title: Development of a robotic endoscope holder for nasal surgery

abstract: This paper presents a new robotic system that assists surgeons in manipulating the endoscope during nasal surgical procedures. The presented robotic system has four degrees of freedom (DOF) arm with a wire-driven double parallelogram mechanism to control the three orientation angles and the insertion depth of the endoscope in the nasal cavity in a decoupled manner. The double parallelogram mechanism leads to a remote center of motion (RCM) at the entry point of the patient's nostril. One of the important features of the developed robot is that the safety of the operation is guaranteed by passive mechanisms and active force control. The passive mechanisms protect safety of the surgeons, the patient and the motors as well. The active force control algorithm monitors the interaction force exerted at the robot by the external environment and control it within a safe threshold. Voice and foot control interfaces have also been developed to facilitate the surgeon to fully control the robotic system without using hands. Experiments with manikin, animal and human cadaver have been conducted to demonstrate the performance of the developed robotic system.


Understanding Information Systems Continuance for Information-Oriented Mobile Applications


title: A Survey of Network Lifetime Maximization Techniques in Wireless Sensor Networks

abstract: Emerging technologies, such as the Internet of Things, smart applications, smart grids, and machine-to-machine networks stimulate the deployment of autonomous, self-configuring, large-scale wireless sensor networks (WSNs). Efficient energy utilization is crucially important in order to maintain a fully operational network for the longest period of time possible. Therefore, network lifetime (NL) maximization techniques have attracted a lot of research attention owing to their importance in terms of extending the flawless operation of battery-constrained WSNs. In this paper, we review the recent developments in WSNs, including their applications, design constraints, and lifetime estimation models. Commencing with the portrayal of rich variety definitions of NL design objective used for WSNs, the family of NL maximization techniques is introduced and some design guidelines with examples are provided to show the potential improvements of the different design criteria.


title: Multicenter Evaluation of a Novel Surveillance Paradigm for Complications of Mechanical Ventilation

abstract: Background ::: Ventilator-associated pneumonia (VAP) surveillance is time consuming, subjective, inaccurate, and inconsistently predicts outcomes. Shifting surveillance from pneumonia in particular to complications in general might circumvent the VAP definition's subjectivity and inaccuracy, facilitate electronic assessment, make interfacility comparisons more meaningful, and encourage broader prevention strategies. We therefore evaluated a novel surveillance paradigm for ventilator-associated complications (VAC) defined by sustained increases in patients' ventilator settings after a period of stable or decreasing support. ::: ::: ::: Methods ::: We assessed 600 mechanically ventilated medical and surgical patients from three hospitals. Each hospital contributed 100 randomly selected patients ventilated 2–7 days and 100 patients ventilated >7 days. All patients were independently assessed for VAP and for VAC. We compared incidence-density, duration of mechanical ventilation, intensive care and hospital lengths of stay, hospital mortality, and time required for surveillance for VAP and for VAC. A subset of patients with VAP and VAC were independently reviewed by a physician to determine possible etiology. ::: ::: ::: Results ::: Of 597 evaluable patients, 9.3% had VAP (8.8 per 1,000 ventilator days) and 23% had VAC (21.2 per 1,000 ventilator days). Compared to matched controls, both VAP and VAC prolonged days to extubation (5.8, 95% CI 4.2–8.0 and 6.0, 95% CI 5.1–7.1 respectively), days to intensive care discharge (5.7, 95% CI 4.2–7.7 and 5.0, 95% CI 4.1–5.9), and days to hospital discharge (4.7, 95% CI 2.6–7.5 and 3.0, 95% CI 2.1–4.0). VAC was associated with increased mortality (OR 2.0, 95% CI 1.3–3.2) but VAP was not (OR 1.1, 95% CI 0.5–2.4). VAC assessment was faster (mean 1.8 versus 39 minutes per patient). Both VAP and VAC events were predominantly attributable to pneumonia, pulmonary edema, ARDS, and atelectasis. ::: ::: ::: Conclusions ::: Screening ventilator settings for VAC captures a similar set of complications to traditional VAP surveillance but is faster, more objective, and a superior predictor of outcomes.


title: Digital reconstruction of multidimensional signals from their projections

abstract: In a wide variety of applications it is necessary to infer the structure of a multidimensional object from a set of its projections. There has been a long-standing interest in this problem and a number of different techniques have been proposed. In this paper, we present a tutorial review of the reconstruction problem and some of the algorithms which have been proposed for its solution. In addition, we present a number of new algorithms that appear to have some advantages over previous algorithms. Some comparisons of these algorithms applied to reconstructions of two-dimensional pictures are given. Furthermore, a number of new theoretical results are presented relating to the minimum number of projections necessary for exact reconstruction.


title: Learning to detect roads in high-resolution aerial images

abstract: Reliably extracting information from aerial imagery is a difficult problem with many practical applications. One specific case of this problem is the task of automatically detecting roads. This task is a difficult vision problem because of occlusions, shadows, and a wide variety of non-road objects. Despite 30 years of work on automatic road detection, no automatic or semi-automatic road detection system is currently on the market and no published method has been shown to work reliably on large datasets of urban imagery. We propose detecting roads using a neural network with millions of trainable weights which looks at a much larger context than was used in previous attempts at learning the task. The network is trained on massive amounts of data using a consumer GPU. We demonstrate that predictive performance can be substantially improved by initializing the feature detectors using recently developed unsupervised learning methods as well as by taking advantage of the local spatial coherence of the output labels.We show that our method works reliably on two challenging urban datasets that are an order of magnitude larger than what was used to evaluate previous approaches.


title: A Decision Tree Framework for Spatiotemporal Sequence Prediction

abstract: We study the problem of learning to predict a spatiotemporal output sequence given an input sequence. In contrast to conventional sequence prediction problems such as part-of-speech tagging (where output sequences are selected using a relatively small set of discrete labels), our goal is to predict sequences that lie within a high-dimensional continuous output space. We present a decision tree framework for learning an accurate non-parametric spatiotemporal sequence predictor. Our approach enjoys several attractive properties, including ease of training, fast performance at test time, and the ability to robustly tolerate corrupted training data using a novel latent variable approach. We evaluate on several datasets, and demonstrate substantial improvements over existing decision tree based sequence learning frameworks such as SEARN and DAgger.


title: Do Industries Explain Momentum?- A Replication of Moskowitz and Grinblatt 2004 ∗

abstract: We replicate Moskowitz and Grinblatt’s (1999) findings on industry momentum using data for US large cap stocks between 1998 and 2007. Moskowitz and Grinblatt find that industry momentum strategies appear to be highly profitable. Moskowitz and Grinblatt also demonstrate that individual stock momentum strategies, which buy past winning stocks and sell past losing stocks, are less profitable after controlling for industry momentum [3]. After replicating Moskowitz and Grinblatt’s results, we find that industry momentum strategies do provide greater returns than individual stock momentum strategies. We also find that the optimal time horizon of an industry momentum strategy is uncorrelated with the size of the industries the strategy is applied to.


title: Robust planning with incomplete domain models

abstract: Most current planners assume complete domain models and focus on generating correct plans. Unfortunately, domain modeling is a laborious and error-prone task, thus real world agents have to plan with incomplete domain models. While domain experts cannot guarantee completeness, often they are able to circumscribe the incompleteness of the model by providing annotations as to which parts of the domain model may be incomplete. In this paper, we study planning problems with incomplete domain models where the annotations specify possible preconditions and effects of actions. We show that the problem of assessing the quality of a plan, or its plan robustness, is #P-complete, establishing its equivalence with the weighted model counting problems. We present two approaches to synthesizing robust plans. While the method based on the compilation to conformant probabilistic planning is much intuitive, its performance appears to be limited to only small problem instances. Our second approach based on stochastic heuristic search works well for much larger problems. It aims to use the robustness measure directly for estimating heuristic distance, which is then used to guide the search. Our planning system, PISA, outperforms a state-of-the-art planner handling incomplete domain models in most of the tested domains, both in terms of plan quality and planning time. Finally, we also present an extension of PISA called CPISA that is able to exploit the available of past successful plan traces to both improve the robustness of the synthesized plans and reduce the domain modeling burden.


title: The Plant Microbiota: Systems-Level Insights and Perspectives

abstract: Plants do not grow as axenic organisms in nature, but host a diverse community of microorganisms, termed the plant microbiota. There is an increasing awareness that the plant microbiota plays a role in plant growth and can provide protection from invading pathogens. Apart from intense research on crop plants, Arabidopsis is emerging as a valuable model system to investigate the drivers shaping stable bacterial communities on leaves and roots and as a tool to decipher the intricate relationship among the host and its colonizing microorganisms. Gnotobiotic experimental systems help establish causal relationships between plant and microbiota genotypes and phenotypes and test hypotheses on biotic and abiotic perturbations in a systematic way. We highlight major recent findings in plant microbiota research using comparative community profiling and omics analyses, and discuss these approaches in light of community establishment and beneficial traits like nutrient acquisition and plant health.


title: The Design of the Borealis Stream Processing Engine

abstract: Borealis is a second-generation distributed stream processing engine that is being developed at Brandeis University, Brown University, and MIT. Borealis inherits core stream processing functionality from Aurora [14] and distribution functionality from Medusa [51]. Borealis modifies and extends both systems in non-trivial and critical ways to provide advanced capabilities that are commonly required by newly-emerging stream processing applications. In this paper, we outline the basic design and functionality of Borealis. Through sample real-world applications, we motivate the need for dynamically revising query results and modifying query specifications. We then describe how Borealis addresses these challenges through an innovative set of features, including revision records, time travel, and control lines. Finally, we present a highly flexible and scalable QoS-based optimization model that operates across server and sensor networks and a new fault-tolerance model with flexible consistency-availability trade-offs.


