title: Shifting Factors and the Ineffectiveness of Third Party Assurance Seals: A Two-Stage Model of Initial Trust in a Web Business

abstract: Little empirical research has addressed how trust building occurs across the early stages of a consumer's web experience. This paper tests the effects of privacy assurance and industry endorsement seals, reputation advertising by a web business, disposition to trust, and structural assurance on consumer trust across two early stages: the introductory stage, in which a consumer has only second‐hand information about the site, and the exploratory stage, when the consumer first visits the website. The study found, first, that disposition to trust and structural assurance had a significant effect on trust in the web business in both the exploratory and introductory stages, showing that their effects did not erode over time. Second, reputation advertising was found to be an effective way to build trust. Third, counter to prevailing opinion and popular practice, neither a noticeable TRUST‐e privacy seal nor a noticeable professional association seal had any significant impact on trust in the web business. When ...


title: Informative Characteristics of 12 Divergent Domains in Complete Large Subunit rDNA Sequences from the Harmful Dinoflagellate Genus, Alexandrium (Dinophyceae)

abstract: . The genus Alexandrium includes organisms of interest, both for the study of dinoflagellate evolution and for their impacts as toxic algae affecting human health and fisheries. Only partial large subunit (LSU) rDNA sequences of Alexandrium and other dinoflagellates are available, although they contain much genetic information. Here, we report complete LSU rDNA sequences from 11 strains of Alexandrium, including Alexandrium affine, Alexandrium catenella, Alexandrium fundyense, Alexandrium minutum, and Alexandrium tamarense, and discuss their segmented domains and structure. Putative LSU rRNA coding regions were recorded to be around 3,400bp long. Their GC content (about 43.7%) is among the lowest when compared with other organisms. Furthermore, no AT-rich regions were found in Alexandrium LSU rDNA, although a low GC content was recorded within the LSU rDNA. No intron-like sequences were found. The secondary structure of the LSU rDNA and parsimony analyses showed that most variation in LSU rDNA is found in the divergent (D) domains with the D2 region being the most informative. This high D domain variability can allow members of the diverse Alexandrium genus to be categorized at the species level. In addition, phylogenetic analysis of the alveolate group using the complete LSU sequences strongly supported previous findings that the dinoflagellates and apicomplexans form a clade.


Ni-doped MnO 2 and Its Electrochemical Properties


title: Adaptive Parallelism and Piranha

abstract: Desktop computers are idle much of the time. Ongoing trends make aggregate LAN "waste"-idle compute cycles-an increasingly attractive target for recycling. Piranha, a software implementation of adaptive parallelism, allows these waste cycles to be recaptured by putting them to work running parallel applications. Most parallel processing is static: programs execute on a fixed set of processors throughout a computation. Adaptive parallelism allows for dynamic processor sets which means that the number of processors working on a computation may vary, depending on availability. With adaptive parallelism, instead of parceling out jobs to idle workstations, a single job is distributed over many workstations. Adaptive parallelism is potentially valuable on dedicated multiprocessors as well, particularly on massively parallel processors. One key Piranha advantage is that task descriptors, not processes, are the basic movable, remappable computation unit. The task descriptor approach supports strong heterogeneity. A process image representing a task in mid computation can't be moved to a machine of a different type, but a task descriptor can be. Thus, a task begun on a Sun computer can be completed by an IBM machine. The authors show that adaptive parallelism has the potential to integrate heterogeneous platforms seamlessly into a unified computing resource and to permit more efficient sharing of traditional parallel processors than is possible with current systems. >


title: Pubertal hormones modulate the addition of new cells to sexually dimorphic brain regions

abstract: New cells, including neurons, arise in several brain regions during puberty in rats. Sex differences in pubertal addition of cells coincide with adult sexual dimorphisms: for each region, the sex that gains more cells during puberty has a larger volume in adulthood. Removing gonadal hormones before puberty eliminates these sex differences, indicating that gonadal steroids direct the addition of new cells during puberty to maintain and accentuate sexual dimorphisms in the adult brain.


title: Anatomy-specific classification of medical images using deep convolutional nets

abstract: Automated classification of human anatomy is an important prerequisite for many computer-aided diagnosis systems. The spatial complexity and variability of anatomy throughout the human body makes classification difficult. “Deep learning” methods such as convolutional networks (ConvNets) outperform other state-of-the-art methods in image classification tasks. In this work, we present a method for organ- or body-part-specific anatomical classification of medical images acquired using computed tomography (CT) with ConvNets. We train a ConvNet, using 4,298 separate axial 2D key-images to learn 5 anatomical classes. Key-images were mined from a hospital PACS archive, using a set of 1,675 patients. We show that a data augmentation approach can help to enrich the data set and improve classification performance. Using ConvNets and data augmentation, we achieve anatomy-specific classification error of 5.9 % and area-under-the-curve (AUC) values of an average of 0.998 in testing. We demonstrate that deep learning can be used to train very reliable and accurate classifiers that could initialize further computer-aided diagnosis.


title: Challenges and needs for automating nano image processing for material characterization

abstract: Image processing techniques are needed to extract critical information pertinent to nano material characterization but the current processing methods are slow, expensive and labor intensive. There is a strong need to develop fast and reliable methods, enabling process control compatible automated processing of nano images. The authors believe specialized techniques are needed to address the challenges, and will discuss the recent development of nano image processing methods as well as the near- and medium-terms needs in the area of nano metrology and imaging. The authors will share their broad perspectives on this research direction.


Influence of Power Delivery Timing on the Energetics and Biomechanics of Humans Wearing a Hip Exoskeleton


title: Prediction of Student Academic Performance by an Application of K-Means Clustering Algorithm

abstract: Data Clustering is used to extract meaningful information and to develop significant relationships among variables stored in large data set/data warehouse. In this paper data clustering technique named k-means clustering is applied to analyze student's learning behavior. The student's evaluation factor like class quizzes, mid and final exam assignment are studied. It is recommended that all these correlated information should be conveyed to the class advisor before the conduction of final exam. This study will help the teachers to reduce the drop out ratio to a significant level and improve the performance of students.


title: Combining space exploration and heuristic search in online motion planning for nonholonomic vehicles

abstract: This paper presents an efficient motion planning method for nonholonomic vehicles, which combines space exploration and heuristic search to achieve online performance. The space exploration employs simple geometric shapes to investigate the collision-free space for the dimension and topology information. Then, the heuristic search is guided by this knowledge to generate vehicle motions under kinodynamic constraints. The overall performance of this framework greatly benefits from the cooperation of these two simple generic algorithms in suitable domains, which sequentially handles the free-space information and kinodynamic constraints. Experimental results show that this method is able to generate motions for nonholonomic vehicles in a time frame of less than 100 milliseconds for the given problem settings. The contribution of this work is the development of a Space Exploration Guided Heuristic Search with a circle-path based heuristics and adaptable search step size. The approach is grid-free and able to plan nonholonomic vehicle motions under kinodynamic constraints.


title: Breaking the fidelity barrier: an examination of our current characterization of prototypes and an example of a mixed-fidelity success

abstract: This paper presents a summary of the space of commonly-used HCI prototyping methods (low-fidelity to high-fidelity) and asserts that with a better understanding of this space, HCI practitioners will be better equipped to direct scarce prototyping resources toward an effort likely to yield specific results. It presents a set of five dimensions along which prototypes can be planned and characterized. The paper then describes an analysis of this space performed by members of the NASA Ames Human-Computer Interaction Group when considering prototyping approaches for a new set of tools for Mars mission planning and scheduling tools. A description is presented of a prototype that demonstrates design solutions that would have been particularly difficult to test given conventional low- or mid- fidelity prototyping methods. The prototype created was "mixed-fidelity," that is, high-fidelity on some dimensions and low-fidelity on others. The prototype is compared to a preexisting tool being redesigned and to a tool that has been developed using the prototype. Experimental data are presented that show the prototype to be a good predictor of eventual user performance with the final application. Given the relative cost of developing prototypes, it is critical to better characterize the space of fidelity in order to more precisely allocate design and development resources.


title: Simultaneous object detection and localization using convolutional neural networks

abstract: Nowadays deep learning is considered as a trendy technique in the computer vision domain. It becomes a pioneer in its main tasks as object classification, object localization, and object detection. Therefore it gave amazing results and records. In this paper, we propose a new approach to identify and localize objects, simultaneously, in a given scene using Convolutional Neural Networks (CNNs). We propose an end-to-end approach for object detection and pose estimation by formulating bounding boxes containing the targeted object and their pose. Our method is based on two main steps, i) produce Bounding boxes on the training images for generating the pose coordinates of each object in the scene and, ii) detect and localize simultaneously each object present in image during the testing step. The contribution performance is assessed on two datasets, Washington RGB scene dataset and LIMIARF dataset that is constructed in our laboratory. We demonstrate that our proposal is able to obtain high precision and reasonable recall levels.


title: Biometrics: a grand challenge

abstract: Reliable person identification is an important problem in diverse businesses. Biometrics, identification based on distinctive personal traits, has the potential to become an irreplaceable part of any identification system. While successful in some niche markets, the biometrics technology has not yet delivered its promise of foolproof automatic identification. With the availability of inexpensive biometric sensors and computing power, it is becoming increasingly clear that widespread usage of biometric person identification is being stymied by our lack of understanding of three fundamental problems; (i) How to accurately and efficiently represent and recognize biometric patterns? (ii) How to guarantee that the sensed measurements are not fraudulent? and (iii) How to make sure that the application is indeed exclusively using pattern recognition for the expressed purpose (function creep (A. K. JAin et al., December 1998))? Solving these core problems will not only catapult biometrics into mainstream applications but will also stimulate adoption of other pattern recognition applications for providing effective automation of sensitive tasks without jeopardizing our individual freedoms. For these reasons, we view biometrics as a grand challenge - "a fundamental problem in science and engineering with broad economic and scientific impact".


