contributing to both the efficiency and usability of many semantic applications. A number of query languages, such as SPARQL, have been developed for the Semantic Web; however, there are, as yet, few tools to support end users with respect to the creation and editing of semantic queries. In this paper we present NITELIGHT, a graphical tool for semantic query design. NITELIGHT uses a Visual Query Language (VQL), called vSPARQL, which provides graphical formalisms for SPARQL query specification. NITELIGHT is a highly reusable Web-based component, and it can be easily embedded in a variety of different Web applications. This paper provides an overview of the NITELIGHT tool and the vSPARQL specification.


title: Multiscale topic tomography

abstract: Modeling the evolution of topics with time is of great value in automatic summarization and analysis of large document collections. In this work, we propose a new probabilistic graphical model to address this issue. The new model, which we call the Multiscale Topic Tomography Model (MTTM), employs non-homogeneous Poisson processes to model generation of word-counts. The evolution of topics is modeled through a multi-scale analysis using Haar wavelets. One of the new features of the model is its modeling the evolution of topics at various time-scales of resolution, allowing the user to zoom in and out of the time-scales. Our experiments on Science data using the new model uncovers some interesting patterns in topics. The new model is also comparable to LDA in predicting unseen data as demonstrated by our perplexity experiments.


Feasibility study large-scale earthquake simulator facility


title: Clinicopathologic and molecular markers in cervical carcinoma: a prospective cohort study

abstract: Background Cervical cancer is a major health problem worldwide. Identification of effective clinicopathologic and molecular markers is vital to improve treatment stratification. Objectives The purpose of this study was to validate a set of well-defined clinicopathologic features in a large population-based, prospectively collected cervical cancer cohort to support their use in the clinic. Further, we explored p53 and human epidermal growth factor receptor 2 as potential prognostic markers in cervical cancer. Study Design Tissue was collected from 401 patients with cervical cancer. Clinical data that included follow-up evaluations were collected from patient journals. Histopathologic data were evaluated and revised by an expert pathologist. The prognostic impact of selected clinicopathologic variables was analyzed in the whole cohort. Tissue microarrays were prepared from 292 carcinomas, and p53 and human epidermal growth factor receptor 2 protein levels were evaluated by immunohistochemistry. Fresh frozen samples from overlapping cervical carcinomas previously were subjected to human papilloma virus typing (n=94), whole exome (n=100) and RNA (n=79) sequencing; the results were available for our analyses. Results Among the clinicopathologic variables, vascular space invasion, histologic type, and tumor size were verified as strong independent prognostic markers. High p53 protein levels were associated significantly with markers for aggressive phenotype and survival, also in multivariate survival analysis, but did not reflect TP53 mutational status. High human epidermal growth factor receptor 2 protein levels were identified in 21% of all tumors. ERBB2 amplification was associated with poor outcome ( P =.003); human epidermal growth factor receptor 2 protein level was not. Conclusions Our findings support that the Federation Internationale de Gynecologie et d'Obstetrique s guidelines should include vascular space invasion and tumor size 2–4 cm and that careful selection of histologic type is essential for stratification of patient risk groups. High p53 levels independently predict poor survival yet do not reflect mutational status in cervical cancer. Amplified ERBB2 significantly links to poor survival, while HercepTest does not. With optimal stratification, human epidermal growth factor receptor 2–based therapy may improve cervical cancer treatment.


title: Braid Effects on Contractile Range and Friction Modeling in Pneumatic Muscle Actuators

abstract: Within braided pneumatic Muscle Actuators (pMA) the braid structure is vital to the actuator's performance, preventing over-inflation, converting radial expansion into axial contraction and setting limits for both dilation and contraction. This paper seeks to explore the nature of the contractile limit and the hysteresis observed by researchers during the actuation cycle. ::: ::: Maximum actuator dilation occurs when adjacent braid strands are forced against one another. Within this work this is analyzed mathematically and it is shown that by halving the number of strands used to create the braided shell the actuator's contractile range can be increased by approximately 7%. This also results in a simultaneous peak contractile force increases of over 16%. These results are verified experimentally. ::: ::: Hysteresis due to friction between braid strands during muscle operation is also explored. The paper will show how consideration of the deformation of the strands allows the contact area and therefore friction to be calculated without the need for experimentally obtained data as in previous research. A mathematical model is produced and verified experimentally.


title: A Conversational Agent Framework using Semantic Analysis

abstract: This paper focuses on the implementation of a novel semantic-based Conversational Agent (CA) framework. Traditional CAs interpret scripts consisting of structural patterns of sentences. User input is matched against such patterns and an associated response is sent as output. This traditional CA approach, which solely takes into account the structural form of a sentence, requires the scripter to anticipate the inordinate ways that a user may send input. This is a tiresome and timeconsuming process. As such, a semantic-based CA that interprets scripts consisting of natural language sentences alleviates this burden by removing the process of pattern generation. The CA was evaluated by participants using a domain of a specific nature, that is, student debt management, which indicated promising results.


title: Impact of artificial "gummy" fingers on fingerprint systems

abstract: Potential threats caused by something like real fingers, which are called fake or artificial fingers, should be crucial for authentication based on fingerprint systems. Security evaluation against attacks using such artificial fingers has been rarely disclosed. Only in patent literature, measures, such as live and well detection, against fake fingers have been proposed. However, the providers of fingerprint systems usually do not mention whether or not these measures are actually implemented in emerging fingerprint systems for PCs or smart cards or portable terminals, which are expected to enhance the grade of personal authentication necessary for digital transactions. As researchers who are pursuing secure systems, we would like to discuss attacks using artificial fingers and conduct experimental research to clarify the reality. This paper reports that gummy fingers, namely artificial fingers that are easily made of cheap and readily available gelatin, were accepted by extremely high rates by 11 particular fingerprint devices with optical or capacitive sensors. We have used the molds, which we made by pressing our live fingers against them or by processing fingerprint images from prints on glass surfaces, etc. We describe how to make the molds, and then show that the gummy fingers, which are made with these molds, can fool the fingerprint devices.


title: A novel cluster-based routing protocol wireless sensor networks using Spider Monkey Optimization

abstract: With rapid development, wireless sensor networks (WSNs) have been focused on improving the performance consist of energy efficiency, communication effectiveness, and system throughput. Many novel mechanisms have been implemented by adapting the social behaviors of natural creatures, such as bats, birds, ants, fish and honeybees. These systems are known as nature inspired systems or swarm intelligence in in order to provide optimization strategies, handle large-scale networks and avoid resource constraints. Spider monkey optimization (SMO) is a recent addition to the family of swarm intelligence algorithms by structuring the social foraging behavior of spider monkeys. In this paper, we aim to study the mechanism of SMO in the field of WSNs, formulating the mathematical model of the behavior patterns which cluster-based Spider Monkey Optimization (SMO-C) approach is adapted. In addition, our proposed methodology based on the Spider Monkey's behavioral structure aims to improve the traditional routing protocols in term of low-energy consumption and system quality of the network.


Bootstrapping Chatbots for Novel Domains


title: Privacy-Preserving Human Activity Recognition from Extreme Low Resolution

abstract: Privacy protection from surreptitious video recordings is an important societal challenge. We desire a computer vision system (e.g., a robot) that can recognize human activities and assist our daily life, yet ensure that it is not recording video that may invade our privacy. This paper presents a fundamental approach to address such contradicting objectives: human activity recognition while only using extreme low-resolution (e.g., 16x12) anonymized videos. We introduce the paradigm of inverse super resolution (ISR), the concept of learning the optimal set of image transformations to generate multiple low-resolution (LR) training videos from a single video. Our ISR learns different types of sub-pixel transformations optimized for the activity classification, allowing the classifier to best take advantage of existing high-resolution videos (e.g., YouTube videos) by creating multiple LR training videos tailored for the problem. We experimentally confirm that the paradigm of inverse super resolution is able to benefit activity recognition from extreme low-resolution videos.


title: Context-Aware Mobile Cloud Computing and Its Challenges

abstract: Cloud computing is gaining popularity due to virtually unlimited resources, low capital cost, ease of adoption, flexible resource provisioning, and high scalability. Considering these benefits, researchers envision the usage of cloud computing for mobile devices to overcome the ever-increasing computational and energy demands of smartphone applications. However, this requires specialized context-ware application development models that can facilitate the development of cloud-enabled applications capable of making context-aware computation offloading decisions. This article provides an overview of mobile cloud computing technology, focusing on its context-awareness aspects and challenges.


title: A Fully Integrated Digital LDO With Coarse–Fine-Tuning and Burst-Mode Operation

abstract: The digital low dropout regulator (D-LDO) has drawn significant attention recently for its low-voltage operation and process scalability. However, the tradeoff between current efficiency and transient response speed has limited its applications. In this brief, a coarse–fine-tuning technique with burst-mode operation is proposed to the D-LDO. Once the voltage undershoot/overshoot is detected, the coarse tuning quickly finds out the coarse control word in which the load current should be located, with large power MOS strength and high sampling frequency for a fixed time. Then, the fine-tuning, with reduced power MOS strength and sampling frequency, regulates the D-LDO to the desired output voltage and takes over the steady-state operation for high accuracy and current efficiency. The proposed D-LDO is verified in a 65-nm CMOS process with a 0.01-mm2 active area. The measured voltage undershoot and overshoot are 55 and 47 mV, respectively, with load steps of 2 to 100 mA with a 20-ns edge time. The quiescent current is 82 $\mu\textrm{A}$ , with a 0.43-ps figure of merit achieved. Moreover, the reference tracking speed is 1.5 $\textrm{V}/\mu\textrm{s}$ .


