title: Transforming the Morbidity and Mortality Conference into an Instrument for Systemwide Improvement

abstract: Objective: The morbidity and mortality conference (M&MC) is a traditional forum that provides clinicians with an opportunity to discuss medical error and adverse events. In an effort to promote patient safety at our institution, we implemented a monthly interdisciplinary morbidity, mortality, and improvement (MM&I) conference, which focused on systemwide problems. The participants included physicians, nursing staff, pharmacy, and other clinical departments, as well as senior hospital administrators. Methods: A Mortality Review Task Force selects cases for presentation at the monthly MM&I. A resident representative presents the case, and a designated senior faculty member facilitates a discussion of the case with audience participation. Key issues that contributed to the undesired outcome of the case are identified and outlined on a cause-andeffect diagram (Ichikawa diagram). Workgroups are created to target systems-based problems. At the end of the conference, attendees are asked to complete an evaluation and provide feedback for subsequent consideration by the task force. Results: Twenty-one cases (12 medical, 9 surgical) representing adverse events were presented at the MM&I conference from January 2005 to February 2007. The mean number of participants per session was 88 (range, 62-115). Adverse events triggering case selection included unexpected deaths (six), unplanned intubations (two), prolonged medical care in the setting of poor prognosis (one), delay in care (nine), and procedural complications (three). The most common factors contributing to adverse or “nearmiss” outcomes in these cases were communication failures and inadequate coordination of care. In all, 33 action items were created, and 23 (70 percent) have been completed to date. Conclusion: A structured hospital-wide MM&I conference is an effective means of engaging physicians, nurses, and key administrative leaders in the discussion of adverse events. The identification of potential system failures and the creation of workgroups to address specific systems-based problems can promote initiatives to improve patient care and safety. Background In order to provide high quality patient care, members of a multidisciplinary health care team must engage in objective, nonjudgmental review of adverse outcomes and commit to systematic process change. The morbidity and mortality conference (M&MC) is one forum that provides clinicians with an opportunity to discuss medical error and adverse events. The M&MC became a major part of physician education in the early 20 century, following the publication of the Flexner report on medical education in 1910 and the creation of the American College of


title: Collaborative multi-bitrate video caching and processing in Mobile-Edge Computing networks

abstract: Recently, Mobile-Edge Computing (MEC) has arisen as an emerging paradigm that extends cloud-computing capabilities to the edge of the Radio Access Network (RAN) by deploying MEC servers right at the Base Stations (BSs). In this paper, we envision a collaborative joint caching and processing strategy for on-demand video streaming in MEC networks. Our design aims at enhancing the widely used Adaptive BitRate (ABR) streaming technology, where multiple bitrate versions of a video can be delivered so as to adapt to the heterogeneity of user capabilities and the varying of network connection bandwidth. The proposed strategy faces two main challenges: (i) not only the videos but their appropriate bitrate versions have to be effectively selected to store in the caches, and (ii) the transcoding relationships among different versions need to be taken into account to effectively utilize the processing capacity at the MEC servers. To this end, we formulate the collaborative joint caching and processing problem as an Integer Linear Program (ILP) that minimizes the backhaul network cost, subject to the cache storage and processing capacity constraints. Due to the NP-completeness of the problem and the impractical overheads of the existing offline approaches, we propose a novel online algorithm that makes cache placement and video scheduling decisions upon the arrival of each new request. Extensive simulations results demonstrate the significant performance improvement of the proposed strategy over traditional approaches in terms of cache hit ratio increase, backhaul traffic and initial access delay reduction.


title: Software defined networking-based vehicular Adhoc Network with Fog Computing

abstract: Vehicular Adhoc Networks (VANETs) have been attracted a lot of research recent years. Although VANETs are deployed in reality offering several services, the current architecture has been facing many difficulties in deployment and management because of poor connectivity, less scalability, less flexibility and less intelligence. We propose a new VANET architecture called FSDN which combines two emergent computing and network paradigm Software Defined Networking (SDN) and Fog Computing as a prospective solution. SDN-based architecture provides flexibility, scalability, programmability and global knowledge while Fog Computing offers delay-sensitive and location-awareness services which could be satisfy the demands of future VANETs scenarios. We figure out all the SDN-based VANET components as well as their functionality in the system. We also consider the system basic operations in which Fog Computing are leveraged to support surveillance services by taking into account resource manager and Fog orchestration models. The proposed architecture could resolve the main challenges in VANETs by augmenting Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I), Vehicle-to-Base Station communications and SDN centralized control while optimizing resources utility and reducing latency by integrating Fog Computing. Two use-cases for non-safety service (data streaming) and safety service (Lane-change assistance) are also presented to illustrate the benefits of our proposed architecture.


title: Exploring the effects of space and place on engagement with an interactive installation

abstract: Very little research has concurrently explored the influence of both physical space and social context (or place) on the way people engage with a public interactive display. We addressed this issue with a novel approach: studying how people engaged with the same interactive installation in ten situations with varying spatial and social properties. The main finding across these studies is that place trumps space: a conducive social context could overcome a poor physical space and encourage interaction; conversely, an inappropriate social context could inhibit interaction in spaces that might normally facilitate engagement. We discuss this finding in terms of the salience of the display within the space, the visibility of incidental interactions with the installation, the different understandings of place that people can have in the same location and the role of emergent champions and comperes in encouraging interaction.


title: Bad to the bone: facial structure predicts unethical behaviour

abstract: Researchers spanning many scientific domains, including primatology, evolutionary biology and psychology, have sought to establish an evolutionary basis for morality. While researchers have identified social and cognitive adaptations that support ethical behaviour, a consensus has emerged that genetically determined physical traits are not reliable signals of unethical intentions or actions. Challenging this view, we show that genetically determined physical traits can serve as reliable predictors of unethical behaviour if they are also associated with positive signals in intersex and intrasex selection. Specifically, we identify a key physical attribute, the facial width-to-height ratio, which predicts unethical behaviour in men. Across two studies, we demonstrate that men with wider faces (relative to facial height) are more likely to explicitly deceive their counterparts in a negotiation, and are more willing to cheat in order to increase their financial gain. Importantly, we provide evidence that the link between facial metrics and unethical behaviour is mediated by a psychological sense of power. Our results demonstrate that static physical attributes can indeed serve as reliable cues of immoral action, and provide additional support for the view that evolutionary forces shape ethical judgement and behaviour.


title: Non-intrusive eye gaze estimation using a projective invariant under head movement

abstract: In this paper, a non-intrusive eye gaze estimation system is proposed. The proposed system consists of five light sources and two cameras, and the direction of the user's eye gaze can be computed by using a projective property without computing the geometrical relation among the eye, the cameras, and the monitor in 3D space. Our method is accurate under head movement, and is comparatively simple and fast. We introduce our method and show experimental results. The experimental results would verify the feasibility of the proposed system as a human-machine interface


The Berkeley Lower Extremity Exoskeleton


title: The association of Klinefelter syndrome and multiple pterygium syndrome: an unusual presentation.

abstract: Multiple pterygium syndrome is characterized by a number of phenotypic features, small stature, webbing of the neck, elbows, and/or knees, and joint contractures. In this report, we present an 11-year-old boy who had the classical findings of multiple pterygium syndrome, and his chromosomal analysis revealed a 47,XXY karyotype. Interestingly, he did not show any of the main clinical signs of Klinefelter syndrome. This patient appears to be the first reported case in the literature in which a non-mosaic 47,XXY karyotype has been found in a patient with multiple pterygium syndrome. The aim of the present report is to describe a non-classic Klinefelter syndrome associated with multiple pterygium syndrome and to emphasize the importance of karyotype analysis in patients with multiple pterygium syndrome.


title: The burden of Clostridium difficile infection in Thais: nationwide survey

abstract: Abstract Background Data on the incidence and burden of Clostridium difficile infection (CDI) in Asia is limited. Objectives To evaluate the incidence and burden of CDI in Thailand. Methods We used 2010 Nationwide Hospital Admission Data, which included the diagnosis of digestive disorders from various causes coded using the ICD-10. Patients with a diagnosis of Clostridium difficile (ICD10-A07) aged >18 years, were included. Their baseline characteristics, clinical outcomes, and risk factors for CDI were analyzed. Length of hospital stay (LOS), mortality rate, and hospital expenses were used as indicators to evaluate the burden of CDI in Thailand. Results Of 4,863,935 admissions in 2010, 554 patients in 570 admissions (0.01%) were diagnosed with CDI. Of these, 106 (19.1%) died during the index hospitalization, and 98.1% had at least one comorbidity. The mean LOS for patients with CDI was longer than with other colitis (P < 0.001) and was also significantly longer for those who died during the index admission, compared with those who survived during the index admission (P = 0.04). The hospital expense for those who died was significantly higher than for those who survived (P < 0.001). From a multivariate analysis, age ≥85 years old, comorbidity, and sepsis were risk factors for mortality during admission with adjusted odds ratios of 2.40, 7.4, and 5.14, respectively. Conclusions The calculated burden of CDI in Thais is high; although the incidence of CDI is lower in Thailand than in Western countries. The mortality relates to the elderly age-group and comorbidity, especially sepsis.