title: Security Enhanced (SE) Android: Bringing Flexible MAC to Android

abstract: The Android software stack for mobile devices defines and enforces its own security model for apps through its application-layer permissions model. However, at its foundation, Android relies upon the Linux kernel to protect the system from malicious or flawed apps and to isolate apps from one another. At present, Android leverages Linux discretionary access control (DAC) to enforce these guarantees, despite the known shortcomings of DAC. In this paper, we motivate and describe our work to bring flexible mandatory access control (MAC) to Android by enabling the effective use of Security Enhanced Linux (SELinux) for kernel-level MAC and by developing a set of middleware MAC extensions to the Android permissions model. We then demonstrate the benefits of our security enhancements for Android through a detailed analysis of how they mitigate a number of previously published exploits and vulnerabilities for Android. Finally, we evaluate the overheads imposed by our security enhancements.


title: Customer Recognition in Experience vs. Inspection Good Markets

abstract: We study the effects of customer recognition and behavior-based price discrimination (BPD) in a two-period experience good duopoly with a discrete value distribution, and we investigate the role of consumers’ ex ante valuation uncertainty in dynamic price competition through comparison with an inspection good duopoly. Several results are reached. First, the firms may reward repeat purchase when the probability of a high value is relatively low and when the high–low value difference is large; otherwise, they may engage in poaching. Second, BPD frequently increases each firm’s total profits, even in the poaching equilibrium. These results contrast with the inspection good duopoly, and the driver is that consumers’ period 2 product preference depends on their realized values in period 1. Third, consumers’ ex ante valuation uncertainty may increase or decrease firm profits without BPD, and it weakly increases firm profits with BPD, relative to the inspection good duopoly. This paper was accepted by J. Miguel Villas-Boas, marketing .


title: Social Media for Government Services

abstract: This book highlights state-of-the-art research, development and implementation efforts concerning social media in government services, bringing together researchers and practitioners in a number of case studies. It elucidatesa number of significant challenges associated with social media specific to government services, such as: benefits and methods of assessing; usability and suitability of tools, technologies and platforms; governance policies and frameworks; opportunities for new services; integrating social media with organisational business processes; and specific case studies. The book also highlights the range of uses and applications of social media in the government domain, at both local and federal levels. As such, it offers a valuable resource for a broad readership including academic researchers, practitioners in the IT industry, developers, and government policy- and decision-makers.


title: Nonlinear ELM simulations based on a nonideal peeling–ballooning model using the BOUT++ code

abstract: A minimum set of equations based on the peeling–ballooning (P–B) model with nonideal physics effects (diamagnetic drift, E × B drift, resistivity and anomalous electron viscosity) is found to simulate pedestal collapse when using the BOUT++ simulation code, developed in part from the original fluid edge code BOUT. Linear simulations of P–B modes find good agreement in growth rate and mode structure with ELITE calculations. The influence of the E × B drift, diamagnetic drift, resistivity, anomalous electron viscosity, ion viscosity and parallel thermal diffusivity on P–B modes is being studied; we find that (1) the diamagnetic drift and E × B drift stabilize the P–B mode in a manner consistent with theoretical expectations; (2) resistivity destabilizes the P–B mode, leading to resistive P–B mode; (3) anomalous electron and parallel ion viscosities destabilize the P–B mode, leading to a viscous P–B mode; (4) perpendicular ion viscosity and parallel thermal diffusivity stabilize the P–B mode. With addition of the anomalous electron viscosity under the assumption that the anomalous kinematic electron viscosity is comparable to the anomalous electron perpendicular thermal diffusivity, or the Prandtl number is close to unity, it is found from nonlinear simulations using a realistic high Lundquist number that the pedestal collapse is limited to the edge region and the ELM size is about 5–10% of the pedestal stored energy. This is consistent with many observations of large ELMs. The estimated island size is consistent with the size of fast pedestal pressure collapse. In the stable α-zones of ideal P–B modes, nonlinear simulations of viscous ballooning modes or current-diffusive ballooning mode (CDBM) for ITER H-mode scenarios are presented.


title: Recommending food: reasoning on recipes and ingredients

abstract: With the number of people considered to be obese rising across the globe, the role of IT solutions in health management has been receiving increased attention by medical professionals in recent years This paper focuses on an initial step toward understanding the applicability of recommender techniques in the food and diet domain By understanding the food preferences and assisting users to plan a healthy and appealing meal, we aim to reduce the effort required of users to change their diet As an initial feasibility study, we evaluate the performance of collaborative filtering, content-based and hybrid recommender algorithms on a dataset of 43,000 ratings from 512 users We report on the accuracy and coverage of the algorithms and show that a content-based approach with a simple mechanism that breaks down recipe ratings into ingredient ratings performs best overall.


title: Conversational Implicatures In Indirect Replies

abstract: In this paper we present algorithms for the interpretation and generation of a kind of particularized conversational implicature occurring in certain indirect replies. Our algorithms make use of discourse expectations, discourse plans, and discourse relations. The algorithms calculate implicatures of discourse units of one or more sentences. Our approach has several advantages. First, by taking discourse relations into account, it can capture a variety of implicatures not handled before. Second, by treating implicatures of discourse units which may consist of more than one sentence, it avoids the limitations of a sentence-at-a-time approach. Third, by making use of properties of discourse which have been used in models of other discourse phenomena, our approach can be integrated with those models. Also, our model permits the same information to be used both in interpretation and generation.


title: Creating a Space for Women: Women's Studies in China in the 1980s

abstract: a new policy issued by the Chinese government for the purpose of revitalizing the economy. The main idea of the reform has been both to adjust the structure of economy mainly by introducing market mechanisms into the planned economy and to change the method of management by giving more power and responsibility to local bureaus and firms. In the rural areas, land was distributed to each household while the collective communes were disbanded. It is not an exaggeration to conclude that the economic reform greatly influenced the lives of Chinese people. In addition, along with economic reform, the doors of the country have been reopened to the Western world. Meanwhile, criticism of Mao and the conservative ideological control, although sporadic, has gradually become possible. Under these special political and socioeconomic circumstances, without getting much attention at the very beginning, women's studies as a new academic discipline is quickly sprouting and blossoming. The questions are, Why did women's studies come into existence in the 1980s but not earlier? What does women's studies involve and what developments have been made? What are its characteristics compared with those of previous Chinese women's liberation movements and those


title: AN EFFECTIVE PRE -PROCESSING ALGORITHM FOR INFORMATION RETRIEVAL SYSTEMS

abstract: The Internet is probably the most successful distributed computing system ever. However, our capabilities for data querying and manipulation on the internet are primordial at best. The user expectations are enhancing over the period of time along with increased amount of operational data past few decades. The data-user expects more deep, exact, and detailed results. Result retrieval for the user query is always relative o the pattern of data storage and index. In Information retrieval systems, tokenization is an integrals part whose prime objective is to identifying the token and their count. In this paper, we have proposed an effective tokenization approach which is based on training vector and result shows that efficiency/ effectiveness of proposed algorithm. Tokenization on documents helps to satisfy user’s information need more precisely and reduced search sharply, is believed to be a part of information retrieval. Pre-processing of input document is an integral part of Tokenization, which involves preprocessing of documents and generates its respective tokens which is the basis of these tokens probabilistic IR generate its scoring and gives reduced search space. The comparative analysis is based on the two parameters; Number of Token generated, Pre-processing time.


title: A New Storage Optimized Honeyword Generation Approach for Enhancing Security and Usability

abstract: Inverting the hash values by performing brute force computation is one of the latest security threats on password based authentication technique. New technologies are being developed for brute force computation and these increase the success rate of inversion attack. Honeyword base authentication protocol can successfully mitigate this threat by making password cracking detectable. However, the existing schemes have several limitations like Multiple System Vulnerability, Weak DoS Resistivity, Storage Overhead, etc. In this paper we have proposed a new honeyword generation approach, identified as Paired Distance Protocol (PDP) which overcomes almost all the drawbacks of previously proposed honeyword generation approaches. The comprehensive analysis shows that PDP not only attains a high detection rate of 97.23% but also reduces the storage cost to a great extent.


title: Network inference using directed information: The deterministic limit

abstract: Consider a network comprised of many variables, which interact with each other following a causal stochastic dynamical system model. Given time-series measurements of these variables, an important task is the inference of the structure of the causal dynamical system. Recently, directed information has been proposed as a generalization of Granger causality to accurately infer the structure of the network. We observe a curious phenomenon: in the limit that the relationships become purely deterministic, this measure loses power completely. In this paper, we study this phenomenon, explore its connection to Taken's delay embedding theorem, propose a remedy called restricted directed information (RDI); and finally, demonstrate its efficacy in simulations. In particular we show that, RDI recovers the graph correctly in all instances, deterministic or stochastic, where there is enough information to recover the graph uniquely.