title: Food Image Recognition with Convolutional Neural Networks

abstract: In this paper, we propose a food image recognition system with convolutional neural networks(CNN), which has been applied to image recognition successfully in the literature. A CNN which consists of five layers has been built and two group of controlled trials have been processed on it. Two datasets are prepared: one is UEC-FOOD100 dataset which is an open 100-class food image dataset including about 15000 images and the other is a fruit dataset that established by ourselves including over 40000 images. We have achieved the best accuracy of 80.8% on the fruit dataset and 60.9% on the multi-food dataset. In addition, we validate the method on two groups of controlled trials and discover the effect of color under various conditions that the color feature is not always helpful for improving the accuracy by comparing the results of two group of controlled trials. As future work, we will combine image segmentation with image recognition to get a better performance.


title: Cutting the Cord in Virtual Reality

abstract: Today's virtual reality (VR) headsets require a cable connection to a PC or game console. This cable significantly limits the player’s mobility and hence her/his VR experience. The high data rate requirement of this link (multiple Gbps) precludes its replacement by WiFi. Thus, in this paper, we focus on using mmWave technology to deliver multi Gbps wireless communication between VR headsets and their game consoles. The challenge, however, is that mmWave signals can be easily blocked by the player's hand or head motion. We describe novel algorithms and system design that allow such mmWave links to sustain high data rates even in the presence of a blockage, enabling a high quality untethered VR experience.


title: Study on spatial variability of soil nutrients in Chongming of Shanghai by using GIS and Kriging method.

abstract: The spatial distributions of the main nutrients in the topsoil of Chongming Island in Shanghai are compreheasively explored in the research,including OM,TN,AN,AP,AK and CEC,based on the combined method of GIS and geostatistics.The Principle of minimized Nugget Effect is used in test the lag as well as other related parameters,which can keep the stability of the interpolation models and the comparability of the precision tests.The result shows:according to the samples,AK and CEC distributed with a mild variation,while AN,TN,AP and OM with an significant variation;by comparing the partial sill,the variation of OM,TN_L(Large scale),AN_L,AK and CEC are mainly impacted by ecological factors at a large scale,with a range over 10 km on the whole,while TN_s (Small scale),AN_s and AP by agricultural factors,with a range below 5 km;CEC bears an insignificant autocorelation,contrasting to the significant autocorelation of TN,and others bear moderate ones;tetra- spherical model is recognized as the most suitable model to OM and AP,spherical model to AK and CEC,and multi-scaled model to TN and AN(to TN,hole effect model to the large scale and spherical model to the small scale;to AN,Gaussian model to both scales);multi-scaled model adopted can harmonize the dilemma of global generalization and local accuracy;the maps of nutrients finally interpolated can illustrate the distribution of them in the continuity of the soil surface.


Estimation of unnormalized statistical models without numerical integration


title: Universal classes of hash functions (Extended Abstract)

abstract: This paper gives an input independent average linear time algorithm for storage and retrieval on keys. The algorithm makes a random choice of hash function from a suitable class of hash functions. Given any sequence of inputs the expected time (averaging over all functions in the class) to store and retrieve elements is linear in the length of the sequence. The number of references to the data base required by the algorithm for any input is extremely close to the theoretical minimum for any possible hash function with randomly distributed inputs. We present three suitable classes of hash functions which also may be evaluated rapidly. The ability to analyze the cost of storage and retrieval without worrying about the distribution of the input allows as corollaries improvements on the bounds of several algorithms.


Panoramic Scenes for Texture Mapping of 3D City Models


title: Putting Pieces Together: Combining FrameNet, VerbNet and WordNet for Robust Semantic Parsing

abstract: This paper describes our work in integrating three different lexical resources: FrameNet, VerbNet, and WordNet, into a unified, richer knowledge-base, to the end of enabling more robust semantic parsing. The construction of each of these lexical resources has required many years of laborious human effort, and they all have their strengths and shortcomings. By linking them together, we build an improved resource in which (1) the coverage of FrameNet is extended, (2) the VerbNet lexicon is augmented with frame semantics, and (3) selectional restrictions are implemented using WordNet semantic classes. The synergistic exploitation of various lexical resources is crucial for many complex language processing applications, and we prove it once again effective in building a robust semantic parser.


Revision Surgery for Recurrent Pain after Excision of the Accessory Navicular and Relocation of the Tibialis Posterior Tendon


title: Enlarged Krylov Subspace Methods and Preconditioners for Avoiding Communication

abstract: The performance of an algorithm on any architecture is dependent on the processing unit’s speed for performing floating point operations (flops) and the speed of accessing memory and disk. As the cost of communication is much higher than arithmetic operations, and since this gap is expected to continue to increase exponentially, communication is often the bottleneck in numerical algorithms. In a quest to address the communication problem, recent research has focused on communication avoiding Krylov subspace methods based on the so called s-step methods. However there are very few communication avoiding preconditioners, and this represents a serious limitation of these methods. In this thesis, we present a communication avoiding ILU0 preconditioner for solving large systems of linear equations (Ax=b) by using iterative Krylov subspace methods. Our preconditioner allows to perform s iterations of the iterative method with no communication, by applying a heuristic alternating min-max layers reordering to the input matrix A, and through ghosting some of the input data and performing redundant computation. We also introduce a new approach for reducing communication in the Krylov subspace methods, that consists of enlarging the Krylov subspace by a maximum of t vectors per iteration, based on the domain decomposition of the graph of A. The enlarged Krylov projection subspace methods lead to faster convergence in terms of iterations and to parallelizable algorithms with less communication, with respect to Krylov methods. We discuss two new versions of Conjugate Gradient, multiple search direction with orthogonalization CG (MSDO-CG) and long recurrence enlarged CG (LRE-CG).


title: A least squares formulation for canonical correlation analysis

abstract: Canonical Correlation Analysis (CCA) is a well-known technique for finding the correlations between two sets of multi-dimensional variables. It projects both sets of variables into a lower-dimensional space in which they are maximally correlated. CCA is commonly applied for supervised dimensionality reduction, in which one of the multi-dimensional variables is derived from the class label. It has been shown that CCA can be formulated as a least squares problem in the binaryclass case. However, their relationship in the more general setting remains unclear. In this paper, we show that, under a mild condition which tends to hold for high-dimensional data, CCA in multi-label classifications can be formulated as a least squares problem. Based on this equivalence relationship, we propose several CCA extensions including sparse CCA using 1-norm regularization. Experiments on multi-label data sets confirm the established equivalence relationship. Results also demonstrate the effectiveness of the proposed CCA extensions.


title: Gestalt interest points for image description in weight-invariant face recognition

abstract: In this work, we propose two improvements of the Gestalt Interest Points (GIP) algorithm for the recognition of faces of people that have underwent significant weight change. The basic assumption is that some interest points contribute more to the description of such objects than others. We assume that we can eliminate certain interest points to make the whole method more efficient while retaining our classification results. To find out which gestalt interest points can be eliminated, we did experiments concerning contrast and orientation of face features. Furthermore, we investigated the robustness of GIP against image rotation. The experiments show that our method is rotational invariant and - in this practically relevant forensic domain - outperforms the state-of-the-art methods such as SIFT, SURF, ORB and FREAK.


title: Toward a Science of Exceptional Achievement Attaining Superior Performance through Deliberate Practice

abstract: Exceptional performance is frequently attributed to genetic differences in talent. Since Sir Francis Galton's book, Hereditary Genius, many scientists have cited heritable factors that set limits of performance and only allow some individuals to attain exceptional levels. However, thus far these accounts have not explicated the causal processes involved in the activation and expression of unique genes in DNA that lead to the emergence of distinctive physiological attributes and cognitive capacities (innate talent). This article argues on the basis of our current knowledge that it is possible to account for the development of elite performance among healthy children without recourse to innate talent (genetic endowment)—excepting the innate determinants of body size. Our account is based on the expert-performance approach and proposes that the distinctive characteristics of exceptional performers are the result of adaptations to extended and intense practice activities that selectively activate dormant genes that are contained within all healthy individuals’ DNA. Furthermore, the theoretical framework of expert performance explains the apparent emergence of early talent by identifying factors that influence starting ages for training and the accumulated engagement in sustained extended deliberate practice, such as motivation, parental support, and access to the best training environments and teachers. In sum, our empirical investigations and extensive reviews show that the development of expert performance will be primarily constrained by individuals’ engagement in deliberate practice and the quality of the available training resources.


title: Development and implement of an inspection robot for power substation

abstract: Along with the development demand of intelligent power substation, mobile robot is implemented to serve power substations. The robot is equipped with inspecting sensors, such as CCD camera and thermal infrared imager which are together referred to as workload. These inspecting sensors are used to detect the environment parameters e.g. reading meter, measuring temperature etc., inside the substation. However, due to the strong electromagnetic interference inside substation, accurate localization of the workload and inspection robot is still a difficult problem to be solved. Existing methods solve the problem by paving magnetic strips or rail. However, in this way, the working area of the robot is limited by the paved infrastructures. In this paper, an inspection robot system is developed for working in substation. In the proposed system, the localization of the robot is realized using adaptive Monte-Carlo localization based on the known environment model. A visual servo system is designed to realize the high accuracy localization of the workload. The proposed robot system should guarantee to obtain fine inspecting results of equipments in substation.