title: Armadillo : An Open Source C + + Linear Algebra Library for Fast Prototyping and Computationally Intensive Experiments Technical Report

abstract: In this report we provide an overview of the open source Armadillo C++ linear algebra library (matrix maths). The library aims to have a good balance between speed and ease of use, and is useful if C++ is the language of choice (due to speed and/or integration capabilities), rather than another language like Matlab or Octave. In particular, Armadillo can be used for fast prototyping and computationally intensive experiments, while at the same time allowing for relatively painless transition of research code into production environments. It is distributed under a license that is applicable in both open source and proprietary software development contexts. The library supports integer, floating point and complex numbers, as well as a subset of trigonometric and statistics functions. Various matrix decompositions are provided through optional integration with LAPACK, or one its high-performance drop-in replacements, such as MKL from Intel or ACML from AMD. A delayed evaluation approach is employed (during compile time) to combine several operations into one and reduce (or eliminate) the need for temporaries. This is accomplished through C++ template meta-programming. Performance comparisons suggest that the library is considerably faster than Matlab and Octave, as well as previous C++ libraries such as IT++ and Newmat. This report reflects a subset of the functionality present in Armadillo 0.9.92.


title: DoS and DDoS in Named Data Networking

abstract: With the growing realization that current Internet protocols are reaching the limits of their senescence, several on-going research efforts aim to design potential next-generation Internet architectures. Although they vary in maturity and scope, in order to avoid past pitfalls, these efforts seek to treat security and privacy as fundamental requirements. Resilience to Denial-of-Service (DoS) attacks that plague today's Internet is a major issue for any new architecture and deserves full attention. In this paper, we focus on DoS in Named Data Networking (NDN) - a specific candidate for next-generation Internet architecture designs. By naming data instead of its locations, NDN transforms data into a first-class entity and makes itself an attractive and viable approach to meet the needs for many current and emerging applications. It also incorporates some basic security features that mitigate classes of attacks that are commonly seen today. However, NDN's resilience to DoS attacks has not been analyzed to-date. This paper represents a first step towards assessment and possible mitigation of DoS in NDN. After identifying and analyzing several new types of attacks, it investigates their variations, effects and counter-measures. This paper also sheds some light on the debate about relative virtues of self-certifying, as opposed to human-readable, names in the context of content-centric networking.


Knowledge Sharing Among High-Tech MNCs in China and India:: Invisible Barriers, Best Practices and Next Steps


title: A 10GHz CMOS RX frontend with spatial cancellation of co-channel interferers for MIMO/digital beamforming arrays

abstract: An architecture for low-noise spatial cancellation of co-channel interferer (CCI) at RF in a digital beamforming (DBF)/MIMO receiver (RX) array is presented. The proposed RF cancellation can attenuate CCI prior to the ADC in a DBF/MIMO RX array while preserving a field-of-view (FoV) in each array element, enabling subsequent DSP for multi-beamforming. A novel hybrid-coupler/polyphase-filter based input coupling scheme that simplifies spatial selection of CCI and enables low-noise cancellation is described. A 4-element 10GHz prototype is implemented in 65nm CMOS that achieves >20dB spatial cancellation of CCI while adding <1.5dB output noise.


title: A Linkset Quality Metric Measuring Multilingual Gain in SKOS Thesauri

abstract: Linked Data is largely adopted to share and make data more accessible on the web. A quite impressive number of datasets has been exposed and interlinked according to the Linked Data paradigm but the quality of these datasets is still a big challenge in the consuming process. Measures for quality of Linked Data datasets have been proposed, mainly by adapting concepts defined in the research field of information systems. However, very limited attention has been dedicated to the quality of linksets, the connections of information belonging to distinct datasets, that might be as important as dataset’s quality when consuming Linked Data. In this paper, we present a first linkset quality measure proposing a function able to estimate the new information gained through linksets among SKOS thesauri. A scoring function, the linkset importing is provided focusing on the multilingual gain, in terms of the new translated labels, obtained by complementing a SKOS thesaurus through skos:exactMatch links. We finally discuss how the linkset importing can be significantly used in the context of the EU project eENVplus.


title: Relationship of Emotional Intelligence with Transformational Leadership and Organizational Citizenship Behavior

abstract: This manuscript examines the relationship of emotional intelligence (El) with transformational leadership (TL) and organizational citizenship behavior (OeB) of the followers. A sample of 57 dyads of managers and their supervisors (i.e. 114 respondents) participated in this study. The reliabilities of the scales were - OeB(0.83), TL (0.88), and El (0.86). El was significantly correlated to conscientiousness, civic virtue, and altruistic behaviors of followers. The method suggested by Barron and Kenny (1986) was used to test mediation of EI between TL and OeB but nothing significant was found. The results indicate that emotional intelligence of leaders enhances the OeB of followers. However, EI of the leader may not be the only factor determining the perception of TL.


JBOSS: The Evolution of Professional Open Source Software


Logging in Java with the JDK 1.4 Logging API and Apache log4j


title: Bilateral Space Video Segmentation

abstract: In this work, we propose a novel approach to video segmentation that operates in bilateral space. We design a new energy on the vertices of a regularly sampled spatiotemporal bilateral grid, which can be solved efficiently using a standard graph cut label assignment. Using a bilateral formulation, the energy that we minimize implicitly approximates long-range, spatio-temporal connections between pixels while still containing only a small number of variables and only local graph edges. We compare to a number of recent methods, and show that our approach achieves state-of-the-art results on multiple benchmarks in a fraction of the runtime. Furthermore, our method scales linearly with image size, allowing for interactive feedback on real-world high resolution video.


title: Design studies of a depth encoding large aperture PET camera

abstract: The feasibility of a wholebody PET tomograph with the capacity to correct for the parallax error induced by the depth-of-interaction of /spl gamma/-rays is assessed through simulation. The experimental energy, depth, and transverse position resolutions of BGO block detector candidates are the main inputs to a simulation that predicts the point source resolution of the depth encoding large aperture camera (DELAC). The results indicate that a measured depth resolution of 7 mm (FWHM) is sufficient to correct a substantial part of the parallax error for a point source at the edge of the field-of-view. A search for the block specifications and camera ring radius that would optimize the spatial resolution and its uniformity across the field-of-view is also presented. >


title: Lesions of the biceps pulley: diagnostic accuracy of MR arthrography of the shoulder and evaluation of previously described and new diagnostic signs.

abstract: PURPOSE ::: To retrospectively determine the diagnostic accuracy of magnetic resonance (MR) arthrography of the shoulder in the evaluation of lesions of the biceps pulley and to evaluate previously described and new diagnostic signs. ::: ::: ::: MATERIALS AND METHODS ::: Institutional review board approval was obtained; the requirement for informed consent was waived. MR arthrograms of 80 consecutive patients (mean age, 34.2 years; 53 male, 27 female) with arthroscopically proved intact or torn pulley systems were assessed for the presence of a pulley lesion by three radiologists who were blinded to arthroscopic results. Criteria evaluated were displacement of the long head of the biceps tendon (LHBT) relative to the subscapularis tendon on oblique sagittal images (displacement sign), medial subluxation of the LHBT on transverse images, nonvisibility or discontinuity of the superior glenohumeral ligament (SGHL), presence of biceps tendinopathy, and rotator cuff tears adjacent to the rotator interval. ::: ::: ::: RESULTS ::: There were 28 pulley lesions noted at arthroscopy. For observers 1, 2, and 3, respectively: MR arthrography showed a sensitivity of 89%, 86%, and 82% and a specificity of 96%, 98%, and 87% in the detection of pulley lesions. Nonvisibility or discontinuity of the SGHL was sensitive (79%, 89%, and 79%) and specific (83%, 79%, and 75%). With the displacement sign, sensitivity was 86%, 82%, and 75% and specificity was 96%, 98%, 90%. Tendinopathy of the LHBT on oblique sagittal images showed a sensitivity of 93%, 82%, 64%; specificity was 81%, 96%, and 85%. Subluxation of the LHBT was insensitive (36%, 50%, and 64%) but specific (100%, 98%, and 96%). ::: ::: ::: CONCLUSION ::: MR arthrography is accurate in the detection of pulley lesions; the displacement sign, nonvisibility or discontinuity of the SGHL, and tendinopathy of the LHBT on oblique sagittal images are the most accurate criteria for the detection of pulley lesions.


title: Biodiversity and Screening of Moderately Halophilic Bacteria with Hydrolytic and Antimicrobial Activities from Yuncheng Salt Lake, China

abstract: 1 Introduction Yuncheng Salt Lake is an old lake with a history of more than 5000 years,which locates in the margins of the central plains of China,with an area of about 130 km.It was an important resource of salt for human consumption from early times.Major chemicals in Yuncheng Salt Lake contain Na Cl,Na2SO4 and Mg SO4(Gao et al.2007).In recent years,some halophilic bacteria and archaea were


title: Inhibition of gastric acid secretion in the rat by high calcium.

