title: Relation extraction and scoring in DeepQA

abstract: Detecting semantic relations in text is an active problem area in natural-language processing and information retrieval. For question answering, there are many advantages of detecting relations in the question text because it allows background relational knowledge to be used to generate potential answers or find additional evidence to score supporting passages. This paper presents two approaches to broad-domain relation extraction and scoring in the DeepQA question-answering framework, i.e., one based on manual pattern specification and the other relying on statistical methods for pattern elicitation, which uses a novel transfer learning technique, i.e., relation topics. These two approaches are complementary; the rule-based approach is more precise and is used by several DeepQA components, but it requires manual effort, which allows for coverage on only a small targeted set of relations (approximately 30). Statistical approaches, on the other hand, automatically learn how to extract semantic relations from the training data and can be applied to detect a large amount of relations (approximately 7,000). Although the precision of the statistical relation detectors is not as high as that of the rule-based approach, their overall impact on the system through passage scoring is statistically significant because of their broad coverage of knowledge.


title: Analysis of back-end flash in a 1.5b/stage pipelined ADC

abstract: An analysis of the impact of last stage flash in a conventional pipeline ADC is performed in this paper. The performance of a pipeline ADC can be altered significantly by calibrating the comparators in the back-end flash. Also, realizing that the input to the back-end flash (in a pipeline ADC) is not uniformly distributed, this paper proposes alternative back-end flash references to improve the overall performance of the ADC. An analysis of the performance of the pipeline with large offsets in the MDAC stages is also presented in this paper.


Nonprehensile Robotic Manipulation: Controllability and Planning


title: Bandwidth and resolution enhancement through pulse compression

abstract: A novel pulse compression technique is developed that improves the axial resolution of an ultrasonic imaging system and provides a boost in the echo signal-to-noise ratio (eSNR). The new technique, called the resolution enhancement compression (REC) technique, was validated with simulations and experimental measurements. Image quality was examined in terms of three metrics: the cSNR, the bandwidth, and the axial resolution through the modulation transfer function (MTF). Simulations were conducted with a weakly-focused, single-element ultrasound source with a center frequency of 2.25 MHz. Experimental measurements were carried out with a single-element transducer (f/3) with a center frequency of 2.25 MHz from a planar reflector and wire targets. In simulations, axial resolution of the ultrasonic imaging system was almost doubled using the REC technique (0.29 mm) versus conventional pulsing techniques (0.60 mm). The -3 dB pulse/echo bandwidth was more than doubled from 48% to 97%, and maximum range sidelobes were -40 dB. Experimental measurements revealed an improvement in axial resolution using the REC technique (0.31 mm) versus conventional pulsing (0.44 mm). The -3 dB pulse/echo bandwidth was doubled from 56% to 113%, and maximum range sidelobes were observed at -45 dB. In addition, a significant gain in eSNR (9 to 16.2 dB) was achieved


title: Coordinating human and machine intelligence to classify microblog communications in crises

abstract: An emerging paradigm for the processing of data streams involves human and machine computation working together, allowing human intelligence to process large-scale data. We apply this approach to the classification of crisis-related messages in microblog streams. We begin by describing the platform AIDR (Artificial Intelligence for Disaster Response), which collects human annotations over time to create and maintain automatic supervised classifiers for social media messages. Next, we study two significant challenges in its design: (1) identifying which elements must be labeled by humans, and (2) determining when to ask for such annotations to be done. The first challenge is selecting the items to be labeled by crowdsourcing workers to maximize the productivity of their work. The second challenge is to schedule the work in order to reliably maintain high classification accuracy over time. We provide and validate answers to these challenges by extensive experimentation on realworld datasets.


title: LSTM-Based Predictions for Proactive Information Retrieval

abstract: We describe a method for proactive information retrieval targeted at retrieving relevant information during a writing task. In our method, the current task and the needs of the user are estimated, and the potential next steps are unobtrusively predicted based on the user's past actions. We focus on the task of writing, in which the user is coalescing previously collected information into a text. Our proactive system automatically recommends the user relevant background information. The proposed system incorporates text input prediction using a long short-term memory (LSTM) network. We present simulations, which show that the system is able to reach higher precision values in an exploratory search setting compared to both a baseline and a comparison system.


title: Learning-based approach for online lane change intention prediction

abstract: Predicting driver behavior is a key component for Advanced Driver Assistance Systems (ADAS). In this paper, a novel approach based on Support Vector Machine and Bayesian filtering is proposed for online lane change intention prediction. The approach uses the multiclass probabilistic outputs of the Support Vector Machine as an input to the Bayesian filter, and the output of the Bayesian filter is used for the final prediction of lane changes. A lane tracker integrated in a passenger vehicle is used for real-world data collection for the purpose of training and testing. Data from different drivers on different highways were used to evaluate the robustness of the approach. The results demonstrate that the proposed approach is able to predict driver intention to change lanes on average 1.3 seconds in advance, with a maximum prediction horizon of 3.29 seconds.


Unravelling Names of Fictional Characters


title: The Open Provenance Model core specification (v1.1)

abstract: The Open Provenance Model is a model of provenance that is designed to meet the following requirements: (1) Allow provenance information to be exchanged between systems, by means of a compatibility layer based on a shared provenance model. (2) Allow developers to build and share tools that operate on such a provenance model. (3) Define provenance in a precise, technology-agnostic manner. (4) Support a digital representation of provenance for any ''thing'', whether produced by computer systems or not. (5) Allow multiple levels of description to coexist. (6) Define a core set of rules that identify the valid inferences that can be made on provenance representation. This document contains the specification of the Open Provenance Model (v1.1) resulting from a community effort to achieve inter-operability in the Provenance Challenge series.


On the Comultiplication in Quantum Affine Algebras


title: Unsupervised link prediction using aggregative statistics on heterogeneous social networks

abstract: The concern of privacy has become an important issue for online social networks. In services such as Foursquare.com, whether a person likes an article is considered private and therefore not disclosed; only the aggregative statistics of articles (i.e., how many people like this article) is revealed. This paper tries to answer a question: can we predict the opinion holder in a heterogeneous social network without any labeled data? This question can be generalized to a link prediction with aggregative statistics problem. This paper devises a novel unsupervised framework to solve this problem, including two main components: (1) a three-layer factor graph model and three types of potential functions; (2) a ranked-margin learning and inference algorithm. Finally, we evaluate our method on four diverse prediction scenarios using four datasets: preference (Foursquare), repost (Twitter), response (Plurk), and citation (DBLP). We further exploit nine unsupervised models to solve this problem as baselines. Our approach not only wins out in all scenarios, but on the average achieves 9.90% AUC and 12.59% NDCG improvement over the best competitors. The resources are available at http://www.csie.ntu.edu.tw/~d97944007/aggregative/


title: Combining creativity and control: Understanding individual motivation in large-scale collaborative creativity

abstract: Recent research has shown that management control systems (MCS) can improve performance in contexts characterized by high levels of task uncertainty. This seems to conflict with a second stream of research, which argues that MCSs risk undermining the intrinsic motivation needed for effective performance in such settings. To solve this puzzle, we build on theories of perceived locus of causality and self-construal and develop an integrative model summarized in 15 propositions. To explicate our proposed solution and to show its robustness, we focus on the class of activities we call large-scale collaborative creativity (LSCC) - contexts where individuals face a dual challenge of demonstrating creativity and embracing the formal controls that coordinate their creative activities with others'. We argue that LSCC requires the simultaneous activation of intrinsic and identified forms of motivation, and simultaneously independent and interdependent self-construals. Against some scholarship that argues or assumes that such simultaneous combinations are infeasible, we argue that they can be fostered through appropriate attraction-selection-attrition policies and management control systems design. We also show how our propositions can enrich our understanding of motivation in other settings, where creativity and/or coordination demands are less pressing.


title: 1 Appears in Computational Intelligence, 4(4):349-355, 1988 PLANS AND RESOURCE-BOUNDED PRACTICAL REASONING

abstract: An architecture for a rational agent must allow for means-end reasoning, for the weighing of competing alternatives, and for interactions betwen these two forms of reasoning. Such an architecture must also address the problem of resource boundedness. We sketch a solution of the first problem that points the way to a solution of the second. In particular, we present a high-level specification of the practical-reasoning component of an architecture for a resource-bounded rational agent. In this architecture, a major role of the agent's plans is to constrain the amount of further practical reasoning she must perform.


title: Recursive Cross-Domain Face/Sketch Generation from Limited Facial Parts

abstract: We start by asking an interesting yet challenging question,"If an eyewitness can only recall the eye features of the suspect, such that the forensic artist can only produce a sketch of the eyes (e.g., the top-left sketch shown in Fig. 1), can advanced computer vision techniques help generate the whole face image?"A more generalized question is that if a large proportion (e.g., more than 50%) of the face/sketch is missing, can a realistic whole face sketch/image still be estimated. Existing face completion and generation methods either do not conduct domain transfer learning or can not handle large missing area. For example, the inpainting approach tends to blur the generated region when the missing area is large (i.e., more than 50%). In this paper, we exploit the potential of deep learning networks in filling large missing region (e.g., as high as 95% missing) and generating realistic faces with high-fidelity in cross domains. We propose the recursive generation by bidirectional transformation networks (r-BTN) that recursively generates a whole face/sketch from a small sketch/face patch. The large missing area and the cross domain challenge make it difficult to generate satisfactory results using a unidirectional cross-domain learning structure. On the other hand, a forward and backward bidirectional learning between the face and sketch domains would enable recursive estimation of the missing region in an incremental manner (Fig. 1) and yield appealing results. r-BTN also adopts an adversarial constraint to encourage the generation of realistic faces/sketches. Extensive experiments have been conducted to demonstrate the superior performance from r-BTN as compared to existing potential solutions.


title: Certifiable Distributional Robustness with Principled Adversarial Training

abstract: Neural networks are vulnerable to adversarial examples and researchers have proposed many heuristic attack and defense mechanisms. We take the principled view of distributionally robust optimization, which guarantees performance under adversarial input perturbations. By considering a Lagrangian penalty formulation of perturbation of the underlying data distribution in a Wasserstein ball, we provide a training procedure that augments model parameter updates with worst-case perturbations of training data. For smooth losses, our procedure provably achieves moderate levels of robustness with little computational or statistical cost relative to empirical risk minimization. Furthermore, our statistical guarantees allow us to efficiently certify robustness for the population loss. For imperceptible perturbations, our method matches or outperforms heuristic approaches.