title: Weakly Supervised Object Localization with Latent Category Learning

abstract: Localizing objects in cluttered backgrounds is a challenging task in weakly supervised localization. Due to large object variations in cluttered images, objects have large ambiguity with backgrounds. However, backgrounds contain useful latent information, e.g., the sky for aeroplanes. If we can learn this latent information, object-background ambiguity can be reduced to suppress the background. In this paper, we propose the latent category learning (LCL), which is an unsupervised learning problem given only image-level class labels. Firstly, inspired by the latent semantic discovery, we use the typical probabilistic Latent Semantic Analysis (pLSA) to learn the latent categories, which can represent objects, object parts or backgrounds. Secondly, to determine which category contains the target object, we propose a category selection method evaluating each category’s discrimination. We evaluate the method on the PASCAL VOC 2007 database and ILSVRC 2013 detection challenge. On VOC 2007, the proposed method yields the annotation accuracy of 48%, which outperforms previous results by 10%. More importantly, we achieve the detection average precision of 30.9%, which improves previous results by 8% and can be competitive with the supervised deformable part model (DPM) 5.0 baseline 33.7%. On ILSVRC 2013 detection, the method yields the precision of 6.0%, which is also competitive with the DPM 5.0.


title: Network-level security and privacy control for smart-home IoT devices

abstract: The increasing uptake of smart home appliances, such as lights, smoke-alarms, power switches, baby monitors, and weighing scales, raises privacy and security concerns at unprecedented scale, allowing legitimate and illegitimate entities to snoop and intrude into the family's activities. In this paper we first illustrate these threats using real devices currently available in the market. We then argue that as more such devices emerge, the attack vectors increase, and ensuring privacy/security of the house becomes more challenging. We therefore advocate that device-level protections be augmented with network-level security solutions, that can monitor network activity to detect suspicious behavior. We further propose that software defined networking technology be used to dynamically block/quarantine devices, based on their network activity and on the context within the house such as time-of-day or occupancy-level. We believe our network-centric approach can augment device-centric security for the emerging smart-home.


title: FIREMAN: a toolkit for firewall modeling and analysis

abstract: Security concerns are becoming increasingly critical in networked systems. Firewalls provide important defense for network security. However, misconfigurations in firewalls are very common and significantly weaken the desired security. This paper introduces FIREMAN, a static analysis toolkit for firewall modeling and analysis. By treating firewall configurations as specialized programs, FIREMAN applies static analysis techniques to check misconfigurations, such as policy violations, inconsistencies, and inefficiencies, in individual firewalls as well as among distributed firewalls. FIREMAN performs symbolic model checking of the firewall configurations for all possible IP packets and along all possible data paths. It is both sound and complete because of the finite state nature of firewall configurations. FIREMAN is implemented by modeling firewall rules using binary decision diagrams (BDDs), which have been used successfully in hardware verification and model checking. We have experimented with FIREMAN and used it to uncover several real misconfigurations in enterprise networks, some of which have been subsequently confirmed and corrected by the administrators of these networks.


title: Monitoring Public Health Concerns Using Twitter Sentiment Classifications

abstract: An important task of public health officials is to keep track of spreading epidemics, and the locations and speed with which they appear. Furthermore, there is interest in understanding how concerned the population is about a disease outbreak. Twitter can serve as an important data source to provide this information in real time. In this paper, we focus on sentiment classification of Twitter messages to measure the Degree of Concern (DOC) of the Twitter users. In order to achieve this goal, we develop a novel two-step sentiment classification workflow to automatically identify personal tweets and negative tweets. Based on this workflow, we present an Epidemic Sentiment Monitoring System (ESMOS) that provides tools for visualizing Twitter users' concern towards different diseases. The visual concern map and chart in ESMOS can help public health officials to identify the progression and peaks of concern for a disease in space and time, so that appropriate preventive actions can be taken. The DOC measure is based on the sentiment-based classifications. We compare clue-based and different Machine Learning methods to classify sentiments of Twitter users regarding diseases, first into personal and neutral tweets and then into negative from neutral personal tweets. In our experiments, Multinomial Naive Bayes achieved overall the best results and took significantly less time to build the classifier than other methods.


title: Robust object tracking via multi-feature adaptive fusion based on stability: contrast analysis

abstract: Object tracking under complex circumstances is a challenging task because of background interference, obstacle occlusion, object deformation, etc. Given such conditions, robustly detecting, locating, and analyzing a target through single-feature representation are difficult tasks. Global features, such as color, are widely used in tracking, but may cause the object to drift under complex circumstances. Local features, such as HOG and SIFT, can precisely represent rigid targets, but these features lack the robustness of an object in motion. An effective method is adaptive fusion of multiple features in representing targets. The process of adaptively fusing different features is the key to robust object tracking. This study uses a multi-feature joint descriptor (MFJD) and the distance between joint histograms to measure the similarity between a target and its candidate patches. Color and HOG features are fused as the tracked object of the joint representation. This study also proposes a self-adaptive multi-feature fusion strategy that can adaptively adjust the joint weight of the fused features based on their stability and contrast measure scores. The mean shift process is adopted as the object tracking framework with multi-feature representation. The experimental results demonstrate that the proposed MFJD tracking method effectively handles background clutter, partial occlusion by obstacles, scale changes, and deformations. The novel method performs better than several state-of-the-art methods in real surveillance scenarios.


title: A Novel Polar Format Algorithm for SAR Images Utilizing Post Azimuth Transform Interpolation

abstract: SAR phase history data represents a polar array in the Fourier space of a scene being imaged. Polar Format processing is about reformatting the collected SAR data to a Cartesian data location array for efficient processing and image formation. In a real-time system, this reformatting or ''re-gridding'' operation is the most processing intensive, consuming the majority of the processing time; it also is a source of error in the final image. Therefore, any effort to reduce processing time while not degrading image quality is valued. What is proposed in this document is a new way of implementing real-time polar-format processing through a variation on the traditional interpolation/2-D Fast Fourier Transform (FFT) algorithm. The proposed change is based upon the frequency scaling property of the Fourier Transform, which allows a post azimuth FFT interpolation. A post azimuth processing interpolation provides overall benefits to image quality and potentially more efficient implementation of the polar format image formation process.


title: The future nursing workforce in Australia: baseline data for a prospective study of the profile, attrition rates and graduate outcomes in a contemporary cohort of undergraduates

abstract: Objective: To gather data from ten universities across two Australian states in order to: provide a descriptive demographic profile of undergraduate Australian nursing students; provide baseline data for a prospective analysis of attrition within undergraduate nursing programs; and to facilitate student recruitment into a prospective cohort study to examine graduate outcomes. Methods: Approval was sought from each Head of School to enable recruitment of undergraduate nursing students as a sub-sample of an ongoing large scale longitudinal e-cohort study involving Australian, New Zealand and United Kingdom nurses and midwives (http://www.e-cohort.net). Each nursing school nominated a contact person to become part of the research team; provide aggregate data on the quantity and demographic profile of currently enrolled undergraduate nursing students; and to facilitate recruitment of students into the cohort study. Results: Two of the ten universities could not supply any demographics of their undergraduate nursing student body and one university could not provide data on year levels. The remaining data revealed an interesting demographic profile in the following areas: the age range of students across both states was 17 to 68 years, with just under half the population of students aged over 25 years. Some universities had a younger cohort of students in comparison to others and this was potentially associated with universities which only offered their program in full-time mode. The high proportion of students choosing to enrol in their undergraduate program part-time in South Australia (22.5%) as well as the large number of international students at one Queensland university (28% in year one) may impact on the future graduate nursing workforce supply. Retrospective analysis of the average attrition rate in Queensland universities was estimated at 24.5% which is consistent with the findings of a recent systematic review of published primary studies. Conclusion: Whilst this preliminary data reveals some interesting issues, in general, there is a paucity of evidence about the demographics of the future Australian nursing workforce, attrition within undergraduate nursing programs and graduate outcomes. Clearly there is a need to systematically track undergraduates and new graduates to quantify student attrition, graduate retention and career plans and begin to build this evidence-base. A minimum demographic dataset of all undergraduate nursing students in Australia should be established to track trends over time that will inform future workforce planning.


title: A New Approach to Track Multiple Vehicles With the Combination of Robust Detection and Two Classifiers

abstract: It plays an important role to accurately track multiple vehicles in intelligent transportation, especially in intelligent vehicles. Due to complicated traffic environments it is difficult to track multiple vehicles accurately and robustly, especially when there are occlusions among vehicles. To alleviate these problems, a new approach is proposed to track multiple vehicles with the combination of robust detection and two classifiers. An improved ViBe algorithm is proposed for robust and accurate detection of multiple vehicles. It uses the gray-scale spatial information to build dictionary of pixel life length to make ghost shadows and object’s residual shadows quickly blended into the samples of the background. The improved algorithm takes good post-processing method to restrain dynamic noise. In this paper, we also design a method using two classifiers to further attack the problem of failure to track vehicles with occlusions and interference. It classifies tracking rectangles with confidence values between two thresholds through combining local binary pattern with support vector machine (SVM) classifier and then using a convolutional neural network (CNN) classifier for the second time to remove the interference areas between vehicles and other moving objects. The two classifiers method has both time efficiency advantage of SVM and high accuracy advantage of CNN. Comparing with several existing methods, the qualitative and quantitative analysis of our experiment results showed that the proposed method not only effectively removed the ghost shadows, and improved the detection accuracy and real-time performance, but also was robust to deal with the occlusion of multiple vehicles in various traffic scenes.