abstract: Abstract This study examined the effect of a high dietary intake of calcium (1.5–2% of diet) or of an i.p. injection of calcium lactate (65 mg Ca/kg) on gastric acid secretion in the conscious, unoperated rat. Two weeks on the high-calcium diet inhibited basal acid secretion in comparison with calcium-free or low-calcium diets. This inhibition was reversed upon feeding a lower level of calcium to the rats. A high-calcium injection into rats fed a commercial diet inhibited histamine-stimulated acid secretion, but not basal secretion. The combined effects of high dietary calcium and a high-calcium injection was also studied. On a calcium-free diet, the calcium injection inhibited both basal and histamine-induced acid secretion. On a 2% calcium diet the high-calcium injection inhibited only the histamine-induced, but not the basal acid secretion.


title: The Pitfalls of Hashing for Privacy

abstract: Boosted by recent legislations, data anonymization is fast becoming a norm. However, as of yet no generic solution has been found to safely release data. As a consequence, data custodians often resort to ad-hoc means to anonymize datasets. Both past and current practices indicate that hashing is often believed to be an effective way to anonymize data. Unfortunately, in practice it is only rarely effective. This paper is a tutorial to explain the limits of cryptographic hash functions as an anonymization technique. Anonymity set is the best privacy model that can be achieved by hash functions. However, this model has several shortcomings. We provide three case studies to illustrate how hashing only yields a weakly anonymized data. The case studies include MAC and email address anonymization as well as the analysis of Google safe browsing.


title: Use of Instructional Technologies in Science Classrooms: Teachers' Perspectives.

abstract: The purpose of this study was to investigate how science teachers use instructional technologies in science classrooms. Participants were 63 teachers who have just completed an alternative teaching certificate program in one of the largest universities in Turkey. They were asked to make a lesson plan based on any topic by assuming that they had an ideal school environment. Based on analysis of all lesson plans, participants were asked to explain their reasons for using those technologies. Findings revealed that PowerPoint was the most widely used instructional technology in their lesson plans. Textbooks and blackboards were other instructional technologies used by participants. Animations were the least used instructional technology by all participants. None of the participants used internet, interactive smart boards, spreadsheets, computer simulations, and educational software in their lesson plans even though they were told to assume that they had an ideal environment in terms of time, resources, and students.


title: Efficient Signature Generation for Classifying Cross-Architecture IoT Malware

abstract: Internet-of-Things IoT devices are increasingly targeted Uy adversaries due to their unique characteristics such as constant online connection, lack of protection, and full integration in people’s daily life. As attackers shift their targets towards IoT devices, malware has been developed to compromise IoT devices equipped with different CPU architectures. While malware detection has been a well-studied area for desktop PCs, heterogeneous processor architecture in IoT devices brings in unique challenges. Existing approaches utilize static or dynamic binary analysis for identifying malware characteristics, Uut they all fall short when dealing with IoT malware compiled for different architectures. In this paper, we propose an efficient signature generation method for IoT malware, which generates distinguishable signatures based on high-level structural, statistical and string feature vectors, as high-level features are more robust against code variations across different architectures. The generated signatures for each malware family can be used for developing lightweight malware detection tools to secure IoT devices. Extensive experiments with two datasets of 5,150 recent IoT malware samples show that our scheme can achieve 95.5% detection rate with 0% false positive rate. Moreover, the proposed scheme can achieve 85.2% detection rate in detecting novel IoT malware.


A Survey on Plagiarism Detection Techniques for Indian Regional Languages


title: Adding dynamics to sketch-based character animations

abstract: Cartoonists and animators often use lines of action to emphasize dynamics in character poses. In this paper, we propose a physically-based model to simulate the line of action's motion, leading to rich motion from simple drawings. Our proposed method is decomposed into three steps. Based on user-provided strokes, we forward simulate 2D elastic motion. To ensure continuity across keyframes, we re-target the forward simulations to the drawn strokes. Finally, we synthesize a 3D character motion matching the dynamic line. The fact that the line can move freely like an elastic band raises new questions about its relationship to the body over time. The line may move faster and leave body parts behind, or the line may slide slowly towards other body parts for support. We conjecture that the artist seeks to maximize the filling of the line (with the character's body)---while respecting basic realism constraints such as balance. Based on these insights, we provide a method that synthesizes 3D character motion, given discontinuously constrained body parts that are specified by the user at key moments.


Corporate Social Responsibility and Stakeholder Value Maximization: Evidence from Mergers


title: A New Graphical Password Scheme Resistant to Shoulder-Surfing

abstract: Shoulder-surfing is a known risk where an attacker can capture a password by direct observation or by recording the authentication session. Due to the visual interface, this problem has become exacerbated in graphical passwords. There have been some graphical schemes resistant or immune to shoulder-surfing, but they have significant usability drawbacks, usually in the time and effort to log in. In this paper, we propose and evaluate a new shoulder-surfing resistant scheme which has a desirable usability for PDAs. Our inspiration comes from the drawing input method in DAS and the association mnemonics in Story for sequence retrieval. The new scheme requires users to draw a curve across their password images orderly rather than click directly on them. The drawing input trick along with the complementary measures, such as erasing the drawing trace, displaying degraded images, and starting and ending with randomly designated images provide a good resistance to shoulder-surfing. A preliminary user study showed that users were able to enter their passwords accurately and to remember them over time.


Applying data mining techniques in property/casualty insurance


title: Genetic variability of DNA repair mechanisms influences chemotherapy outcome of gastric cancer.

abstract: Genetic variability of DNA repair mechanisms influences chemotherapy treatment outcome of gastric cancer. We conducted a cohort study to investigate the role of ERCC1-ERCC2 gene polymorphisms in the chemotherapy response and clinic outcome of gastric cancer. Between March 2011 and March 2013, 228 gastric patients who were newly diagnosed with histopathology were enrolled in our study. Genotypes of ERCC1 rs11615, rs3212986, rs2298881 and ERCC2 rs3212986 were conducted by polymerase chain reaction restriction fragment length polymorphism (PCR-RFLP) assay. We found that individuals carrying TT genotype of ERCC1 rs11615 and CC genotype of ERCC1 rs2298881 were associated with better response to chemotherapy and longer survival time of gastric cancer. Moreover, individuals with AA genotype of ERCC2 rs1799793 were correlated with shorter survival of gastric cancer. In conclusion, ERCC1 rs11615, rs2298881 and ERCC2 rs1799793 polymorphism play an important role in the treatment outcome of gastric cancer.


Program Synthesis from Natural Language Using Recurrent Neural Networks


title: Improved visual SLAM: a novel approach to mapping and localization using visual landmarks in consecutive frames

abstract: Pathfinding is becoming more and more common in autonomous vehicle navigation, robot localization, and other computer vision applications. In this paper, a novel approach to mapping and localization is presented that extracts visual landmarks from a robot dataset acquired by a Kinect sensor. The visual landmarks are detected and recognized using the improved scale-invariant feature transform (I-SIFT) method. The methodology is based on detecting stable and invariant landmarks in consecutive (red-green-blue depth) RGB-D frames of the robot dataset. These landmarks are then used to determine the robot path, and a map is constructed by using the visual landmarks. A number of experiments were performed on various datasets in an indoor environment. The proposed method performs efficient landmark detection in various environments, which includes changes in rotation and illumination. The experimental results show that the proposed method can solve the simultaneous localization and mapping (SLAM) problem using stable visual landmarks, but with less computation time.


title: A Study on Developing CRM Scorecard

abstract: As more firms embrace CRM as a core business strategy, it is getting more important to assess firms' CRM readiness and performance for diagnosing, guiding, managing, and evaluating their current CRM practices. Previous CRM studies have been limited to extracting a few success factors or focus only on specific implementation methodologies without any theoretical underpinning. In this paper, we suggest a performance measurement framework called CRM scorecard as an integrative organizational artifact to diagnose and assess a firm's CRM practice. The CRM scorecard was developed through comprehensive literature review and in-depth interviews with CRM practitioners, with specific objective and subjective instruments added to measure corporate CRM capability and readiness. Additionally, we conducted an analytic hierarchy process (AHP) analysis to prioritize the evaluation factors in the CRM scorecard. To illustrate the applicability of the proposed CRM scorecard, we apply the artifact to a consumer bank, which is well known for an exemplary CRM strategy in Korea


title: Traffic Sign Detection for Advanced Driver Assistance System

abstract: Development of safety features so as to prevent ignorance of traffic sign boards mounted on road is one of the major technical challenges in the automobile industry. Ignoring traffic signs can lead to major road accidents. Therefore, using driver assistance system to timely assist with warning and information road signs can be of significant help in prevention of accidents. This paper aims towards the detection of road signs using contour analysis approach. In this paper Blue, Green, Red (BGR) to Hue, Saturation, Value (HSV) conversion model and morphological filter for noise filtering are used to make the result more robust. The system has a voice trigger to alert the driver of road signs via audio message and thus helping in prevention of accidents.