ReTiCaM: Real-time Human Performance Capture from Monocular Video


title: Real-time decompression and visualization of animated volume data

abstract: Interactive exploration of animated volume data is required by many application, but the huge amount of computational time and storage space needed for rendering does not yet allow the visualization of animated volumes. In this paper, we introduce an algorithm running at interactive frame rates using 3D wavelet transforms that allows for any wavelet, motion compensation techniques and various encoding schemes of the resulting wavelet coefficients to be used. We analyze different families and orders of wavelets for compression ratio and the introduced error. We use a quantization that has been optimized for the visual impression of the reconstructed volume, independent of the viewing algorithm. This enables us to achieve very high compression ratios while still being able to reconstruct the volume with as few visual artifacts as possible. A further improvement of the compression ratio has been achieved by applying a motion compensation scheme to exploit temporal coherency. Using these schemes, we are able to decompress each volume of our animation at interactive frame rates, while visualizing these decompressed volumes on a single PC. We also present a number of improved visualization algorithms for high-quality display using OpenGL hardware running at interactive frame rates on a standard PC.


title: Bipartite network projection and personal recommendation

abstract: One-mode projecting is extensively used to compress bipartite networks. Since one-mode projection is always less informative than the bipartite representation, a proper weighting method is required to better retain the original information. In this article, inspired by the network-based resource-allocation dynamics, we raise a weighting method which can be directly applied in extracting the hidden information of networks, with remarkably better performance than the widely used global ranking method as well as collaborative filtering. This work not only provides a creditable method for compressing bipartite networks, but also highlights a possible way for the better solution of a long-standing challenge in modern information science: How to do a personal recommendation.


title: EEG feature comparison and classification of simple and compound limb motor imagery

abstract: BackgroundMotor imagery can elicit brain oscillations in Rolandic mu rhythm and central beta rhythm, both originating in the sensorimotor cortex. In contrast with simple limb motor imagery, less work was reported about compound limb motor imagery which involves several parts of limbs. The goal of this study was to investigate the differences of the EEG patterns between simple limb motor imagery and compound limb motor imagery, and discuss the separability of multiple types of mental tasks.MethodsTen subjects participated in the experiment involving three tasks of simple limb motor imagery (left hand, right hand, feet), three tasks of compound limb motor imagery (both hands, left hand combined with right foot, right hand combined with left foot) and rest state. Event-related spectral perturbation (ERSP), power spectral entropy (PSE) and spatial distribution coefficient were adopted to analyze these seven EEG patterns. Then three algorithms of modified multi-class common spatial patterns (CSP) were used for feature extraction and classification was implemented by support vector machine (SVM).ResultsThe induced event-related desynchronization (ERD) affects more components within both alpha and beta bands resulting in more broad ERD bands at electrode positions C3, Cz and C4 during left/right hand combined with contralateral foot imagery, whose PSE values are significant higher than that of simple limb motor imagery. From the topographical distribution, simultaneous imagination of upper limb and contralateral lower limb certainly contributes to the activation of more areas on cerebral cortex. Classification result shows that multi-class stationary Tikhonov regularized CSP (Multi-sTRCSP) outperforms other two multi-class CSP methods, with the highest accuracy of 84% and mean accuracy of 70%.ConclusionsThe work implies that there exist the separable differences between simple limb motor imagery and compound limb motor imagery, which can be utilized to build a multimodal classification paradigm in motor imagery based brain-computer interface (BCI) systems.


title: Biologically-inspired dynamical systems for movement generation: Automatic real-time goal adaptation and obstacle avoidance

abstract: Dynamical systems can generate movement trajectories that are robust against perturbations. This article presents an improved modification of the original dynamic movement primitive (DMP) framework by Ijspeert et al [1], [2]. The new equations can generalize movements to new targets without singularities and large accelerations. Furthermore, the new equations can represent a movement in 3D task space without depending on the choice of coordinate system (invariance under invertible affine transformations). Our modified DMP is motivated from biological data (spinal-cord stimulation in frogs) and human behavioral experiments. We further extend the formalism to obstacle avoidance by exploiting the robustness against perturbations: an additional term is added to the differential equations to make the robot steer around an obstacle. This additional term empirically describes human obstacle avoidance. We demonstrate the feasibility of our approach using the Sarcos Slave robot arm: after learning a single placing movement, the robot placed a cup between two arbitrarily given positions and avoided approaching obstacles.


Network Slicing for 5 G : Challenges and Opportunities


title: Haraka v2 – Efficient Short-Input Hashing for Post-Quantum Applications

abstract: Recently, many efficient cryptographic hash function design strategies have been explored, not least because of the SHA-3 competition. These designs are, almost exclusively, geared towards high performance on long inputs. However, various applications exist where the performance on short (fixed length) inputs matters more. Such hash functions are the bottleneck in hash-based signature schemes like SPHINCS or XMSS, which is currently under standardization. Secure functions specifically designed for such applications are scarce. We attend to this gap by proposing two short-input hash functions (or rather simply compression functions). By utilizing AES instructions on modern CPUs, our proposals are the fastest on such platforms, reaching throughputs below one cycle per hashed byte even for short inputs, while still having a very low latency of less than 60 cycles. Under the hood, this results comes with several innovations. First, we study whether the number of rounds for our hash functions can be reduced, if only second-preimage resistance (and not collision resistance) is required. The conclusion is: only a little. Second, since their inception, AES-like designs allow for supportive security arguments by means of counting and bounding the number of active S-boxes. However, this ignores powerful attack vectors using truncated differentials, including the powerful rebound attacks. We develop a general tool-based method to include arguments against attack vectors using truncated differentials.


title: Sensr: evaluating a flexible framework for authoring mobile data-collection tools for citizen science

abstract: Across HCI and social computing platforms, mobile applications that support citizen science, empowering non-experts to explore, collect, and share data have emerged. While many of these efforts have been successful, it remains difficult to create citizen science applications without extensive programming expertise. To address this concern, we present Sensr, an authoring environment that enables people without programming skills to build mobile data collection and management tools for citizen science. We demonstrate how Sensr allows people without technical skills to create mobile applications. Findings from our case study demonstrate that our system successfully overcomes technical constraints and provides a simple way to create mobile data collection tools.


title: Stator flux based speed sensorless control of single phase induction motor drives

abstract: Single phase induction motors (SPIM) are widely used at low power levels because of their simple construction, robust nature and nominal price. Conventionally, they have one operational stator winding and the speed of the motor is not controlled. But SPIM drives for higher power levels have both main and auxiliary winding remaining operational throughout and can be treated as asymmetrical, two-phase machines. High performance operation of these asymmetrical machines is associated with the problems of modeling the asymmetry and controlling the electromagnetic torque oscillations. Different research groups have proposed different schemes to tackle the above said difficulties. In this paper, a novel vector control scheme is proposed based on the simplest mathematical model that works for diverse speed ranges far below and much above the base speed and excludes the traditional complexities found in high performance SPIM logics. Stress has been given on simplicity in modeling the asymmetry and designing the controllers. The controllers are simulated in synchronously rotating reference frame for which the stator voltages are transformed to synchronous reference frame by removing the asymmetrical terms and adding them later as decoupled voltages. The simulation results are given for various speeds below and above base speed to indicate the versatility of the scheme. To improve its adaptability, speed sensorless algorithm has also been added.


title: Mining Discriminative Triplets of Patches for Fine-Grained Classification

abstract: Fine-grained classification involves distinguishing between similar sub-categories based on subtle differences in highly localized regions; therefore, accurate localization of discriminative regions remains a major challenge. We describe a patch-based framework to address this problem. We introduce triplets of patches with geometric constraints to improve the accuracy of patch localization, and automatically mine discriminative geometrically-constrained triplets for classification. The resulting approach only requires object bounding boxes. Its effectiveness is demonstrated using four publicly available fine-grained datasets, on which it outperforms or achieves comparable performance to the state-of-the-art in classification.


title: Municipal solid waste management in China: status, problems and challenges.

abstract: This paper presents an examination of MSW generation and composition in China, providing an overview of the current state of MSW management, an analysis of existing problems in MSW collection, separation, recycling and disposal, and some suggestions for improving MSW systems in the future. In China, along with urbanization, population growth and industrialization, the quantity of municipal solid waste (MSW) generation has been increasing rapidly. The total MSW amount increased from 31.3 million tonnes in 1980 to 212 million tonnes in 2006, and the waste generation rate increased from 0.50 kg/capita/day in 1980 to 0.98 kg/capita/year in 2006. Currently, waste composition in China is dominated by a high organic and moisture content, since the concentration of kitchen waste in urban solid waste makes up the highest proportion (at approximately 60%) of the waste stream. The total amount of MSW collected and transported was 148 million tonnes in 2006, of which 91.4% was landfilled, 6.4% was incinerated and 2.2% was composted. The overall MSW treatment rate in China was approximately 62% in 2007. In 2007, there were 460 facilities, including 366 landfill sites, 17 composing plants, and 66 incineration plants. This paper also considers the challenges faced and opportunities for MSW management in China, and a number of recommendations are made aimed at improving the MSW management system.