title: Open Source Task Profiling by Extending the OpenMP Runtime API

abstract: The introduction of tasks in the OpenMP programming model brings a new level of parallelism. This also creates new challenges with respect to its meanings and applicability through an event-based performance profiling. The OpenMP Architecture Review Board (ARB) has approved an interface specification known as the “OpenMP Runtime API for Profiling” to enable performance tools to collect performance data for OpenMP programs. In this paper, we propose new extensions to the OpenMP Runtime API for profiling task level parallelism. We present an efficient method to distinguish individual task instances in order to track their associated events at the micro level. We implement the proposed extensions in the OpenUH compiler which is an open-source OpenMP compiler. With negligible overheads, we are able to capture important events like task creation, execution, suspension, and exiting. These events help in identifying overheads associated with the OpenMP tasking model, e.g., task waiting until a task starts execution or task cleanup etc. These events also help in constructing important parent-child relationships that define tasks’ call paths. The proposed extensions are in line with the newest specifications recently proposed by the OpenMP tools committee for task profiling.


title: Gender and website design in e-business

abstract: Although online shopping has increased exponentially in recent years, very little is known about the difference in male and female perceptions of this experience. This investigation is focused on examining how perceptions between the genders differ concerning transaction security, website design elements, and how the experience of online shopping results in perceptions of website trust, website satisfaction, and e-loyalty. Results of the research indicate that transaction security was not as large a concern as expected, and perceptions about it did not differ between men and women. Alternately, in support of the research hypotheses, there are significant differences in perceptions of website design and website satisfaction between the genders, but not for e-loyalty.


title: A Survey on Botnet Command and Control Traffic Detection

abstract: Internet users have been attacked by widespread email viruses ::: earlier, but now scenario has been changed. Now attackers are ::: no more interested to just attract media attention by infecting ::: a large number of computers on the network; in fact, their ::: interest has been shifted to compromising and controlling the ::: infected computers for their personal profits. This new attack ::: trend brings the concept of botnets over the global network of ::: computers. With the high reported infection rates, the vast ::: range of illegal activities and powerful comebacks, botnets are ::: one of the main threats against the cyber security. This paper ::: provides the readers with a background on botnet life-cycle, ::: architecture and malicious activities. It also classifies ::: botnet detection techniques, reviews the recent research works ::: on botnet traffic detection and finally indicates some ::: challenges posed to future work on botnet detection.


title: A navigation device utilizing body communication channel for mobile wearable systems

abstract: A novel touch sensor utilizing a body communication technology is presented in the paper. The proposed device accepts orders (gestures) only from a person wearing it. Moreover, when comparing it to a similar, however an optical one, it appears as a less power consumable. Preliminary results of its properties examination are presented and discussed. Additionally, the developed sensor allows to measure a human body the electrical passive parameters thus, aside it performs a basic functionality it also delivers additional information on the user.


title: Simultaneous analysis of sildenafil, vardenafil, tadalafil, and their desalkyl metabolites in human whole blood and urine by isotope dilution LC–MS–MS

abstract: Given that there are many autopsy cases in which erectile dysfunction (ED) treatment drugs can be detected from elderly men who are diagnosed to have died of cardiovascular disorders, the degree of cardiovascular risk posed by ED treatment drugs is still controversial. Moreover, counterfeit ED drugs or illegal dietary supplements containing ED drugs are also threats to public health. In this study, we established a detailed procedure for simultaneous analysis of typical ED drugs (sildenafil, vardenafil, tadalafil) and their metabolites in human blood and urine by isotope dilution liquid chromatography-tandem mass spectrometry (LC–MS–MS). Each sample of whole blood and urine containing the three ED treatment drugs, their metabolites, and deuterated internal standards (ISs) was diluted with alkalinized water, loaded onto an Oasis HLB cartridge, washed with dilute ammonium hydroxide solution, and eluted with chloroform. The eluate was acidified with methanol and concentrated HCl and evaporated to dryness. The resulting residue was reconstituted with methanol and mobile phase solution, and 5 μl of the solution was injected into an LC–MS–MS instrument. The determinations were made by multiple reaction monitoring using each product ion. The recovery rates of the drugs, metabolites, and ISs from whole blood and urine ranged from 80.7 to 127%. Good linearity was obtained for all drugs and their metabolites in the range of 1–100 ng/ml in whole blood and urine with correlation coefficients greater than 0.99. The detection limits (signal-to-noise ratio = 3) for all compounds were not higher than 0.05 ng/ml. Intraday and interday accuracy and precision data were also satisfactory for all compounds in whole blood and urine. Actual measurements of sildenafil and N-desmethyl sildenafil were also demonstrated by analysis of whole blood and urine specimens from two male volunteers after ingestion of a 25-mg tablet of sildenafil.


title: A Malaysian vehicle license plate localization and recognition system

abstract: Technological intelligence is a highly sought after commodity even in traffic-based systems. These intelligent systems do not only help in traffic monitoring but also in commuter safety, law enforcement and commercial applications. In this paper, a license plate localization and recognition system for vehicles in Malaysia is proposed. This system is developed based on digital images and can be easily applied to commercial car park systems for the use of documenting access of parking services, secure usage of parking houses and also to prevent car theft issues. The proposed license plate localization algorithm is based on a combination of morphological processes with a modified Hough Transform approach and the recognition of the license plates is achieved by the implementation of the feed-forward backpropagation artificial neural network. Experimental results show an average of 95% successful license plate localization and recognition in a total of 589 images captured from a complex outdoor environment.


title: A beacon-less location discovery scheme for wireless sensor networks

abstract: In wireless sensor networks (WSNs), sensor location plays a critical role in many applications. Having a GPS receiver on every sensor node is costly. In the past, a number of location discovery schemes have been proposed. Most of these schemes share a common feature: they use some special nodes, called beacon nodes, which are assumed to know their own locations (e.g., through GPS receivers or manual configuration). Other sensors discover their locations based on the information provided by these beacon nodes. In this paper, we show that efficient location discovery can be achieved in sensor networks without using beacons. We propose a beacon-less location discovery scheme, based on the following observations: in practice, it is quite common that sensors are deployed in groups, i.e., sensors are put into n groups, and sensors in the same group are deployed together at the same deployment point (the deployment point is different from the sensors' final resident location). Sensors from the same group can land in different locations, and those locations usually follow a probability distribution that can be known a priori. With this prior deployment knowledge, we show that sensors can discover their locations by observing the group memberships of its neighbors. We model the location discovery problem as a statistical estimation problem, and we use the maximum likelihood estimation method to estimate the location. We have conducted experiments to evaluate our scheme.


title: An analysis of requirements evolution in open source projects: recommendations for issue trackers

abstract: While requirements for open source projects originate from a variety of sources like e.g. mailing lists or blogs, typically, they eventually end up as feature requests in an issue tracking system. When analyzing how these issue trackers are used for requirements evolution, we witnessed a high percentage of duplicates in a number of high-prole projects. Further investigation of six open source projects and their users led us to a number of important observations and a categorization of the root causes of these duplicates. Based on this, we propose a set of improvements for future issue tracking systems.


title: Types of arcs in a fuzzy graph

abstract: The concept of connectivity plays an important role in both theory and applications of fuzzy graphs. Depending on the strength of an arc, this paper classifies arcs of a fuzzy graph into three types namely @a-strong, @b-strong and @d-arcs. The advantage of this type of classification is that it helps in understanding the basic structure of a fuzzy graph completely. We analyze the relation between strong paths and strongest paths in a fuzzy graph and obtain characterizations for fuzzy bridges, fuzzy trees and fuzzy cycles using the concept of @a-strong, @b-strong and @d-arcs. An arc of a fuzzy tree is @a-strong if and only if it is an arc of its unique maximum spanning tree. Also we identify different types of arcs in complete fuzzy graphs.


title: Generalized schema-mappings: from termination to tractability

abstract: Data-Exchange is the problem of creating new databases according to a high-level specification called a schema-mapping while preserving the information encoded in a source database. This paper introduces a notion of generalized schema-mapping that enriches the standard schema-mappings (as defined by Fagin et al) with more expressive power. It then proposes a more general and arguably more intuitive notion of semantics that rely on three criteria: Soundness, Completeness and Laconicity (non-redundancy and minimal size). These semantics are shown to coincide precisely with the notion of cores of universal solutions in the framework of Fagin, Kolaitis and Popa. It is also well-defined and of interest for larger classes of schema-mappings and more expressive source databases (with null-values and equality constraints). After an investigation of the key properties of generalized schema-mappings and their semantics, a criterion called Termination of the Oblivious Chase (TOC) is identified that ensures polynomial data-complexity. This criterion strictly generalizes the previously known criterion of Weak-Acyclicity. To prove the tractability of TOC schema-mappings, a new polynomial time algorithm is provided that, unlike the algorithm of Gottlob and Nash from which it is inspired, does not rely on the syntactic property of Weak-Acyclicity. As the problem of deciding whether a Schema-mapping satisfies the TOC criterion is only recursively enumerable, a more restrictive criterion called Super-weak Acylicity (SwA) is identified that can be decided in Polynomial-time while generalizing substantially the notion of Weak-Acyclicity.