A phase‐field approach to pneumatic fracture


title: Mutual Information Optimally Local Private Discrete Distribution Estimation

abstract: Consider statistical learning (e.g. discrete distribution estimation) with local $\epsilon$-differential privacy, which preserves each data provider's privacy locally, we aim to optimize statistical data utility under the privacy constraints. Specifically, we study maximizing mutual information between a provider's data and its private view, and give the exact mutual information bound along with an attainable mechanism: $k$-subset mechanism as results. The mutual information optimal mechanism randomly outputs a size $k$ subset of the original data domain with delicate probability assignment, where $k$ varies with the privacy level $\epsilon$ and the data domain size $d$. After analysing the limitations of existing local private mechanisms from mutual information perspective, we propose an efficient implementation of the $k$-subset mechanism for discrete distribution estimation, and show its optimality guarantees over existing approaches.


title: Learning a Metric Embedding for Face Recognition using the Multibatch Method

abstract: This work is motivated by the engineering task of achieving a near state-of-the-art face recognition on a minimal computing budget running on an embedded system. Our main technical contribution centers around a novel training method, called Multibatch, for similarity learning, i.e., for the task of generating an invariant "face signature" through training pairs of "same" and "not-same" face images. The Multibatch method first generates signatures for a mini-batch of $k$ face images and then constructs an unbiased estimate of the full gradient by relying on all $k^2-k$ pairs from the mini-batch. We prove that the variance of the Multibatch estimator is bounded by $O(1/k^2)$, under some mild conditions. In contrast, the standard gradient estimator that relies on random $k/2$ pairs has a variance of order $1/k$. The smaller variance of the Multibatch estimator significantly speeds up the convergence rate of stochastic gradient descent. Using the Multibatch method we train a deep convolutional neural network that achieves an accuracy of $98.2\%$ on the LFW benchmark, while its prediction runtime takes only $30$msec on a single ARM Cortex A9 core. Furthermore, the entire training process took only 12 hours on a single Titan X GPU.


Image Fusion Using Kuwahara Filter


title: Dynamic balancing and walking for real-time 3d characters

abstract: This paper describes the real-time modeling of 3D skeletal motion with balancing properties. Our goal is to mimic human responsiveness when external forces are applied to the model. To achieve this we use an inverted pendulum as a basis for achieving a self-balancing model. We demonstrate responsiveness in stepping and posture control via a simplified biped skeletal model using our technique.


title: Harnessing Object and Scene Semantics for Large-Scale Video Understanding

abstract: Large-scale action recognition and video categorization are important problems in computer vision. To address these problems, we propose a novel object-and scene-based semantic fusion network and representation. Our semantic fusion network combines three streams of information using a three-layer neural network: (i) frame-based low-level CNN features, (ii) object features from a state-of-the-art large-scale CNN object-detector trained to recognize 20K classes, and (iii) scene features from a state-of-the-art CNN scene-detector trained to recognize 205 scenes. The trained network achieves improvements in supervised activity and video categorization in two complex large-scale datasets - ActivityNet and FCVID, respectively. Further, by examining and back propagating information through the fusion network, semantic relationships (correlations) between video classes and objects/scenes can be discovered. These video class-object/video class-scene relationships can in turn be used as semantic representation for the video classes themselves. We illustrate effectiveness of this semantic representation through experiments on zero-shot action/video classification and clustering.


title: Special Report: NCBI disease corpus: A resource for disease name recognition and concept normalization

abstract: Information encoded in natural language in biomedical literature publications is only useful if efficient and reliable ways of accessing and analyzing that information are available. Natural language processing and text mining tools are therefore essential for extracting valuable information, however, the development of powerful, highly effective tools to automatically detect central biomedical concepts such as diseases is conditional on the availability of annotated corpora. This paper presents the disease name and concept annotations of the NCBI disease corpus, a collection of 793 PubMed abstracts fully annotated at the mention and concept level to serve as a research resource for the biomedical natural language processing community. Each PubMed abstract was manually annotated by two annotators with disease mentions and their corresponding concepts in Medical Subject Headings (MeSH(R)) or Online Mendelian Inheritance in Man (OMIM(R)). Manual curation was performed using PubTator, which allowed the use of pre-annotations as a pre-step to manual annotations. Fourteen annotators were randomly paired and differing annotations were discussed for reaching a consensus in two annotation phases. In this setting, a high inter-annotator agreement was observed. Finally, all results were checked against annotations of the rest of the corpus to assure corpus-wide consistency. The public release of the NCBI disease corpus contains 6892 disease mentions, which are mapped to 790 unique disease concepts. Of these, 88% link to a MeSH identifier, while the rest contain an OMIM identifier. We were able to link 91% of the mentions to a single disease concept, while the rest are described as a combination of concepts. In order to help researchers use the corpus to design and test disease identification methods, we have prepared the corpus as training, testing and development sets. To demonstrate its utility, we conducted a benchmarking experiment where we compared three different knowledge-based disease normalization methods with a best performance in F-measure of 63.7%. These results show that the NCBI disease corpus has the potential to significantly improve the state-of-the-art in disease name recognition and normalization research, by providing a high-quality gold standard thus enabling the development of machine-learning based approaches for such tasks. The NCBI disease corpus, guidelines and other associated resources are available at: http://www.ncbi.nlm.nih.gov/CBBresearch/Dogan/DISEASE/.


title: Improving the accuracy of a flood forecasting model by means of machine learning and chaos theory

abstract: Monitoring natural environments is a challenging task on account of their hostile features. The use of wireless sensor networks (WSNs) for data collection is a feasible method since these domains lack any infrastructure. However, further studies are required to handle the data collected for a better modeling of behavior and thus make it possible to forecast impending disasters. In light of this, in this paper an analysis is conducted on the use of data gathered from urban rivers to forecast flooding with a view to reducing the damage it causes. The data were collected by means of a WSN in Sao Carlos, Sao Paulo State, Brazil, which gathered and processed data about the river level and rainfall by means of machine learning techniques and employing chaos theory to model the time series; this meant that the inputs of the machine learning technique were the time series gathered by the WSN modeled on the basis of the immersion theorem. The WSNs were deployed by our group in the city of Sao Carlos where there have been serious problems caused by floods. After the data interdependence had been established by the immersion theorem, the artificial neural networks were investigated to determine their degree of accuracy in the forecasting models.


title: IDBA- A Practical Iterative de Bruijn Graph De Novo Assembler

abstract: The de Bruijn graph assembly approach breaks reads into k-mers before assembling them into contigs The string graph approach forms contigs by connecting two reads with k or more overlapping nucleotides Both approaches must deal with the following problems: false-positive vertices, due to erroneous reads; gap problem, due to non-uniform coverage; branching problem, due to erroneous reads and repeat regions A proper choice of k is crucial but for single k there is always a trade-off: a small k favors the situation of erroneous reads and non-uniform coverage, and a large k favors short repeat regions. ::: ::: We propose an iterative de Bruijn graph approach iterating from small to large k exploring the advantages of the in between values Our IDBA outperforms the existing algorithms by constructing longer contigs with similar accuracy and using less memory, both with real and simulated data The running time of the algorithm is comparable to existing algorithms. ::: ::: Availability: IDBA is available at http://www.cs.hku.hk/~alse/idba/


title: Cyberbullying: An overrated phenomenon?

abstract: The paper argues that several claims about cyberbullying made in the media and elsewhere are greatly exaggerated and have little empirical scientific support. Contradicting these claims, it turns out that cyberbullying, when studied in proper context, is a low-prevalence phenomenon, which has not increased over time and has not created many “new” victims and bullies, that is, children and youth who are not also involved in some form of traditional bullying. These conclusions are based on two quite large samples of students, one from the USA and one from Norway, both of which have time series data for periods of four or five years. It is further argued that the issue of possible negative effects of cyberbullying has not received much serious research attention and a couple of strategies for such research are suggested together with some methodological recommendations. Finally, it is generally recommended that schools direct most of their anti-bullying efforts to counteracting traditional bullying, combined...


title: WCBA: Weighted classification based on association rules algorithm for breast cancer disease

abstract: Abstract Breast cancer is the second most frequent human neoplasm that accounts for one quarter of all cancers in females. Among the other types of cancers, it is considered to be the main cause of death in women in most countries. An efficient classifier for accurately helping physicians to predict this chronic disease is in high demand. One approach for solving this problem has been tackled by many scholars using Association Classification (AC) techniques to enhance the classification process through applying association rules. However, most AC algorithms are suffering from the estimated measures used in the rule evaluation process and the prioritization techniques used at the attributes level, which could play a critical role in the rule generation process. In this article we attempt to solve this problem through an efficient weighted classification based on association rules algorithm, named WCBA. We also present a new pruning and prediction technique based on statistical measures to generate more accurate association rules to enhance the accuracy level of the AC classifiers. As a case study, we used WCBA to classify breast cancer instances with the help of subject matter experts from King Hussein Cancer Center (KHCC) located in Amman, Jordan. We compare WCBA with five well-known AC algorithms: CBA, CMAR, MCAR, FACA and ECBA running on two breast cancer datasets from UCI machine learning data repository. Experimental results show that WCBA, in most cases, outperformed the other AC algorithms for this case study. In addition, WCBA generates more accurate rules that contain the most efficient attributes for predicting breast cancer. WCBA algorithm aims to predict breast cancer in a patient. It serves all breast cancer patients by reducing the fear of the possibility of the recurrence of the disease and takes the necessary measures to prevent the progression of the disease and to predict breast cancer in a patient. The algorithm can be generalized to work on different domains with the help of subject matter experts.