Deep Gaussian Process for Crop Yield Prediction Based on Remote Sensing Data.


title: DPP-4 (CD26) inhibitor alogliptin inhibits atherosclerosis in diabetic apolipoprotein E-deficient mice.

abstract: Dipeptidyl peptidase-4 (DPP-4 or CD26) inhibitors, a new class of antidiabetic compounds, are effective in the treatment of hyperglycemia. Because atherosclerosis-related cardiovascular diseases are the major complications of diabetes, it is important to determine the effect of DPP-4 inhibitors on atherosclerosis. In this study, nondiabetic and diabetic apolipoprotein E-deficient mice were treated with DPP-4 inhibitor alogliptin for 24 weeks, and atherosclerotic lesions in aortic origins were examined. Results showed that diabetes significantly increased atherosclerotic lesions, but alogliptin treatment reduced atherosclerotic lesions in diabetic mice. Metabolic studies showed that diabetes increased plasma glucose and that alogliptin treatment reduced glucose. Furthermore, immunohistochemistry study showed that diabetes increased interleukin-6 (IL-6) and IL-1β protein expression in atherosclerotic plaques, but alogliptin treatment attenuated diabetes-augmented IL-6 and IL-1β expression. In consistence with the observations from the mouse models, our in vitro studies showed that alogliptin-inhibited toll-like receptor 4 (TLR-4)-mediated upregulation of IL-6, IL-1β, and other proinflammatory cytokines by mononuclear cells. Taken together, our findings showed that alogliptin-inhibited atherosclerosis in diabetic apolipoprotein E-deficient mice and that the actions of alogliptin on both glucose and inflammation may contribute to the inhibition.


title: Estimating county health statistics with twitter

abstract: Understanding the relationships among environment, behavior, and health is a core concern of public health researchers. While a number of recent studies have investigated the use of social media to track infectious diseases such as influenza, little work has been done to determine if other health concerns can be inferred. In this paper, we present a large-scale study of 27 health-related statistics, including obesity, health insurance coverage, access to healthy foods, and teen birth rates. We perform a linguistic analysis of the Twitter activity in the top 100 most populous counties in the U.S., and find a significant correlation with 6 of the 27 health statistics. When compared to traditional models based on demographic variables alone, we find that augmenting models with Twitter-derived information improves predictive accuracy for 20 of 27 statistics, suggesting that this new methodology can complement existing approaches.


title: Clustering improved grid map registration using the normal distribution transform

abstract: Grid map registration is an important field in mobile robotics. Applications in which multiple robots are involved benefit from multiple aligned grid maps as they provide an efficient exploration of the environment in parallel. In this paper, a normal distribution transform (NDT)-based approach for grid map registration is presented. For simultaneous mapping and localization approaches on laser data, the NDT is widely used to align new laser scans to reference scans. The original grid quantization-based NDT results in good registration performances but has poor convergence properties due to discontinuities of the optimization function and absolute grid resolution. This paper shows that clustering techniques overcome disadvantages of the original NDT by significantly improving the convergence basin for aligning grid maps. A multi-scale clustering method results in an improved registration performance which is shown on real world experiments on radar data.


A Neural Chatbot with Personality


title: The ACL Anthology Network

abstract: We introduce the ACL Anthology Network (AAN), a manually curated networked database of citations, collaborations, and summaries in the field of Computational Linguistics. We also present a number of statistics about the network including the most cited authors, the most central collaborators, as well as network statistics about the paper citation, author citation, and author collaboration networks.


title: Effective Large-Scale Online Influence Maximization

abstract: In this paper, we study a highly generic version of influence maximization (IM), one of optimizing influence campaigns by sequentially selecting "spread seeds" from a set of candidates, a small subset of the node population, under the hypothesis that, in a given campaign, previously activated nodes remain "persistently" active throughout and thus do not yield further rewards. We call this problem online influence maximization with persistence. We introduce an estimator on the candidates' missing mass – the expected number of nodes that can still be reached from a given seed candidate – and justify its strength to rapidly estimate the desired value. We then describe a novel algorithm, GT-UCB, relying on upper confidence bounds on the missing mass. We show that our approach leads to high-quality spreads on classic IM datasets, even though it makes almost no assumptions on the diffusion medium. Importantly, it is orders of magnitude faster than state-of-the-art IM methods.


title: Multi class adult image classification using neural networks

abstract: As the Internet became popular, the volume of digital multimedia data is exponentially increased in all aspects of our life This drastic increment in multimedia data causes unwelcome deliveries of adult image contents to the Internet Consequently, a large number of children are wide-open to these harmful contents In this paper, we propose an efficient classification system that can categorize the images into multiple classes such as swimming suit, topless, nude, sexual act, and normal The experiment shows that this system achieved more than 80% of the success rate Thus, the proposed system can be used as a framework for web contents rating systems.


title: Bahía Engaño: a north Patagonian nursery area for the smoothhound shark Mustelus schmitti (Carcharhiniformes: Triakidae)

abstract: Mustelus schmitti (Carcharhiniformes: Triakidae) studied in the present work were caught incidentally in the shrimp fishery of the south of Bahia Engano (Patagonia, Argentina). This bay is a shallow water, high productivity coastal area where shrimps and small fish are abundant throughout the warmer months. Young of M. schmitti are found in the bay from late spring to mid autumn. The analysis of stomach contents showed that the young feed mainly on crustaceans; the shrimp Artemesia longinaris being the main prey item. The bay serves as a nursery area for M. schmitti , and the length range and umbilical scars present in all the specimens studied revealed that the individuals were newborns. At Bahia Engano, fishing trawls in the intertidal and surf zone are common, and ∼80% of the total number of species caught is considered as by-catch. The newborns and juvenile M. schmitti are an important element of this by-catch. Since the reproductive potential of the smoothhound shark is low, the protection of these newborns and juveniles from fishing pressure is of great importance.


title: Chinese Registry of rheumatoid arthritis (CREDIT): II. prevalence and risk factors of major comorbidities in Chinese patients with rheumatoid arthritis

abstract: BACKGROUND ::: Rheumatoid arthritis patients are at higher risk of developing comorbidities. The main objective of this study was to evaluate the prevalence of major comorbidities in Chinese rheumatoid arthritis patients. We also aimed to identify factors associated with these comorbidities. ::: ::: ::: METHODS ::: Baseline demographic, clinical characteristics and comorbidity data from RA patients enrolled in the Chinese Registry of rhEumatoiD arthrITis (CREDIT) from Nov 2016 to August 2017 were presented and compared with those from five other registries across the world. Possible factors related to three major comorbidities (cardiovascular disease, fragility fracture and malignancy) were identified using multivariate logistic regression analyses. ::: ::: ::: RESULTS ::: A total of 13,210 RA patients were included (80.6% female, mean age 52.9 years and median RA duration 4.0 years). Baseline prevalence rates of major comorbidities were calculated: CVD, 2.2% (95% CI 2.0-2.5%); fragility fracture, 1.7% (95% CI 1.5-1.9%); malignancy, 0.6% (95% CI 0.5-0.7%); overall major comorbidities, 4.2% (95% CI 3.9-4.6%). Advanced age was associated with all comorbidities. Male gender and disease duration were positively related to CVD. Female sex and longer disease duration were potential risk factors for fragility fractures. Ever use of methotrexate (MTX) was negatively related to baseline comorbidities. ::: ::: ::: CONCLUSIONS ::: Patients with rheumatoid arthritis in China have similar prevalence of comorbidities with other Asian countries. Advanced age and long disease duration are possible risk factors for comorbidities. On the contrary, MTX may protect RA patients from several major comorbidities, supporting its central role in the management of rheumatoid arthritis.


title: Novel paradigms for advanced distribution grid energy management

abstract: The electricity distribution grid was not designed to cope with load dynamics imposed by high penetration of electric vehicles, neither to deal with the increasing deployment of distributed Renewable Energy Sources. Distribution System Operators (DSO) will increasingly rely on flexible Distributed Energy Resources (flexible loads, controllable generation and storage) to keep the grid stable and to ensure quality of supply. In order to properly integrate demand-side flexibility, DSOs need new energy management architectures, capable of fostering collaboration with wholesale market actors and pro-sumers. We propose the creation of Virtual Distribution Grids (VDG) over a common physical infrastructure , to cope with heterogeneity of resources and actors, and with the increasing complexity of distribution grid management and related resources allocation problems. Focusing on residential VDG, we propose an agent-based hierarchical architecture for providing Demand-Side Management services through a market-based approach, where households transact their surplus/lack of energy and their flexibility with neighbours, aggregators, utilities and DSOs. For implementing the overall solution, we consider fine-grained control of smart homes based on Inter-net of Things technology. Homes seamlessly transact self-enforcing smart contracts over a blockchain-based generic platform. Finally, we extend the architecture to solve existing problems on smart home control, beyond energy management.