title: Learning to Optimize via Information-Directed Sampling

abstract: We propose information-directed sampling - a new algorithm for online optimization problems in which a decision-maker must balance between exploration and exploitation while learning from partial feedback. Each action is sampled in a manner that minimizes the ratio between the square of expected single-period regret and a measure of information gain: the mutual information between the optimal action and the next observation. ::: ::: We establish an expected regret bound for information-directed sampling that applies across a very general class of models and scales with the entropy of the optimal action distribution. For the widely studied Bernoulli and linear bandit models, we demonstrate simulation performance surpassing popular approaches, including upper confidence bound algorithms, Thompson sampling, and knowledge gradient. Further, we present simple analytic examples illustrating that information-directed sampling can dramatically outperform upper confidence bound algorithms and Thompson sampling due to the way it measures information gain.


title: Efficient View-Dependent Image-Based Rendering with Projective Texture-Mapping

abstract: This paper presents how the image-based rendering technique of view-dependent texture-mapping (VDTM) can be efficiently implemented using projective texture mapping, a feature commonly available in polygon graphics hardware. VDTM is a technique for generating novel views of a scene with approximately known geometry making maximal use of a sparse set of original views. The original presentation of VDTM in by Debevec, Taylor, and Malik required significant per-pixel computation and did not scale well with the number of original images. In our technique, we precompute for each polygon the set of original images in which it is visible and create a "view map" data structure that encodes the best texture map to use for a regularly sampled set of possible viewing directions. To generate a novel view, the view map for each polygon is queried to determine a set of no more than three original images to blended together in order to render the polygon with projective texture-mapping. Invisible triangles are shaded using an object-space hole-filling method. We show how the rendering process can be streamlined for implementation on standard polygon graphics hardware. We present results of using the method to render a large-scale model of the Berkeley bell tower and its surrounding campus enironment.


title: Decoding mixed emotions from expression map of face images

abstract: In real life facial expressions show mixture of emotions. This paper proposes a novel expression descriptor based expression map that can efficiently represent pure, mixture and transition of facial expressions. The expression descriptor is the integration of optic flow and image gradient values and the descriptor value is accumulated in temporal scale. The expression map is realized using self-organizing map. We develop an objective scheme to find the percentage of different prototypical pure emotions (e.g., happiness, surprise, disgust etc.) that mix up to generate a real facial expression. Experimental results show that the expression map can be used as an effective classifier for facial expressions.


title: Duplication Detection for Software Bug Reports Based on BM25 Term Weighting

abstract: Handling bug reports is an important issue in software maintenance. Recently, detection on duplicate bug reports has received much attention. There are two main reasons. First, duplicate bug reports may waste human resource to process these redundant reports. Second, duplicate bug reports may provide abundant information for further software maintenance. In the past studies, many schemes have been proposed using the information retrieval and natural language processing techniques. In this thesis, we propose a novel detection scheme based on a BM25 term weighting scheme. We have conducted empirical experiments on three open source projects, Apache, ArgoUML, and SVN. The experimental results show that the BM25-based scheme can effectively improve the detection performance in nearly all cases.


title: Sequential churn prediction and analysis of cellular network users — A multi-class, multi-label perspective

abstract: We investigate the problem of churn detection and prediction using sequential cellular network data. We introduce a cleaning and preprocessing of the dataset that makes it suitable for the analysis. We draw a comparison of the churn prediction results from the-state-of-the-art algorithms such as the Gradient Boosting Trees, Random Forests, basic Long Short-Term Memory (LSTM) and Support Vector Machines (SVM). We achieve significant performance boost by incorporating the sequential nature of the data, imputing missing information and analyzing the effects of various features. This in turns makes the classifier rigorous enough to give highly accurate results. We emphasize on the sequential nature of the problem and seek algorithms that can track the variations in the data. We test and compare the performance of proposed algorithms using performance measures on real life cellular network data for churn detection.


title: Photosynthetic acclimation to shade: probing the role of phytochromes using photomorphogenic mutants of tomato

abstract: Photomorphogenic mutants of tomato were used to probe the possible involvement of phytochrome A in photosynthetic acclimation to shade. Plants of wild-type Lycopersicon esculentum, the near isogenic mutants aurea (au, deficient in phytochrome A, and also possibly other phytochromes) and high pigment (hp, exhibiting exaggerated photomorphogenic responses), and the au/hp double mutant were grown in a greenhouse under either unfiltered daylight, or daylight attenuated by neutral density filters or green filters that simulated the spectral effects of vegetation shade. Growth and photosynthetic parameters were measured over a growing period of 15 d. Typical effects of simulated vegetation shade on extension growth were observed only in the wild-type and hp strains; the presence of the au lesion resulted in reduced capacity to respond to vegetation shade by increased extension. Photosynthetic light saturation curves obtained using infrared gas analysis at 15 d indicated that neither the au nor the hp mutations caused serious reductions in maximum net photosynthetic rate in plants acclimated to full daylight, although the au/hp double mutant showed a significantly reduced maximum rate. In the wild-type and hp strains grown under both neutral and simulated vegetation shade, the photosynthetic light saturation curves were similar. Plants with the au lesion showed a significantly lower capacity to acclimate to vegetation shade, as seen by their reduced maximum rate of net photosynthesis. However, in situ measurements of photosynthetic rate in the greenhouse were indistinguishable between the four strains. Thylakoid stoichiometry measurements revealed a very large increase in PSII/PSI ratio under simulated vegetation shade as compared with either unfiltered daylight or neutral density shade. These increases were not diminished in the phytochrome-deficient au strains, and were even larger in the hp strains. The results indicate that the au lesion has little effect on the capacity of tomato to exhibit photoadaptation to simulated vegetational shade when measured at the thylakoid level. On the other hand, net leaf photosynthesis under vegetation shade can be significantly affected by the phytochrome deficiency caused by the au mutation, implying a possible role for one or more of the phytochromes in photoadaptation at the leaf level.


title: Cat swarm optimization

abstract: In this paper, we present a new algorithm of swarm intelligence, namely, Cat Swarm Optimization (CSO). CSO is generated by observing the behaviors of cats, and composed of two sub-models, i.e., tracing mode and seeking mode, which model upon the behaviors of cats. Experimental results using six test functions demonstrate that CSO has much better performance than Particle Swarm Optimization (PSO).


title: A Smart and Passive Floor-Vibration Based Fall Detector for Elderly

abstract: Falls are very prevalent among the elderly. They are the second leading cause of unintentional-injury death for people of all ages and the leading cause of death for elders 79 years and older. Studies have shown that the medical outcome of a fall is largely dependent upon the response and rescue time. Hence, a highly accurate automatic fall detector is an important component of the living setting for older adult to expedite and improve the medical care provided to this population. Though there are several kinds of fall detectors currently available, they suffer from various drawbacks. Some of them are intrusive while others require the user to wear and activate the devices, and hence may fail in the event of user non-compliance. This paper describes the working principle and the design of a floor vibration-based fall detector that is completely passive and unobtrusive to the resident. The detector was designed to overcome some of the common drawbacks of the earlier fall detectors. The performance of the detector is evaluated by conducting controlled laboratory tests using anthropomorphic dummies. The results showed 100% fall detection rate with minimum potential for false alarms


title: Adaptive statistical detection of false data injection attacks in smart grids

abstract: The smart power grid is a synergistic system that integrates diverse network components for power generation, transmission, and distribution. Its advanced metering infrastructure (AMI) enables the grid's efficient and reliable operation. Nevertheless, it is amenable to advanced cyber threats; malicious actors can compromise vulnerable meters and arbitrarily alter their readings. These orchestrated “false data injection attacks” can lead to power outages and service interruption. We propose a framework that uses measurements from trusted (secure) nodes in order to detect abnormal “spoofing” activity of other nodes, possibly tampered. Our model considers the structural similarities in the electricity consumption of AMI nodes, and exploits the spatial correlation amongst meters. To alleviate the problem's large-dimensionality aspect, the meters are clustered into classes of similar energy patterns. We evaluate our algorithms using real-world building data obtained from a large university campus.


title: Knowledge-based reasoning in a unified feature modeling scheme

abstract: Feature-based modeling is an accepted approach to include high-level geometric information in product models as well as to facilitate a parameterized and constraint-based product development process. Moreover, features are suitable as an intermediate layer between a product’s knowledge model and its geometry model for effective and efficient information management. To achieve this, traditional feature technology must be extended to align with the approach of knowledge-based reasoning. In this paper, based on a previously proposed unified feature modeling scheme, feature definitions are extended to support knowledge-based reasoning. In addition, a communication mechanism between the knowledge-based system and the feature model is established. The methods to embed and use knowledge for information consistency control are described.


title: Microbial fuel cells: novel biotechnology for energy generation.

abstract: Microbial fuel cells (MFCs) provide new opportunities for the sustainable production of energy from biodegradable, reduced compounds. MFCs function on different carbohydrates but also on complex substrates present in wastewaters. As yet there is limited information available about the energy metabolism and nature of the bacteria using the anode as electron acceptor; few electron transfer mechanisms have been established unequivocally. To optimize and develop energy production by MFCs fully this knowledge is essential. Depending on the operational parameters of the MFC, different metabolic pathways are used by the bacteria. This determines the selection and performance of specific organisms. Here we discuss how bacteria use an anode as an electron acceptor and to what extent they generate electrical output. The MFC technology is evaluated relative to current alternatives for energy generation.