title: A systematic review of zirconia as an implant material.

abstract: The purpose of this systematic review was to assess the published data concerning zirconia dental implants from various aspects. To identify relevant literature an electronic search was performed of PubMed. Titles and abstracts were screened and articles that fulfilled the inclusion criteria were selected for a full-text reading. Articles were divided into four groups: 1) studies evaluating the mechanical properties of zirconia implants, 2) studies on osseointegration of zirconia, 3) studies on peri-implant tissue response to zirconia implant, and 4) studies on plaque accumulation with zirconia. Review of the selected articles showed that zirconia implants are reliable for placement in the jaw bone. Furthermore, zirconia implants present a material surface that is compatible with the peri-implant tissue and relatively less attractive to plaque. Based on the reviewed literature, it appears that zirconia has the potential to become the dental implant material of choice, especially for aesthetic restorations; however, some issues need to be studied further.


title: Recent Named Entity Recognition and Classification techniques: A systematic review

abstract: Abstract Textual information is becoming available in abundance on the web, arising the requirement of techniques and tools to extract the meaningful information. One of such an important information extraction task is Named Entity Recognition and Classification. It is the problem of finding the members of various predetermined classes, such as person, organization, location, date/time, quantities, numbers etc. The concept of named entity extraction was first proposed in Sixth Message Understanding Conference in 1996. Since then, a number of techniques have been developed by many researchers for extracting diversity of entities from different languages and genres of text. Still, there is a growing interest among research community to develop more new approaches to extract diverse named entities which are helpful in various natural language applications. Here we present a survey of developments and progresses made in Named Entity Recognition and Classification research.


title: Functional dissociations within the inferior parietal cortex in verbal working memory

abstract: Neuroimaging studies of working memory have revealed two sites in the left supramarginal gyrus that may support the short-term storage of phonological information. Activation in the left dorsal aspect of the inferior parietal cortex (DIPC) has been observed in contrasts of working memory load, whereas activation in the ventral aspect of the inferior parietal cortex (VIPC) has been found primarily in contrast of information type (verbal vs. nonverbal). Our goal was to determine whether these two areas are functionally distinct or if instead they are part of a homogeneous region with large variations in the focus of peak activity. Toward this end, we used fMRI to assess the neural response in two working memory tasks (N-back and item recognition) in which we also manipulated memory load and the type of information to be recalled (verbal vs. nonverbal). We found both DIPC and VIPC activation in the same group of subjects and further demonstrated that they have differential sensitivity to our experimental factors. Only the DIPC showed robust load effects, whereas only the VIPC showed reliable effects of information type. These results help to account for the differences observed in between-subject comparisons, and they indicate that the two regions are functionally dissociable. In contrast to the DIPC, activity of the VIPC was also recruited in the fixation and low-load conditions, a surprising result that has not been fully explored in prior studies. Despite their distinctive patterns of performance, neither of these regions displayed a pattern of activity that entirely corresponds to common assumptions of a dedicated phonological short-term store (STS). Instead, we hypothesize that the DIPC may support domain-general executive processes, while the VIPC may support phonological encoding–recoding processes central to a variety of language tasks.


title: Data-Driven Synthesis of Cartoon Faces Using Different Styles

abstract: This paper presents a data-driven approach for automatically generating cartoon faces in different styles from a given portrait image. Our stylization pipeline consists of two steps: an offline analysis step to learn about how to select and compose facial components from the databases; a runtime synthesis step to generate the cartoon face by assembling parts from a database of stylized facial components. We propose an optimization framework that, for a given artistic style, simultaneously considers the desired image-cartoon relationships of the facial components and a proper adjustment of the image composition. We measure the similarity between facial components of the input image and our cartoon database via image feature matching, and introduce a probabilistic framework for modeling the relationships between cartoon facial components. We incorporate prior knowledge about image-cartoon relationships and the optimal composition of facial components extracted from a set of cartoon faces to maintain a natural, consistent, and attractive look of the results. We demonstrate generality and robustness of our approach by applying it to a variety of portrait images and compare our output with stylized results created by artists via a comprehensive user study.


A Formal Technique for Automated Dialogue Development


title: An evaluation of an eye tracker as a device for computer input2

abstract: Since humans direct their visual attention by means of eye movements, a device which monitors eye movements should be a natural “pick” device for selecting objects visually present on a monitor. The results from an experimental investigation of an eye tracker as a computer input device are presented. Three different methods were used to select the object looked at; these were a button press, prolonged fixation or “dwell” and an on screen select button. The results show that an eye tracker can be used as a fast selection device providing that the target size is not too small. If the targets are small speed declines and errors increase rapidly.


title: Constructions of gender in the history of artificial intelligence

abstract: Key developments in the history of artificial intelligence are described in terms of a model of gender (Man of Reason), drawn from the work of philosopher Genevieve Lloyd, and informed by research in gender and technology and feminist epistemology. Significantly, the model demonstrates the elevation of mental knowledge over corporeal knowledge. Recent attempts to address the problem of embodiment in situated robotics go some way toward overcoming this problem but have yet to address the question of situatedness, which feminist and other social science research suggests should be an integral part of our models of knowers.


Bootstrapping Fine-Grained Classifiers : Active Learning with a Crowd in the Loop


title: Can crowdsourcing really be used in B2B innovation?

abstract: The aim of this research is to explore the use of crowdsourcing especially from business-to-business companies' innovation perspective, and to create a more comprehensive picture of the possibilities of crowdsourcing for companies operating in business-to-business markets. Business-to-business context was chosen because it is in many ways a very different environment for crowdsourcing than business-to-consumer context, and we found no academic studies on the topic. A systematic literature review was performed to gain an understanding of the state-of-the-art, and to create a research framework on the concept of crowdsourcing in innovation covering e.g. the type of crowdsourcing used, which crowds were used and in what innovation process phase crowds were utilized. Concerning the current ways of using 'crowds' and crowdsourcing in B2B innovation process, we found evidence of using crowdsourcing in B2B's in all the three innovation process phases: front-end, product development, and commercialization. Furthermore, evidence was found for crowdsourcing to be used in innovation mainly in the manner of crowd creation, crowd wisdom and crowd funding. We found that the role of social media was quite essential in all the found B2B crowdsourcing examples.


title: Procedural approach to volumetric terrain generation

abstract: The recent outbreak of indie games has popularized volumetric terrains to a new level, although video games have used them for decades. These terrains contain geological data, such as materials or cave systems. To improve the exploration experience and due to the large amount of data needed to construct volumetric terrains, industry uses procedural methods to generate them. However, they use their own methods, which are focused on their specific problem domains, lacking customization features. Besides, the evaluation of the procedural terrain generators remains an open issue in this field since no standard metrics have been established yet. In this paper, we propose a new approach to procedural volumetric terrains. It generates completely customizable volumetric terrains with layered materials and other features (e.g., mineral veins, underground caves, material mixtures and underground material flow). The method allows the designer to specify the characteristics of the terrain using intuitive parameters. Additionally, it uses a specific representation for the terrain based on stacked material structures, reducing memory requirements. To overcome the problem in the evaluation of the generators, we propose a new set of metrics for the generated content.


title: Towards Query Efficient Black-box Attacks: An Input-free Perspective

abstract: Recent studies have highlighted that deep neural networks (DNNs) are vulnerable to adversarial attacks, even in a black-box scenario. However, most of the existing black-box attack algorithms need to make a huge amount of queries to perform attacks, which is not practical in the real world. We note one of the main reasons for the massive queries is that the adversarial example is required to be visually similar to the original image, but in many cases, how adversarial examples look like does not matter much. It inspires us to introduce a new attack called \emph{input-free} attack, under which an adversary can choose an arbitrary image to start with and is allowed to add perceptible perturbations on it. Following this approach, we propose two techniques to significantly reduce the query complexity. First, we initialize an adversarial example with a gray color image on which every pixel has roughly the same importance for the target model. Then we shrink the dimension of the attack space by perturbing a small region and tiling it to cover the input image. To make our algorithm more effective, we stabilize a projected gradient ascent algorithm with momentum, and also propose a heuristic approach for region size selection. Through extensive experiments, we show that with only 1,701 queries on average, we can perturb a gray image to any target class of ImageNet with a 100\% success rate on InceptionV3. Besides, our algorithm has successfully defeated two real-world systems, the Clarifai food detection API and the Baidu Animal Identification API.


title: Deep Single-View 3D Object Reconstruction with Visual Hull Embedding

abstract: 3D object reconstruction is a fundamental task of many robotics and AI problems. With the aid of deep convolutional neural networks (CNNs), 3D object reconstruction has witnessed a significant progress in recent years. However, possibly due to the prohibitively high dimension of the 3D object space, the results from deep CNNs are often prone to missing some shape details. In this paper, we present an approach which aims to preserve more shape details and improve the reconstruction quality. The key idea of our method is to leverage object mask and pose estimation from CNNs to assist the 3D shape learning by constructing a probabilistic singleview visual hull inside of the network. Our method works by first predicting a coarse shape as well as the object pose and silhouette using CNNs, followed by a novel 3D refinement CNN which refines the coarse shapes using the constructed probabilistic visual hulls. Experiment on both synthetic data and real images show that embedding a single-view visual hull for shape refinement can significantly improve the reconstruction quality by recovering more shapes details and improving shape consistency with the input image.