title: Clinical Concepts Emerging from fMRI Functional Connectomics

abstract: Recent advances in connectomics have led to a synthesis of perspectives regarding the brain's functional organization that reconciles classical concepts of localized specialization with an appreciation for properties that emerge from interactions across distributed functional networks. This provides a more comprehensive framework for understanding neural mechanisms of normal cognition and disease. Although fMRI has not become a routine clinical tool, research has already had important influences on clinical concepts guiding diagnosis and patient management. Here we review illustrative examples. Studies demonstrating the network plasticity possible in adults and the global consequences of even focal brain injuries or disease both have had substantial impact on modern concepts of disease evolution and expression. Applications of functional connectomics in studies of clinical populations are challenging traditional disease classifications and helping to clarify biological relationships between clinical syndromes (and thus also ways of extending indications for, or "re-purposing," current treatments). Large datasets from prospective, longitudinal studies promise to enable the discovery and validation of functional connectomic biomarkers with the potential to identify people at high risk of disease before clinical onset, at a time when treatments may be most effective. Studies of pain and consciousness have catalyzed reconsiderations of approaches to clinical management, but also have stimulated debate about the clinical meaningfulness of differences in internal perceptual or cognitive states inferred from functional connectomics or other physiological correlates. By way of a closing summary, we offer a personal view of immediate challenges and potential opportunities for clinically relevant applications of fMRI-based functional connectomics.


title: Knowledge sharing in virtual communities – a review of the empirical research

abstract: This study reviews the recent empirical studies on knowledge sharing in Virtual Communities (VCs). The paper begins with an analysis of the VC conceptualisation and the focal phenomenon of knowledge sharing. Secondly, the factors that seem to facilitate knowledge-sharing activities in VCs are identified and categorised as individual motivations, personal characteristics, technical attributes and community-level social capital. Overall, the results demonstrate a strong emphasis on why individuals engage in such activities, but less attention is given to what is being shared and how the processes of sharing are manifested in practice. The paper concludes with some suggestions for further research.


title: Modeling the stimulators of the behavioral intention to use mobile entertainment: Does gender really matter?

abstract: Abstract This research aims to empirically examine the stimulators that influence consumers’ behavioral intention to use (IU) mobile entertainment (ME) in Malaysia. The proposed stimulators are perceived usefulness (PU), perceived ease of use (PEOU), social influence (SI), perceived self-efficacy (PSE) and perceived enjoyment (PE). PU and PEOU were derived from TAM, SI was taken from the TRA, TPB and DOI model, PE was obtained from the extended-TAM model and PSE was taken from Bandura’s theory. This is among the first study that uses a model consisting of integrated constructs from TAM, extended-TAM, TRA, TPB, DOI and Bandura’s theory in predicting acceptance of ME. Empirical data were analyzed by employing Structural Equation Modeling (SEM) analysis. Gender moderating effect was examined via multiple group analysis (MGA). The findings revealed that PU, PEOU, SI and PE are positively associated with consumer IU of ME. Surprisingly, there were no significant moderating effects of gender. The control variables (i.e. age, marital status, education level, number of mobile phone and experience) were found to have no confounding effects on the ME adoption. The findings have contributed theoretical and managerial implications to ME providers, mobile phone manufacturers, the music, movie and gaming industry players.


title: Local Binary Patterns and Extreme Learning Machine for Hyperspectral Imagery Classification

abstract: It is of great interest in exploiting texture information for classification of hyperspectral imagery (HSI) at high spatial resolution. In this paper, a classification paradigm to exploit rich texture information of HSI is proposed. The proposed framework employs local binary patterns (LBPs) to extract local image features, such as edges, corners, and spots. Two levels of fusion (i.e., feature-level fusion and decision-level fusion) are applied to the extracted LBP features along with global Gabor features and original spectral features, where feature-level fusion involves concatenation of multiple features before the pattern classification process while decision-level fusion performs on probability outputs of each individual classification pipeline and soft-decision fusion rule is adopted to merge results from the classifier ensemble. Moreover, the efficient extreme learning machine with a very simple structure is employed as the classifier. Experimental results on several HSI data sets demonstrate that the proposed framework is superior to some traditional alternatives.


title: Color QR Codes: Increased Capacity Via Per-Channel Data Encoding and Interference Cancellation

abstract: Monochrome 2-D barcodes have recently become extremely popular in mobile imaging applications. To accommodate higher data rates, we present a methodology for extending these barcodes to color. We use independent per channel data encoding in cyan, magenta, and yellow print colorant channels and decode the information in the complementary red, green, and blue channels used in capture, effectively increasing the data rate by a factor of three. To overcome unavoidable inter-channel interference, our framework utilizesadaptive thresholding and model-based interference cancellation at the decoder which are facilitated by an intelligent barcode design that allows estimation of parameters required for thresholding and cancellation. We present the framework by constructing and evaluating the performance for a color version of the QR code, the most commonly deployed monochrome barcode in mobile applications. Experimental results demonstrate that the framework is effective and allows recovery of embedded data with low bit error rates, which can be readily handled by the builtin error correction withinthe QRcode standard.


Beyond Sentential Semantic Parsing: Tackling the Math SAT with a Cascade of Tree Transducers.


title: Students ’ Dropout Risk Assessment in Undergraduate Courses of ICT at Residential University – A Case Study

abstract: The present case study describes the results of the educational data mining aimed at predicting the undergraduate courses of computer science (BCA and B.Tech.) students’ instant dropout or after first semester. For this purpose, the preliminary data of 220 students collected randomly in prescheduled format on personal interview to find out dropout rates and dropout reasons. The simple and intuitive classifiers (decision trees) ID3 and J48 were used in this paper. The main reason recorded for dropout of students at this residential university were personal factor viz; illness & homesickness, Educational factors viz; learning problems & difficult courses, change of Institution with present goal and low placement rate and institutional factors such as campus environment, too many rules in hostel life and poor entertainment facilities. The information generated will be useful for better planning and implementation of educational program and infrastructure under measurable condition to increase the enrollment rate of students in ICT courses at this university.


title: Recurrent neural network training with preconditioned stochastic gradient descent

abstract: This paper studies the performance of a recently proposed preconditioned stochastic gradient descent (PSGD) algorithm on recurrent neural network (RNN) training. PSGD adaptively estimates a preconditioner to accelerate gradient descent, and is designed to be simple, general and easy to use, as stochastic gradient descent (SGD). RNNs, especially the ones requiring extremely long term memories, are difficult to train. We have tested PSGD on a set of synthetic pathological RNN learning problems and the real world MNIST handwritten digit recognition task. Experimental results suggest that PSGD is able to achieve highly competitive performance without using any trick like preprocessing, pretraining or parameter tweaking.


title: Large Scale Data Mining: Challenges and Responses

abstract: Data mining over large data-sets is important due to its obvious commercial potential. However, it is also a major challenge due to its computational complexity. Exploiting the inherent parallelism of data mining algorithms provides a direct solution by utilising the large data retrieval and processing power of parallel architectures. In this paper, we present some results of our intensive research on parallelising data mining algorithms. In particular, we also present a methodology for determining the proper parallelisatlon strategy based on the idea of algorithmic skeletons and performance modelling. This research aims to provide a systematic way to develop parallel data mining algorithms and applications.


Poster : Low-latency blockchain consensus


title: JSON data management: supporting schema-less development in RDBMS

abstract: Relational Database Management Systems (RDBMS) have been very successful at managing structured data with well-defined schemas. Despite this, relational systems are generally not the first choice for management of data where schemas are not pre-defined or must be flexible in the face of variations and changes. Instead, No-SQL database systems supporting JSON are often selected to provide persistence to such applications. JSON is a light-weight and flexible semi-structured data format supporting constructs common in most programming languages. In this paper, we analyze the way in which requirements differ between management of relational data and management of JSON data. We present three architectural principles that facilitate a schema-less development style within an RDBMS so that RDBMS users can store, query, and index JSON data without requiring schemas. We show how these three principles can be applied to industry-leading RDBMS platforms, such as the Oracle RDBMS Server, with relatively little effort. Consequently, an RDBMS can unify the management of both relational data and JSON data in one platform and use SQL with an embedded JSON path language as a single declarative language to query both relational data and JSON data. This SQL/JSON approach offers significant benefits to application developers as they can use one product to manage both relational data and semi-structured flexible schema data.


title: Genetic Algorithms for Evolving Deep Neural Networks

abstract: In recent years, deep learning methods applying unsupervised learning to train deep layers of neural networks have achieved remarkable results in numerous fields. In the past, many genetic algorithms based methods have been successfully applied to training neural networks. In this paper, we extend previous work and propose a GA-assisted method for deep learning. Our experimental results indicate that this GA-assisted approach improves the performance of a deep autoencoder, producing a sparser neural network.


title: Automatic semantic modeling of indoor scenes from low-quality RGB-D data using contextual information

abstract: We present a novel solution to automatic semantic modeling of indoor scenes from a sparse set of low-quality RGB-D images. Such data presents challenges due to noise, low resolution, occlusion and missing depth information. We exploit the knowledge in a scene database containing 100s of indoor scenes with over 10,000 manually segmented and labeled mesh models of objects. In seconds, we output a visually plausible 3D scene, adapting these models and their parts to fit the input scans. Contextual relationships learned from the database are used to constrain reconstruction, ensuring semantic compatibility between both object models and parts. Small objects and objects with incomplete depth information which are difficult to recover reliably are processed with a two-stage approach. Major objects are recognized first, providing a known scene structure. 2D contour-based model retrieval is then used to recover smaller objects. Evaluations using our own data and two public datasets show that our approach can model typical real-world indoor scenes efficiently and robustly.