title: Farthest-Point Heuristic based Initialization Methods for K-Modes Clustering

abstract: The k-modes algorithm has become a popular technique in solving categorical data clustering problems in different application domains. However, the algorithm requires random selection of initial points for the clusters. Different initial points often lead to considerable distinct clustering results. In this paper we present an experimental study on applying a farthest-point heuristic based initialization method to k-modes clustering to improve its performance. Experiments show that new initialization method leads to better clustering accuracy than random selection initialization method for k-modes clustering.


title: Should I stay or should I go? How the human brain manages the trade-off between exploitation and exploration

abstract: Many large and small decisions we make in our daily lives—which ice cream to choose, what research projects to pursue, which partner to marry—require an exploration of alternatives before committing to and exploiting the benefits of a particular choice. Furthermore, many decisions require re-evaluation, and further exploration of alternatives, in the face of changing needs or circumstances. That is, often our decisions depend on a higher level choice: whether to exploit well known but possibly suboptimal alternatives or to explore risky but potentially more profitable ones. How adaptive agents choose between exploitation and exploration remains an important and open question that has received relatively limited attention in the behavioural and brain sciences. The choice could depend on a number of factors, including the familiarity of the environment, how quickly the environment is likely to change and the relative value of exploiting known sources of reward versus the cost of reducing uncertainty through exploration. There is no known generally optimal solution to the exploration versus exploitation problem, and a solution to the general case may indeed not be possible. However, there have been formal analyses of the optimal policy under constrained circumstances. There have also been specific suggestions of how humans and animals may respond to this problem under particular experimental conditions as well as proposals about the brain mechanisms involved. Here, we provide a brief review of this work, discuss how exploration and exploitation may be mediated in the brain and highlight some promising future directions for research.


title: Fisheye stereo camera using equirectangular images

abstract: This paper proposes making a stereo camera using two fisheye cameras. The measurement range of the stereo camera is increased by using fisheye cameras. Fisheye images are deformed by using an equirectangular projection to simplify the stereo correspondence search in the images. Perspective transformation is applied to images so that feature points of far objects coincide in the images of the two cameras in order to remove mismatches due to the errors of intrinsic and extrinsic parameters. The corresponding points are calculated by using stereo matching in the deformed image, and the distance is measured from the disparity. The accuracy of the constructed stereo camera's distance measuring is evaluated by experiments. The performance of the constructed stereo camera is evaluated by indoor and outdoor experiments.


title: Enriching Word Embeddings Using Knowledge Graph for Semantic Tagging in Conversational Dialog Systems

abstract: Unsupervised word embeddings provide rich linguistic and conceptual information about words. However, they may provide weak information about domain specific semantic relations for certain tasks such as semantic parsing of natural language queries, where such information about words can be valuable. To encode the prior knowledge about the semantic word relations, we present new method as follows: We extend the neural network based lexical word embedding objective function Mikolov, et.al. 2013 by incorporating the information about relationship between entities that we extract from knowledge bases. Our model can jointly learn lexical word representations from free text enriched by the relational word embeddings from relational data (e.g., Freebase) for each type of entity relations. We empirically show on the task of semantic tagging of natural language queries that our enriched embeddings can provide information about not only short-range syntactic dependencies but also long-range semantic dependencies between words. Using the enriched embeddings, we obtain an average of 2% improvement in F-score compared to the previous baselines.


title: Development of Working Memory Maintenance

abstract: The neural circuitry supporting mature visual spatial working memory (VSWM) has been well delineated in nonhuman primates and in human adults. However, we still have limited understanding about developmental change through adolescence in this network. We present results from a fast event-related functional MRI (fMRI) study aimed at characterizing developmental changes in brain mechanisms supporting VSWM across different delay periods. Forty-three healthy subjects (17 adults, 18–30 yr; 13 adolescents, 13–17 yr; 13 children, 8–12 yr) were scanned as they performed an oculomotor delayed response (ODR) task with short (2.5 s) and long (10 s) delay period trials. Results showed that all age groups recruited a common network of regions to support both delay trials, including frontal, parietal, and temporal regions, indicative of a core circuitry needed to perform the task. Several age-related differences were found in the recruitment of regions, supporting short delay trials, including fronto-caudal areas, which could contribute to known differences in initial memory-guided saccade precision. To support extended delay trials, adults primarily recruited additional posterior parietal cortex (PPC), whereas children and adolescents recruited a considerably more extensive distributed circuitry. Our findings indicate that brain processes supporting basic aspects of working memory across cortex are established by childhood. We also find evidence for continued immaturities in systems supporting working memory precision, reflected by differences in the circuitry recruited by children and by continued refinement of fronto-insular-temporal regions recruited by adolescents. Taken together, these results suggest distinct developmental changes in the circuitry supporting visual spatial working memory.


title: Practical Bayesian optimization in the presence of outliers

abstract: Inference in the presence of outliers is an important field of research as outliers are ubiquitous and may arise across a variety of problems and domains. Bayesian optimization is method that heavily relies on probabilistic inference. This allows outstanding sample efficiency because the probabilistic machinery provides a memory of the whole optimization process. However, that virtue becomes a disadvantage when the memory is populated with outliers, inducing bias in the estimation. In this paper, we present an empirical evaluation of Bayesian optimization methods in the presence of outliers. The empirical evidence shows that Bayesian optimization with robust regression often produces suboptimal results. We then propose a new algorithm which combines robust regression (a Gaussian process with Student-t likelihood) with outlier diagnostics to classify data points as outliers or inliers. By using an scheduler for the classification of outliers, our method is more efficient and has better convergence over the standard robust regression. Furthermore, we show that even in controlled situations with no expected outliers, our method is able to produce better results.


title: Electrostatic Basis for Separation of Wheat Bran Tissues

abstract: New fractionation technologies are presently under investigation in order to enhance the wheat bran nutrient value. The explicit aim of this paper is to evaluate the possibility of using electric field forces for the separation of the fine particles generated from each wheat bran tissue during the milling process. Corona-charging and surface-potential-decay experiments were performed on samples of finely ground wheat bran and aleurone tissues (maximum size of 0.3 mm). Ground bran, which has a higher moisture content, more rapidly loses the charge imparted by corona discharge. For both kinds of samples, finer particles charged better than larger particles, and lyophilized samples were found to better preserve their charge than those not subjected to a freeze-drying process. An experiment carried out using a belt-type corona electrostatic separator validated the possibility of sorting these tissues based on the differences in their charge decay characteristics. Tribocharging experiments performed on the same tissues have also led to encouraging results: The charge/mass ratio of ground bran tissues was twice that of aleurone. These differences in the physical properties of the two types of tissues provide the technical basis for the development of electrostatic-separation technologies as part of more efficient bran-cracking strategies.


title: Graph Signal Processing: Overview, Challenges, and Applications

abstract: Research in Graph Signal Processing (GSP) aims to develop tools for processing data defined on irregular graph domains. In this paper we first provide an overview of core ideas in GSP and their connection to conventional digital signal processing. We then summarize recent developments in developing basic GSP tools, including methods for sampling, filtering or graph learning. Next, we review progress in several application areas using GSP, including processing and analysis of sensor network data, biological data, and applications to image processing and machine learning. We finish by providing a brief historical perspective to highlight how concepts recently developed in GSP build on top of prior research in other areas.


title: Cyber-physical security for smart cars: taxonomy of vulnerabilities, threats, and attacks

abstract: As the passenger vehicles evolve to be "smart", electronic components, including communication and intelligent software, are continuously introduced to new models and concept vehicles. The new paradigm introduces new features and benefits, but also brings new security concerns. Smart cars are considered cyber-physical systems (CPS) because of their integration of cyber- and physical-components. In recent years, various threats, vulnerabilities, and attacks have been discovered from different models of smart cars. In the worst-case scenario, external attackers may remotely obtain full control of the vehicle by exploiting an existing vulnerability. In this poster, we examine smart car security from a CPS' perspective, and derive a taxonomy of threats, vulnerabilities, and attacks. We demonstrate a systematic model of smart car security by distinguishing between cyber, cyber-physical, and physical (C-CP-P) components and their interactions. We present our reflections on how the systematic model and taxonomy could be utilized to help the development of effective control mechanisms.


title: Distributed traffic light control at uncoupled intersections with real-world topology by deep reinforcement learning

abstract: This work examines the implications of uncoupled intersections with local real-world topology and sensor setup on traffic light control approaches. Control approaches are evaluated with respect to: Traffic flow, fuel consumption and noise emission at intersections. ::: The real-world road network of Friedrichshafen is depicted, preprocessed and the present traffic light controlled intersections are modeled with respect to state space and action space. ::: Different strategies, containing fixed-time, gap-based and time-based control approaches as well as our deep reinforcement learning based control approach, are implemented and assessed. Our novel DRL approach allows for modeling the TLC action space, with respect to phase selection as well as selection of transition timings. It was found that real-world topologies, and thus irregularly arranged intersections have an influence on the performance of traffic light control approaches. This is even to be observed within the same intersection types (n-arm, m-phases). Moreover we could show, that these influences can be efficiently dealt with by our deep reinforcement learning based control approach.