title: Classical and Molecular Cytogenetics of Disorders of Sex Development in Domestic Animals

abstract: The association of abnormal chromosome constitutions and disorders of sex development in domestic animals has been recorded since the beginnings of conventional cytogenetic analysis. Deviated karyotypes consisting of abnormal sex chromosome sets (e.g. aneuploidy) and/or the coexistence of cells with different sex chromosome constitutions (e.g. mosaicism or chimerism) in an individual seem to be the main causes of anomalies of sex determination and sex differentiation. Molecular cytogenetics and genetics have increased our understanding of these pathologies, where human and mouse models have provided a substantial amount of knowledge, leading to the discovery of a number of genes implicated in mammalian sex determination and differentiation. Additionally, other genes, which appeared to be involved in ovary differentiation, have been found by investigations in domestic species such as the goat. In this paper, we present an overview of the biology of mammalian sex development as a scientific background for better understanding the body of knowledge of the clinical cytogenetics of disorders of sex development in domestic animals. An attempt to summarize of what has been described in that particular subject of veterinary medicine for each of the main mammalian domestic species is presented here.


title: A Reference Model of Information Assurance & Security

abstract: Information Assurance & Security (IAS) is a dynamic domain which changes continuously in response to the evolution of society, business needs and technology. This paper proposes a Reference Model of Information Assurance & Security (RMIAS), which endeavours to address the recent trends in the IAS evolution, namely diversification and deperimetrisation. The model incorporates four dimensions: Information System Security Life Cycle, Information Taxonomy, Security Goals and Security Countermeasures. In addition to the descriptive knowledge, the RMIAS embeds the methodological knowledge. A case study demonstrate show the RMIAS assists with the development and revision of an Information Security Policy Document.


title: Effects of fenfluramine, m-chlorophenylpiperazine, and other serotonin-related agonists and antagonists on penile erections in nonhuman primates.

abstract: Fenfluramine, m-chlorophenylpiperazine (m-CPP), 1-phenylpiperazine, and the buspirone metabolite, 1-(2-pyrimidyl)piperazine given intravenously to adult rhesus monkeys regularly elicited penile erections. In contrast, serotonin (5-HT) agonists with 5-HT1A site specificity (8-OH-DPAT, buspirone) as well as trazodone, ritanserin, and metergoline were no different from saline in producing penile erections. Fenfluramine's effects were blocked by the 5-HT2 antagonists, ritanserin and metergoline, while m-CPP's effects were not blocked by the peripheral 5-HT antagonist, xylamidine, indicating that tumescence can be elicited by serotonergic agents which act at non-5-HT1A sites in the central nervous system.


title: Towards Optimal Worker Assistance: A Framework for Adaptive Selection and Presentation of Assembly Instructions

abstract: Traditional systems for digital assistance in manual assembly, e.g. optical displays at the work place, are inherently suboptimal for providing efficient and ergonomically feasible worker guidance. The display of sequential instructions does not offer an increase in productivity beyond a certain degree. Little situational support and the resulting deterministic guidance lead to a reduced acceptance by the worker. A solution to this discrepancy is seen in an adaptive and cognitive system for worker guidance. Information from an adaptive process model and findings from experiments about human cognition are facilitated to provide the worker with assistive information, adapted to the situation and cognitive state of the worker. I.


title: Simple photonic emission analysis of AES

abstract: This work presents a novel low-cost optoelectronic setup for time- and spatially resolved analysis of photonic emissions and a corresponding methodology, Simple Photonic Emission Analysis (SPEA). Observing the backside of ICs, the system captures extremly weak photo-emissions from switching transistors and relates them to code running in the chip. SPEA utilizes both spatial and temporal information about these emissions to perform side channel analysis of ICs. We successfully performed SPEA of a proof-of-concept AES implementation and were able to recover the full AES secret key by monitoring accesses to the S-Box. This attack directly exploits the side channel leakage of a single transistor and requires no additional data processing. The system costs and the necessary time for an attack are comparable to power analysis techniques. The presented approach significantly reduces the amount of effort required to perform attacks based on photonic emission analysis and allows AES key recovery in a relevant amount of time. We present practical results for the AVR ATMega328P and the AVR XMega128A1.


title: Engineering economic analysis of biomass IGCC with carbon capture and storage

abstract: Abstract Integration of biomass energy technologies with carbon capture and sequestration could yield useful energy products and negative net atmospheric carbon emissions. We survey the methods of integrating biomass technologies with carbon dioxide capture, and model an IGCC electric power system in detail. Our engineering process model, based on analysis and operational results of the Battelle/Future Energy Resources Corporation gasifier technology, integrates gasification, syngas conditioning, and carbon capture with a combined cycle gas turbine to generate electricity with negative net carbon emissions. Our baseline system has a net generation of 123 MW e , 28% thermal efficiency, 44% carbon capture efficiency, and specific capital cost of 1,730 $ kW e −1 . Economic analysis suggests this technology could be roughly cost competitive with more conventional methods of achieving deep reductions in CO 2 emissions from electric power. The potential to generate negative emissions could provide cost-effective emissions offsets for sources where direct mitigation is expected to be difficult, and will be increasingly important as mitigation targets become more stringent.


title: Control of a Powered Ankle–Foot Prosthesis Based on a Neuromuscular Model

abstract: Control schemes for powered ankle-foot prostheses rely upon fixed torque-ankle state relationships obtained from measurements of intact humans walking at target speeds and across known terrains. Although effective at their intended gait speed and terrain, these controllers do not allow for adaptation to environmental disturbances such as speed transients and terrain variation. Here we present an adaptive muscle-reflex controller, based on simulation studies, that utilizes an ankle plantar flexor comprising a Hill-type muscle with a positive force feedback reflex. The model's parameters were fitted to match the human ankle's torque-angle profile as obtained from level-ground walking measurements of a weight and height-matched intact subject walking at 1 m/s. Using this single parameter set, clinical trials were conducted with a transtibial amputee walking on level ground, ramp ascent, and ramp descent conditions. During these trials, an adaptation of prosthetic ankle work was observed in response to ground slope variation, in a manner comparable to intact subjects, without the difficulties of explicit terrain sensing. Specifically, the energy provided by the prosthesis was directly correlated to the ground slope angle. This study highlights the importance of neuromuscular controllers for enhancing the adaptiveness of powered prosthetic devices across varied terrain surfaces.


title: Responses of midbrain dopamine neurons to behavioral trigger stimuli in the monkey

abstract: Destruction of the midbrain dopamine (DA) system in Parkinsonian man and experimental animals leads to deficits in initiation of behavior, motor performance, and cognitive mechanisms. We have investigated the extracellular impulse activity of single midbrain DA neurons in unlesioned monkeys performing in a controlled behavioral task that was designed to paradigmatically test behavioral reactivity. Animals were trained to execute natural forelimb reaching movements for food reward in response to a trigger stimulus. Presumptive DA neurons were histologically located in the pars compacta of substantia nigra and in neighboring areas A8 and A10. They spontaneously discharged polyphasic impulses of relatively long duration (1.4-3.6 ms) and at low frequencies (0.5-8.5/s). Systemic injections of low doses of the DA autoreceptor agonist apomorphine (0.05-0.2 mg/kg) depressed the activity of virtually all thus tested DA neurons. In following established criteria, these characteristics strongly suggest the DAergic nature of the recorded neurons. The majority of midbrain DA neurons (70 of 128) responded to the behavioral trigger stimulus of the task with a short burst of impulses. Latencies ranged from 39 to 105 ms (median 65 ms) for onset and from 65 to 165 ms (median 95 ms) for peak of responses. Responses occurred before arm movement and at the time of or before onset of electromyographic (EMG) activity in prime mover muscles. Responses were time-locked to the stimulus and not to the onset of movement or EMG. Responses remained present in most neurons but were reduced when vision of the behavioral trigger stimulus was prevented while maintaining the associated acoustic signals. In another variation of the task, most neurons also responded to a stimulus that was physically identical to the behavioral trigger but to which the animal made no movement. The activity of a few DA neurons (11 of 128) was reduced following presentation of the behavioral trigger stimulus, with latencies comparable to those of activations. The activity of many DA neurons was increased (40 of 128) or reduced (22 of 128) during execution of the forelimb reaching movement. These changes were of a slow and moderate nature, and were minor compared with responses to the behavioral trigger stimulus. About half of movement-related neurons also responded to the behavioral trigger. The activity of a few DA neurons was increased (11 to 128) or reduced (1 to 128) when the food reward reached the mouth. These changes did not occur with spontaneous mouth movements. About half of these neurons also responded to the behavioral trigger.(ABSTRACT TRUNCATED AT 400 WORDS)


title: Variable stiffness control and implementation of hydraulic SEA based on virtual spring leg

abstract: SLIP model with compliance describes human locomotion very well. A Legged robot's variable stiffness joint could produce an equivalent virtual spring leg, which is the foundation for SLIP model's physical implementation. This paper presents a single leg with knee joint actuated by hydraulic SEA and proposes a variable stiffness control method that is applied to the virtual spring leg. The experiment results prove the effectiveness of proposed control method and also present that the designed hydraulic SEA could realize virtual spring leg with stiffness adjustable from 5KN/m to 20KN/m, which is the stiffness needed in human's normal walking. The robot's single leg weighs about 6kg and could burden at least 60kg load, which provides a way to realize the heavy load legged robot with variable stiffness joint.