title: Biological Assay and Detection Methods for Marine “Shellfish” Toxins

abstract: Marine organisms produce a wide range of bioactive compounds. There is an equally wide array of methods for their detection and quantification. The toxins range from low molecular-weight compounds produced by unicellular cyanobacteria, diatoms, and micro-algae to large polypeptide and protein toxins produced by higher organisms such as sea anemones. This review will focus on the low molecular-weight toxins, which are of particular interest because of their harmful effects in humans following consumption of contaminated shellfish or fish. Most are neurotoxins, and in many instances the common names given to the classes of toxin based on the observed symptoms (paralytic shellfish poisons [PSP], neurotoxic shellfish poisons [NSP], and amnesic shellfish poisons [ASP]) point to an interaction between the toxin and the nervous system. In other cases, toxins with in vitro cytotoxic or hemolytic activity can cause equally dramatic effects, as instanced by the discomfort that diarrhetic shellfish poisons (DSP) cause consumers and the economic losses that result from large fish kills.


title: The role of the decision maker in DSSs and representation levels

abstract: We claim that the role of the decision maker in a DSS is to control a heuristic search. In this context, the models involved in a DSS naturally appear as evaluation functions driving the end-user control. This interpretation, which seems to be natural for P. Humphreys' (1989) lower representation levels, highlights the links between models and control and explains the prominent role of the "what if" concept in the DSS literature of the seventies. The focus now however, is rather on modelling, and will turn to the control which is related to flexibility. Hence, our analysis paves the way to the design of more flexible knowledge-based or "intelligent" DSSs. We moreover examine the difficult question of the decision support for higher representation levels. >


Probing sentence embeddings for structure-dependent tense


title: Alcohol: Effects on Neurobehavioral Functions and the Brain

abstract: Alcoholism results from an interplay between genetic and environmental factors, and is linked to brain defects and associated cognitive, emotional, and behavioral impairments. A confluence of findings from neuroimaging, physiological, neuropathological, and neuropsychological studies of alcoholics indicate that the frontal lobes, limbic system, and cerebellum are particularly vulnerable to damage and dysfunction. An integrative approach employing a variety of neuroscientific technologies is essential for recognizing the interconnectivity of the different functional systems affected by alcoholism. In that way, relevant experimental techniques can be applied to assist in determining the degree to which abstinence and treatment contribute to the reversal of atrophy and dysfunction.


title: The Effect of Programming Language on Error Rates of Novice Programmers

abstract: This paper describes the design and testing of a new introductory programming language, GRAIL1. GRAIL was designed to minimise student syntax errors, and hence allow the study of the impact of syntax errors on learning to program. An experiment was conducted using students learning programming for the first time. The students were split into two groups, one group learning LOGO and the other GRAIL. The resulting code was then analysed for syntax and logic errors. The groups using LOGO made more errors than the groups using GRAIL, which shows that choice of programming language can have a substantial impact on error rates of novice programmers.


title: Internet of Things (IoT) sensors for smart home electric energy usage management

abstract: Going home will take on a new meaning and inviting with the increasing prevalence of IoT sensors being incorporated into the modern household. A smart home is comprised of IoT sensors, IoT actuators and a computational system thinking machine for communication, computation, control and visualizations (in-house and remote), which work together to benefit the homeowner in multiple ways. Three of the major benefits include improving the energy efficiency of a smart home, actively monitoring the home environment, and enhancing the residents' social well-being. A survey of existing IoT sensors, an assessment of their technical and economical parameters, the prominent research initiatives to enhance the functionality of IoT sensors and key features of future IoT sensors are presented in this paper.


title: Data Compression and Harmonic Analysis

abstract: In this paper we review some recent interactions between harmonic analysis and data compression. The story goes back of course to Shannon's R(D) theory in the case of Gaussian stationary processes, which says that transforming into a Fourier basis followed by block coding gives an optimal lossy compression technique; practical developments like transform-based image compression have been inspired by this result. In this paper we also discuss connections perhaps less familiar to the information theory community, growing out of the field of harmonic analysis. Recent harmonic analysis constructions, such as wavelet transforms and Gabor transforms, are essentially optimal transforms for transform coding in certain settings. Some of these transforms are under consideration for future compression standards. We discuss some of the lessons of harmonic analysis in this century. Typically, the problems and achievements of this field have involved goals that were not obviously related to practical data compression, and have used a language not immediately accessible to outsiders. Nevertheless, through an extensive generalization of what Shannon called the "sampling theorem", harmonic analysis has succeeded in developing new forms of functional representation which turn out to have significant data compression interpretations. We explain why harmonic analysis has interacted with data compression, and we describe some interesting recent ideas in the field that may affect data compression in the future.


title: Improving Write Performance of LSMT-Based Key-Value Store

abstract: Key-value stores are widely used to provide much higher read and write throughput than traditional SQL databases. LSMT (log structure merge tree) based key-value store, as one type of key-value stores, is applied in many practical systems since it could eliminate random writes and provide good read performance at the same time. However, the data residing in disk needs compaction operations from time to time, which takes a large amount of I/O resources. Since disk access speed is much slower than DRAM and most data resides in disks, the compaction operation will significantly influence the system performance. In this paper, we propose a grouped level structure, which divides each level in LSMT into multiple groups. Also, we propose a new compaction method for the grouped level structure to reduce the compaction I/O overhead. Our experiments show that the grouped level structure saves about 55% to 78% I/O resource of compaction, so it improves the write throughput by 69% to 284%, but only reduces the read throughput by 5% to 9%. It improves the overall throughput by 30% to 69% with read dominated workloads of 25% write operations and 75% read operations.


title: Theoretical Robopsychology: Samu Has Learned Turing Machines

abstract: From the point of view of a programmer, the robopsychology is a synonym for the activity is done by developers to implement their machine learning applications. This robopsychological approach raises some fundamental theoretical questions of machine learning. Our discussion of these questions is constrained to Turing machines. Alan Turing had given an algorithm (aka the Turing Machine) to describe algorithms. If it has been applied to describe itself then this brings us to Turing's notion of the universal machine. In the present paper, we investigate algorithms to write algorithms. From a pedagogy point of view, this way of writing programs can be considered as a combination of learning by listening and learning by doing due to it is based on applying agent technology and machine learning. As the main result we introduce the problem of learning and then we show that it cannot easily be handled in reality therefore it is reasonable to use machine learning algorithm for learning Turing machines.


title: An Adaptive Background Modeling Method for Foreground Segmentation

abstract: Background modeling has played an important role in detecting the foreground for video analysis. In this paper, we presented a novel background modeling method for foreground segmentation. The innovations of the proposed method lie in the joint usage of the pixel-based adaptive segmentation method and the background updating strategy, which is performed in both pixel and object levels. Current pixel-based adaptive segmentation method only updates the background at the pixel level and does not take into account the physical changes of the object, which may result in a series of problems in foreground detection, e.g., a static or low-speed object is updated too fast or merely a partial foreground region is properly detected. To avoid these deficiencies, we used a counter to place the foreground pixels into two categories (illumination and object). The proposed method extracted a correct foreground object by controlling the updating time of the pixels belonging to an object or an illumination region respectively. Extensive experiments showed that our method is more competitive than the state-of-the-art foreground detection methods, particularly in the intermittent object motion scenario. Moreover, we also analyzed the efficiency of our method in different situations to show that the proposed method is available for real-time applications.


title: Combining smart card data and household travel survey to analyze jobs-housing relationships in Beijing

abstract: Abstract Location Based Services (LBS) provide a new perspective for spatiotemporally analyzing dynamic urban systems. Research has investigated urban dynamics using LBS. However, less attention has been paid to the analysis of urban structure (especially commuting pattern) using smart card data (SCD), which are widely available in most large cities in China, and even in the world. This paper combines bus SCD for a one-week period with a oneday household travel survey, as well as a parcel-level land use map to identify job–housing locations and commuting trip routes in Beijing. Two data forms are proposed, one for jobs–housing identification and the other for commuting trip route identification. The results of the identification are aggregated in the bus stop and traffic analysis zone (TAZ) scales, respectively. Particularly, commuting trips from three typical residential communities to six main business zones are mapped and compared to analyze commuting patterns in Beijing. The identified commuting trips are validated by comparison with those from the survey in terms of commuting time and distance, and the positive validation results prove the applicability of our approach. Our experiment, as a first step toward enriching LBS data using conventional survey and urban GIS data, can obtain solid identification results based on rules extracted from existing surveys or censuses.


title: An Empirical Study of Consumer Perceptions and Comprehension of Web Site Privacy Policies

abstract: The U.S. legislation at both the federal and state levels mandates certain organizations to inform customers about information uses and disclosures. Such disclosures are typically accomplished through privacy policies, both online and offline. Unfortunately, the policies are not easy to comprehend, and, as a result, online consumers frequently do not read the policies provided at healthcare Web sites. Because these policies are often required by law, they should be clear so that consumers are likely to read them and to ensure that consumers can comprehend these policies. This, in turn, may increase consumer trust and encourage consumers to feel more comfortable when interacting with online organizations. In this paper, we present results of an empirical study, involving 993 Internet users, which compared various ways to present privacy policy information to online consumers. Our findings suggest that users perceive typical, paragraph-form policies to be more secure than other forms of policy representation, yet user comprehension of such paragraph-form policies is poor as compared to other policy representations. The results of this study can help managers create more trustworthy policies, aid compliance officers in detecting deceptive organizations, and serve legislative bodies by providing tangible evidence as to the ineffectiveness of current privacy policies.