title: The New Generation of Operations Research Methods in Supply Chain Optimization: A Review

abstract: The possibilities of applying Operations Research (O.R.) techniques in the design of real-world systems are vast. The optimization and design of the supply chain network (SCN) is one of the relevant topics that has directed the attention of many scholars. Sound decisions in this regard, including the proper selection of the facility’s location, transportation modes and routes and inventory management policies, can noticeably improve the systems performance. Over 380 articles published between 2005 and 2016 in the ISI/Web of Science database have applied advanced O.R. techniques in SCN optimization studies. This paper offers a systematic review of these published contributions by focusing on two categories of O.R. approaches most recently applied for the design of SC systems: integrated mathematical modeling and simulation-optimization (S-O) frameworks. A taxonomy analysis of the mentioned approaches is presented based on the supply chain elements. A bibliometric analysis is also conducted to provide technical insights into the possible gaps in the field. Moreover, the relevant studies on SC sustainability are highlighted. The research results are supportive of the S-O frameworks as either an alternative approach or an effective solution method for the integrated problems. The research outcomes can provide researchers in the field with useful details of the integrated problems and S-O frameworks as the most recent O.R. methodologies in the field of SC optimization.


title: Some Theoretical Underpinnings of Knowledge Translation

abstract: A careful analysis of the definition of knowledge translation highlights the importance of the judicious translation of research into practice and policy. There is, however, a considerable gap between research and practice. Closing the research-to-practice gap involves changing clinical practice, a complex and challenging endeavor. There is increasing recognition that efforts to change practice should be guided by conceptual models or frameworks to better understand the process of change. The authors conducted a focused literature search, developed inclusion criteria to identify planned action theories, and then extracted data from each theory to determine the origins, examine the meaning, judge the logical consistency, and define the degree of generalizability, parsimony, and testability. An analysis was conducted of the concepts found in each theory, and a set of action categories was developed that form the phases of planned action. Thirty-one planned action theories were identified that formed the basis of the analyses. An Access database was created, as well as a KT Theories User's Guide that synthesizes all the planned change models and theories, identifies common elements of each, and provides information on their use. There are many planned change models and frameworks with many common elements and action categories. Whenever any planned change model is used, change agents should consider documenting their experiences with the model so as to advance understanding of how useful the model is and to provide information to others who are attempting a similar project.


title: Direct trimming of NURBS surfaces on the GPU

abstract: This paper presents a highly efficient direct trimming technique for NURBS surfaces, which is applicable to tessellation-based rendering as well as ray tracing systems. The central idea is to split the trim curves into monotonic segments with respect to the two parameter dimensions of the surface patches. We use an optimized bisection method to classify a point with respect to each monotonic trim curve segment without performing an actual intersection test. Our hierarchical acceleration structure allows the use of a large number of such curve segments and performs the bisection method only for points contained in the bounding boxes of the curve segments. ::: We have integrated our novel point classification scheme into a GPU-based NURBS ray casting system and implemented the entire trimmed NURBS rendering algorithm in a single OpenGL GLSL shader. The shader can handle surfaces and trim curves of arbitrary degrees, which allows the use of original CAD data without incorporating any approximations. Performance data confirms that our trimming approach can deal with hundreds of thousands of trim curves at interactive rates. Our point classification scheme can be applied to other application domains dealing with complex curved regions including flood fills, font rendering and vector graphics mapped on arbitrary surfaces.


title: Automated Identification of Diabetic Retinopathy Stages Using Digital Fundus Images

abstract: Diabetic retinopathy (DR) is caused by damage to the small blood vessels of the retina in the posterior part of the eye of the diabetic patient. The main stages of diabetic retinopathy are non-proliferate diabetes retinopathy (NPDR) and proliferate diabetes retinopathy (PDR). The retinal fundus photographs are widely used in the diagnosis and treatment of various eye diseases in clinics. It is also one of the main resources for mass screening of diabetic retinopathy. In this work, we have proposed a computer-based approach for the detection of diabetic retinopathy stage using fundus images. Image preprocessing, morphological processing techniques and texture analysis methods are applied on the fundus images to detect the features such as area of hard exudates, area of the blood vessels and the contrast. Our protocol uses total of 140 subjects consisting of two stages of DR and normal. Our extracted features are statistically significant (p?<?0.0001) with distinct mean?±?SD as shown in Table 1. These features are then used as an input to the artificial neural network (ANN) for an automatic classification. The detection results are validated by comparing it with expert ophthalmologists. We demonstrated a classification accuracy of 93%, sensitivity of 90% and specificity of 100%.


title: Recognition of Indian Sign Language using feature fusion

abstract: Sign Language is the most natural and expressive way for the hearing impaired. This paper presents a method for automatic recognition of two handed signs of Indian Sign Language (ISL). The method consists of three phases: Segmentation, Feature Extraction and Recognition. The segmentation is done through Otsu's algorithm. In the feature extraction phase, shape descriptors, HOG descriptors (Histogram of Oriented Gradient) and SIFT (Scale Invariant Feature Transform) feature have been fused to compute a feature vector. In the recognition phase, a multi-class Support Vector Machine (MSVM) is used for training and classifying signs of ISL. The experimental results provide evidence of the effectiveness of the proposed approach with 93% recognition rate.


title: On the Convergence of Adam and Beyond

abstract: Several recently proposed stochastic optimization methods that have been successfully used in training deep networks such as RMSProp, Adam, Adadelta, Nadam are based on using gradient updates scaled by square roots of exponential moving averages of squared past gradients. In many applications, e.g. learning with large output spaces, it has been empirically observed that these algorithms fail to converge to an optimal solution (or a critical point in nonconvex settings). We show that one cause for such failures is the exponential moving average used in the algorithms. We provide an explicit example of a simple convex optimization setting where Adam does not converge to the optimal solution, and describe the precise problems with the previous analysis of Adam algorithm. Our analysis suggests that the convergence issues can be fixed by endowing such algorithms with `long-term memory' of past gradients, and propose new variants of the Adam algorithm which not only fix the convergence issues but often also lead to improved empirical performance.


title: Separation of Pt(IV), Pd(II), Ru(III) and Rh(III) from model chloride solutions by liquid-liquid extraction with phosphonium ionic liquids

abstract: Abstract The recovery of metals from waste is profitable not only from an economic but also from an ecological point of view. The natural resources of metals (especially platinum group metals – PGM) are limited, while the amount of waste containing the desired metals is still growing. The purpose of the work is the use of quaternary phosphonium salts, i.e. Cyphos IL 101 (trihexyl(tetradecyl)phosphonium chloride), Cyphos IL 102 (trihexyl(tetradecyl)phosphonium bromide) and Cyphos IL 104 (trihexyl(tetradecyl)phosphonium bis(2,4,4-trimethylpentyl)phosphinate), for the separation of palladium(II), platinum(IV), rhodium(III) and ruthenium(III) from their multi-component model mixtures of composition based on real solutions after leaching of PGM-containing wastes (e.g. automobile catalytic converters). Though, Pd(II) could not be separated from Pt(IV) by one-stage extraction, separation of Pd(II) from Pt(IV) was possible by stripping with 0.1 M thiourea in 0.5 M HCl. The extraction of Rh(III) from the four-component solution practically did not occur (Rh(III) stayed in raffinate), therefore Pt(IV) and Pd(II) could be selectively separated from Rh(III). As a result of the work, a separation procedure of successful separatation of Pd(II), Pt(IV), Ru(III) Rh(III) from four-component solutions has been developed based on two stages of extraction with ionic liquid solution (trihexyl(tetradecyl)phosphonium chloride) and two stages of stripping.


title: Assisting Users in a World Full of Cameras: A Privacy-Aware Infrastructure for Computer Vision Applications

abstract: Computer vision based technologies have seen widespread adoption over the recent years. This use is not limited to the rapid adoption of facial recognition technology but extends to facial expression recognition, scene recognition and more. These developments raise privacy concerns and call for novel solutions to ensure adequate user awareness, and ideally, control over the resulting collection and use of potentially sensitive data. While cameras have become ubiquitous, most of the time users are not even aware of their presence. In this paper we introduce a novel distributed privacy infrastructure for the Internet-of-Things and discuss in particular how it can help enhance user's awareness of and control over the collection and use of video data about them. The infrastructure, which has undergone early deployment and evaluation on two campuses, supports the automated discovery of IoT resources and the selective notification of users. This includes the presence of computer vision applications that collect data about users. In particular, we describe an implementation of functionality that helps users discover nearby cameras and choose whether or not they want their faces to be denatured in the video streams.


title: Looking at emotional words is not the same as reading emotional words: Behavioral and neural correlates

abstract: Recent research suggests that the allocation of attentional resources to emotional content during word processing might be sensitive to task requirements. This question was investigated in two tasks with similar instructions. The stimuli were positive, negative, and neutral nouns. Participants had to identify meaningful words embedded in a stream of non-recognizable stimuli (task 1) or pseudowords (task 2). Task 1 could be successfully performed on the basis of the perceptual features whereas a lexico-semantic analysis was required in task 2. Effects were found only in task 2. Positive nouns were identified faster, with fewer errors and elicited larger amplitude in an early negativity. Also, the amplitude of a late positivity was larger for both positive and negative nouns than for neutral nouns. It is concluded that some degree of linguistic processing is needed to direct attention to the affective content during word processing. Descriptors: Emotion, Language, ERPs


title: How to measure comorbiditya critical review of available methods

abstract: The object of this article was to systematically review available methods to measure comorbidity and to assess their validity and reliability. A search was made in Medline and Embase, with the keywords comorbidity and multi-morbidity, to identify articles in which a method to measure comorbidity was described. The references of these articles were also checked, and using a standardized checklist the relevant data were extracted from these articles. An assessment was made of the content, concurrent, predictive and construct validity, and the reliability. Thirteen different methods to measure comorbidity were identified: one disease count and 12 indexes. Data on content and predictive validity were available for all measures, while data on construct validity were available for nine methods, data on concurrent validity, and interrater reliability for eight methods, and data on intrarater reliability for three methods. The Charlson Index is the most extensively studied comorbidity index for predicting mortality. The Cumulative Illness Rating Scale (CIRS) addresses all relevant body systems without using specific diagnoses. The Index of Coexisting Disease (ICED) has a two-dimensional structure, measuring disease severity and disability, which can be useful when mortality and disability are the outcomes of interest. The Kaplan Index was specifically developed for use in diabetes research. The Charlson Index, the CIRS, the ICED and the Kaplan Index are valid and reliable methods to measure comorbidity that can be used in clinical research. For the other indexes, insufficient data on the clinimetric properties are available.