Image Downsampling and Upsampling Methods 1


title: Micro Phase Shifting

abstract: We consider the problem of shape recovery for real world scenes, where a variety of global illumination (interreflections, subsurface scattering, etc.) and illumination defocus effects are present. These effects introduce systematic and often significant errors in the recovered shape. We introduce a structured light technique called Micro Phase Shifting, which overcomes these problems. The key idea is to project sinusoidal patterns with frequencies limited to a narrow, high-frequency band. These patterns produce a set of images over which global illumination and defocus effects remain constant for each point in the scene. This enables high quality reconstructions of scenes which have traditionally been considered hard, using only a small number of images. We also derive theoretical lower bounds on the number of input images needed for phase shifting and show that Micro PS achieves the bound.


title: The Ethical Challenges of Direct-to-Consumer Genetic Testing

abstract: Genetic testing is currently subject to little oversight, despite the significant ethical issues involved. Repeated recommendations for increased regulation of the genetic testing market have led to little progress in the policy arena. A 2005 Internet search identified 13 websites offering health-related genetic testing for direct purchase by the consumer. Further examination of these sites showed that overall, biotech companies are not providing enough information for consumers to make well-informed decisions; they are not consistently offering genetic counseling services; and some sites even offer tests with little evidence of clinical value. This article aims to raise company and consumer awareness about the ethical concerns surrounding the direct-to-consumer marketing of health-related genetic tests. It also suggests ways that biotech companies can bring their services to the public in an ethically responsible manner, without increased regulatory oversight.


title: Robust and optimal control of longitudinal dynamics of automotive vehicle

abstract: This paper describes the modeling and controlling of longitudinal vehicle model. State space averaging is used to get the nominal model of vehicle, while bond graph methodology is adopted to model the actuator dynamics of the vehicle. Robust control theory is applied to modify the nominal model to standard format used for parametric and multiplicative input uncertainties. H∞ control technique is adopted to control the states of the model in the presence of uncertainties and disturbance. Concept of servo mechanism is used to check velocity-tracking performance, while μ analysis is performed on the controlled model to check the robust stability and robust performance of the model.


title: Competitive caching with machine learned advice

abstract: Traditional online algorithms encapsulate decision making under uncertainty, and give ways to hedge against all possible future events, while guaranteeing a nearly optimal solution as compared to an offline optimum. On the other hand, machine learning algorithms are in the business of extrapolating patterns found in the data to predict the future, and usually come with strong guarantees on the expected generalization error. In this work we develop a framework for augmenting online algorithms with a machine learned oracle to achieve competitive ratios that provably improve upon unconditional worst case lower bounds when the oracle has low error. Our approach treats the oracle as a complete black box, and is not dependent on its inner workings, or the exact distribution of its errors. We apply this framework to the traditional caching problem -- creating an eviction strategy for a cache of size $k$. We demonstrate that naively following the oracle's recommendations may lead to very poor performance, even when the average error is quite low. Instead we show how to modify the Marker algorithm to take into account the oracle's predictions, and prove that this combined approach achieves a competitive ratio that both (i) decreases as the oracle's error decreases, and (ii) is always capped by $O(\log k)$, which can be achieved without any oracle input. We complement our results with an empirical evaluation of our algorithm on real world datasets, and show that it performs well empirically even using simple off-the-shelf predictions.


title: Deep contextualized word representations

abstract: We introduce a new type of deep contextualized word representation that models both (1) complex characteristics of word use (e.g., syntax and semantics), and (2) how these uses vary across linguistic contexts (i.e., to model polysemy). Our word vectors are learned functions of the internal states of a deep bidirectional language model (biLM), which is pre-trained on a large text corpus. We show that these representations can be easily added to existing models and significantly improve the state of the art across six challenging NLP problems, including question answering, textual entailment and sentiment analysis. We also present an analysis showing that exposing the deep internals of the pre-trained network is crucial, allowing downstream models to mix different types of semi-supervision signals.


title: GIS-Based Multiple-Criteria Decision Analysis

abstract: Important and complex spatial decisions, such as allocating land to development or conservationoriented goals, require information and tools to aid in understanding the inherent tradeoffs. They also require mechanisms for incorporating and documenting the value judgements of interest groups and decision makers. Multiple-criteria decision analysis (MCDA) is a family of techniques that aid decision makers in formally structuring multi-faceted decisions and evaluating the alternatives. It has been used for about two decades with geographic information systems (GIS) to analyse spatial problems. However, the variety and complexity of MCDA methods, with their varying terminologies, means that this rich set of tools is not easily accessible to the untrained. This paper provides background for GIS users, analysts and researchers to quickly get up to speed on MCDA, supporting the ultimate goal of making it more accessible to decision makers. A number of factors for describing MCDA problems and selecting methods are outlined then simplified into a decision tree, which organises an introduction of key methods. Approaches range from mathematical programming and heuristic algorithms for simultaneously optimising multiple goals, to more common single-objective techniques based on weighted addition of criteria values, attainment of criteria thresholds, or outranking of alternatives. There is substantial research that demonstrates ways to couple GIS with multi-criteria methods, and to adapt MCDA for use in spatially continuous problems. Increasing the accessibility of GIS-based MCDA provides new opportunities for researchers and practitioners, including web-based participation and advanced visualisation of decision processes.


title: WikiTaxonomy: A Large Scale Knowledge Resource

abstract: We present a taxonomy automatically generated from the system of categories in Wikipedia. Categories in the resource are identified as either classes or instances and included in a large subsumption, i.e. isa, hierarchy. The taxonomy is made available in RDFS format to the research community, e.g. for direct use within AI applications or to bootstrap the process of manual ontology creation.


title: Supervised Classification of Remotely Sensed Imagery Using a Modified $k$-NN Technique

abstract: Nearest neighbor (NN) techniques are commonly used in remote sensing, pattern recognition, and statistics to classify objects into a predefined number of categories based on a given set of predictors. These techniques are particularly useful in those cases exhibiting a highly nonlinear relationship between variables. In most studies, the distance measure is adopted a priori. In contrast, we propose a general procedure to find Euclidean metrics in a low-dimensional space (i.e., one in which the number of dimensions is less than the number of predictor variables) whose main characteristic is to minimize the variance of a given class label of all those pairs of points whose distance is less than a predefined value. k-NN is used in each embedded space to determine the possibility that a query belongs to a given class label. The class estimation is carried out by an ensemble of predictions. To illustrate the application of this technique, a typical land cover classification using a Landsat-5 Thematic Mapper scene is presented. Experimental results indicate substantial improvement with regard to the classification accuracy as compared with approaches such as maximum likelihood, linear discriminant analysis, standard k-NN, and adaptive quasi-conformal kernel k-NN.


Commentary: Shaping men's memory: the effects of a female's waist-to-hip ratio on men's memory for her appearance and biographical information


title: The Integration of Safety and Security Requirements

abstract: This paper investigates safety and security requirements specification methods, and proposed techniques for the integration of contrasting methodologies. The nature of interaction between safety and security requirements, and problems relating to their independent development, are discussed. The requirements specifications of an Air Traffic Control system are used to highlight the problems inherent in the independent approach to requirements development. From investigation of the literature and the case study, we identify several areas that can cause problems when we attempt to harmonize safety and security requirements techniques. The most important of these are: different system models used for safety and security; different documentation structures for the analyses and their results; the interaction of safety and security requirements; isolation of safety and security requirements processes.


title: Colour appeal in website design within and across cultures: A multi-method evaluation

abstract: Colour has the potential to elicit emotions or behaviors, yet there is little research in which colour treatments in website design are systematically tested. Little is known about how colour affects trust or satisfaction on the part of the viewer. Although the Internet is increasingly global, few systematic studies have been undertaken in which the impact of colour on culturally diverse viewers is investigated in website design. In this research three website colour treatments are tested across three culturally distinct viewer groups for their impact on user trust, satisfaction, and e-loyalty. To gather data, a rich multi-method approach is used including eye-tracking, a survey, and interviews. Results reveal that website colour appeal is a significant determinant for website trust and satisfaction with differences noted across cultures. The findings have practical value for web marketers and interface designers concerning effective colour use in website development.


title: Multi-Scale Multi-Task FCN for Semantic Page Segmentation and Table Detection

abstract: Page segmentation and table detection play an important role in understanding the structure of documents. We present a page segmentation algorithm that incorporates state-of-the-art deep learning methods for segmenting three types of document elements: text blocks, tables, and figures. We propose a multi-scale, multi-task fully convolutional neural network (FCN) for the tasks of semantic page segmentation and element contour detection. The semantic segmentation network accurately predicts the probability at each pixel of the three element classes. The contour detection network accurately predicts instance level "edges" around each element occurrence. We propose a conditional random field (CRF) that uses features output from the semantic segmentation and contour networks to improve upon the semantic segmentation network output. Given the semantic segmentation output, we also extract individual table instances from the page using some heuristic rules and a verification network to remove false positives. We show that although we only consider a page image as input, we produce comparable results with other methods that relies on PDF file information and heuristics and hand crafted features tailored to specific types of documents. Our approach learns the representative features for page segmentation from real and synthetic training data. %, and produces good results on real documents. The learning-based property makes it a more general method than existing methods in terms of document types and element appearances. For example, our method reliably detects sparsely lined tables which are hard for rule-based or heuristic methods.