title: Foundations of Language: Brain, Meaning, Grammar, Evolution

abstract: Foundations of Language shows one of the most fundamental new thinkings in linguistics since Noam Chomskyis Aspects of the Theory of Syntax in 1965. Foundations of Language opens up vivid new perspectives on every major aspect of language and communication: grammar vocabulary, learning, origins of language, and the relationship of language and thought to the real world. It puts linguistics at the centre of the search for understanding human nature and human cognition. In my opinion, the author presents in this book a broad survey in linguistics, introducing his personal perspective in the field of theoretical linguistics. Ray Jackendoff proposes a new holistic theory of the relation between the sounds, structure, and meaning of language and their relation to the mind and the brain. Jackendoff goes on to defend a radical conception of how the brain stores and processes language, and how grammar relies on parallel generative systems that are integrated through interface components. The interdisciplinary approach of Foundations of Language makes the book specially up to date, recognizing the interaction of psychology, neuroscience, biology, and philosophy, setting a groundbreaking agenda for close co-operation of these knowledge areas. According to the author, this book has been written with three concentric audiences in mind, being the most central, of course, linguistics of all specialities and all persuasions. The next ring includes those disciplines that look to linguistics for theoretical models: psycholinguistics, neurolinguistics, language acquisition and computational linguistics. The outer ring includes all those having some professional concern with languages, including psychologists, cognitive scientists, neuroscientists, philosophers of language and philosophers of mind, and, perhaps, evolutionary biologists. The general structure of the book has been designed to be read and re-read by anyone seriously interested in the state of the art of research on language. The book is divided into three parts:


title: Web mining: information and pattern discovery on the World Wide Web

abstract: Application of data mining techniques to the World Wide Web, referred to as Web mining, has been the focus of several recent research projects and papers. However, there is no established vocabulary, leading to confusion when comparing research efforts. The term Web mining has been used in two distinct ways. The first, called Web content mining in this paper, is the process of information discovery from sources across the World Wide Web. The second, called Web usage mining, is the process of mining for user browsing and access patterns. We define Web mining and present an overview of the various research issues, techniques, and development efforts. We briefly describe WEBMINER, a system for Web usage mining, and conclude the paper by listing research issues.


Anytime Algorithms for Multi-core Architectures


title: A Variational Inequality Perspective on Generative Adversarial Networks

abstract: Stability has been a recurrent issue in training generative adversarial networks (GANs). One common way to tackle this issue has been to propose new formulations of the GAN objective. Yet, surprisingly few studies have looked at optimization methods specifically designed for this adversarial training. In this work, we review the "variational inequality" framework which contains most formulations of the GAN objective introduced so far. Taping into the mathematical programming literature, we counter some common misconceptions about the difficulties of saddle point optimization and propose to extend standard methods designed for variational inequalities to GANs training, such as a stochastic version of the extragradient method, and empirically investigate their behavior on GANs.


title: Aviation Noise Impacts: State of the Science

abstract: Noise is defined as 'unwanted sound.' Aircraft noise is one, if not the most detrimental environmental effect of aviation. It can cause community annoyance, disrupt sleep, adversely affect academic performance of children, and could increase the risk for cardiovascular disease of people living in the vicinity of airports. In some airports, noise constrains air traffic growth. This consensus paper was prepared by the Impacts of Science Group of the Committee for Aviation Environmental Protection of the International Civil Aviation Organization and summarizes the state of the science of noise effects research in the areas of noise measurement and prediction, community annoyance, children's learning, sleep disturbance, and health. It also briefly discusses civilian supersonic aircraft as a future source of aviation noise. © 2017 Noise & Health | Published by Wolters Kluwer - Medknow.


title: A high-isolation eight-way Power Combiner

abstract: A high-isolation eight-way power combiner is proposed in this paper. It is constructed by cascading three-stage power combiners in series. Its high isolation and low return loss characteristics are realized by the lumped isolators, side isolated network, and resistive septum. The eight-way power combiner is substantially a 17-port mode network. It consists of eight input ports, eight matching ports, and one output port. The mode network matrix cascade computation clearly reveals its working mechanism. Based on the 17-port network, an eight-way power combiner is realized with eight matching ports connected to loads. The model has been constructed and simulated in Hfss software. Simulation shows that from 8 to 10 GHz, the return loss is greater than 20 dB, the isolation is greater than 20 dB, and insertion loss is less than 0.15 dB. Its cross-section areas are approximately 0.3 λ×1.5 λ for the central frequency of 9 GHz. It is suitable for the integration of high power amplifiers.


title: Prediction, operations, and condition monitoring in wind energy

abstract: Recent developments in wind energy research including wind speed prediction, wind turbine control, operations of hybrid power systems, as well as condition monitoring and fault detection are surveyed. Approaches based on statistics, physics, and data mining for wind speed prediction at different time scales are reviewed. Comparative analysis of prediction results reported in the literature is presented. Studies of classical and intelligent control of wind turbines involving different objectives and strategies are reported. Models for planning operations of different hybrid power systems including wind generation for various objectives are addressed. Methodologies for condition monitoring and fault detection are discussed. Future research directions in wind energy are proposed.


title: Form Follows Sound: Designing Interactions from Sonic Memories

abstract: Sonic interaction is the continuous relationship between user actions and sound, mediated by some technology. Because interaction with sound may be task oriented or experience-based it is important to understand the nature of action-sound relationships in order to design rich sonic interactions. We propose a participatory approach to sonic interaction design that first considers the affordances of sounds in order to imagine embodied interaction, and based on this, generates interaction models for interaction designers wishing to work with sound. We describe a series of workshops, called Form Follows Sound, where participants ideate imagined sonic interactions, and then realize working interactive sound prototypes. We introduce the Sonic Incident technique, as a way to recall memorable sound experiences. We identified three interaction models for sonic interaction design: conducting; manipulating; substituting. These three interaction models offer interaction designers and developers a framework on which they can build richer sonic interactions.


title: Traffic Classification of Mobile Apps through Multi-Classification

abstract: The wide spreading and growing usage of smartphones are deeply changing the kind of traffic that traverses home and enterprise networks and the Internet. Tools that base their functions on the knowledge of the application generating the traffic (performance enhancement proxies, network monitors, policy enforcement devices) imply traffic classification, and are thus limited or impaired when dealing with the daily expanding set of mobile apps. Besides the moving-target nature of mobile apps traffic, the increasing adoption of encrypted protocols (TLS) makes classification even more challenging, defeating established approaches (DPI, statistical classifiers). In this paper we aim to improve the classification performance of mobile apps classifiers adopting a multi-classification approach, intelligently-combining decisions from state-of-art classifiers proposed for mobile and encrypted traffic classification. Based on a dataset of users' activity collected by a mobile solutions provider, our results demonstrate that classification performance can be improved according to all considered metrics, up to +8.1% F-measure score with respect to the best base classifier. Further room for improvements is also evidenced by the ideal combiner performance (oracle).


title: Alzheimer's disease: early alterations in brain DNA methylation at ANK1, BIN1, RHBDF2 and other loci

abstract: Aging can lead to cognitive decline associated with neural pathology and Alzheimer's disease (AD). Here the authors scan the methylation status of CpGs across the entire genome of brain samples from aged subjects in an epigenome-wide association study (EWAS). Several loci, including ANK1, were associated with AD pathology, gene expression and AD genetic risk networks.


title: LPDDR4X (3732 Mbps) DBI impact on SI/PI and power

abstract: Co-SI/PI analysis of a LPDDR4X SoC-PoP DRAM system operating at 0.6V VDDQ and data rate of 3732 Mbps is presented, quantifying the impact of Data Bus Inversion (DBI) coding on signal and power integrity. DBI significantly mitigates Victim/Aggressor and SSN scenarios. Eye-aperture improvements observed can be ∼15% for an 80% Vic/Agg plus SSN pattern with-DBI. Additionally, VDDQ power savings due to DBI is summarized and can be ∼50% for a full-channel running a 100% SSN data pattern.


title: Mesosternal bristle number in a cosmopolitan drosophilid: an X-linked variable trait independent of sternopleural bristles

abstract: Mesosternal (MS) bristles in Drosophila are a pair of machrochaetae found at the sternal end of the sternopleural (STP) microchaetae, and are thought to be invariable. In a closely related drosophilid genus, Zaprionus, their number is four and, in contrast to Drosophila, they show interspecific and intraspecific variability. The genetic basis of MS bristle number variability was studied in Z. indianus, the only cosmopolitan species of the genus. The trait responded rapidly to selection and two lines were obtained, one lacking any bristles (0-0) and the other bearing the normal phenotype (2-2). Other symmetrical phenotypes, (1-1) and (3-3), could also be selected for, but with lesser success. By contrast, STP bristle number did not vary significantly between the two lines (0-0) and (2-2), revealing its genetic independence from MS bristle number. Reciprocal crosses between these two lines showed that MS bristle number is mainly influenced by a major gene on the X chromosome (i.e. F(1) males always resembled their mothers) with codominant expression (i.e. heterozygous F(1) females harboured an average phenotype of 2 bristles). However, trait penetrance was incomplete and backcrosses revealed that this variability was partly due to genetic modifiers, most likely autosomal. The canalization of MS bristle number was investigated under different temperatures, and the increased appearance of abnormal phenotypes mainly occurred at extreme temperatures. There was a bias, however, towards bristle loss, as shown by a liability (developmental map) analysis. Finally, when ancestral and introduced populations were compared, the latter were far less stable, suggesting that genetic bottlenecks may perturb the MS bristle number canalization system. MS bristle number, thus, appears to be an excellent model for investigating developmental canalization at both the quantitative and the molecular level.