title: Achieving Human Parity on Automatic Chinese to English News Translation

abstract: Machine translation has made rapid advances in recent years. Millions of people are using it today in online translation systems and mobile applications in order to communicate across language barriers. The question naturally arises whether such systems can approach or achieve parity with human translations. In this paper, we first address the problem of how to define and accurately measure human parity in translation. We then describe Microsoft's machine translation system and measure the quality of its translations on the widely used WMT 2017 news translation task from Chinese to English. We find that our latest neural machine translation system has reached a new state-of-the-art, and that the translation quality is at human parity when compared to professional human translations. We also find that it significantly exceeds the quality of crowd-sourced non-professional translations.


title: Does ease-of-use contributes to the perception of enjoyment? A case of gamification in e-banking

abstract: The success of online games encouraged the development of gamification software in e-banking. Beside the growing trend of gamification, it is important understand how bank customers face the gamified applications, particularly as enjoyment and ease-of-use. To assess the determinants that influence the adoption of gamification in e-banking, we developed a research to propose a conceptual model that illustrates the adoption of gamified business applications by bank customers, in e-banking context. We conducted two quantitative studies (A and B) to understand how bank customers represent a gamified business software and its changes (or improvements) over time. Study A was performed in 2012 (Nź=ź183), and study B in 2015 (Nź=ź219). Online bank customers were invited to rate the importance of variables related to: socialness, ease-of-use, usefulness, enjoyment and intention to use e-banking systems with game features and social cues. The results show that ease-of-use and enjoyment are interrelated, and both have influence in e-banking usage. This study present theoretical ground of the conceptual model, and discuss two empirical studies, aiming to analyse the ease-of-use and enjoyment influence on bank customers. These findings will contribute directly to explain of adoption hedonic business software in e-banking. Two studies, are presented to analyse the ease-of-use and enjoyment influence.A conceptual model was validated in two consequent studies in e-banking.The conceptual model for hedonic business software is not fully recursive.The ease-of-use and enjoyment have a positive influence on each other.The influence of ease-of-use on enjoyment is stronger, than the reverse.


title: Autopoiesis, Structural Coupling and Cognition: A history of these and other notions in the biology of cognition

abstract: A liquid crystal cell comprises a transparent top support plate having a transparent conductive coating thereon, a bottom support plate smaller than the first support plate and having a reflective conductive coating on one surface thereof and a black coating on its opposite surface is centered with respect to and spaced from the top support plate, a layer of a liquid crystal composition fills the space between the support plates, a metal cover encloses the bottom support plate and liquid crystal layer and is sealed around its periphery to the top support plate by means of a metal frit and solder. The cover is insulated from the top support plate by means of a dielectric insulating layer on the support plate. Spring means are provided which are bonded to the cover and the bottom support plate for holding the bottom support plate in position. A method for assembling the structure is described.


title: Extending OpenStack Access Control with Domain Trust

abstract: OpenStack has been rapidly established as the most popular open-source platform for cloud Infrastrusture-as-a-Service in this fast moving industry. In response to increasing access control requirements from its users, the OpenStack identity service Keystone has introduced several entities, such as domains and projects in addition to roles, resulting in a rather complex and somewhat obscure authorization model. In this paper, we present a formalized description of the core OpenStack access control (OSAC). We further propose a domain trust extension for OSAC to facilitate secure cross-domain authorization. We have implemented a proof-of-concept prototype of this trust extension based on Keystone. The authorization delay introduced by the domain trusts is 0.7 percent on average in our experiments.


title: Stock-out Prediction in Multi-echelon Networks

abstract: In multi-echelon inventory systems the performance of a given node is affected by events that occur at many other nodes and at many other time periods. For example, a supply disruption upstream will have an effect on downstream, customer-facing nodes several periods later as the disruption"cascades"through the system. There is very little research on stock-out prediction in single-echelon systems and (to the best of our knowledge) none on multi-echelon systems. However, in real the world, it is clear that there is significant interest in techniques for this sort of stock-out prediction. Therefore, our research aims to fill this gap by using DNN to predict stock-outs in multi-echelon supply chains.


Analysis and Performance Evaluation of 1-bit Full Adder Using Different Topologies


title: Unsupervised Imitation Learning

abstract: We introduce a novel method to learn a policy from unsupervised demonstrations of a process. Given a model of the system and a set of sequences of outputs, we find a policy that has a comparable performance to the original policy, without requiring access to the inputs of these demonstrations. We do so by first estimating the inputs of the system from observed unsupervised demonstrations. Then, we learn a policy by applying vanilla supervised learning algorithms to the (estimated)input-output pairs. For the input estimation, we present a new adaptive linear estimator (AdaL-IE) that explicitly trades-off variance and bias in the estimation. As we show empirically, AdaL-IE produces estimates with lower error compared to the state-of-the-art input estimation method, (UMV-IE) [Gillijns and De Moor, 2007]. Using AdaL-IE in conjunction with imitation learning enables us to successfully learn control policies that consistently outperform those using UMV-IE.


title: Learning to Parse Database Queries Using Inductive Logic Programming

abstract: This paper presents recent work using the CHILL parser acquisition system to automate the construction of a natural-language interface for database queries. CHILL treats parser acquisition as the learning of search-control rules within a logic program representing a shift-reduce parser and uses techniques from Inductive Logic Programming to learn relational control knowledge. Starting with a general framework for constructing a suitable logical form, CHILL is able to train on a corpus comprising sentences paired with database queries and induce parsers that map subsequent sentences directly into executable queries. Experimental results with a complete database-query application for U.S. geography show that CHILL is able to learn parsers that outperform a preexisting, hand-crafted counterpart. These results demonstrate the ability of a corpus-based system to produce more than purely syntactic representations. They also provide direct evidence of the utility of an empirical approach at the level of a complete natural language application.


title: IP Covert Channel Detection

abstract: A covert channel can occur when an attacker finds and exploits a shared resource that is not designed to be a communication mechanism. A network covert channel operates by altering the timing of otherwise legitimate network traffic so that the arrival times of packets encode confidential data that an attacker wants to exfiltrate from a secure area from which she has no other means of communication. In this article, we present the first public implementation of an IP covert channel, discuss the subtle issues that arose in its design, and present a discussion on its efficacy. We then show that an IP covert channel can be differentiated from legitimate channels and present new detection measures that provide detection rates over 95p. We next take the simple step an attacker would of adding noise to the channel to attempt to conceal the covert communication. For these noisy IP covert timing channels, we show that our online detection measures can fail to identify the covert channel for noise levels higher than 10p. We then provide effective offline search mechanisms that identify the noisy channels.


title: Original and Repair Bulk Fracture Resistance of Particle Filler and Short Fiber–Reinforced Composites

abstract: Clinical Relevance Longevity of repaired direct composite restorations may be improved by including a short E-glass fiber–reinforced composite with a semi-interpenetrating network matrix as the substrate material in bilayered restorations.


title: CSE: Conceptual Sentence Embeddings based on Attention Model

abstract: Most sentence embedding models typically represent each sentence only using word surface, which makes these models indiscriminative for ubiquitous homonymy and polysemy. In order to enhance representation capability of sentence, we employ conceptualization model to assign associated concepts for each sentence in the text corpus, and then learn conceptual sentence embedding (CSE). Hence, this semantic representation is more expressive than some widely-used text representation models such as latent topic model, especially for short-text. Moreover, we further extend CSE models by utilizing a local attention-based model that select relevant words within the context to make more efficient prediction. In the experiments, we evaluate the CSE models on two tasks, text classification and information retrieval. The experimental results show that the proposed models outperform typical sentence embed-ding models.


Preferences, Homophily, and Social Learning


title: Deep Networks for Image Super-Resolution with Sparse Prior

abstract: Deep learning techniques have been successfully applied in many areas of computer vision, including low-level image restoration problems. For image super-resolution, several models based on deep neural networks have been recently proposed and attained superior performance that overshadows all previous handcrafted models. The question then arises whether large-capacity and data-driven models have become the dominant solution to the ill-posed super-resolution problem. In this paper, we argue that domain expertise represented by the conventional sparse coding model is still valuable, and it can be combined with the key ingredients of deep learning to achieve further improved results. We show that a sparse coding model particularly designed for super-resolution can be incarnated as a neural network, and trained in a cascaded structure from end to end. The interpretation of the network based on sparse coding leads to much more efficient and effective training, as well as a reduced model size. Our model is evaluated on a wide range of images, and shows clear advantage over existing state-of-the-art methods in terms of both restoration accuracy and human subjective quality.


title: A Metaheuristically Tuned Interval Type 2 Fuzzy System to Reduce Segmentation Uncertainty in Brain MRI Images

abstract: Precise segmentation of magnetic resonance image (MRI) seems challenging because of the complex structure of the brain, non-uniform field in images, and noise. As a result, decision-making is associated with uncertainty. Fuzzy based approaches have been developed to overcome this problem, though most of them use fuzzy type 1 method, and sometimes contain a pre-processing step. This paper "modified type 2 fuzzy system" (MT2FS) declares a state-of-the-art method to segment MRI images using interval fuzzy type-2. Furthermore, Genetic algorithm has been employed to specify the best values for mean and variance of upper and lower membership functions. This strategy will determine discrimination boundaries for different brain tissues to be less independent from the training set. Finally, the result of fuzzy rules is extracted by using Dempster-Shafer rule combination method. Simulation results demonstrate a satisfactory output on both simulated and real MRI images in comparison with previously conducted research works without the need for a pre-processing stage.