title: Reducing Task Completion Time in Mobile Offloading Systems through Online Adaptive Local Restart

abstract: Offloading is an advanced technique to improve the performance of mobile devices. In a mobile offloading system, heavy computations are migrated from resource constrained mobile devices to powerful cloud servers through a wireless network connection. The unreliable wireless network often disturbs system operation. Task completion can be delayed or interrupted by congestion or packet loss in the network. To deal with this problem the offloaded jobs can be locally restarted and completed in the mobile device itself. In this paper, we propose a dynamic scheme to determine whether and when to locally restart a task. First, we design an experiment to explore the impact of packet loss and delay in unreliable networks on the completion time of an offloading task. Then, we mathematically derive the prerequisites for local restart and selection of the optimal timeout. The analysis result confirms that local restart is beneficial when the distribution of task completion time has high variance. Further, a dynamic local restart scheme is proposed for mobile applications. This scheme keeps track of the variance of the probability density function of the distribution of task completion time. This is done using a dynamic histogram, which collects and updates data at run time. The efficiency of the local restart scheme is confirmed by experimental results. The experiment shows that local restart at the right time achieves better performance than always offloading.


title: TurkDeck: Physical Virtual Reality Based on People

abstract: TurkDeck is an immersive virtual reality system that reproduces not only what users see and hear, but also what users feel. TurkDeck produces the haptic sensation using props, i.e., when users touch or manipulate an object in the virtual world, they simultaneously also touch or manipulate a corresponding object in the physical world. Unlike previous work on prop-based virtual reality, however, TurkDeck allows creating arbitrarily large virtual worlds in finite space and using a finite set of physical props. The key idea behind TurkDeck is that it creates these physical representations on the fly by making a group of human workers present and operate the props only when and where the user can actually reach them. TurkDeck manages these so-called "human actuators" by displaying visual instructions that tell the human actuators when and where to place props and how to actuate them. We demonstrate TurkDeck at the example of an immersive 300m2 experience in 25m2 physical space. We show how to simulate a wide range of physical objects and effects, including walls, doors, ledges, steps, beams, switches, stompers, portals, zip lines, and wind. In a user study, participants rated the realism/immersion of TurkDeck higher than a traditional prop-less baseline condition (4.9 vs. 3.6 on 7 item Likert).


title: Agent-Based Interactions and Economic Encounters in an Intelligent InterCloud

abstract: An InterCloud is an interconnected global “cloud of clouds” that enables each cloud to tap into resources of other clouds. This is the earliest work to devise an agent-based InterCloud economic model for analyzing consumer-to-cloud and cloud-to-cloud interactions. While economic encounters between consumers and cloud providers are modeled as a many-to-many negotiation, economic encounters among clouds are modeled as a coalition game. To bolster many-to-many consumer-to-cloud negotiations, this work devises a novel interaction protocol and a novel negotiation strategy that is characterized by both 1) adaptive concession rate ( ACR ) and 2) minimally sufficient concession ( MSC ). Mathematical proofs show that agents adopting the ACR - MSC strategy negotiate optimally because they make minimum amounts of concession. By automatically controlling concession rates, empirical results show that the ACR - MSC strategy is efficient because it achieves significantly higher utilities than the fixed-concession-rate time-dependent strategy. To facilitate the formation of InterCloud coalitions, this work devises a novel four-stage cloud-to-cloud interaction protocol and a set of novel strategies for InterCloud agents. Mathematical proofs show that these InterCloud coalition formation strategies 1) converge to a subgame perfect equilibrium and 2) result in every cloud agent in an InterCloud coalition receiving a payoff that is equal to its Shapley value .


Reply to ‘A lack of evidence for six times more anxiety and depression in US graduate students than in the general population’


Complementary Split Ring Resonator Based Triple Band Microstrip Antenna for WLAN/WiMAX Applications


title: Science, technology and the future of small autonomous drones

abstract: We are witnessing the advent of a new era of robots - drones - that can autonomously fly in natural and man-made environments. These robots, often associated with defence applications, could have a major impact on civilian tasks, including transportation, communication, agriculture, disaster mitigation and environment preservation. Autonomous flight in confined spaces presents great scientific and technical challenges owing to the energetic cost of staying airborne and to the perceptual intelligence required to negotiate complex environments. We identify scientific and technological advances that are expected to translate, within appropriate regulatory frameworks, into pervasive use of autonomous drones for civilian applications.


title: Entity Extraction via Ensemble Semantics

abstract: Combining information extraction systems yields significantly higher quality resources than each system in isolation. In this paper, we generalize such a mixing of sources and features in a framework called Ensemble Semantics. We show very large gains in entity extraction by combining state-of-the-art distributional and pattern-based systems with a large set of features from a webcrawl, query logs, and Wikipedia. Experimental results on a web-scale extraction of actors, athletes and musicians show significantly higher mean average precision scores (29% gain) compared with the current state of the art.


A Machine Learning-Based Design Representation Method for Designing Heterogeneous Microstructures


title: Semantic Facial Expression Editing using Autoencoded Flow

abstract: High-level manipulation of facial expressions in images --- such as changing a smile to a neutral expression --- is challenging because facial expression changes are highly non-linear, and vary depending on the appearance of the face. We present a fully automatic approach to editing faces that combines the advantages of flow-based face manipulation with the more recent generative capabilities of Variational Autoencoders (VAEs). During training, our model learns to encode the flow from one expression to another over a low-dimensional latent space. At test time, expression editing can be done simply using latent vector arithmetic. We evaluate our methods on two applications: 1) single-image facial expression editing, and 2) facial expression interpolation between two images. We demonstrate that our method generates images of higher perceptual quality than previous VAE and flow-based methods.


title: Economic development, urban expansion, and sustainable development in Shanghai

abstract: Studies of urbanization effects in Chinese cities from the aspect of the coupled development of economy and environment are rare due to data limitations. This paper studied Shanghai’s fast urban expansion and examined the dynamic relationship between economic growth and environment consequences at the district level. We extracted data on urban built-up area and land surface temperature from remote sensing images. We analyzed the patterns of urban expansion and land use change and explained the dynamic relationship between economic development and environment conditions. We attributed the uneven economic development and environmental change in districts of Shanghai to four main institutional factors: (1) the role of the government, (2) the multi-level urban planning system, (3) land market reform, and (4) the economic restructuring.


title: Password-authenticated key exchange based on RSA

abstract: There have been many proposals in recent years for password-authenticated key exchange protocols, i.e., protocols in which two parties who share only a short secret password perform a key exchange authenticated with the password. However, the only ones that have been proven secured against offline dictionary attacks were based on Diffie–Hellman key exchange. We examine how to design a secure password-authenticated key exchange protocol based on RSA. In this paper, we first look at the OKE and protected-OKE protocols (both RSA-based) and show that they are insecure. Then we show how to modify the OKE protocol to obtain a password-authenticated key exchange protocol that can be proven secure (in the random oracle model). This protocol is very practical; in fact, it requires about the same amount of computation as the Diffie–Hellman-based protocols. Finally, we present an augmented protocol that is resilient to server compromise, meaning (informally) that an attacker who compromises a server would not be able to impersonate a client, at least not without running an offline dictionary attack against that client’s password.


title: Precise vehicle localization in dense urban environments

abstract: In this contribution we introduce a framework for precise vehicle localization in dense urban environments which are characterized by high rates of dynamic and semi-static objects. The proposed localization method is specifically designed to handle inconsistencies between map material and sensor measurements. This is achieved by means of a robust map matching procedure based on the Fourier-Mellin transformation (FMT) for global vehicle pose estimation. Accurate and reliable relative localization is obtained from a LiDAR odometry. Consistency checks based on the cumulative sum (CUSUM) test are instrumented for rejection of inconsistent map matching results from the fusion procedure. Our key contributions are: i) Introduction and adaptation of a spectral map matching procedure based on the FMT for urban automated driving, ii) Presentation of a framework for efficient and robust localization in dense urban environments based on a novel LiDAR odometry, map matching, wheel odometry and GPS, iii) Proposal of a procedure for localization integrity monitoring which leads to significantly increased pose estimation accuracy. Evaluation results show the superior performance of the proposed approach compared to another state-of-the-art localization algorithm for a challenging urban dataset. All maps were recorded two years in advance of the evaluation test run. Furthermore, different LiDAR-based sensor setups were used for mapping and localization.


title: Gyrophone: recognizing speech from gyroscope signals

abstract: We show that the MEMS gyroscopes found on modern smart phones are sufficiently sensitive to measure acoustic signals in the vicinity of the phone. The resulting signals contain only very low-frequency information (<200Hz). Nevertheless we show, using signal processing and machine learning, that this information is sufficient to identify speaker information and even parse speech. Since iOS and Android require no special permissions to access the gyro, our results show that apps and active web content that cannot access the microphone can nevertheless eavesdrop on speech in the vicinity of the phone.


title: Imputation-boosted collaborative filtering using machine learning classifiers

abstract: As data sparsity remains a significant challenge for collaborative filtering (CF, we conjecture that predicted ratings based on imputed data may be more accurate than those based on the originally very sparse rating data. In this paper, we propose a framework of imputation-boosted collaborative filtering (IBCF), which first uses an imputation technique, or perhaps machine learned classifier, to fill-in the sparse user-item rating matrix, then runs a traditional Pearson correlation-based CF algorithm on this matrix to predict a novel rating. Empirical results show that IBCF using machine learning classifiers can improve predictive accuracy of CF tasks. In particular, IBCF using a classifier capable of dealing well with missing data, such as naive Bayes, can outperform the content-boosted CF (a representative hybrid CF algorithm) and IBCF using PMM (predictive mean matching, a state-of-the-art imputation technique), without using external content information.


title: Brexit, Trump, and ‘methodological whiteness’: on the misrecognition of race and class

abstract: The rhetoric of both the Brexit and Trump campaigns was grounded in conceptions of the past as the basis for political claims in the present. Both established the past as constituted by nations that were represented as ‘white’ into which racialized others had insinuated themselves and gained disproportionate advantage. Hence, the resonant claim that was broadcast primarily to white audiences in each place ‘to take our country back’. The politics of both campaigns was also echoed in those social scientific analyses that sought to focus on the ‘legitimate’ claims of the ‘left behind’ or those who had come to see themselves as ‘strangers in their own land’. The skewing of white majority political action as the action of a more narrowly defined white working class served to legitimize analyses that might otherwise have been regarded as racist. In effect, I argue that a pervasive ‘methodological whiteness’ has distorted social scientific accounts of both Brexit and Trump’s election victory and that this needs to be taken account of in our discussion of both phenomena.