title: Prevalence and determinants of hypertension in Myanmar - a nationwide cross-sectional study

abstract: Non-communicable diseases (NCDs), malaria and tuberculosis dominate the disease pattern in Myanmar. Due to urbanization, westernized lifestyle and economic development, it is likely that NCDs such as cerebrovascular disease and ischemic heart disease are on a rise. The leading behavioral- and metabolic NCDs risk factors are tobacco smoke, dietary risks and alcohol use, and high blood pressure and body mass index, respectively. The study aimed at estimating the prevalence and determinants of hypertension, including metabolic-, behavioral- and socio-demographic risk factors. A nationwide, cross-sectional study of 7429 citizens of Myanmar aged 15–64 years were examined in 2009, using the WHO STEPS methodology. In separate analyses by gender, odds radios (ORs) and 95 % confidence intervals (CIs) for determinants of hypertension were estimated using logistic regression analyses. Confounders included in analyses were chosen based on Directed acyclic graphs (DAGs). The prevalence of hypertension was 30.1 % (95 % CI: 28.4–31.8) in males and 29.8 % (28.5–31.1) in females. The mean BMI was 21.7 (SD 4.3) kg/m2 for males and 23.0 (5.1) kg/m2 for females. In fully adjusted analyses, we found in both genders increased OR for hypertension if the participants had high BMI (males: OR = 2.6; 95 % CI 2.1–3.3, females: OR = 2.3; 2.0–2.7) and high waist circumference (males: OR = 3.4; 1.8–6.8, females: OR = 2.7; 2.2–3.3). In both sexes, associations were also found between hypertension and low physical activity at work, or living in urban areas or the delta region. Being underweight and use of sesame oil in cooking was associated with lower odds for hypertension. The prevalence of hypertension was high and associated with metabolic-, behavioral- and socio-demographic factors. Due to expected rapid economic growth in Myanmar we recommend similar studies in the future to follow up and describe trends in the risk factors, especially modifiable factors, which will most likely be on rise. Studies on effectiveness on interventions are needed, and policies to reduce the burden of NCD risk factors should be implemented if proven effective in similar settings.


title: A tree parameterization for efficiently computing maximum likelihood maps using gradient descent

abstract: In 2006, Olson et al. presented a novel approach to address the graph-based simultaneous localization and mapping problem by applying stochastic gradient descent to minimize the error introduced by constraints. Together with multi-level relaxation, this is one of the most robust and efficient maximum likelihood techniques published so far. In this paper, we present an extension of Olson's algorithm. It applies a novel parameterization of the nodes in the graph that significantly improves the performance and enables us to cope with arbitrary network topologies. The latter allows us to bound the complexity of the algorithm to the size of the mapped area and not to the length of the trajectory as it is the case with both previous approaches. We implemented our technique and compared it to multi-level relaxation and Olson's algorithm. As we demonstrate in simulated and in real world experiments, our approach converges faster than the other approaches and yields accurate maps of the environment.


title: Outdoor flight testing of a pole inspection UAV incorporating high-speed vision

abstract: We present a pole inspection system for outdoor environments comprising a high-speed camera on a vertical take-off and landing (VTOL) aerial platform. The pole inspection task requires a vehicle to fly close to a structure while maintaining a fixed stand-off distance from it. Typical GPS errors make GPS-based navigation unsuitable for this task however. When flying outdoors a vehicle is also affected by aerodynamics disturbances such as wind gusts, so the onboard controller must be robust to these disturbances in order to maintain the stand-off distance. Two problems must therefor be addressed: fast and accurate state estimation without GPS, and the design of a robust controller. We resolve these problems by a) performing visual + inertial relative state estimation and b) using a robust line tracker and a nested controller design. Our state estimation exploits high-speed camera images (100Hz) and 70Hz IMU data fused in an Extended Kalman Filter (EKF). We demonstrate results from outdoor experiments for pole-relative hovering, and pole circumnavigation where the operator provides only yaw commands. Lastly, we show results for image-based 3D reconstruction and texture mapping of a pole to demonstrate the usefulness for inspection tasks.


MEMS Capacitive Pressure Sensors: A Review on Recent Development and Prospective


title: Sentiment Analysis by Capsules

abstract: In this paper, we propose RNN-Capsule, a capsule model based on Recurrent Neural Network (RNN) for sentiment analysis. For a given problem, one capsule is built for each sentiment category e.g., ‘positive’, ‘neutral’, and ‘negative’. Each capsule has an attribute, a state, and three modules: representation module, probability module, and reconstruction module. The attribute of a capsule is the assigned sentiment category. Given an instance encoded in hidden vectors by a typical RNN, the representation module builds capsule representation by the attention mechanism. Based on capsule representation, the probability module computes the capsule’s state probability. A capsule’s state is active if its state probability is the largest among all capsules for the given instance, and inactive otherwise. On two benchmark datasets (i.e., Movie Review and Stanford Sentiment Treebank) and one proprietary dataset (i.e., Hospital Feedback), we show that RNN-Capsule achieves state-of-the-art performance on sentiment classification. More importantly, without using any linguistic knowledge, RNN-Capsule is capable of outputting words with sentiment tendencies reflecting capsules’ attributes. The words well reflect the domain specificity of the dataset. To the best of our knowledge, this is the first capsule model for sentiment analysis.


title: A survey of deep learning for polyphonic sound event detection

abstract: Deep learning has achieved state of the art in various machine learning problems, such as computer vision, speech recognition, and natural language processing. Sound event detection (SED), which is about recognizing audio events in real-life environments, has attracted a lot of attention recently. Many works have been successful when applying deep learning techniques for the SED problem as can be seen in Detection and Classification of Acoustic Scenes and Events (DCASE) challenge 2016–2017. In this paper, we present a review of the SED problem and discuss different deep learning approaches for the problem.


title: 2.4 Gbps, 7 mW All-Digital PVT-Variation Tolerant True Random Number Generator for 45 nm CMOS High-Performance Microprocessors

abstract: This paper describes an all-digital PVT-variation tolerant true-random number generator (TRNG), fabricated in 45 nm high-k/metal-gate CMOS, targeted for on-die entropy generation in high-performance microprocessors. The TRNG harvests differential thermal-noise at the diffusion nodes of a pre-charged cross-coupled inverter pair to resolve out of metastability, generating one random bit/cycle. A self-calibrating 2-step tuning mechanism using coarse-grained configurable inverters and fine-grained programmable clock delay generators, along with an entropy-tracking feedback loop provide tolerance to 20% PVT variation-induced device mismatches, enabling lowest-reported energy-consumption of 2.9 pJ/bit with a dense layout occupying 4004 μm2, while achieving: (i) 2.4 Gbps random bit throughput, 7 mW total power consumption with 0.7 mW leakage power component, measured at 1.1 V, 50°C, (ii) random bitstreams that passes all NIST RNG tests with raw entropy/bit measured up to 0.9999999993, (iii) good distribution of 1's with 4-bit entropy of 3.97996 and high-entropy pattern probability of 0.066 (iv) wide operating supply voltage range with robust sub-threshold voltage performance of 14 Mbps, 5.6 μW, measured at 280 mV, 50°C, (v) 12 fine-grained high-entropy settings for the TRNG to dither in during steady-state operation, (vi) <;3% error while using an analytical ergodic Markov chain model for predicting pattern probabilities and (vii) 200x higher throughput and 9x higher energy-efficiency than previously reported implementations. Design modifications for robust operation in 22 nm high-volume manufacturing in the presence of 3σ process variations demonstrate scalability of the all-digital design to future technologies.


title: The flash tube and its applications

abstract: A flash tube is a gas-filled device designed to produce a brilliant flash of light when it is used to short-circuit a charged capacitor. The discharge is characterized by a high output of radiation in the infrared and ultra-violet as well as in the visible regions.A brief historical review is followed by an outline of the general construction of modern flash tubes. Their electrical characteristics are given together with curves showing current, voltage and power against time. The effect of the tube parameters on the complete circuit characteristics are examined.Some measurements of light intensity, luminous energy and efficiency are given, and the development of the typical high-current arc spectra of xenon under flash-tube conditions is illustrated by a series of spectrograms.Circuits for the control of these tubes are described, suitable for both isolated-flash and repetition-flash applications.The concluding Sections deal with a number of recent applications and with an examination of the possibilities of the flash tube as an aid to the examination of high-speed phenomena, both photographically and visually.