title: NXgraph: An efficient graph processing system on a single machine

abstract: Recent studies show that graph processing systems on a single machine can achieve competitive performance compared with cluster-based graph processing systems. In this paper, we present NXgraph, an efficient graph processing system on a single machine. We propose the Destination-Sorted Sub-Shard (DSSS) structure to store a graph. To ensure graph data access locality and enable fine-grained scheduling, NXgraph divides vertices and edges into intervals and sub-shards. To reduce write conflicts among different threads and achieve a high degree of parallelism, NXgraph sorts edges within each sub-shard according to their destination vertices. Then, three updating strategies, i.e., Single-Phase Update (SPU), Double-Phase Update (DPU), and Mixed-Phase Update (MPU), are proposed in this paper. NXgraph can adaptively choose the fastest strategy for different graph problems according to the graph size and the available memory resources to fully utilize the memory space and reduce the amount of data transfer. All these three strategies exploit streamlined disk access patterns. Extensive experiments on three real-world graphs and five synthetic graphs show that NXgraph outperforms GraphChi, TurboGraph, VENUS, and GridGraph in various situations. Moreover, NXgraph, running on a single commodity PC, can finish an iteration of PageRank on the Twitter [1] graph with 1.5 billion edges in 2.05 seconds; while PowerGraph, a distributed graph processing system, needs 3.6s to finish the same task on a 64-node cluster.


title: Diagnosing Voice Disorder with Machine Learning

abstract: The goal of this study is to build an effective model to identify the types of voice disorder, include Normal, Neoplas, Phonotrauma and Vocal palsy, from FEMH dataset. To deal with this classification problem, we use several Machine learning methods, such as Support Vector Machine, Random Forest, K-Nearest Neighbor, and Gradient Boosting. Meanwhile, we also try the Ensemble Learning algorithm, which combines these learners and selects the most popular prediction.


title: Null space approach of Fisher discriminant analysis for face recognition

abstract: The null space of the within-class scatter matrix is found to express most discriminative information for the small sample size problem (SSSP). The null space-based LDA takes full advantage of the null space while the other methods remove the null space. It proves to be optimal in performance. From the theoretical analysis, we present the NLDA algorithm and the most suitable situation for NLDA. Our method is simpler than all other null space approaches, it saves the computational cost and maintains the performance simultaneously. Furthermore, kernel technique is incorporated into discriminant analysis in the null space. Firstly, all samples are mapped to the kernel space through a better kernel function, called Cosine kernel, which is proposed to increase the discriminating capability of the original polynomial kernel function. Secondly, a truncated NLDA is employed. The novel approach only requires one eigenvalue analysis and is also applicable to the large sample size problem. Experiments are carried out on different face data sets to demonstrate the effectiveness of the proposed methods.


title: The effect of overt and covert narcissism on self-esteem and self-efficacy beyond self-esteem

abstract: Abstract Past literature has suggested a dual nature of trait based narcissism, comprising overt and covert forms. While several studies have examined the two subtypes in relation to self-esteem, very few studies have examined narcissistic subtypes and self-efficacy. 115 Psychology undergraduates filled in self-report measures of overt narcissism, covert narcissism, self-esteem and self-efficacy. Results demonstrated no significant relationship between overt and covert narcissism, suggesting two distinct subtypes. Overt and covert forms of narcissism were found to significantly contribute to self-efficacy beyond self-esteem. Further, overt narcissism positively predicted both self-esteem and self-efficacy beyond self-esteem. Conversely, covert narcissism was found to negatively predict self-esteem and self-efficacy beyond self-esteem. Overt narcissism subscale associations were also computed, with Power being associated with higher self-efficacy but not self-esteem, suggesting Power to be a more adaptive subscale. The Special Person subscale was associated with higher self-esteem but not self-efficacy, suggesting it forms the maladaptive core of overt narcissism. Exhibitionism was not associated with either self-esteem or self-efficacy. Results appear congruent with past literature, and have given an additional insight into the implications of trait based narcissism regarding self-efficacy. Findings appear to suggest trait based overt narcissism is a more adaptive construct to individual self-concept than covert narcissism.


title: Light reflection functions for simulation of clouds and dusty surfaces

abstract: The study of the physical process of light interacting with matter is an important part of computer image synthesis since it forms the basis for calculations of intensities in the picture. The simpler models used in the past are being augmented by more complex models gleaned from the physics literature. This paper is another step in the direction of assimilating such knowledge. It concerns the statistical simulation of light passing through and being reflected by clouds of similar small particles. (It does not, however, address the cloud structure modeling problem). By extension it can be applied to surfaces completely covered by dust and is therefore a physical basis for various theories of diffuse reflection.


title: RUIS: a toolkit for developing virtual reality applications with spatial interaction

abstract: We introduce Reality-based User Interface System (RUIS), a virtual reality (VR) toolkit aimed for students and hobbyists, which we have used in an annually organized VR course for the past four years. RUIS toolkit provides 3D user interface building blocks for creating immersive VR applications with spatial interaction and stereo 3D graphics, while supporting affordable VR peripherals like Kinect, PlayStation Move, Razer Hydra, and Oculus Rift. We describe a novel spatial interaction scheme that combines freeform, full-body interaction with traditional video game locomotion, which can be easily implemented with RUIS. We also discuss the specific challenges associated with developing VR applications, and how they relate to the design principles behind RUIS. Finally, we validate our toolkit by comparing development difficulties experienced by users of different software toolkits, and by presenting several VR applications created with RUIS, demonstrating a variety of spatial user interfaces that it can produce.


title: Electrochemical detection of hydrogen peroxide via hemoglobin–DNA/pyterpy-modified gold electrode

abstract: Abstract Hydrogen peroxide (H 2 O 2 ) biosensors are some of the most promising sensors being developed, because they are highly selective and respond quickly to a specific substrate. Hemoglobin (Hb) has been used as an ideal biomolecule for the construction of hydrogen peroxide biosensors because of its high sensitivity to H 2 O 2 . Direct electron transfer from Hb has been widely investigated for application in the detection of H 2 O 2 because of the reaction’s simplicity, high selectivity and intrinsic sensitivity. A method for the electrochemical detection of hydrogen peroxide based on the immobilization of hemoglobin on a DNA/Fe(pyterpy) 2+ -modified gold electrode was investigated. The pyterpy monolayers were primary electron depositions on the gold electrode surface of a quartz crystal microbalance (QCM). This layer offers a template to which negatively charged DNA attaches. The process of fabricating the electrode was verified by a quartz crystal analyzer (QCA). The experimental parameters, such as pH, applied potential and amperometric response, were evaluated and optimized. Under the optimized conditions, this sensor shows a linear response between 3.0 × 10 −6 and 9.0 × 10 −4 M concentrations of H 2 O 2 . The detection limit was determined to be 7 × 10 −7 M (based on S / N = 3).


title: Are You Sure That This Happened? Assessing the Factuality Degree of Events in Text

abstract: Identifying the veracity, or factuality, of event mentions in text is fundamental for reasoning about eventualities in discourse. Inferences derived from events judged as not having happened, or as being only possible, are different from those derived from events evaluated as factual. Event factuality involves two separate levels of information. On the one hand, it deals with polarity, which distinguishes between positive and negative instantiations of events. On the other, it has to do with degrees of certainty (e.g., possible, probable), an information level generally subsumed under the category of epistemic modality. This article aims at contributing to a better understanding of how event factuality is articulated in natural language. For that purpose, we put forward a linguistic-oriented computational model which has at its core an algorithm articulating the effect of factuality relations across levels of syntactic embedding. As a proof of concept, this model has been implemented in De Facto, a factuality profiler for eventualities mentioned in text, and tested against a corpus built specifically for the task, yielding an F1 of 0.70 (macro-averaging) and 0.80 (micro-averaging). These two measures mutually compensate for an over-emphasis present in the other (either on the lesser or greater populated categories), and can therefore be interpreted as the lower and upper bounds of the De Facto's performance.


title: A Double-Sided LCC Compensation Network and Its Tuning Method for Wireless Power Transfer

abstract: This paper proposes a double-sided LCC compensation network and its tuning method for wireless power transfer (WPT). With the proposed topology and its tuning method, the resonant frequency is irrelevant with the coupling coefficient between the two coils and is also independent of the load condition, which means that the system can work at a constant switching frequency. Analysis in frequency domain is given to show the characteristics of the proposed method. We also propose a method to tune the network to realize zero voltage switching (ZVS) for the Primary-side switches. Simulation and experimental results verified analysis and validity of the proposed compensation network and the tuning method. A wireless charging system with output power of up to 7.7 kW for electric vehicles was built, and 96% efficiency from dc power source to battery load is achieved.


title: Cache-oblivious streaming B-trees

abstract: A streaming B-tree is a dictionary that efficiently implements insertions and range queries. We present two cache-oblivious streaming B-trees, the shuttle tree, and the cache-oblivious lookahead array (COLA). For block-transfer size B and on N elements, the shuttle tree implements searches in optimal O(log B+1N) transfers, range queries of L successive elements in optimal O(log B+1N +L/B) transfers, and insertions in O((log B+1N)/BΘ(1/(log log B)2)+(log2N)/B) transfers, which is an asymptotic speedup over traditional B-trees if B ≥ (log N)1+c log log log2 N for any constant c >1. A COLA implements searches in O(log N) transfers, range queries in O(log N + L/B) transfers, and insertions in amortized O((log N)/B) transfers, matching the bounds for a (cache-aware) buffered repository tree. A partially deamortized COLA matches these bounds but reduces the worst-case insertion cost to O(log N) if memory size M = Ω(log N). We also present a cache-aware version of the COLA, the lookahead array, which achieves the same bounds as Brodal and Fagerberg's (cache-aware) Be-tree. We compare our COLA implementation to a traditional B-tree. Our COLA implementation runs 790 times faster for random inser-tions, 3.1 times slower for insertions of sorted data, and 3.5 times slower for searches.