Combining Hyperband and Bayesian Optimization


title: Stylizing animation by example

abstract: Skilled artists, using traditional media or modern computer painting tools, can create a variety of expressive styles that are very appealing in still images, but have been unsuitable for animation. The key difficulty is that existing techniques lack adequate temporal coherence to animate these styles effectively. Here we augment the range of practical animation styles by extending the guided texture synthesis method of Image Analogies [Hertzmann et al. 2001] to create temporally coherent animation sequences. To make the method art directable, we allow artists to paint portions of keyframes that are used as constraints. The in-betweens calculated by our method maintain stylistic continuity and yet change no more than necessary over time.


title: Limitations in working memory : implications for language development

abstract: In this study, the proposal that individual differences in spoken language acquisition may be due to limitations in short-term memory abilities was investigated within a working memory framework. The relationship between speech production skills and working memory abilities was examined in two groups of 4-yearold children, matched for non-verbal ability but who had either relatively good or poor non-word repetition skills. Children with better non-word repetition skills produced speech that comprised a wider repertoire of words, on average longer utterances and a greater range of syntactic constructions than did children with relatively poor non-word repetition skills. The significant association found between these indices of language development and verbal short-term memory span assessed with non-spoken recall, suggested that this relationship was not merely due to the common output requirements of the language and memory tasks. Inconsistent associations between language performance and two tasks of vis...


title: Aspectual Coercion and Eventuality Structure*

abstract: The focus of this paper is on the system of aspectual coercion connected with durative adverbials (like for one day), time-span adverbials (like in ten minutes), and time-point adverbials (like at seven o’clock) as well as the progressive in English and its roots in the domain of eventualities. Aspectual coercions are regarded as operations which are executed in order to prevent a conflict between the aspects of a verbal expression and the aspectual requirements of the context. The meaning resulting from such an adjustment involves material which is not contributed by the constituents but originates from knowledge of eventualities. Thus, leading questions of the paper are what the overall structure of eventualities is and how this structure determines the aspectual meaning of expressions as well as the way in which it can be contextually shifted. Recurring to prior work in the field of research, several sorts of eventuality and general relations that exist between eventualities of different sorts are distinguished. On this basis, by examining a sample of data, the paper identifies a multitude of operations for systematically transforming the (e.g., iterative coercion, habitual coercion, inchoative coercion, additive coercion, and subtractive coercion). Finally, taking into account that the operations have basically the same formal structure and, therefore, give reason to generalization, a two-stage approach to aspectual coercion is presented. Its most notably features are that, first, the principle of semantic compositionality is entirely maintained in its validity and, second, adjusting by coercion is turned out to be ultimately a pragmatic phenomenon.


title: Inhibition of ethanol-producing yeast and bacteria by degradation products produced during pre-treatment of biomass

abstract: An overview of the different inhibitors formed by pre-treatment of lignocellulosic materials and their inhibition of ethanol production in yeast and bacteria is given. Different high temperature physical pre-treatment methods are available to render the carbohydrates in lignocellulose accessible for ethanol fermentation. The resulting hydrolyzsates contain substances inhibitory to fermentation—depending on both the raw material (biomass) and the pre-treatment applied. An overview of the inhibitory effect on ethanol production by yeast and bacteria is presented. Apart from furans formed by sugar degradation, phenol monomers from lignin degradation are important co-factors in hydrolysate inhibition, and inhibitory effects of these aromatic compounds on different ethanol producing microorganisms is reviewed. The furans and phenols generally inhibited growth and ethanol production rate (QEtOH) but not the ethanol yields (YEtOH) in Saccharomyces cerevisiae. Within the same phenol functional group (aldehyde, ketone, and acid) the inhibition of volumetric ethanol productivity was found to depend on the amount of methoxyl substituents and hence hydrophobicity (log P). Many pentose-utilizing strains Escherichia coli, Pichia stipititis, and Zymomonas mobilis produce ethanol in concentrated hemicellulose liquors but detoxification by overliming is needed. Thermoanaerobacter mathranii A3M3 can grow on pentoses and produce ethanol in hydrolysate without any need for detoxification.


title: Peacock: Learning Long-Tail Topic Features for Industrial Applications

abstract: Latent Dirichlet allocation (LDA) is a popular topic modeling technique in academia but less so in industry, especially in large-scale applications involving search engine and online advertising systems. A main underlying reason is that the topic models used have been too small in scale to be useful; for example, some of the largest LDA models reported in literature have up to 103 topics, which difficultly cover the long-tail semantic word sets. In this article, we show that the number of topics is a key factor that can significantly boost the utility of topic-modeling systems. In particular, we show that a “big” LDA model with at least 105 topics inferred from 109 search queries can achieve a significant improvement on industrial search engine and online advertising systems, both of which serve hundreds of millions of users. We develop a novel distributed system called Peacock to learn big LDA models from big data. The main features of Peacock include hierarchical distributed architecture, real-time prediction, and topic de-duplication. We empirically demonstrate that the Peacock system is capable of providing significant benefits via highly scalable LDA topic models for several industrial applications.


title: Digital Behaviour Change Interventions to Break and Form Habits

abstract: Digital behaviour change interventions, particularly those using pervasive computing technology, hold great promise in supporting users to change their behaviour. However, most interventions fail to take habitual behaviour into account, limiting their potential impact. This failure is partly driven by a plethora of overlapping behaviour change theories and related strategies that do not consider the role of habits. We critically review the main theories and models used in the research to analyse their application to designing effective habitual behaviour change interventions. We highlight the potential for Dual Process Theory, modern habit theory, and Goal Setting Theory, which together model how users form and break habits, to drive effective digital interventions. We synthesise these theories into an explanatory framework, the Habit Alteration Model, and use it to outline the state of the art. We identify the opportunities and challenges of habit-focused interventions.


title: TRANSMISSION THEORY OF PLANE ELECTROMAGNETIC WAVES* BY

abstract: This paper deals with transmission theory of plane electromagnetic waves in free space and in cylindrical regions of arbitrary cross section. Transmission properties of such waves can be expressed very simply in the same terms as the properties of electric waves guided by a pair of parallel wires. The earlier parts of the paper are concerned with general theorems and the latter parts with their application to plane waves in metal tubes of circular and rectangular cross sections.


title: Computer-Supported Cooperative Work- Concepts and Trends

abstract: The research field Computer-Supported Cooperative Work (CSCW) is concerned with understanding social interaction and the design, development, and evaluation of technical systems supporting social interaction in teams and communities - or in other words it is about researching the use of computer-based technology for supporting collaboration. The field was coined in the 1980th by researchers from computer science, information science and social science. In this paper we will briefly introduce CSCW - its concepts and current trends - and thereby focus on the role of Informatics in the field - from application integration to ubiquitous user interfaces.


title: Stress enhances reconsolidation of declarative memory

abstract: Summary Retrieval of negative emotional memories is often accompanied by the experience of stress. Upon retrieval, a memory trace can temporarily return into a labile state, where it is vulnerable to change. An unresolved question is whether post-retrieval stress may affect the strength of declarative memory in humans by modulating the reconsolidation process. Here, we tested in two experiments whether post-reactivation stress may affect the strength of declarative memory in humans. In both experiments, participants were instructed to learn neutral, positive and negative words. Approximately 24 h later, participants received a reminder of the word list followed by exposure to the social evaluative cold pressor task (reactivation/stress group, n exp1 = 20; n exp2 = 18) or control task (reactivation/no-stress group, n exp1 = 23; n exp2 = 18). An additional control group was solely exposed to the stress task, without memory reactivation (no-reactivation/stress group, n exp1 = 23; n exp2 = 21). The next day, memory performance was tested using a free recall and a recognition task. In the first experiment we showed that participants in the reactivation/stress group recalled more words than participants in the reactivation/no-stress and no-reactivation/stress group, irrespective of valence of the word stimuli. Furthermore, participants in the reactivation/stress group made more false recognition errors. In the second experiment we replicated our observations on the free recall task for a new set of word stimuli, but we did not find any differences in false recognition. The current findings indicate that post-reactivation stress can improve declarative memory performance by modulating the process of reconsolidation. This finding contributes to our understanding why some memories are more persistent than others.