Choosing a Subset Regression


title: Adaptation algorithm for adaptive streaming over HTTP

abstract: Internet video makes up a significant part of the Internet traffic and its fraction is constantly growing. In order to guarantee best user experience throughout different network access technologies with dynamically varying network conditions, it is fundamental to adopt technologies enabling a proper delivery of the media content. One of such technologies is adaptive streaming. It allows to dynamically adapt the bit-rate of the stream to varying network conditions. There are various approaches to adaptive streaming. In our work, we focus on the receiver-driven approach where the media file is subdivided into segments, each of the segments is provided at multiple bit-rates, and the task of the client is to select the appropriate bit-rate for each of the segments. With this approach, the challenges are (i) to properly estimate the dynamics of the available network throughput, (ii) to control the filling level of the client buffer in order to avoid underflows resulting in playback interruptions, (iii) to maximize the quality of the stream, while avoiding unnecessary quality fluctuations, and, finally, (iv) to minimize the delay between the user's request and the start of the playback. During our work, we designed and implemented a receiver-driven adaptation algorithm for adaptive streaming that does not rely on cross-layer information or server assistance. We integrated the algorithm with a prototype implementation of a streaming client based on the MPEG DASH (Dynamic Adaptive Streaming over HTTP) standard. We evaluated the implemented prototype in real-world scenarios and found that it performes remarkably well even under challenging network conditions. Further, it exhibits stable and fair operation if a common link is shared among multiple clients.


title: Policy development and implementation in health promotion--from theory to practice: the ADEPT model.

abstract: SUMMARY There is a growing interest among health promotion researchers to better understand and influence the policy process. However, at this point in time, theoretical concepts enabling researchers to do so are still rare and underused, suggesting a need for new, easy-to-use concepts to explain successes or failures of health promotion policies. This article presents the ADEPT (Analysis of Determinants of Policy Impact) approach, which aims to explain and influence policy development and policy impact implementation with four determinants: goals, obligations, resources and opportunities. ADEPT provides a detailed operationalization for both quantitative and qualitative use. An empirical test of the ADEPT model using a quantitative survey of 719 policy-makers from four health promotion policy fields and six European nations indicated that both policy outputs and policy outcomes are influenced by the four determinants. The approach has, in the meantime, been successfully utilized to analyze and initiate policy development in a number of health promotion projects. Despite a number of limitations, ADEPT provides an easy-to-use, theory-based and parsimonious tool for understanding and influencing policy processes in health promotion. Moreover, as it identifies potential ‘levers of influence’ and can easily be connected to existing methods of community development or capacity building, it is a particularly powerful tool for policy development.


title: ON THE QUANTUM MECHANICAL WAVE FUNCTION AS A LINK BETWEEN COGNITION AND THE PHYSICAL WORLD: A ROLE FOR PSYCHOLOGY

abstract: A straightforward explanation of fundamental tenets of quantum mechanics concerning the wave function results in the thesis that the quantum mechanical wave function is a link between human cognition and the physical world. The reticence on the part of physicists to adopt this thesis is discussed. A comparison is made to the behaviorists' consideration of mind, and the historical roots of how the problem concerning the quantum mechanical wave function arose are discussed. The basis for an empirical demonstration that the wave function is a link between human cognition and the physical world is provided through developing an experiment using methodology from psychology and physics. Based on research in psychology and physics that relied on this methodology, it is likely that Einstein, Podolsky, and Rosen's theoretical result that mutually exclusive wave functions can simultaneously apply to the same concrete physical circumstances can be implemented on an empirical level.


title: Pathological narcissism and narcissistic personality disorder.

abstract: We review the literature on pathological narcissism and narcissistic personality disorder (NPD) and describe a significant criterion problem related to four inconsistencies in phenotypic descriptions and taxonomic models across clinical theory, research, and practice; psychiatric diagnosis; and social/personality psychology. This impedes scientific synthesis, weakens narcissism's nomological net, and contributes to a discrepancy between low prevalence rates of NPD and higher rates of practitioner-diagnosed pathological narcissism, along with an enormous clinical literature on narcissistic disturbances. Criterion issues must be resolved, including clarification of the nature of normal and pathological narcissism, incorporation of the two broad phenotypic themes of narcissistic grandiosity and narcissistic vulnerability into revised diagnostic criteria and assessment instruments, elimination of references to overt and covert narcissism that reify these modes of expression as distinct narcissistic types, and...


title: Design considerations for a wireless sensor network for locating parking spaces

abstract: This paper considers the design of a real time parking space locating system using a network of wireless sensor nodes that are equipped with magnetic sensors. Considerations for designing a reliable detection scheme for cars using magnetic signatures obtained by the wireless sensors are presented. All design considerations are derived from experimental data obtained from a campus parking garage. Results indicate that the proposed detection algorithm can effectively detect a wide range of passing vehicles with small error probability.


title: A measure of regularity for polygonal mosaics in biological systems

abstract: BackgroundThe quantification of the spatial order of biological patterns or mosaics provides useful information as many properties are determined by the spatial distribution of their constituent elements. These are usually characterised by methods based on nearest neighbours distances, by the number of sides of cells, or by angles defined by the adjacent cells.MethodsA measure of regularity in polygonal mosaics of different kinds in biological systems is proposed. It is based on the condition of eutacticity, expressed in terms of eutactic stars, which is closely related to regularity of polytopes. Thus it constitutes a natural measure of regularity. The proposed measure is tested with numerical and real data. Numerically is tested with a hexagonal lattice that is distorted progressively and with a non-periodic regular tiling. With real data, the distribution of oak trees in forests from three locations in the State of Querétaro, Mexico, and the spiral pattern of florets in a flowering plant are characterised.ResultsThe proposed measure performs well and as expected while tested with a numerical experiment, as well as when applied to a known non-periodic tiling of the plane. Concerning real data, the measure is sensitive to the degree of perturbation observed in the distribution of oak trees and detects high regularity in a phyllotactic pattern studied.ConclusionsThe measure here proposed has a clear geometrical meaning, establishing what regularity means, and constitute an advantageous general purposes alternative to analyse spatial distributions, capable to indicate the degree of regularity of a mosaic or an array of points.


title: Design implications for enterprise storage systems via multi-dimensional trace analysis

abstract: Enterprise storage systems are facing enormous challenges due to increasing growth and heterogeneity of the data stored. Designing future storage systems requires comprehensive insights that existing trace analysis methods are ill-equipped to supply. In this paper, we seek to provide such insights by using a new methodology that leverages an objective, multi-dimensional statistical technique to extract data access patterns from network storage system traces. We apply our method on two large-scale real-world production network storage system traces to obtain comprehensive access patterns and design insights at user, application, file, and directory levels. We derive simple, easily implementable, threshold-based design optimizations that enable efficient data placement and capacity optimization strategies for servers, consolidation policies for clients, and improved caching performance for both.


title: Efficient iris recognition by characterizing key local variations

abstract: Unlike other biometrics such as fingerprints and face, the distinct aspect of iris comes from randomly distributed features. This leads to its high reliability for personal identification, and at the same time, the difficulty in effectively representing such details in an image. This paper describes an efficient algorithm for iris recognition by characterizing key local variations. The basic idea is that local sharp variation points, denoting the appearing or vanishing of an important image structure, are utilized to represent the characteristics of the iris. The whole procedure of feature extraction includes two steps: 1) a set of one-dimensional intensity signals is constructed to effectively characterize the most important information of the original two-dimensional image; 2) using a particular class of wavelets, a position sequence of local sharp variation points in such signals is recorded as features. We also present a fast matching scheme based on exclusive OR operation to compute the similarity between a pair of position sequences. Experimental results on 2 255 iris images show that the performance of the proposed method is encouraging and comparable to the best iris recognition algorithm found in the current literature.


