title: Large-scale FFT on GPU clusters

abstract: A GPU cluster is a cluster equipped with GPU devices. Excellent acceleration is achievable for computation-intensive tasks (e. g. matrix multiplication and LINPACK) and bandwidth-intensive tasks with data locality (e. g. finite-difference simulation). Bandwidth-intensive tasks such as large-scale FFTs without data locality are harder to accelerate, as the bottleneck often lies with the PCI between main memory and GPU device memory or the communication network between workstation nodes. That means optimizing the performance of FFT for a single GPU device will not improve the overall performance. This paper uses large-scale FFT as an example to show how to achieve substantial speedups for these more challenging tasks on a GPU cluster. Three GPU-related factors lead to better performance: firstly the use of GPU devices improves the sustained memory bandwidth for processing large-size data; secondly GPU device memory allows larger subtasks to be processed in whole and hence reduces repeated data transfers between memory and processors; and finally some costly main-memory operations such as matrix transposition can be significantly sped up by GPUs if necessary data adjustment is performed during data transfers. This technique of manipulating array dimensions during data transfer is the main technical contribution of this paper. These factors (as well as the improved communication library in our implementation) attribute to 24.3x speedup with respect to FFTW and 7x speedup with respect to Intel MKL for 4096 3D single-precision FFT on a 16-node cluster with 32 GPUs. Around 5x speedup with respect to both standard libraries are achieved for double precision.


title: Empirical study of topic modeling in Twitter

abstract: Social networks such as Facebook, LinkedIn, and Twitter have been a crucial source of information for a wide spectrum of users. In Twitter, popular information that is deemed important by the community propagates through the network. Studying the characteristics of content in the messages becomes important for a number of tasks, such as breaking news detection, personalized message recommendation, friends recommendation, sentiment analysis and others. While many researchers wish to use standard text mining tools to understand messages on Twitter, the restricted length of those messages prevents them from being employed to their full potential. We address the problem of using standard topic models in micro-blogging environments by studying how the models can be trained on the dataset. We propose several schemes to train a standard topic model and compare their quality and effectiveness through a set of carefully designed experiments from both qualitative and quantitative perspectives. We show that by training a topic model on aggregated messages we can obtain a higher quality of learned model which results in significantly better performance in two real-world classification problems. We also discuss how the state-of-the-art Author-Topic model fails to model hierarchical relationships between entities in Social Media.


title: Scaffolding students' learning using test my code

abstract: As programming is the basis of many CS courses, meaningful activities in supporting students on their journey towards being better programmers is a matter of utmost importance. Programming is not only about learning simple syntax constructs and their applications, but about honing practical problem-solving skills in meaningful contexts. In this article, we describe our current work on an automated assessment system called Test My Code (TMC), which is one of the feedback and support mechanisms that we use in our programming courses. TMC is an assessment service that (1) enables building of scaffolding into programming exercises; (2) retrieves and updates tasks into the students' programming environment as students work on them, and (3) causes no additional overhead to students' programming process. Instructors benefit from TMC as it can be used to perform code reviews, and collect and send feedback even on fully on-line courses.


title: Locating Rogue Access Point Using Fine-Grained Channel Information

abstract: Rogue access point (AP) has emerged as an important security problem in WLANs. However, it is a challenge task to localize the rogue AP with both high accuracy and minimal infrastructure cost. Either expensive professional infrastructure (e.g., multiple wireless sniffers) or additional hardware (e.g., directional antenna) need to be pre-deployed for rogue AP localization with high cost. Moreover, existing methods using Received Signal Strength (RSS) result in a large error as RSS is suffered from the multipath and shadowing effects in complex wireless environment. In this work, we exploit the channel state information (CSI), which is readily available from commercial Wi-Fi devices, to locate the rogue AP with high accuracy. We use only a single off-the-shelf Wi-Fi device for rogue AP localization which involves minimal infrastructure requirement. Our proposed rogue AP localization framework consists of two components: direction determination and position estimation. The direction determination can be carried out by using the human blocking effect on the CSI amplitude or phase. The multiple antennas on the Wi-Fi devices can be further utilized to enhance the rogue AP direction estimation. Given the estimated direction, two schemes are proposed to pinpoint the position of the rogue AP: determining directions at multiple locations grounded on triangulation and walking towards the rogue AP with direction adjustment. Results from extensive experiments in both indoor and outdoor environments show that our framework can achieve more practical and accurate rogue AP localization when comparing with the existing RSS-based approach.


title: A simple post-embedding system for the rapid demonstration of tissue antigens under the electron microscope

abstract: A simple and versatile technique for the preparation of ultra-thin sections, which can be stained immunohistochemically directly on electron microscope grids, is presented. An anti-hapten immunoperoxidase procedure has been adapted for use on tissue fixed in a purified monomeric glutaraldehyde--picric acid mixture, and embedded in 'L R White', a recently formulated plastic resin. This plastic tolerates the use of partial dehydration of tissue, resulting in higher antigenic yields. In addition, no etching of ultra-thin sections is necessary, and the whole immunostaining procedure can be completed in less than 2 h. A comparison of commonly used fixatives is discussed. High-resolution micrographs showing general staining (uranyl acetate--lead citrate) of rat pancreas, and immunostaining of insulin and TSH in storage granules in perfusion-fixed rat tissue and of lambda-chain immunoreactive cells in immersion-fixed human tonsil are included as examples.