title: PageRank On Semantic Networks, With Application To Word Sense Disambiguation

abstract: This paper presents a new open text word sense disambiguation method that combines the use of logical inferences with PageRank-style algorithms applied on graphs extracted from natural language documents. We evaluate the accuracy of the proposed algorithm on several sense-annotated texts, and show that it consistently outperforms the accuracy of other previously proposed knowledge-based word sense disambiguation methods. We also explore and evaluate methods that combine several open-text word sense disambiguation algorithms.


title: Automated colon cancer detection using hybrid of novel geometric features and some traditional features

abstract: Automatic classification of colon into normal and malignant classes is complex due to numerous factors including similar colors in different biological constituents of histopathological imagery. Therefore, such techniques, which exploit the textural and geometric properties of constituents of colon tissues, are desired. In this paper, a novel feature extraction strategy that mathematically models the geometric characteristics of constituents of colon tissues is proposed. In this study, we also show that the hybrid feature space encompassing diverse knowledge about the tissues' characteristics is quite promising for classification of colon biopsy images. This paper thus presents a hybrid feature space based colon classification (HFS-CC) technique, which utilizes hybrid features for differentiating normal and malignant colon samples. The hybrid feature space is formed to provide the classifier different types of discriminative features such as features having rich information about geometric structure and image texture. Along with the proposed geometric features, a few conventional features such as morphological, texture, scale invariant feature transform (SIFT), and elliptic Fourier descriptors (EFDs) are also used to develop a hybrid feature set. The SIFT features are reduced using minimum redundancy and maximum relevancy (mRMR). Various kernels of support vector machines (SVM) are employed as classifiers, and their performance is analyzed on 174 colon biopsy images. The proposed geometric features have achieved an accuracy of 92.62%, thereby showing their effectiveness. Moreover, the proposed HFS-CC technique achieves 98.07% testing and 99.18% training accuracy. The better performance of HFS-CC is largely due to the discerning ability of the proposed geometric features and the developed hybrid feature space. A novel colon cancer detection system has been proposed.The proposed system utilizes a hybrid of traditional and novel features for classification.Novel geometric features, which mathematically quantify the variation in the structure of normal and malignant colon tissues, have been proposed.The proposed geometric features show superior classification performance compared to traditional features.RBF kernel of SVM shows promising results for classification of colon cancer data.


title: Predictability analysis on expressway vehicle mobility using electronic toll collection data

abstract: This paper assesses the predictability of individual vehicle's mobility in Beijing expressway system using Electronic Toll Collection (ETC) data records. By examining the uncertainties of movements using entropy, considering both the frequencies and sequential correlations of vehicles' trajectories, we draw to the conclusion that the average limit of predictability of expressway vehicles mobility is 91%. Furthermore, we concluded that the individual property is negatively correlated to its mobility property such as visited station, average travel distance and radius of gyration. Finally, we applied Markov chain (MC) based models to predict the actual accuracy of predictability and found that MC(1) model is adequate to produce a good result and extending the order of the model won't give substantial bonus, thus the first-order Markov property of expressway vehicles' trajectory is proved. Our findings indicate that individual vehicles' mobility in expressway system is far from random and highly dependent on its historical trajectory


title: A Randomized Concurrent Algorithm for Disjoint Set Union

abstract: Disjoint set union is a basic problem in data structures with a wide variety of applications. We extend a known efficient sequential algorithm for this problem to obtain a simple and efficient concurrent wait-free algorithm running on an asynchronous parallel random access machine (APRAM). Crucial to our result is the use of randomization. Under a certain independence assumption, for a problem instance in which there are n elements, m operations, and l processes, our algorithm does θ(m (α{n, m/nl) + log (nl/m + 1 ))) expected work, where the expectation is over the random choices made by the algorithm and α is a functional inverse of Ackermann's function. In addition, each operation takes O(log n) steps with high probability. We point out some gaps in the earlier work on this problem by Anderson and Woll [1]. More importantly, our algorithm is significantly simpler than theirs. Finally, under our independence assumption our algorithm achieves speedup close to linear for applications in which all or most of the processes can be kept busy, thereby partially answering an open problem posed by them.


Workshop track -ICLR 2017 NEURAL COMBINATORIAL OPTIMIZATION WITH REINFORCEMENT LEARNING


title: Forecasting emerging technologies: Use of bibliometrics and patent analysis

abstract: It is rather difficult to forecast emerging technologies as there is no historical data available. In such cases, the use of bibliometrics and patent analysis have provided useful data. This paper presents the forecasts for three emerging technology areas by integrating the use of bibliometrics and patent analysis into well-known technology forecasting tools such as scenario planning, growth curves and analogies. System dynamics is also used to be able to model the dynamic ecosystem of the technologies and their diffusion. Technologies being forecasted are fuel cell, food safety and optical storage technologies. Results from these three applications help us to validate the proposed methods as appropriate tools to forecast emerging technologies.


title: SEA-CNN: scalable processing of continuous k-nearest neighbor queries in spatio-temporal databases

abstract: Location-aware environments are characterized by a large number of objects and a large number of continuous queries. Both the objects and continuous queries may change their locations over time. In this paper, we focus on continuous k-nearest neighbor queries (CKNN, for short). We present a new algorithm, termed SEA-CNN, for answering continuously a collection of concurrent CKNN queries. SEA-CNN has two important features: incremental evaluation and shared execution. SEA-CNN achieves both efficiency and scalability in the presence of a set of concurrent queries. Furthermore, SEA-CNN does not make any assumptions about the movement of objects, e.g., the objects velocities and shapes of trajectories, or about the mutability of the objects and/or the queries, i.e., moving or stationary queries issued on moving or stationary objects. We provide theoretical analysis of SEA-CNN with respect to the execution costs, memory requirements and effects of tunable parameters. Comprehensive experimentation shows that SEA-CNN is highly scalable and is more efficient in terms of both I/O and CPU costs in comparison to other R-tree-based CKNN techniques.


title: Revisiting Faster R-CNN: A Deeper Look at Region Proposal Network

abstract: Currently, state-of-the-art object detectors are based on Faster R-CNN. We firstly revisit Faster R-CNN and explore problems in it, e.g., coarseness of feature maps for accurate localization, fixed-window feature extraction in RPN and insensitivity for small scale objects. Then a novel object detection network is proposed to address these problems. Specifically, we utilize a two-stage cascade multi-scale proposal generation network to get high accurate proposals: an original RPN is adopted to initially generate coarse proposals, then another network with multi-layer features and RoI pooling layer are introduced to refine these proposals. We also generate small scale proposals in the second stage simultaneously. After that, a detection network with multi-layer features further classifies and refines proposals. A novel 3-step joint training algorithm is introduced to optimize our model. Experiments on PASCAL VOC 2007 and 2012 demonstrate the effectiveness of our network.


title: Recognising Textual Entailment With Logical Inference

abstract: We use logical inference techniques for recognising textual entailment. As the performance of theorem proving turns out to be highly dependent on not readily available background knowledge, we incorporate model building, a technique borrowed from automated reasoning, and show that it is a useful robust method to approximate entailment. Finally, we use machine learning to combine these deep semantic analysis techniques with simple shallow word overlap; the resulting hybrid model achieves high accuracy on the RTE testset, given the state of the art. Our results also show that the different techniques that we employ perform very differently on some of the subsets of the RTE corpus and as a result, it is useful to use the nature of the dataset as a feature.


title: Legal Document Retrieval using Document Vector Embeddings and Deep Learning

abstract: Domain specific information retrieval process has been a prominent and ongoing research in the field of natural language processing. Many researchers have incorporated different techniques to overcome the technical and domain specificity and provide a mature model for various domains of interest. The main bottleneck in these studies is the heavy coupling of domain experts, that makes the entire process to be time consuming and cumbersome. In this study, we have developed three novel models which are compared against a golden standard generated via the on line repositories provided, specifically for the legal domain. The three different models incorporated vector space representations of the legal domain, where document vector generation was done in two different mechanisms and as an ensemble of the above two. This study contains the research being carried out in the process of representing legal case documents into different vector spaces, whilst incorporating semantic word measures and natural language processing techniques. The ensemble model built in this study, shows a significantly higher accuracy level, which indeed proves the need for incorporation of domain specific semantic similarity measures into the information retrieval process. This study also shows, the impact of varying distribution of the word similarity measures, against varying document vector dimensions, which can lead to improvements in the process of legal information retrieval.


title: Attribute-Based Encryption for Circuits

abstract: In an attribute-based encryption (ABE) scheme, a ciphertext is associated with an l-bit public index pind and a message m, and a secret key is associated with a Boolean predicate P. The secret key allows to decrypt the ciphertext and learn m iff P(pind) = 1. Moreover, the scheme should be secure against collusions of users, namely, given secret keys for polynomially many predicates, an adversary learns nothing about the message if none of the secret keys can individually decrypt the ciphertext. We present attribute-based encryption schemes for circuits of any arbitrary polynomial size, where the public parameters and the ciphertext grow linearly with the depth of the circuit. Our construction is secure under the standard learning with errors (LWE) assumption. Previous constructions of attribute-based encryption were for Boolean formulas, captured by the complexity class NC1. In the course of our construction, we present a new framework for constructing ABE schemes. As a by-product of our framework, we obtain ABE schemes for polynomial-size branching programs, corresponding to the complexity class LOGSPACE, under quantitatively better assumptions.