title: Employee Retention: A Review of Literature

abstract: Human resources are the life-blood of any organization. Even though most of the organizations are now a days, found to be technology driven, yet human resources are required to run the technology. They are the most vital and dynamic resources of any organization. With all round development in each and every area of the economy, there is stiff competition in the market. With this development and competition, there are lots and lots of avenues and opportunities available in the hands of the human resources. The biggest challenge that organizations are facing today is not only managing these resources but also retaining them. Securing and retaining skilled employees plays an important role for any organization, because employees' knowledge and skills are central to companies' ability to be economically competitive. Besides, continuously satisfying the employees is another challenge that the employers are facing today. Keeping into account the importance and sensitivity of the issue of retention to any organization, the present study tries to review the various available literature and research work on employee retention and the factors affecting employee retention and job satisfaction among the employees.


title: An Empirical Comparison of Four Text Mining Methods

abstract: The amount of textual data that is available for researchers and businesses to analyze is increasing at a dramatic rate. This reality has led IS researchers to investigate various text mining techniques. This essay examines four text mining methods that are frequently used in order to identify their advantages and limitations. The four methods that we examine are (1) latent semantic analysis, (2) probabilistic latent semantic analysis, (3) latent Dirichlet allocation, and (4) the correlated topic model. We compare these four methods and highlight the optimal conditions under which to apply the various methods. Our paper sheds light on the theory that underlies text mining methods and provides guidance for researchers who seek to apply these methods.


title: Convolutional Scale Invariance for Semantic Segmentation

abstract: We propose an effective technique to address large scale variation in images taken from a moving car by cross-breeding deep learning with stereo reconstruction. Our main contribution is a novel scale selection layer which extracts convolutional features at the scale which matches the corresponding reconstructed depth. The recovered scale-invariant representation disentangles appearance from scale and frees the pixel-level classifier from the need to learn the laws of the perspective. This results in improved segmentation results due to more efficient exploitation of representation capacity and training data. We perform experiments on two challenging stereoscopic datasets (KITTI and Cityscapes) and report competitive class-level IoU performance.


title: Discovering physical concepts with neural networks

abstract: Despite the success of neural networks at solving concrete physics problems, their use as a general-purpose tool for scientific discovery is still in its infancy. Here, we approach this problem by modeling a neural network architecture after the human physical reasoning process, which has similarities to representation learning. This allows us to make progress towards the long-term goal of machine-assisted scientific discovery from experimental data without making prior assumptions about the system. We apply this method to toy examples and show that the network finds the physically relevant parameters, exploits conservation laws to make predictions, and can help to gain conceptual insights, e.g., Copernicus' conclusion that the solar system is heliocentric.


title: Sensor fusion for semantic segmentation of urban scenes

abstract: Semantic understanding of environments is an important problem in robotics in general and intelligent autonomous systems in particular. In this paper, we propose a semantic segmentation algorithm which effectively fuses information from images and 3D point clouds. The proposed method incorporates information from multiple scales in an intuitive and effective manner. A late-fusion architecture is proposed to maximally leverage the training data in each modality. Finally, a pairwise Conditional Random Field (CRF) is used as a post-processing step to enforce spatial consistency in the structured prediction. The proposed algorithm is evaluated on the publicly available KITTI dataset [1] [2], augmented with additional pixel and point-wise semantic labels for building, sky, road, vegetation, sidewalk, car, pedestrian, cyclist, sign/pole, and fence regions. A per-pixel accuracy of 89.3% and average class accuracy of 65.4% is achieved, well above current state-of-the-art [3].


Attentional Alignment Networks


title: A Graph-CNN for 3D Point Cloud Classification

abstract: Graph convolutional neural networks (Graph-CNNs) extend traditional CNNs to handle data that is supported on a graph. Major challenges when working with data on graphs are that the support set (the vertices of the graph) do not typically have a natural ordering, and in general, the topology of the graph is not regular (i.e., vertices do not all have the same number of neighbors). Thus, Graph-CNNs have huge potential to deal with 3D point cloud data which has been obtained from sampling a manifold. In this paper, we develop a Graph-CNN for classifying 3D point cloud data, called PointGCN. The architecture combines localized graph convolutions with two types of graph downsampling operations (also known as pooling). By the effective exploration of the point cloud local structure using the Graph-CNN, the proposed architecture achieves competitive performance on the 3D object classification benchmark ModelNet, and our architecture is more stable than competing schemes.


title: Constant Effective Duty Cycle Control for Flying Capacitor Balancing in flying Capacitor Multi-Level Converters

abstract: One of the challenges of utilizing Flying Capacitor Multi-Level (FCML) converters is the flying capacitor balancing. Poorly balanced flying capacitors increase the switch voltage stress, which is detrimental to performance and device rating requirements. Previously, valley current detection was shown as a potential method to balance flying capacitors, but the method suffers from poor flying capacitor balancing performance at light load. Here, we investigate the light load conditions that lead to poor balancing and propose a new method, constant effective duty cycle (CEDC) compensation, which provides active balancing for the full load range of the converter. The proposed method is validated with an 4-level FCML experimental prototype, demonstrating excellent flying capacitor balancing over all load ranges and across multiple induced flying capacitor imbalances.


title: Domain Adaptation for Face Recognition: Targetize Source Domain Bridged by Common Subspace

abstract: In many applications, a face recognition model learned on a source domain but applied to a novel target domain degenerates even significantly due to the mismatch between the two domains. Aiming at learning a better face recognition model for the target domain, this paper proposes a simple but effective domain adaptation approach that transfers the supervision knowledge from a labeled source domain to the unlabeled target domain. Our basic idea is to convert the source domain images to target domain (termed as targetize the source domain hereinafter), and at the same time keep its supervision information. For this purpose, each source domain image is simply represented as a linear combination of sparse target domain neighbors in the image space, with the combination coefficients however learnt in a common subspace. The principle behind this strategy is that, the common knowledge is only favorable for accurate cross-domain reconstruction, but for the classification in the target domain, the specific knowledge of the target domain is also essential and thus should be mostly preserved (through targetization in the image space in this work). To discover the common knowledge, specifically, a common subspace is learnt, in which the structures of both domains are preserved and meanwhile the disparity of source and target domains is reduced. The proposed method is extensively evaluated under three face recognition scenarios, i.e., domain adaptation across view angle, domain adaptation across ethnicity and domain adaptation across imaging condition. The experimental results illustrate the superiority of our method over those competitive ones.


title: When Firms Change Direction

abstract: The authors advance a new theory of the firm that incorporates behavioural and economic theories with cognitive theory. The book makes a case for predicting strategic change in firms.


Design and Implementation of a Path Finding Robot Using Flood Fill Algorithm


title: The application of data mining techniques to support customer relationship management: the case of ethiopian revenue and customs authority

abstract: The application of data mining technique has been widely applied in different business areas such as health, education and finance for the purpose of data analysis and then to support and maximizes the organizations customer satisfaction in an effort to increase loyalty and retain customers business over their lifetimes . The researchers primary objective, in this paper is to classify customers based on their common attributes since customer grouping is the main part of customer relationship management. In this study, different characteristics of the ERCA customers data were collected from the customs database called ASYCUDA. Once the customers data were collected, the necessary data preparation steps were conducted on it and finally a data set consisting of 46748 records was attained. The classification modeling was built by using J48 decision tree and multi layer perceptron ANN algorithms with 10-fold cross-validation and splitting (70% training and 30% testing) techniques. Among these models, a model which was built using J48 decision tree algorithm with default 10-fold cross-validation outperforms 99.95% of overall accuracy rate; while the classification accuracy of ANN is 99.71%. So decision tree has better accuracy than ANN for classifying ERCA customers data.


title: A Privacy-Preserving and Copy-Deterrence Content-Based Image Retrieval Scheme in Cloud Computing

abstract: With the increasing importance of images in people’s daily life, content-based image retrieval (CBIR) has been widely studied. Compared with text documents, images consume much more storage space. Hence, its maintenance is considered to be a typical example for cloud storage outsourcing. For privacy-preserving purposes, sensitive images, such as medical and personal images, need to be encrypted before outsourcing, which makes the CBIR technologies in plaintext domain to be unusable. In this paper, we propose a scheme that supports CBIR over encrypted images without leaking the sensitive information to the cloud server. First, feature vectors are extracted to represent the corresponding images. After that, the pre-filter tables are constructed by locality-sensitive hashing to increase search efficiency. Moreover, the feature vectors are protected by the secure kNN algorithm, and image pixels are encrypted by a standard stream cipher. In addition, considering the case that the authorized query users may illegally copy and distribute the retrieved images to someone unauthorized, we propose a watermark-based protocol to deter such illegal distributions. In our watermark-based protocol, a unique watermark is directly embedded into the encrypted images by the cloud server before images are sent to the query user. Hence, when image copy is found, the unlawful query user who distributed the image can be traced by the watermark extraction. The security analysis and the experiments show the security and efficiency of the proposed scheme.