Tooling Framework for Instantiating Natural Language Querying System


title: Relational versus non-relational database systems for data warehousing

abstract: Relational database systems have been the dominating technology to manage and analyze large data warehouses. Moreover, the ER model, the standard in database design has a close relationship with the relational model. Recently, there has been a surge of alternative technologies for large scale analytic processing, most of which are not based on the relational model. Out of these proposals, distributed file systems together with MapReduce have become strong competitors to relational database systems to analyze large data sets, exploiting parallel processing. Moreover, there is progress on using MapReduce to evaluate relational queries. With that motivation in mind, this panel will compare pros and cons of each technology for data warehousing and will identify research issues, considering practical aspects like ease of use, programming flexibility and cost; as well as technical aspects like data modeling, storage, hardware, scalability, query processing, fault tolerance and data mining.


title: Android Implicit Information Flow Demystified

abstract: In this paper, a comprehensive analysis of implicit information flow (IIF) on the Android bytecode is presented to identify all potential IIF forms, determine their exploitability, and mitigate the potential threat. By applying control-transfer-oriented semantic analysis of the bytecode language, we identify five IIF forms, some of which are not studied by existing IIF literature. We develop proof-of-concepts (PoCs) for each IIF form to demonstrate their exploitability. The experimental results show that all these PoCs can effectively and efficiently transmit sensitive data, as well as successfully evade the detection of a state-of-the-art privacy monitor TaintDroid. To mitigate the threat of IIF, we propose a solution to defending against IIF leveraging a special control dependence tracking technique and implement a prototype system. The evaluation shows that the prototype can effectively detect information leak by all the identified IIF forms and also real-world malware with an acceptable overhead. In summary, our study gives in-depth insight into Android IIF from both offensive and defensive perspectives, and provides a foundation for further research on Android IIF.


title: TAC Entity Linking by Performing Full-document Entity Extraction and Disambiguation

abstract: The paper describes the system submitted to TAC 2011 for the English entity linking task of the Knowledge Base Population track. Instead of focusing only on the provided target strings, this system extracts and disambiguates globally all entities from each target document and then maps the target string to one of the entities extracted from the document. The main features employed by the system are topics associated with the entities in the knowledge base, which are derived from Wikipedia categories, list pages, and lexicosyntactic link patterns. The submitted run achieved high scores on the official test data (accuracy of 86.8% and B-cubed+ F-measure of 0.841).


title: Maps for the Visualization of high-dimensional Data Spaces

abstract: -The U-Matrix is a canonical tool for the display of distance structures in data space using emergent SOM (ESOM). The U-Matrix defined originally for planar map spaces is extended in this work to toroid neuron spaces. Embedding the neuron space in a finite but borderless space, such as a torus, avoids border effects of planar spaces. A planar display of a toroid map space disrupts, however, coherent U-Matrix structures. Tiling multiple instances of the U-Matrix solves this problem at the cost of multiple images of data points. The P-Matrix, as defined here, is a display of the density relationships in the data space using Pareto Density Estimation. While the P-Matrix is useful for clustering, it can also be used for a non-ambiguous display of a non planar neuron space. Centering the display for high density regions and removing ambiguous images of data points leads to U-Maps and P-Maps. U-Maps depict the distance structure of a data space as a borderless three dimensional landscape whose floor space is ordered according to the topology preserving features of ESOM. P-Maps display the density structures. Both maps are specially suited for data mining and knowledge discovery.


title: UAV Avionics “Hardware in the Loop ” Simulator

abstract: The avionics systems, especially the flight management and control systems, are some of the most critical elements of any aircraft, more so for an autonomous aircraft. Testing these systems in a realistic synthetic environment is an important process. It is logical therefore, to develop a suitable simulation environment in which the avionics systems can be tested. It is desirable for this system to be easy to implement, cost effective and representative of the aircraft being simulated. ::: ::: This paper discusses the development of such a simulation system, known as the Aircraft ::: Simulation And Testing Environment (ASATE). ASATE is the continuation of work initiated in 2001 for the testing of avionics developed as part of the QUT UAV project. ASATE ::: incorporates commercially available software packages including X-Plane for the flight visualisation and Matlab Simulink for the implementation of the dynamic flight model, sensor models and control algorithms. The mission management computer and other systems including the flight control system, are connected to the Simulink/X-Plane systems via ASATE. ASATE provides the means for designers to construct mission and control algorithms and to test the avionics hardware and software systems in real time, by simulating full missions, with realistic visual feedback of the aircraft’s response. ::: ASATE has been successfully employed to verify the flight management system and the flight control system of the QUT UAV. A set of control system tutorials, based on ASATE, have also been developed to assist in the teaching of flight control systems at QUT. This paper describes the development of ASATE and its usage for simulating aircraft missions (excluding takeoff and landing) and as an educational tool for students.


title: Monotone Operator Methods for Nash Equilibria in Non-Potential Games

abstract: We observe that a significant class of Nash equilibrium problems in non-potential games can be associated with monotone inclusion problems. We propose splitting techniques to solve such problems and establish their convergence. Applications to generalized Nash equilibria, zero-sum games, and cyclic proximity problems are demonstrated.


title: A contact force measure sensor based on resistance-array-type sensor

abstract: As an ideal candidate for biology information detection, flexible resistance-array-type sensors have attracted extensive attention. But owing to the Neighbor Disturbance in resistance array, the accuracy of the sensors has been limited greatly. In the presented work, a novel resistance measuring circuit has been developed with equipotential shielding method, which shows a better accuracy in detecting. Furthermore, the variable accuracy of the circuit can be realized by utilizing different resistance. Meanwhile, a contact force measure sensor was designed following the proposed principle. Finally, a set of experiments have been conducted by detecting hard cores buried in soft gel to test and verify the sensitivity and feasibility of 2D shape recognition of the sensor.


title: MLE for moving target’s profile stitching in SFR

abstract: In stepped-frequency imaging radar, stitching the high range resolution profiles of different sampling intervals is very important to obtain the whole profile of the range gate. Current stitching methods usually operate under the assumption of over sampling and low target-to-radar velocity to avoid profile distortion. Therefore, these methods are under critical challenges in application. This paper proposes a new stitching method based on phase compensation and maximum likelihood estimation. It generally satisfies the imaging requirement under lower sampling rate and higher velocity conditions with less profile distortion. It is shown that the performance of this method can reach Cramer-Rao low bound under stationary Gaussian noise. Performances of several methods are compared by theoretical analysis and simulations. The results verify the advantages of the method proposed in this paper.


title: Nerve Root Sedimentation Sign : Evaluation of a New Radiological Sign in Lumbar Spinal Stenosis

abstract: Study Design. Retrospective case-referent study. Objective. To assess whether the new sedimentation sign discriminates between nonspecific low back pain (LBP) and symptomatic lumbar spinal stenosis (LSS). Summary of Background Data. In the diagnosis of LSS, radiologic findings do not always correlate with clinical symptoms, and additional diagnostic signs are needed. In patients without LSS, we observe the sedimentation of lumbar nerve roots to the dorsal part of the dural sac on supine magnetic resonance image scans. In patients with symptomatic and morphologic central LSS, this sedimentation is rarely seen. We named this phenomenon "sedimentation sign" and defined the absence of sedimenting nerve roots as positive sedimentation sign for the diagnosis of LSS. Methods. This study included 200 patients. Patients in the LSS group (n = 100) showed claudication with or without LBP and leg pain, a cross-sectional area 120 mm 2 , and a walking distance >1000 m. The frequency of a positive sedimentation sign was compared between the 2 groups, and intraobserver and interobserver reliability were assessed in a random subsample (n = 20). Results. A positive sedimentation sign was identified in 94 patients in the LSS group (94%; 95% confidence interval, 90%-99%) but none in the LBP group (0%; 95% confidence interval, 0%-4%). Reliability was kappa = 1.0 (intraobserver) and kappa = 0.93 (interobserver), respectively. There was no difference in the detection of the sign between segmental levels L1-L5 in the LSS group. Conclusion. A positive sedimentation sign exclusively and reliably occurs in patients with LSS, suggesting its usefulness in clinical practice. Future accuracy studies will address its sensitivity and specificity. If they confirm the sign's high specificity, a positive sedimentation sign can rule in LSS, and, with a high sensitivity, a negative sedimentation sign can rule out LSS.


title: Cooperation Performance Evaluation between Seaport and Dry Port; Case of Qingdao Port and Xi'an Port

abstract: Abstract Along with the drastic competition among ports, the strive for the vast economic hinterland and the supply of goods have become strategic problems for port operators. At the same time, port enterprises are paying more and more attention to the construction of dry ports. This article establishes the port cooperation performance evaluation index based on the balanced score card method and uses the grey relational degree method to evaluate the cooperation performance between seaports and dry ports. Finally using Qingdao port and Xi'an port as an example, and Zhengzhou port and Lanzhou port as benchmarks, the application of this evaluation method is introduced in detail. The conclusion reveals that cooperation between Qingdao port and Xi'an port has deficiencies in customer satisfaction, financial cooperation and non-market tools. Alongside this, the author proposes related issues about information management in the supply chain, competition position and the scope of hinterland. This article, combined with the related theory of supply chain and performance evaluation, puts forward a set of relatively complete cooperation performance evaluations between seaports and dry ports, which provide scientific theory support for better cooperation.