title: The QSM algorithm and its application to software behavior model induction

abstract: This article presents a novel application of grammatical inference techniques to the synthesis of behavior models of software systems. This synthesis is used for the elicitation of software requirements. This problem is formulated as a deterministic finite-state automaton induction problem from positive and negative scenarios provided by an end user of the software-to-be. A query-driven state merging (QSM) algorithm is proposed. It extends the Regular Positive and Negative Inference (RPNI) and blue-fringe algorithms by allowing membership queries to be submitted to the end user. State merging operations can be further constrained by some prior domain knowledge formulated as fluents, goals, domain properties, and models of external software components. The incorporation of domain knowledge both reduces the number of queries and guarantees that the induced model is consistent with such knowledge. The proposed techniques are implemented in the ISIS tool and practical evaluations on standard requirements engineering test cases and synthetic data illustrate the interest of this approach.


title: Deep convolutional networks for automated detection of posterior-element fractures on spine CT

abstract: Injuries of the spine, and its posterior elements in particular, are a common occurrence in trauma patients, with potentially devastating consequences. Computer-aided detection (CADe) could assist in the detection and classification of spine fractures. Furthermore, CAD could help assess the stability and chronicity of fractures, as well as facilitate research into optimization of treatment paradigms. In this work, we apply deep convolutional networks (ConvNets) for the automated detection of posterior element fractures of the spine. First, the vertebra bodies of the spine with its posterior elements are segmented in spine CT using multi-atlas label fusion. Then, edge maps of the posterior elements are computed. These edge maps serve as candidate regions for predicting a set of probabilities for fractures along the image edges using ConvNets in a 2.5D fashion (three orthogonal patches in axial, coronal and sagittal planes). We explore three different methods for training the ConvNet using 2.5D patches along the edge maps of `positive', i.e. fractured posterior-elements and `negative', i.e. non-fractured elements. An experienced radiologist retrospectively marked the location of 55 displaced posterior-element fractures in 18 trauma patients. We randomly split the data into training and testing cases. In testing, we achieve an area-under-the-curve of 0.857. This corresponds to 71% or 81% sensitivities at 5 or 10 false-positives per patient, respectively. Analysis of our set of trauma patients demonstrates the feasibility of detecting posterior-element fractures in spine CT images using computer vision techniques such as deep convolutional networks.


title: Interest classification of Twitter users using Wikipedia

abstract: We present a framework for (automatically) classifying the relative interests of Twitter users using information from Wikipedia. Our proposed framework first uses Wikipedia to automatically classify a user's celebrity followings into various interest categories, followed by determining the relative interests of the user with a weighting compared to his/her other interests. Our preliminary evaluation on Twitter shows that this framework is able to correctly classify users' interests and that these users frequently converse about topics that reflect both their (detected) interest and a related real-life event.


title: Advances in data stream mining

abstract: Mining data streams has been a focal point of research interest over the past decade. Hardware and software advances have contributed to the significance of this area of research by introducing faster than ever data generation. This rapidly generated data has been termed as data streams. Credit card transactions, Google searches, phone calls in a city, and many others are typical data streams. In many important applications, it is inevitable to analyze this streaming data in real time. Traditional data mining techniques have fallen short in addressing the needs of data stream mining. Randomization, approximation, and adaptation have been used extensively in developing new techniques or adopting exiting ones to enable them to operate in a streaming environment. This paper reviews key milestones and state of the art in the data stream mining area. Future insights are also be presented.


title: Data uncertainties in material flow analysis: Municipal solid waste management system in Maputo City, Mozambique

abstract: Material flow analysis can effectively trace and quantify the flows and stocks of materials such as solid wastes in urban environments. However, the integrity of material flow analysis results is compromised by data uncertainties, an occurrence that is particularly acute in low-and-middle-income study contexts. This article investigates the uncertainties in the input data and their effects in a material flow analysis study of municipal solid waste management in Maputo City, the capital of Mozambique. The analysis is based on data collected in 2007 and 2014. Initially, the uncertainties and their ranges were identified by the data classification model of Hedbrant and Sörme, followed by the application of sensitivity analysis. The average lower and upper bounds were 29% and 71%, respectively, in 2007, increasing to 41% and 96%, respectively, in 2014. This indicates higher data quality in 2007 than in 2014. Results also show that not only data are partially missing from the established flows such as waste generation to final disposal, but also that they are limited and inconsistent in emerging flows and processes such as waste generation to material recovery (hence the wider variation in the 2014 parameters). The sensitivity analysis further clarified the most influencing parameter and the degree of influence of each parameter on the waste flows and the interrelations among the parameters. The findings highlight the need for an integrated municipal solid waste management approach to avoid transferring or worsening the negative impacts among the parameters and flows.


title: Vision-based Virtual Touch Screen Interface

abstract: To utilize the augmented reality with the portable environment, human-computer interaction interface that satisfies the mobility, the convenience, as well as the accuracy is required. In this paper, we propose a vision-based virtual touch screen interface that ensures the requirements. The virtual touch screen is implemented on a stereo cameras installed see-through head mounted display device using an adaptive gesture recognition algorithm. Since it provides a natural way to interact with computers through the vision-based select, click, and drag&drop operations, it can be applied to various augmented reality environments without the restriction of the mobility and the inconvenience.


title: Advanced Algorithms for Wind Turbine Power Curve Modeling

abstract: A wind turbine power curve essentially captures the performance of the wind turbine. The power curve depicts the relationship between the wind speed and output power of the turbine. Modeling of wind turbine power curve aids in performance monitoring of the turbine and also in forecasting of power. This paper presents the development of parametric and nonparametric models of wind turbine power curves. Parametric models of the wind turbine power curve have been developed using four and five parameter logistic expressions. The parameters of these expressions have been solved using advanced algorithms like genetic algorithm (GA), evolutionary programming (EP), particle swarm optimization (PSO), and differential evolution (DE). Nonparametric models have been evolved using algorithms like neural networks, fuzzy c-means clustering, and data mining. The modeling of wind turbine power curve is done using five sets of data; one is a statistically generated set and the others are real-time data sets. The results obtained have been compared using suitable performance metrics and the best method for modeling of the power curve has been obtained.


title: Passenger expectations and airline services: a Hong Kong based study

abstract: Abstract The airline industry is undergoing a very difficult time and many companies are in search of service segmentation strategies that will satisfy different target market segments. This study attempts to identify the service dimensions that matter most to current airline passengers. The research measures and compares differences in passengers’ expectations of the desired airline service quality in terms of the dimensions of reliability; assurance; facilities; employees; flight patterns; customization and responsiveness. Primary data were collected from passengers departing Hong Kong airport. Regarding the service dimension expectations, differences analysis shows that there are no statistically significant differences between passengers who made their own airline choice (decision makers) and those who did not (non-decision makers). However, there are significant differences among passengers of different ethnic groups/nationalities as well as among passengers who travel for different purposes, such as business, holiday and visiting friends/relatives. The findings also indicate that passengers consistently rank ‘assurance’ as the most important service dimension. This indicates that passengers are concerned about the safety and security aspect and this may indicate why there has been such a downturn in demand as this study was conducted just prior to the World Trade Center incident on the 11th September 2001.


title: Artist Agent: A Reinforcement Learning Approach to Automatic Stroke Generation in Oriental Ink Painting

abstract: Oriental ink painting, called Sumi-e, is one of the most appealing painting styles that has attracted artists around the world. Major challenges in computer-based Sumi-e simulation are to abstract complex scene information and draw smooth and natural brush strokes. To automatically generate such strokes, we propose to model a brush as a reinforcement learning agent, and learn desired brush-trajectories by maximizing the sum of rewards in the policy search framework. We also elaborate on the design of actions, states, and rewards tailored for a Sumi-e agent. The effectiveness of our proposed approach is demonstrated through simulated Sumi-e experiments.


title: Summary - TerpreT: A Probabilistic Programming Language for Program Induction

abstract: We study machine learning formulations of inductive program synthesis; that is, given input-output examples, synthesize source code that maps inputs to corresponding outputs. Our key contribution is TerpreT, a domain-specific language for expressing program synthesis problems. A TerpreT model is composed of a specification of a program representation and an interpreter that describes how programs map inputs to outputs. The inference task is to observe a set of input-output examples and infer the underlying program. From a TerpreT model we automatically perform inference using four different back-ends: gradient descent (thus each TerpreT model can be seen as defining a differentiable interpreter), linear program (LP) relaxations for graphical models, discrete satisfiability solving, and the Sketch program synthesis system. TerpreT has two main benefits. First, it enables rapid exploration of a range of domains, program representations, and interpreter models. Second, it separates the model specification from the inference algorithm, allowing proper comparisons between different approaches to inference. ::: We illustrate the value of TerpreT by developing several interpreter models and performing an extensive empirical comparison between alternative inference algorithms on a variety of program models. To our knowledge, this is the first work to compare gradient-based search over program space to traditional search-based alternatives. Our key empirical finding is that constraint solvers dominate the gradient descent and LP-based formulations. ::: This is a workshop summary of a longer report at arXiv:1608.04428


title: In-Field Test for Permanent Faults in FIFO Buffers of NoC Routers

abstract: The Network-on-Chip (NoC) communication architecture is a packet based network where cores communicate among themselves by sending and receiving packets. High parallelism, smaller latency in data transmission and facility of Intellectual Property (IP) re-use have made NoCs overcome the problem of bandwidth and latency in conventional bus-based interconnects. In this concise proposes an on-line straightforward test method for recognition of idle hard blames which create in first info initially yield cradles of switches amid field operation of NoC. The strategy includes rehashing tests intermittently to anticipate aggregation of deficiencies. A model execution of the proposed test calculation has been incorporated into the switch channel interface and on-line test has been performed with manufactured self-comparative information movement. The execution of the NoC after expansion of the test circuit has been researched as far as throughput while the region overhead has been examined by incorporating the test equipment. What's more, an on-line test system for the directing rationale has been proposed which considers using the header bounces of the information activity development in transporting the test designs.