Design and Evaluation of an RDMA-aware Data Shuffling Operator for Parallel Database Systems Feilong


title: Narrative Planning: Balancing Plot and Character

abstract: Narrative, and in particular storytelling, is an important part of the human experience. Consequently, computational systems that can reason about narrative can be more effective communicators, entertainers, educators, and trainers. One of the central challenges in computational narrative reasoning is narrative generation, the automated creation of meaningful event sequences. There are many factors - logical and aesthetic - that contribute to the success of a narrative artifact. Central to this success is its understandability. We argue that the following two attributes of narratives are universal: (a) the logical causal progression of plot, and (b) character believability. Character believability is the perception by the audience that the actions performed by characters do not negatively impact the audience's suspension of disbelief. Specifically, characters must be perceived by the audience to be intentional agents. In this article, we explore the use of refinement search as a technique for solving the narrative generation problem - to find a sound and believable sequence of character actions that transforms an initial world state into a world state in which goal propositions hold. We describe a novel refinement search planning algorithm - the Intent-based Partial Order Causal Link (IPOCL) planner - that, in addition to creating causally sound plot progression, reasons about character intentionality by identifying possible character goals that explain their actions and creating plan structures that explain why those characters commit to their goals. We present the results of an empirical evaluation that demonstrates that narrative plans generated by the IPOCL algorithm support audience comprehension of character intentions better than plans generated by conventional partial-order planners.


title: Comparison of detrended fluctuation analysis and spectral analysis for heart rate variability in sleep and sleep apnea

abstract: Sleep has been regarded as a testing situation for the autonomic nervous system, because its activity is modulated by sleep stages. Sleep-related breathing disorders also influence the autonomic nervous system and can cause heart rate changes known as cyclical variation. We investigated the effect of sleep stages and sleep apnea on autonomic activity by analyzing heart rate variability (HRV). Since spectral analysis is suited for the identification of cyclical variations and detrended fluctuation analysis can analyze the scaling behavior and detect long-range correlations, we compared the results of both complementary techniques in 14 healthy subjects, 33 patients with moderate, and 31 patients with severe sleep apnea. The spectral parameters VLF, LF, HF, and LF/HF confirmed increasing parasympathetic activity from wakefulness and REM over light sleep to deep sleep, which is reduced in patients with sleep apnea. Discriminance analysis was used on a person and sleep stage basis to determine the best method for the separation of sleep stages and sleep apnea severity. Using spectral parameters 69.7% of the apnea severity assignments and 54.6% of the sleep stage assignments were correct, while using scaling analysis these numbers increased to 74.4% and 85.0%, respectively. We conclude that changes in HRV are better quantified by scaling analysis than by spectral analysis.


title: Peptide-functionalized nanoparticles for selective targeting of pancreatic tumor

abstract: Abstract Chemotherapy for pancreatic cancer is hampered by the tumor's physio-pathological complexity. Here we show a targeted nanomedicine using a new ligand, the CKAAKN peptide, which had been identified by phage display, as an efficient homing device within the pancreatic pathological microenvironment. Taking advantage of the squalenoylation platform, the CKAAKN peptide was conjugated to squalene (SQCKAAKN) and then co-nanoprecipitated with the squalenoyl prodrug of gemcitabine (SQdFdC) giving near monodisperse nanoparticles (NPs) for safe intravenous injection. By interacting with a novel target pathway, the Wnt-2, the CKAAKN functionalization enabled nanoparticles: (i) to specifically interact with both tumor cells and angiogenic vessels and (ii) to simultaneously promote pericyte coverage, thus leading to the normalization of the vasculature likely improving the tumor accessibility for therapy. All together, this approach represents a unique targeted nanoparticle design with remarkable selectivity towards pancreatic cancer and multiple mechanisms of action.


LD-VOWL: Extracting and Visualizing Schema Information for Linked Data Endpoints


title: An Anatomical Analysis of the Supratrochlear Artery: Considerations in Facial Filler Injections and Preventing Vision Loss

abstract: Background Embolia cutis medicamentosa (ECM) is a rare phenomenon attributed to intra-arterial drug injection. Glabellar filler injections can result in potentially devastating visual loss from inadvertent retrograde arteriolar embolization due to the extensive vasculature within the upper face. The minimum amount of filler necessary to potentiate this complication has not yet been reported. ::: ::: Objectives We aim to determine the volume of filler necessary to occupy the supratrochlear artery from the glabella to the bifurcation of the ophthalmic and central retinal arteries. We specifically examine the volume of the supratrochlear artery from the glabella to orbital apex. ::: ::: Methods The study was approved by Duke University Institutional Review Board and involved surgical dissection of six fresh tissue cadaver heads (12 hemifaces). The arterial system in each cadaver head was injected with latex for visualization. The supratrochlear arteries were isolated anteriorly from the glabella to the orbital apex posteriorly. Intra-orbital vessel radius and length were measured. The vessel volume was calculated by water displacement of the intra-arterial latex. ::: ::: Results The vessel volumes ranged from 0.04 to 0.12 mL. The average vessel volume was calculated as 0.085 mL, the average length as 51.75 mm, and the average radius as 0.72 mm. ::: ::: Conclusions Vascular occlusion from filler injections can lead to devastating visual consequences due to inadvertent retrograde intra-arterial embolization. Our findings indicate that the average entire volume of the supratrochlear artery from the glabella to the orbital apex is 0.085 mL. Injectors should be aware that a bolus of this critical volume may lead to a significant adverse outcome.


title: Structured Control Nets for Deep Reinforcement Learning

abstract: In recent years, Deep Reinforcement Learning has made impressive advances in solving several important benchmark problems for sequential decision making. Many control applications use a generic multilayer perceptron (MLP) for non-vision parts of the policy network. In this work, we propose a new neural network architecture for the policy network representation that is simple yet effective. The proposed Structured Control Net (SCN) splits the generic MLP into two separate sub-modules: a nonlinear control module and a linear control module. Intuitively, the nonlinear control is for forward-looking and global control, while the linear control stabilizes the local dynamics around the residual of global control. We hypothesize that this will bring together the benefits of both linear and nonlinear policies: improve training sample efficiency, final episodic reward, and generalization of learned policy, while requiring a smaller network and being generally applicable to different training methods. We validated our hypothesis with competitive results on simulations from OpenAI MuJoCo, Roboschool, Atari, and a custom 2D urban driving environment, with various ablation and generalization tests, trained with multiple black-box and policy gradient training methods. The proposed architecture has the potential to improve upon broader control tasks by incorporating problem specific priors into the architecture. As a case study, we demonstrate much improved performance for locomotion tasks by emulating the biological central pattern generators (CPGs) as the nonlinear part of the architecture.


MEASUREMENT AND ANALYSIS OF P2P ACTIVITY AGAINST PAEDOPHILE CONTENT


title: A User Model of Psycho-physiological Measure of Emotion

abstract: The interpretation of physiological signals in terms of emotion requires an appropriate mapping between physiological features and emotion representations. We present a user model associating psychological and physiological representation of emotion in order to bring findings from the psychophysiology domain into User-Modeling computational techniques. We discuss results based on an experiment we performed based on bio-sensors to get physiological measure of emotion, involving 40 subjects.


Aligning Entity Names with Online Aliases on Twitter


title: A survey of noise reduction methods for distant supervision

abstract: We survey recent approaches to noise reduction in distant supervision learning for relation extraction. We group them according to the principles they are based on: at-least-one constraints, topic-based models, or pattern correlations. Besides describing them, we illustrate the fundamental differences and attempt to give an outlook to potentially fruitful further research. In addition, we identify related work in sentiment analysis which could profit from approaches to noise reduction.


title: Let's Talk About Storage & Recovery Methods for Non-Volatile Memory Database Systems

abstract: The advent of non-volatile memory (NVM) will fundamentally change the dichotomy between memory and durable storage in database management systems (DBMSs). These new NVM devices are almost as fast as DRAM, but all writes to it are potentially persistent even after power loss. Existing DBMSs are unable to take full advantage of this technology because their internal architectures are predicated on the assumption that memory is volatile. With NVM, many of the components of legacy DBMSs are unnecessary and will degrade the performance of data intensive applications. To better understand these issues, we implemented three engines in a modular DBMS testbed that are based on different storage management architectures: (1) in-place updates, (2) copy-on-write updates, and (3) log-structured updates. We then present NVM-aware variants of these architectures that leverage the persistence and byte-addressability properties of NVM in their storage and recovery methods. Our experimental evaluation on an NVM hardware emulator shows that these engines achieve up to 5.5X higher throughput than their traditional counterparts while reducing the amount of wear due to write operations by up to 2X. We also demonstrate that our NVM-aware recovery protocols allow these engines to recover almost instantaneously after the DBMS restarts.


title: A Survey on Mobile Social Networks: Applications, Platforms, System Architectures, and Future Research Directions

abstract: Mobile social networks (MSNs) have become increasingly popular in supporting many novel applications since emerging in the recent years. Their applications and services are of great interest to service providers, application developers, and users. This paper distinguishes MSNs from conventional social networks and provides a comprehensive survey of MSNs with regard to platforms, solutions, and designs of the overall system architecture. We review the popular MSN platforms and experimental solutions for existing MSN applications and services and present the dominant mobile operating systems on which MSNs are implemented. We then analyze and propose the overall architectural designs of conventional and future MSN systems. In particular, we present the architectural designs from two perspectives: from the client side to the server side, and from the wireless data transmission level to the terminal utilization level. We further introduce and compare the unique features, services, and key technologies of two generations of architectural designs of MSN systems. Then, we classify the existing MSN applications and propose one special form of MSN, i.e., vehicular social network, and demonstrate its unique features and challenges compared with common MSNs. Finally, we summarize the major challenges for on-going MSN research and outline possible future research directions.


An Audio to Score Alignment Framework Using Spectral Factorization and Dynamic Time Warping


title: Image interpretation by a single bottom-up top-down cycle

abstract: The human visual system recognizes objects and their constituent parts rapidly and with high accuracy. Standard models of recognition by the visual cortex use feed-forward processing, in which an object's parts are detected before the complete object. However, parts are often ambiguous on their own and require the prior detection and localization of the entire object. We show how a cortical-like hierarchy obtains recognition and localization of objects and parts at multiple levels nearly simultaneously by a single feed-forward sweep from low to high levels of the hierarchy, followed by a feedback sweep from high- to low-level areas.