title: Online Constrained Model-based Reinforcement Learning

abstract: Applying reinforcement learning to robotic systems poses a number of challenging problems. A key requirement is the ability to handle continuous state and action spaces while remaining within a limited time and resource budget. Additionally, for safe operation, the system must make robust decisions under hard constraints. To address these challenges, we propose a model based approach that combines Gaussian Process regression and Receding Horizon Control. Using sparse spectrum Gaussian Processes, we extend previous work by updating the dynamics model incrementally from a stream of sensory data. This results in an agent that can learn and plan in real-time under non-linear constraints. We test our approach on a cart pole swing-up environment and demonstrate the benefits of online learning on an autonomous racing task. The environment's dynamics are learned from limited training data and can be reused in new task instances without retraining.


title: Adaptive droop control for accurate power sharing in islanded microgrid using virtual impedance

abstract: In this paper, the relationship between real and reactive power sharing in islanded microgrid considering the complex impedance effect is analyzed. It is determined that traditional droop control has drawbacks in achieving accurate power sharing caused by the mismatched feeder line effect. In order to enhance the power sharing accuracy, an adaptive droop control using virtual impedance for islanded microgrid is proposed. The method utilizes real and reactive power mismatch, which fed to integral controllers, and then generates the virtual inductive and resistive components. Furthermore, the virtual impedance loop is added to the drooping equation to decouple real and reactive power functions. This proposed method has the advantage of enhancing the load power sharing accuracy among distributed generations (DGs) without requiring knowledge of the line impedances. This is more adaptive to complex impedance situations. The simulation results show the feasibility and correctness of the proposed strategy.


title: Spatial Computing for Design — an Artificial Intelligence Perspective

abstract: The articulation of the Science of Design by Herbert Simon and the paradigmatic relevance of Artificial Intelligence in that context are closely intertwined topics: Simon elaborates the ‘Sciences of the Artificial’ in the context of the design of artefacts. Situated in this AI-centric view of design, we characterize “spatial computing for design” as a specialisation concerned with the development of the general representational and computational apparatus necessary for solving modelling and reasoning problems in spatial design. Several representation and reasoning problems are dis-cussed in the backdrop of relevant examples involving the formal modelling of structural form with respect to a desired/anticipated artefactual function. The discussion, although applicable to any spatial design activity, is grounded in the domain of assistive decision-support in the context of a conventional computer-aided architecture design workflow.


Graphical Model for Baskeball Match Simulation


title: Self-Reported Symptoms of Depression and PTSD Are Associated with Reduced Vowel Space in Screening Interviews

abstract: Reduced frequency range in vowel production is a well documented speech characteristic of individuals with psychological and neurological disorders. Affective disorders such as depression and post-traumatic stress disorder (PTSD) are known to influence motor control and in particular speech production. The assessment and documentation of reduced vowel space and reduced expressivity often either rely on subjective assessments or on analysis of speech under constrained laboratory conditions (e.g. sustained vowel production, reading tasks). These constraints render the analysis of such measures expensive and impractical. Within this work, we investigate an automatic unsupervised machine learning based approach to assess a speaker's vowel space. Our experiments are based on recordings of 253 individuals. Symptoms of depression and PTSD are assessed using standard self-assessment questionnaires and their cut-off scores. The experiments show a significantly reduced vowel space in subjects that scored positively on the questionnaires. We show the measure's statistical robustness against varying demographics of individuals and articulation rate. The reduced vowel space for subjects with symptoms of depression can be explained by the common condition of psychomotor retardation influencing articulation and motor control. These findings could potentially support treatment of affective disorders, like depression and PTSD in the future.


title: Dynamic Task Prioritization for Multitask Learning

abstract: We propose dynamic task prioritization for multitask learning. This allows a model to dynamically prioritize difficult tasks during training, where difficulty is inversely proportional to performance, and where difficulty changes over time. In contrast to curriculum learning, where easy tasks are prioritized above difficult tasks, we present several studies showing the importance of prioritizing difficult tasks first. We observe that imbalances in task difficulty can lead to unnecessary emphasis on easier tasks, thus neglecting and slowing progress on difficult tasks. Motivated by this finding, we introduce a notion of dynamic task prioritization to automatically prioritize more difficult tasks by adaptively adjusting the mixing weight of each task’s loss objective. Additional ablation studies show the impact of the task hierarchy, or the task ordering, when explicitly encoded in the network architecture. Our method outperforms existing multitask methods and demonstrates competitive results with modern single-task models on the COCO and MPII datasets.


title: Dual-Agent Deep Reinforcement Learning for Deformable Face Tracking

abstract: In this paper, we propose a dual-agent deep reinforcement learning (DADRL) method for deformable face tracking, which generates bounding boxes and detects facial landmarks interactively from face videos. Most existing deformable face tracking methods learn models for these two tasks individually, and perform these two procedures subsequently during the testing phase, which ignore the intrinsic connections of these two tasks. Motivated by the fact that the performance of facial landmark detection depends heavily on the accuracy of the generated bounding boxes, we exploit the interactions of these two tasks in probabilistic manner by following a Bayesian model and propose a unified framework for simultaneous bounding box tracking and landmark detection. By formulating it as a Markov decision process, we define two agents to exploit the relationships and pass messages via an adaptive sequence of actions under a deep reinforcement learning framework to iteratively adjust the positions of the bounding boxes and facial landmarks. Our proposed DADRL achieves performance improvements over the state-of-the-art deformable face tracking methods on the most challenging category of the 300-VW dataset.


title: Parallel Manipulators Applications—A Survey

abstract: This paper presents the comparison between serial and parallel manipulators. Day by day, the applications of the parallel manipulator in various field is become apparent and with a rapid rate utilized in precise manufacturing, medical science and in space exploration equipments. A parallel manipulator can be defined as a closed loop kinematic chain mechanism whose end effector is linked to the base by several independent kinematic chains. The classification of various parallel manipulators is presented herewith. The prime focus of the paper is to realize the parallel manipulators applications for industry, space, medical science or commercial usage by orienting manipulator in the space at the high speed with a desired accuracy.


title: A Little Honesty Goes a Long Way - The Two-Tier Model for Secure Multiparty Computation.

abstract: A fundamental result in secure multiparty computation (MPC) is that in order to achieve full security, it is necessary that a majority of the parties behave honestly. There are settings, however, where the condition of an honest majority might be overly restrictive, and there is a need to define and investigate other plausible adversarial models in order to circumvent the above impossibility. To this end, we introduce the two-tier model for MPC, where some small subset of servers is guaranteed to be honest at the beginning of the computation (the first tier), while the corruption state of the other servers (the second tier) is unknown. The two-tier model naturally arises in various settings, such as for example when a service provider wishes to utilize a large pre-existing set of servers, while being able to trust only a small fraction of them. The first tier is responsible for performing the secure computation while the second tier serves as a disguise: using novel anonymization techniques, servers in the first tier remain undetected to an adaptive adversary, preventing a targeted attack on these critical servers. Specifically, given n servers and assuming αn of them are corrupt at the onset (where α ∈ (0, 1)), we present an MPC protocol that can withstand an optimal amount of less than (1− α)n/2 additional adaptive corruptions, provided the first tier is of size ω(logn). This allows us to perform MPC in a fully secure manner even when the total number of corruptions exceeds n/2 across both tiers, thus evading the honest majority requirement.


title: OutlinAR: an assisted interactive model building system with reduced computational effort

abstract: This paper presents a system that allows online building of 3D wireframe models through a combination of user interaction and automated methods from a handheld camera-mouse. Crucially, the model being built is used to concurrently compute camera pose, permitting extendable tracking while enabling the user to edit the model interactively. In contrast to other model building methods that are either off-line and/or automated but computationally intensive, the aim here is to have a system that has low computational requirements and that enables the user to define what is relevant (and what is not) at the time the model is being built. OutlinAR hardware is also developed which simply consists of the combination of a camera with a wide field of view lens and a wheeled computer mouse.


title: Effect of cationic lipid and matrix lipid composition on solid lipid nanoparticle-mediated gene transfer.

abstract: This investigation is focused on the enhancement of in vitro transfection activity by optimizing cationic lipid and matrix lipid composition of solid lipid nanoparticles (SLN). For this purpose SLN were formulated by using two different matrix lipids and six different cationic detergents. These 12 formulations were tested for physical parameters such as particle size, zeta potential and DNA-binding capacity, and also for their biological properties such as cytotoxicity and in vitro transfection efficiency. The SLN were produced by hot high-pressure homogenization, all formulations were physically stable and showed a highly positive surface charge (+34 to +45 mV). In vitro cytotoxicity measurements on COS-1 cells revealed that cytotoxicity is strongly dependent on the cationic lipid used. SLN made from one-tailed cationic detergents were highly cytotoxic. In contrast the two-tailed cationic lipids were all well tolerated. Transfection activity seems to be determined by both the cationic lipid and the matrix lipid used. Here, the combination of cetylpalmitate and N-[1-(2,3-dioleoyloxy)propyl]-N,N,N-trimethylammonium chloride led to significantly higher transfection efficiencies than in all other tested combinations. These results indicate that well tolerated and highly efficient in vitro transfection could be achieved with SLN whenever selecting good combinations of two-tailed cationic lipids and matrix lipids.