title: Internet of Vehicles: Architecture, Protocols, and Security

abstract: Today, vehicles are increasingly being connected to the Internet of Things which enable them to provide ubiquitous access to information to drivers and passengers while on the move. However, as the number of connected vehicles keeps increasing, new requirements (such as seamless, secure, robust, scalable information exchange among vehicles, humans, and roadside infrastructures) of vehicular networks are emerging. In this context, the original concept of vehicular ad-hoc networks is being transformed into a new concept called the Internet of Vehicles (IoV). We discuss the benefits of IoV along with recent industry standards developed to promote its implementation. We further present recently proposed communication protocols to enable the seamless integration and operation of the IoV. Finally, we present future research directions of IoV that require further consideration from the vehicular research community.


title: Data Mining for the Analysis of Eye Tracking Records

abstract: It is proposed the implementation of a methodology for the analysis and classification of large volumes of records. It is studied and evaluated the application of DM as a tool to analysis qualitatively and quantitatively the register obtained by an eye movement tracking device, eye-tracking, when bring under to people with different levels of orthographic knowledge (OK: High, Medium and Low), in the face of two tasks; (i) detection of spelling error and (ii) in the detection of a simple character, in the brief exposure (1500 milliseconds) of words without and with misspelling. It used some analytical procedure series of DM such as: the search for response patterns; the creation of secondary variables; the use of classification of trees and grouping the data (k-means). New models were created as of the distance between the position of the spelling error and the position of the gaze of the participants. Differences in the visual attention were found between the participants; in the same way, it was observed that the misspelling influences the performance of the task (ii), diverting visual attention to spelling error, in the participants with High OK. It is concluded that the DM helps to find the particularities of eye movements from large volumes of data that generates eye-tracking, which cannot be analysed with simple procedures.


The Art of Scheduling for Big Data Science.


Infinite dimensional stochastic analysis


title: Unsupervised aspect level sentiment analysis using Ant Clustering and Self-organizing Maps

abstract: We present an approach for aspect based opinion mining, which uses an unsupervised neural network as the opinion classifier. To identify the aspects, we use the Ant Clustering Algorithm. It is able to group similar sentences into clusters and then to extract from each cluster one different aspect of the opinion target object. The neural model used for sentiment analysis is an extension of the Growing Hierarchical Self-organizing Maps. In our aspect based sentiment analysis method, we assume that different sentences in a product review refer to the different aspects of the reviewed product. We use the Growing Hierarchical Self-organizing Maps in order to classify the review sentences. This way we determine whether the various aspects of the target entity (e.g. a product) are opinionated with positive or negative sentiment in the review sentences. We classify the sentences against a domain specific tree-like ontological taxonomy of aspects and (positive/ negative) opinions associated with the aspects. As a consequence, we really classify the sentiment polarity about the different aspects of the target object, as expressed in the sentences. Moreover, being based on a classification against an ontology of aspects, our approach is semantic oriented, where the aspects themselves are also defined semantically. The approach proposed has been tested on a collection of product reviews, more exactly reviews about photo cameras.


title: Gravity-balancing of classes of industrial robots

abstract: Gravity balancing of industrial robots is an important issue because these robots may have massive links in order to manipulate large payloads. In this paper, we present techniques to gravity balance the weight of moving links in industrial robots with various arrangements of joints. In a typical industrial robot, the first joint axis is parallel to the gravity vector, hence, the weight of the first moving link does not have any effect on gravity balancing of the robot. Our approach for gravity balancing of industrial robots uses two steps: (i) we locate the center of mass of distal segments of the robot using auxiliary parallelograms; (ii) we connect springs between the center of mass and other members in the robot such that the total potential energy of the system is invariant with configuration. In this paper, we present designs for gravity balancing of classes of industrial robots with revolute and prismatic joints


title: RECENT ADVANCES IN EEG MONITORING FOR GENERAL ANAESTHESIA, ALTERED STATES OF CONSCIOUSNESS AND SPORTS PERFORMANCE SCIENCE

abstract: A novel approach for electroencephalogram (EEG) processing is presented. Along with the theoretical development of stochastic signal processing techniques, three application areas are suggested: general anaesthesia monitoring, altered states of consciousness e.g. hypnosis/sedation and sports performance medicine e.g. training to be in "the zone". A number of methods already exist to analyze EEG signals. However, they are generally considered to be difficult to interpret and suffer from lack of noise immunity. Spectral analysis certainly aids interpretation but most methods employing the Fast Fourier Transform (FFT) suffer from some well known drawbacks, such as spectral leakage and cross modulation. The method developed here is a modified form of the auto-correlation Yule-Walker algorithm we have named the Bristol Process. It has been found to be stable and able to recover rapidly from even large scale electromagnetic interference (such as surgical diathermy) and from electrical artefacts induced by eye-blinks and body movement. This novel approach permits stable calculation of power relative to total power (an index) at any chosen frequency or frequency band and makes interpretation of EEG easier. Continuous recording of an index (trend) for immediate or post-event review improves interpretation and may be used in association with video recording of sports performance. Future developments are planned with a commercial organisation.


title: Predicting Adversarial Examples with High Confidence

abstract: It has been suggested that adversarial examples cause deep learning models to make incorrect predictions with high confidence. In this work, we take the opposite stance: an overly confident model is more likely to be vulnerable to adversarial examples. This work is one of the most proactive approaches taken to date, as we link robustness with non-calibrated model confidence on noisy images, providing a data-augmentation-free path forward. The adversarial examples phenomenon is most easily explained by the trend of increasing non-regularized model capacity, while the diversity and number of samples in common datasets has remained flat. Test accuracy has incorrectly been associated with true generalization performance, ignoring that training and test splits are often extremely similar in terms of the overall representation space. The transferability property of adversarial examples was previously used as evidence against overfitting arguments, a perceived random effect, but overfitting is not always random.


title: Adaptation and validation of the depression, anxiety and stress scale (DASS) to Brazilian Portuguese.

abstract: Abstract Background Depression and anxiety have been associated with a range of symptoms that often overlap. Depression, Anxiety and Stress Scale‐21 (DASS-21) is a single instrument to assess symptoms of depression, anxiety and stress. This study aimed to adapt and validate the DASS-21 for use in the Brazilian Portuguese language. Methods The DASS-21 has been adapted following the translation–back translation methodology from English to Portuguese. 242 subjects completed the following assessments: the DASS-21, the Beck Depression Index (BDI), Beck Anxiety Index (BAI) and the Inventory of Stress Symptoms of Lipp (ISSL). Results The Kaiser–Meyer–Olkin (KMO) result was .949, indicating that the adequacy of the model was high. Cronbach's alpha was .92 for the depression, .90 for the stress, and .86 for the anxiety, indicating a good internal consistency for each subscale. The correlations between DASS scale and BDI scale, BAI scale and ISSL inventory were strong. The factorial analysis and distribution of factors among the subscales indicated that the structure of three distinct factors is adequate. Limitations Older subjects over 65 years of age were not largely represented in this sample. A study specific to this elderly population should be conducted. Another limitation of the study was education level. The impact of low education in its applicability should be considered. Conclusions The findings support the validity of the Brazilian Portuguese version of the DASS-21 and add to the evidence of the DASS-21 quality and ability to assess emotional states separately, eliminating the use of different instruments to assess these states.


Survey on Privacy Preserving Data Mining


title: Intelligent and Expert Systems in Medicine - A Review

abstract: Although medicine is an ancient science, only since the twentieth century there have been technological advances with widespread use in diagnosis, treatment and rehabilitation. However, the introduction of Articial Intelligence in Medicine did not take place until the second half of the past century. The purpose of this study is to provide an overview of the role of intelligent tools in modern health care systems and to review the current challenges for the eld.


title: Deletion Without Rebalancing in Binary Search Trees

abstract: We address the vexing issue of deletions in balanced trees. Rebalancing after a deletion is generally more complicated than rebalancing after an insertion. Textbooks neglect deletion rebalancing, and many B-tree--based database systems do not do it. We describe a relaxation of AVL trees in which rebalancing is done after insertions but not after deletions, yet worst-case access time remains logarithmic in the number of insertions. For any application of balanced trees in which the number of updates is polynomial in the tree size, our structure offers performance competitive with that of classical balanced trees. With the addition of periodic rebuilding, the performance of our structure is theoretically superior to that of many, if not all, classic balanced tree structures. Our structure needs lg lg m+ 1 bits of balance information per node, where m is the number of insertions and lg is the base-two logarithm, or lg lg n+ O(1) with periodic rebuilding, where n is the number of nodes. An insertion takes up to two rotations and O(1) amortized time, not counting the time to find the insertion position. This is the same as in standard AVL trees. Using an analysis that relies on an exponential potential function, we show that rebalancing steps occur with a frequency that is exponentially small in the height of the affected node. Our techniques apply to other types of balanced trees, notably B-trees, as we show in a companion article, and particularly red-black trees, which can be viewed as a special case of B-trees.