Factors Affecting Customer Loyalty of Using Internet Banking in Malaysia


title: Automated tree detection and density calculation using unmanned aerial vehicles

abstract: Growth monitoring in tree plantation farms plays a crucial role in taking necessary actions from early stages to improve harvesting efficiency. Unmanned aerial vehicles (UAVs) provide a fast and efficient way to acquire data from large farms with difficult access. We propose a novel end-to-end system composed of multiple stages to identify areas where seeds have not grown as expected in young tree farms. The system acquires data from UAV flights to generate georeferenced orthophoto images. Next, local binary patterns, distance transform, and watershed segmentation methods are applied on images to detect trees. Finally, tree density distributions are calculated in the granularity of approximately 90 m2 tiles. We assessed the system performance by comparing detection results against a ground truth set of over 50000 trees from 16 orthophoto images for two tree species. The median deviation was 2 trees per tile for both species, where the median tree count was 14 and 9 per tile, respectively.


title: Pyrite nanoparticles: an Earth-abundant mineral catalyst for activation of molecular hydrogen and hydrogenation of nitroaromatics

abstract: Pyrite (FeS2) nanoparticles, a kind of Earth-abundant mineral, can efficiently activate molecular hydrogen under mild conditions. At ambient pressure, H2 can be dissociatively adsorbed by FeS2 nanoparticles, react with nitrobenzene, and selectively produce aniline. The catalytic activity for hydrogenation of nitrobenzene is comparable to most non-precious metal catalysts.


title: Feedback-error-learning neural network for trajectory control of a robotic manipulator

abstract: Abstract A neural network model for generation of motor command was proposed in our earlier paper. This model contains a feedback loop (transcortical loop), an internal neural model of motor system (spinocerebellum and magnocellular part of the red nucleus) and an internal neural model of inverse dynamics of the motor system (cerebrocerebellum and parvocellular part of the red nucleus). The inverse-dynamics model is acquired by heterosynaptic plasticity using feedback motor command (torque) as an error signal. In this paper, we apply hierarchical arrangement of the transcortical loop and the inverse-dynamics model for learning trajectory control of an industrial robotic manipulator. Although neither strict modeling of the manipulator nor precise parameter estimation was required, the control performance by the neural-network model improved gradually during 30 minutes of learning. Once the neural-network model learned to control some movement, it could control quite different and faster movements. That is, the neural-network model has capability to generalize learned movements. Advantages of the neural-network control over conventional control methods are discussed.


title: Tracking the Trackers: An Analysis of the State of the Art in Multiple Object Tracking

abstract: Standardized benchmarks are crucial for the majority of computer vision applications. Although leaderboards and ranking tables should not be over-claimed, benchmarks often provide the most objective measure of performance and are therefore important guides for research. We present a benchmark for Multiple Object Tracking launched in the late 2014, with the goal of creating a framework for the standardized evaluation of multiple object tracking methods. This paper collects the two releases of the benchmark made so far, and provides an in-depth analysis of almost 50 state-of-the-art trackers that were tested on over 11000 frames. We show the current trends and weaknesses of multiple people tracking methods, and provide pointers of what researchers should be focusing on to push the field forward.


title: Mace: language support for building distributed systems

abstract: Building distributed systems is particularly difficult because of the asynchronous, heterogeneous, and failure-prone environment where these systemsmust run. Tools for building distributed systems must strike a compromise between reducing programmer effort and increasing system efficiency. We present Mace, a C++ language extension and source-to-source compiler that translates a concise but expressive distributed system specification into a C++ implementation. Mace overcomes the limitations of low-level languages by providing a unified framework for networking and event handling, and the limitations of high-level languages by allowing programmers to write program components in a controlled and structured manner in C++. By imposing structure and restrictions on how applications can be written, Mace supports debugging at a higher level, including support for efficient model checking and causal-path debugging. Because Mace programs compile to C++, programmers can use existing C++ tools, including optimizers, profilers, and debuggers to analyze their systems.


title: A modeling capacity of vertebral fractures exists during growth: an up-to-47-year follow-up.

abstract: STUDY DESIGN ::: The study is an observational cohort study. ::: ::: ::: OBJECTIVES ::: To determine the incidence and the long-term outcome of thoracic or lumbar vertebral fractures in children. ::: ::: ::: SUMMARY OF BACKGROUND DATA ::: The incidence of vertebral fractures in children is described as rare and the outcome as favorable. However, no studies evaluate the clinical and radiographic long-term outcome and if a fractured vertebra could be rebuilt during growth. ::: ::: ::: METHOD ::: The incidence of vertebral fractures in children was evaluated through the radiographic archives. Twelve boys and 12 girls, aged 7-16 when sustaining the fracture, 21 one-column compression fractures and 3 burst fractures Denis type B, all without neurologic deficits, attended the follow-up. Primary treatment consisted of immediate mobilization without brace. Clinical and radiographic examinations were performed 27-47 years after the injury. ::: ::: ::: RESULTS ::: The annual incidence of thoracic and lumbar vertebral body fractures in individuals below age 16 was 0.07%. Twenty-one individuals had, at follow-up, no subjective complaints, 3 had occasional back pain (Oswestry Scores 8, 22, and 26), 23 were classified as Frankel E, and 1 as Frankel D. The radiographic ratio anterior height/posterior height of the fractured vertebral body increased from 0.75 after injury to 0.87 at follow-up (P < 0.001). The posttraumatic kyphosis in the fractured region decreased in 8 individuals (33%), all aged 13 or less at fracture. No increased disc degeneration was observed. ::: ::: ::: CONCLUSION ::: Thoracolumbar vertebral fractures without neurologic deficits, sustained during growth, have a favorable long-term outcome. A modeling capacity, reducing the fracture deformity, exists at least in the youngest patients.


title: Improving the post-acute care discharge score (PACD) by adding patients’ self-care abilities: A prospective cohort study

abstract: Background ::: Reducing delays in hospital discharge is important to improve transition processes and reduce health care costs. The recently proposed post-acute care discharge score focusing on the self-care abilities before hospital admission allows early identification of patients with a need for post-acute care. New limitations in self-care abilities identified during hospitalization may also indicate a risk. Our aim was to investigate whether the addition of the post-acute care discharge score and a validated self-care instrument would improve the prognostic accuracy to predict post-acute discharge needs in unselected medical inpatients. ::: ::: ::: Methods ::: We included consecutive adult medical and neurological inpatients. Logistic regression models with area under the receiver operating characteristic curve were calculated to study associations of post-acute discharge score and self-care index with post-acute discharge risk. We calculated joint regression models and reclassification statistics including the net reclassification index and integrated discrimination improvement to investigate whether merging the self-care index and the post-acute discharge score leads to better diagnostic accuracy. ::: ::: ::: Results ::: Out of 1342 medical and 402 neurological patients, 150 (11.18%) and 94 (23.38%) have reached the primary endpoint of being discharged to a post-acute care facility. Multivariate analysis showed that the self-care index is an outcome predictor (OR 0.897, 95%CI 0.864–0.930). By combining the self-care index and the post-acute care discharge score discrimination for medical (from area under the curve 0.77 to 0.83) and neurological patients (from area under the curve 0.68 to 0.78) could be significantly improved. Reclassification statistics also showed significant improvements with regard to net reclassification index (14.2%, p<0.05) and integrated discrimination improvement (4.83%, p<0.05). ::: ::: ::: Conclusions ::: Incorporating an early assessment of patients’ actual intrahospital self-care ability to the post-acute care discharge score led to an improved prognostic accuracy for identifying adult, medical and neurological patients at risk for discharge to a post-acute care facility.


title: A Pay-As-You-Go Methodology for Ontology-Based Data Access

abstract: A successfully repeated use case for Semantic Web technologies is Ontology-Based Data Access for data integration. In this approach, an ontology serves as a uniform conceptual federating model, which is accessible to both IT developers and business users. Here, two challenges for developing an OBDA system are considered: ontology and mapping engineering, along with a pay-as-you-go methodology that addresses these challenges and enables agility.