First Record of a Mangrove Frog Fejervarya cancrivora (Amphibia: Ranidae) In the Pondicherry Mangroves, Bay of Bengal-India


title: ClusCite: effective citation recommendation by information network-based clustering

abstract: Citation recommendation is an interesting but challenging research problem. Most existing studies assume that all papers adopt the same criterion and follow the same behavioral pattern in deciding relevance and authority of a paper. However, in reality, papers have distinct citation behavioral patterns when looking for different references, depending on paper content, authors and target venues. In this study, we investigate the problem in the context of heterogeneous bibliographic networks and propose a novel cluster-based citation recommendation framework, called ClusCite, which explores the principle that citations tend to be softly clustered into interest groups based on multiple types of relationships in the network. Therefore, we predict each query's citations based on related interest groups, each having its own model for paper authority and relevance. Specifically, we learn group memberships for objects and the significance of relevance features for each interest group, while also propagating relative authority between objects, by solving a joint optimization problem. Experiments on both DBLP and PubMed datasets demonstrate the power of the proposed approach, with 17.68% improvement in Recall@50 and 9.57% growth in MRR over the best performing baseline.


title: Wearable Gait Measurement System with an Instrumented Cane for Exoskeleton Control

abstract: In this research we introduce a wearable sensory system for motion intention estimation and control of exoskeleton robot. The system comprises wearable inertial motion sensors and shoe-embedded force sensors. The system utilizes an instrumented cane as a part of the interface between the user and the robot. The cane reflects the motion of upper limbs, and is used in terms of human inter-limb synergies. The developed control system provides assisted motion in coherence with the motion of other unassisted limbs. The system utilizes the instrumented cane together with body worn sensors, and provides assistance for start, stop and continuous walking. We verified the function of the proposed method and the developed wearable system through gait trials on treadmill and on ground. The achievement contributes to finding an intuitive and feasible interface between human and robot through wearable gait sensors for practical use of assistive technology. It also contributes to the technology for cognitively assisted locomotion, which helps the locomotion of physically challenged people.


title: EasyGO: Gene Ontology-based annotation and functional enrichment analysis tool for agronomical species

abstract: BackgroundIt is always difficult to interpret microarray results. Recently, a handful of tools have been developed to meet this need, but almost none of them were designed to support agronomical species.DescriptionThis paper presents EasyGO, a web server to perform Gene Ontology based functional interpretation on groups of genes or GeneChip probe sets. EasyGO makes a special contribution to the agronomical research community by supporting Affymetrix GeneChips of both crops and farm animals and by providing stronger capabilities for results visualization and user interaction. Currently it supports 11 agronomical plants, 3 farm animals, and the model plant Arabidopsis. The authors demonstrated EasyGO's ability to uncover hidden knowledge by analyzing a group of probe sets with similar expression profiles.ConclusionEasyGO is a good tool for helping biologists and agricultural scientists to discover enriched biological knowledge that can provide solutions or suggestions for original problems. It is freely available to all users at http://bioinformatics.cau.edu.cn/easygo/.


abstract: Du, H., King, R.B. & Chi, P. (2012). The development and validation of the Relational Self-Esteem Scale. Scandinavian Journal of Psychology 53, 258–264. ::: ::: According to the tripartite model of the self (Brewer & Gardner, 1996), the self consists of three aspects: personal, relational, and collective. Correspondingly, individuals can achieve a sense of self-worth through their personal attributes (personal self-esteem), relationship with significant others (relational self-esteem), or social group membership (collective self-esteem). Existing measures on personal and collective self-esteem are available in the literature; however, no scale exists that assesses relational self-esteem. The authors developed a scale to measure individual differences in relational self-esteem and tested it with two samples of Chinese university students. Between and within-network approaches to construct validation were used. The scale showed adequate internal consistency reliability and results of the confirmatory factor analysis showed good fit. It also exhibited meaningful correlations with theoretically relevant constructs in the nomological network. Implications and directions for future research are discussed.


title: Discovering author impact: A PageRank perspective

abstract: This article provides an alternative perspective for measuring author impact by applying PageRank algorithm to a coauthorship network. A weighted PageRank algorithm considering citation and coauthorship network topology is proposed. We test this algorithm under different damping factors by evaluating author impact in the informetrics research community. In addition, we also compare this weighted PageRank with the h-index, citation, and program committee (PC) membership of the International Society for Scientometrics and Informetrics (ISSI) conferences. Findings show that this weighted PageRank algorithm provides reliable results in measuring author impact.


title: Deep Factors with Gaussian Processes for Forecasting

abstract: A large collection of time series poses significant challenges for classical and neural forecasting approaches. Classical time series models fail to fit data well and to scale to large problems, but succeed at providing uncertainty estimates. The converse is true for deep neural networks. In this paper, we propose a hybrid model that incorporates the benefits of both approaches. Our new method is data-driven and scalable via a latent, global, deep component. It also handles uncertainty through a local classical Gaussian Process model. Our experiments demonstrate that our method obtains higher accuracy than state-of-the-art methods.


Intellectual property law : principles in practice


title: A rough TOPSIS Approach for Failure Mode and Effects Analysis in Uncertain Environments

abstract: This study aims at improving the effectiveness of failure mode and effect analysis (FMEA) technique. FMEA is a widely used technique for identifying and eliminating known or potential failures from system, design, and process. However, in conventional FMEA, risk factors of Severity (S), Occurrence (O), and Detection difficulty (D) are simply multiplied to obtain a crisp risk priority number without considering the subjectivity and vagueness in decision makers’ judgments. Besides, the weights for risk factors S, O, and D are also ignored. As a result, the effectiveness and accuracy of the FMEA are affected. To solve this problem, a novel FMEA approach for obtaining a more rational rank of failure modes is proposed. Basically, two stages of evaluation process are described: the determination of risk factors’ weights and ranking the risk for the failure modes. A rough group ‘Technique for Order Performance by Similarity to Ideal Solution’ (TOPSIS) method is used to evaluate the risk of failure mode. The novel approach integrates the strength of rough set theory in handling vagueness and the merit of TOPSIS in modeling multi-criteria decision making. Finally, an application in steam valve system is provided to demonstrate the potential of the methodology under vague and subjective environment. Copyright © 2013 John Wiley & Sons, Ltd.


title: A conceptual comparison of firefly algorithm, bat algorithm and cuckoo search

abstract: There are various mathematical optimization problems that can be effectively solved by metaheuristic algorithms. The advantage of these algorithms is that they perform iterative search processes which efficiently perform exploration and exploitation in the domain space containing local and global optima. In this context, three types of metaheuristic algorithms called firefly algorithm, bat algorithm and cuckoo search algorithm were used to find optimal solutions. Firefly is inspired by behavior of flies, bat algorithm is based on the echolocation behavior of bats while in cuckoo search, a pattern corresponds to a nest and similarly each individual attribute of the pattern corresponds to a cuckoo-egg. A series of computational experiments using each algorithm were conducted. Experimental results were analyzed and it is observed that firefly algorithm seems to perform better than bat algorithm and cuckoo search.


title: Clustervision: Visual Supervision of Unsupervised Clustering

abstract: Clustering, the process of grouping together similar items into distinct partitions, is a common type of unsupervised machine learning that can be useful for summarizing and aggregating complex multi-dimensional data. However, data can be clustered in many ways, and there exist a large body of algorithms designed to reveal different patterns. While having access to a wide variety of algorithms is helpful, in practice, it is quite difficult for data scientists to choose and parameterize algorithms to get the clustering results relevant for their dataset and analytical tasks. To alleviate this problem, we built Clustervision , a visual analytics tool that helps ensure data scientists find the right clustering among the large amount of techniques and parameters available. Our system clusters data using a variety of clustering techniques and parameters and then ranks clustering results utilizing five quality metrics. In addition, users can guide the system to produce more relevant results by providing task-relevant constraints on the data. Our visual user interface allows users to find high quality clustering results, explore the clusters using several coordinated visualization techniques, and select the cluster result that best suits their task. We demonstrate this novel approach using a case study with a team of researchers in the medical domain and showcase that our system empowers users to choose an effective representation of their complex data.


title: How to manage project opportunity and risk

abstract: This re-titled and extensively revised book builds on the success of an established classic text. It also builds on more than thirty five years of successful consulting practice, addressing practical situations that range from major offshore oil development projects to projects limited to replacing a domestic bathroom floor covering. It synthesises this practical experience with a very broad relevant literature. The target audience includes board level senior managers responsible for project, programme and project portfolio aspects of corporate policy, and their integration with corporate strategy and operations. It includes those charged with implementing projects at all levels, including uncertainty, opportunity and risk management professionals. And it includes aspiring members of these groups. It shows why current project risk management practice, and related enterprise risk management practice, starts in the wrong place, pursues an inappropriate set of goals, uses the wrong tools, and fails to deliver what is needed. This book goes beyond current project risk management orthodoxy in a number of ways. One is the use of an ‘uncertainty management’ perspective which transforms the scope of opportunities to enhance corporate performance, a central theme. A second is a ‘whole asset lifecycle’ perspective on all aspects of change management. A third is a holistic integration of quantitative and qualitative uncertainty management processes which clarify opportunity and risk recognising the subjective issues involved. A fourth is showing how uncertainty management and the rest of project management can be integrated, and all aspects of corporate uncertainty, opportunity and risk management can be integrated. It shows how surprisingly simple approaches can lead to surprisingly powerful insights and results – used in the right place. It also shows why some impressively sophisticated and costly approaches involving common practice tools can create confusion and divert management effort away from what really matters. Uncertainty management as described in this book is a process driven approach using concepts and tools which replace many common practice risk management ideas. They can be used to make better decisions with less cost, realising more opportunities for less effort, and taking less risk for more reward, to transform corporate performance


title: Quantification of training load during one-, two- and three-game week schedules in professional soccer players from the English Premier League: implications for carbohydrate periodisation

abstract: ABSTRACTMuscle glycogen is the predominant energy source for soccer match play, though its importance for soccer training (where lower loads are observed) is not well known. In an attempt to better inform carbohydrate (CHO) guidelines, we quantified training load in English Premier League soccer players (n = 12) during a one-, two- and three-game week schedule (weekly training frequency was four, four and two, respectively). In a one-game week, training load was progressively reduced (P < 0.05) in 3 days prior to match day (total distance = 5223 ± 406, 3097 ± 149 and 2912 ± 192 m for day 1, 2 and 3, respectively). Whilst daily training load and periodisation was similar in the one- and two-game weeks, total accumulative distance (inclusive of both match and training load) was higher in a two-game week (32.5 ± 4.1 km) versus one-game week (25.9 ± 2 km). In contrast, daily training total distance was lower in the three-game week (2422 ± 251 m) versus the one- and two-game weeks, though accumulative weekly d...