title: RELATIONAL JOB DESIGN AND THE MOTIVATION TO MAKE A PROSOCIAL DIFFERENCE.

abstract: This article illustrates how work contexts motivate employees to care about making a positive difference in other people's lives. I introduce a model of relational job design to describe how jobs spark the motivation to make a prosocial difference, and how this motivation affects employees' actions and identities. Whereas existing research focuses on individual differences and the task structures of jobs, I illuminate how the relational architecture of jobs shapes the motivation to make a prosocial difference.


title: Torque Ripple Suppression in Flux-Switching PM Motor by Harmonic Current Injection Based on Voltage Space-Vector Modulation

abstract: The flux-switching permanent-magnet (FSPM) machine is a relatively novel brushless machine having magnets and concentrated windings in the stator instead of rotor, which exhibits inherently sinusoidal back-EMF waveform and high torque capability. However, due to the high airgap flux density produced by magnets and the salient poles in both stator and rotor, the resultant torque ripple is relatively large, which is unfavorable for high performance drive system. In this paper, instead of conventional optimization on the machine itself, a new torque ripple suppression approach is proposed in which a series of specific harmonic currents are added into q-axis reference current, resulting in additional torque components to counteract the fundamental and second-order harmonic components of cogging torque. Both the simulation and experimental results confirm that the proposed approach can effectively suppress the torque ripple. It should be emphasized that this method is applicable to all PM machines having relatively large cogging torque.


title: Big Data Technologies and Applications

abstract: The objective of this book is to introduce the basic concepts of big data computing and then to describe the total solution of big data problems using HPCC, an open-source computing platform. The book comprises 15 chapters broken into three parts. The first part, Big Data Technologies, includes introductions to big data concepts and techniques; big data analytics; and visualization and learning techniques. The second part, LexisNexis Risk Solution to Big Data, focuses on specific technologies and techniques developed at LexisNexis to solve critical problems that use big data analytics. It covers the open source High Performance Computing Cluster (HPCC Systems) platform and its architecture, as well as parallel data languages ECL and KEL, developed to effectively solve big data problems. The third part, Big Data Applications, describes various data intensive applications solved on HPCC Systems. It includes applications such as cyber security, social network analytics including fraud, Ebola spread modeling using big data analytics, unsupervised learning, and image classification. The book is intended for a wide variety of people including researchers, scientists, programmers, engineers, designers, developers, educators, and students. This book can also be beneficial for business managers, entrepreneurs, and investors.


title: Learning to rank audience for behavioral targeting in display ads

abstract: Behavioral targeting (BT), which aims to sell advertisers those behaviorally related user segments to deliver their advertisements, is facing a bottleneck in serving the rapid growth of long tail advertisers. Due to the small business nature of the tail advertisers, they generally expect to accurately reach a small group of audience, which is hard to be satisfied by classical BT solutions with large size user segments. In this paper, we propose a novel probabilistic generative model named Rank Latent Dirichlet Allocation (RANKLDA) to rank audience according to their ads click probabilities for the long tail advertisers to deliver their ads. Based on the basic assumption that users who clicked the same group of ads will have a higher probability of sharing similar latent search topical interests, RANKLDA combines topic discovery from users' search behaviors and learning to rank users from their ads click behaviors together. In computation, the topic learning could be enhanced by the supervised information of the rank learning and simultaneously, the rank learning could be better optimized by considering the discovered topics as features. This co-optimization scheme enhances each other iteratively. Experiments over the real click-through log of display ads in a public ad network show that the proposed RANKLDA model can effectively rank the audience for the tail advertisers.


title: Web Security: Detection of Cross Site Scripting in PHP Web Application using Genetic Algorithm

abstract: Cross site scripting (XSS) is one of the major threats to the web application security, where the research is still underway for an effective and useful way to analyse the source code of web application and removes this threat. XSS occurs by injecting the malicious scripts into web application and it can lead to significant violations at the site or for the user. Several solutions have been recommended for their detection. However, their results do not appear to be effective enough to resolve the issue. This paper recommended a methodology for the detection of XSS from the PHP web application using genetic algorithm (GA) and static analysis. The methodology enhances the earlier approaches of determining XSS vulnerability in the web application by eliminating the infeasible paths from the control flow graph (CFG). This aids in reducing the false positive rate in the outcomes. The results of the experiments indicated that our methodology is more effectual in detecting XSS vulnerability from the PHP web application compared to the earlier studies, in terms of the false positive rates and the concrete susceptible paths determined by GA Generator.


title: The narrative constitution of identity: A relational and network approach

abstract: justice. Women, by contrast, were believed to be at a lower stage because they were found to have a sense of agency still tied primarily to their social relationships and to make political and moral decisions based on context-specific principles based on these relationships rather than on the grounds of their own autonomous judgments. Students of gender studies know well just how busy social scientists have been kept by their efforts to come up with ever more sociological "alibis" for the question of why women did not act like men. Gilligan's response was to refuse the terms of the debate altogether. She thus did not develop yet another explanation for why women are "deviant." Instead, she turned the question on its head by asking what was wrong with the theory a theory whose central premises defines 50% of social beings as "abnormal." Gilligan translated this question into research by subjecting the abstraction of universal and discrete agency to comparative research into female behavior evaluated on its own terms The new research revealed women to be more "concrete" in their thinking and more attuned to "fairness" while men acted on "abstract reasoning" and "rules of justice." These research findings transformed female otherness into variation and difference but difference now freed from the normative de-


title: A Secure Two-Phase Data Deduplication Scheme

abstract: Data grows at the impressive rate of 50% per year, and 75% of the digital world is a copy! Although keeping multiple copies of data is necessary to guarantee their availability and long term durability, in many situations the amount of data redundancy is immoderate. By keeping a single copy of repeated data, data deduplication is considered as one of the most promising solutions to reduce the storage costs, and improve users experience by saving network bandwidth and reducing backup time. However, this solution must now solve many security issues to be completely satisfying. In this paper we target the attacks from malicious clients that are based on the manipulation of data identifiers and those based on backup time and network traffic observation. We present a deduplication scheme mixing an intraand an inter-user deduplication in order to build a storage system that is secure against the aforementioned type of attacks by controlling the correspondence between files and their identifiers, and making the inter-user deduplication unnoticeable to clients using deduplication proxies. Our method provides global storage space savings, per-client bandwidth network savings between clients and deduplication proxies, and global network bandwidth savings between deduplication proxies and the storage server. The evaluation of our solution compared to a classic system shows that the overhead introduced by our scheme is mostly due to data encryption which is necessary to ensure confidentiality.


title: A Review of Challenges and Opportunities in Machine Learning for Health

abstract: Modern electronic health records (EHRs) provide data to answer clinically meaningful questions. The growing data in EHRs makes healthcare ripe for the use of machine learning. However, learning in a clinical setting presents unique challenges that complicate the use of common machine learning methodologies. For example, diseases in EHRs are poorly labeled, conditions can encompass multiple underlying endotypes, and healthy individuals are underrepresented. This article serves as a primer to illuminate these challenges and highlights opportunities for members of the machine learning community to contribute to healthcare.


title: The role of spatial selective attention in working memory for locations: evidence from event-related potentials

abstract: We investigated the hypothesis that the covert focusing of spatial attention mediates the on-line maintenance of location information in spatial working memory. During the delay period of a spatial working-memory task, behaviorally irrelevant probe stimuli were flashed at both memorized and nonmemorized locations. Multichannel recordings of event-related potentials (ERPs) were used to assess visual processing of the probes at the different locations. Consistent with the hypothesis of attention-based rehearsal, early ERP components were enlarged in response to probes that appeared at memorized locations. These visual modulations were similar in latency and topography to those observed after explicit manipulations of spatial selective attention in a parallel experimental condition that employed an identical stimulus display.


title: Rijke, “MoodViews: Tools for Blog Mood Analysis

abstract: We demonstrate a system for tracking and analyzing moods of bloggers worldwide, as reflected in the largest blogging community, LiveJournal. Our system collects thousands of blog posts every hour, performs various analyses on the posts and presents the results graphically. Exploring the Blogspace From the point of view of information access, the blogspace offers many natural opportunities beyond traditional search facilities, such as trend detection, topic tracking, link tracking, feed generation, etc. But there is more. Many blog authoring environments allow bloggers to tag their entries with highly individual (and personal) features. Users of LiveJournal, currently the largest weblog community, have the option of reporting their mood at the time of the post; users can either select a mood from a predefined list of 132 common moods such as “amused” or “angry,” or enter free-text. A large percentage of LiveJournal users chooses to utilize this option, tagging their postings with a mood. This results in a stream of hundreds of weblog posts tagged with mood information per minute, from hundreds of thousands of different users across the globe. Our focus in this demo is on providing access to the blogspace using moods as the “central” dimension. The type of information needs that we are interested in are best illustrated by questions such as: How do moods develop? How are they related? How do global events impact moods? And: Can global mood swings be traced back to global events? We describe MoodViews, a collection of tools for analyzing, tracking and visualizing moods and mood changes in blogs posted by LiveJournal users.