title: Individual differences and Information Security Awareness

abstract: The main purpose of this study was to examine the relationship between individuals' Information Security Awareness (ISA) and individual difference variables, namely age, gender, personality and risk-taking propensity. Within this study, ISA was defined as individuals' knowledge of what policies and procedures they should follow, their understanding of why they should adhere to them (their attitude) and what they actually do (their behaviour). This was measured using the Human Aspects of Information Security Questionnaire (HAIS-Q). Individual difference variables were examined via a survey of 505 working Australians. It was found that conscientiousness, agreeableness, emotional stability and risk-taking propensity significantly explained variance in individuals ISA, while age and gender did not. Findings highlighted the need for future research to examine individual differences and their impact on ISA. Results of the study can be applied by industry to develop tailored InfoSec training programs. Emotional stability was positively associated with Information Security Awareness (ISA).Conscientiousness and agreeableness were positively associated with ISA.Individuals with a propensity to take fewer risks scored higher on ISA.


title: MDA Model Transformation Methods for EDOC ER Models

abstract: The Model Driven Architecture(MDA)proposed by OMG uses software models as the primary artifacts of software development.In MDA,application system models are first constructed independently of specific platforms and implementation technologies.These high abstract models can be automatically mapped into low-level models or declarative codes by model transformation technologies.The efficacy of Model Driven Architecture is mainly determined by the efficacy of model transformation tools.The main difficult problem of model transformation is to maintain semantic consistency between models of different abstract levels during transformation.We use the EDOC profile,a platform-independent standard adopted by OMG,to describe PIMs.This standard is designed for modeling distributed enterprise applications.In this paper,we focus on the problem of how to define transformation rules from EDOC entity and relationship models to concrete implementations on J2EE platform.Several design patterns are introduced into model transformation rules in order to improve transformation efficacy.Using these transformation rules,platform-independent models(PIMs)can be transformed to J2EE platform dependent models(PSMs)automatically.


title: Biopesticides in the framework of the European Pesticide Regulation (EC) No. 1107/2009.

abstract: BACKGROUND ::: The European Pesticide Regulation (EC) No. 1107/2009 encourages the use of less harmful active substances. Two main concerns involve the application of cut-off criteria for pesticides without losing tools for future agriculture (especially for minor uses) and the implementation of zonal evaluations. Biopesticides are considered to have lower risks than synthetic pesticides; consequently, there is strong interest for their use in integrated pest management practices. ::: ::: ::: RESULTS ::: This paper provides an analysis of the current European situation, starting with the first attempts to regulate the use of plant protection products and focusing on the implications of the new legislative criteria for biopesticides. ::: ::: ::: CONCLUSION ::: It is important to be aware that biopesticides are still pesticides and fall under the same regulations as their synthetic counterparts. Although manufacturers are still reluctant to commit to such alternatives due to difficulties with approval and registration, biopesticides could be alternatives for traditional plant protection products, either as a base for the synthesis of new products or integrated with traditional plant protection products. In addition, biopesticides have to be used only as indicated on the label, which provides critical information about how to safely handle and use plant protection products.


Theorizing Toward a Theory of Social Practices : A Development in Culturalist


title: PROMETHEE and AHP: The design of operational synergies in multicriteria analysis.: Strengthening PROMETHEE with ideas of AHP

abstract: Abstract This paper discusses the strengths and weaknesses of the Preference Ranking Organisation MeTHod for Enrichment Evaluations (PROMETHEE) and analytic hierarchy process (AHP) methods. Building upon this analysis, recommendations are formulated to integrate into PROMETHEE a number of useful AHP features, especially as regards the design of the decision-making hierarchy (ordering of goals, sub-goals, dimensions, criteria, projects, etc.) and the determination of weights. As a result of mixing basis features of both methods, operational synergies can be achieved in MCA.


title: A joint backhaul and RAN perspective on the benefits of centralised RAN functions

abstract: Cloud-RAN (C-RAN) is considered a prime 5G enabler with promising resource pooling gains, tighter coordination among cells, and cost saving in remote radio heads and corresponding deployment and operation. However, C-RAN brings stringent requirements on the backhaul last mile, or the fronthaul, in terms of capacity, latency, and synchronisation, to the extent that direct fibre is believed to be the only plausible fronthaul solution. Knowing that more often than not, fibre to the home is not available and is a cumbersome and costly technology to provide, what are the alternatives for deploying C-RAN? How much loss is incurred in a 5G network if C-RAN is not available? On the other hand, the distributed RAN (D-RAN) is less demanding on the backhaul but is believed to lack in performance in terms of resource usage and efficiency of RAN deployment. In this work we address the comparison between C-RAN and D-RAN from a joint RAN and backhaul perspective in a quantitative manner, using a case study approach. Our results show that C-RAN is indeed cost effective and advantageous from a joint perspective; moreover, intermediate functional splits between the C-RAN and the D-RAN are promising as an evolution path towards 5G, in the absence of fibre.


title: Clipper: A Low-Latency Online Prediction Serving System

abstract: Machine learning is being deployed in a growing number of applications which demand real-time, accurate, and robust predictions under heavy query load. However, most machine learning frameworks and systems only address model training and not deployment. ::: In this paper, we introduce Clipper, a general-purpose low-latency prediction serving system. Interposing between end-user applications and a wide range of machine learning frameworks, Clipper introduces a modular architecture to simplify model deployment across frameworks and applications. Furthermore, by introducing caching, batching, and adaptive model selection techniques, Clipper reduces prediction latency and improves prediction throughput, accuracy, and robustness without modifying the underlying machine learning frameworks. We evaluate Clipper on four common machine learning benchmark datasets and demonstrate its ability to meet the latency, accuracy, and throughput demands of online serving applications. Finally, we compare Clipper to the TensorFlow Serving system and demonstrate that we are able to achieve comparable throughput and latency while enabling model composition and online learning to improve accuracy and render more robust predictions.


Proxy Caching Mechanism for Multimedia Playback Streams in the Internet


title: Convergence Analysis of Inexact Randomized Iterative Methods

abstract: The first author would like to acknowledge Robert Mansel Gower, Georgios Loizou and Rachael Tappenden for useful discussions.


title: Repairing Reed-Solomon Codes With Multiple Erasures

abstract: Despite their exceptional error-correcting properties, Reed-Solomon (RS) codes have been overlooked in distributed storage applications due to the common belief that they have poor repair bandwidth: A naive repair approach would require the whole file to be reconstructed in order to recover a single erased codeword symbol. In a recent work, Guruswami and Wootters (STOC'16) proposed a single-erasure repair method for RS codes that achieves the optimal repair bandwidth amongst all linear encoding schemes. Their key idea is to recover the erased symbol by collecting a sufficiently large number of its traces, each of which can be constructed from a number of traces of other symbols. As all traces belong to a subfield of the defining field of the RS code and many of them are linearly dependent, the total repair bandwidth is significantly reduced compared to that of the naive repair scheme. We extend the trace collection technique to cope with multiple erasures.


title: Normalized metal artifact reduction (NMAR) in computed tomography

abstract: Purpose: While modern clinical CT scanners under normal circumstances produce high quality images, severe artifacts degrade the image quality and the diagnostic value if metal prostheses or other metal objects are present in the field of measurement. Standard methods for metal artifact reduction (MAR) replace those parts of the projection data that are affected by metal (the so-called metal trace or metal shadow) by interpolation. However, while sinogram interpolation methods efficiently remove metal artifacts, new artifacts are often introduced, as interpolation cannot completely recover the information from the metal trace. The purpose of this work is to introduce a generalized normalization technique for MAR, allowing for efficient reduction of metal artifacts while adding almost no new ones. The method presented is compared to a standard MAR method, as well as MAR using simple length normalization. ::: ::: Methods: In the first step, metal is segmented in the image domain by thresholding. A 3D forward projection identifies the metal trace in the original projections. Before interpolation, the projections are normalized based on a 3D forward projection of a prior image. This prior image is obtained, for example, by a multithreshold segmentation of the initial image. The original rawdata are divided by the projection data of the prior image and, after interpolation, denormalized again. Simulations and measurements are performed to compare normalized metal artifact reduction (NMAR) to standard MAR with linear interpolation and MAR based on simple length normalization. ::: ::: Results: Promising results for clinical spiral cone-beam data are presented in this work. Included are patients with hip prostheses, dental fillings, and spine fixation, which were scanned at pitch values ranging from 0.9 to 3.2. Image quality is improved considerably, particularly for metal implants within bone structures or in their proximity. The improvements are evaluated by comparing profiles through images and sinograms for the different methods and by inspecting ROIs. NMAR outperforms both other methods in all cases. It reduces metal artifacts to a minimum, even close to metal regions. Even for patients with dental fillings, which cause most severe artifacts, satisfactory results are obtained with NMAR. In contrast to other methods, NMAR prevents the usual blurring of structures close to metal implants if the metal artifacts are moderate. ::: ::: Conclusions: NMAR clearly outperforms the other methods for both moderate and severe artifacts. The proposed method reliably reduces metal artifacts from simulated as well as from clinical CT data. Computationally efficient and inexpensive compared to iterative methods, NMAR can be used as an additional step in any conventional sinogram inpainting-based MAR method.


Segmentation-Free Word Embedding for Unsegmented Languages


The influence of cognitive skills and team cohesion on player performance in Multiplayer Online Battle Arena