title: Infrared Image Colorization Based on a Triplet DCGAN Architecture

abstract: This paper proposes a novel approach for colorizing near infrared (NIR) images using a Deep Convolutional Generative Adversarial Network (GAN) architecture. The proposed approach is based on the usage of a triplet model for learning each color channel independently, in a more homogeneous way. It allows a fast convergence during the training, obtaining a greater similarity between the colored NIR image and the corresponding ground truth. The proposed approach has been evaluated with a large data set of NIR images and compared with a recent approach, which is also based on a GAN architecture where all the color channels are obtained at the same time.


title: Fast databases with fast durability and recovery through multicore parallelism

abstract: Multicore in-memory databases for modern machines can support extraordinarily high transaction rates for online transaction processing workloads. A potential weakness, however, is recovery from crash failures. Can classical techniques, such as checkpoints, be made both efficient enough to keep up with current systems' memory sizes and transaction rates, and smart enough to avoid additional contention? Starting from an efficient multicore database system, we show that naive logging and checkpoints make normal-case execution slower, but that frequent disk synchronization allows us to keep up with many workloads with only a modest reduction in throughput. We design throughout for parallelism: during logging, during checkpointing, and during recovery. The result is fast. Given appropriate hardware (three SSDs and a RAID), a 32-core system can recover a 43.2 GB key-value database in 106 seconds, and a > 70 GB TPC-C database in 211 seconds.


title: A Manufacturing Core Concepts Ontology for Product Lifecycle Interoperability

abstract: This paper proposes a manufacturing core concepts ontology (MCCO) aimed at providing support for product life cycle interoperability. The potential focus of the work is interoperability across the production and design domains of product lifecycle. A core set of manufacturing concepts and their key relationships are identified in MCCO. Semantics are captured formally through heavyweight logic using rigorous rules and axioms. Three different levels of specialization have been identified according to the degree of specialization required. Each level provides an immediate route to interoperability for the concepts specialized from that level. MCCO enable knowledge sharing across design and production domains through core concepts. A successful initial experimental implementation has been done to demonstrate the working of MCCO.


title: Design Thing'ing: methodology for understanding and discovering Use cases in IoT scenarios

abstract: In this paper we suggest a design research method for discovering and eliciting details of use cases in the Internet of Things Era. The method adds value at the Research and synthesis stage of a UCD or Design Thinking Process where the objective is not to simply understand the requirements and constraints of the problem -- but to really discover the emotions, motivations and context that are all part of the design challenge, and identify the most important nuances that gives an advantage when designing solutions. The research methodology discussed here helps in understanding the problem specifically for Internet of Things scenarios and uncovers layered insights related to context, motivations and related use cases. Not in terms of solutions, but in identifying the best opportunities to address, where this process helps.


title: Banking on Blockchain: Costs Savings Thanks to the Blockchain Technology

abstract: This paper looks at the challenges and opportunities of implementing blockchain technology across banking, providing food for thought about the potentialities of this disruptive technology. The blockchain technology can optimize the global financial infrastructure, achieving sustainable development, using more efficient systems than at present. In fact, many banks are currently focusing on blockchain technology to promote economic growth and accelerate the development of green technologies. In order to understand the potential of blockchain technology to support the financial system, we studied the actual performance of the Bitcoin system, also highlighting its major limitations, such as the significant energy consumption due to the high computing power required, and the high cost of hardware. We estimated the electrical power and the hash rate of the Bitcoin network, over time, and, in order to evaluate the efficiency of the Bitcoin system in its actual operation, we defined three quantities: “economic efficiency”, “operational efficiency”, and “efficient service”. The obtained results show that by overcoming the disadvantages of the Bitcoin system, and therefore of blockchain technology, we could be able to handle financial processes in a more efficient way than under the current system.


title: Video-Based Human Behavior Understanding: A Survey

abstract: Understanding human behaviors is a challenging problem in computer vision that has recently seen important advances. Human behavior understanding combines image and signal processing, feature extraction, machine learning, and 3-D geometry. Application scenarios range from surveillance to indexing and retrieval, from patient care to industrial safety and sports analysis. Given the broad set of techniques used in video-based behavior understanding and the fast progress in this area, in this paper we organize and survey the corresponding literature, define unambiguous key terms, and discuss links among fundamental building blocks ranging from human detection to action and interaction recognition. The advantages and the drawbacks of the methods are critically discussed, providing a comprehensive coverage of key aspects of video-based human behavior understanding, available datasets for experimentation and comparisons, and important open research issues.


title: Using Wikipedia at the TREC QA Track

abstract: We describe our participation in the TREC 2004 Question Answering track. We provide a detailed account of the ideas underlying our approach to the QA task, especially to the so-called ?other? questions. This year we made essential use of Wikipedia, the free online encyclopedia, both as a source of answers to factoid questions and as an importance model to help us identify material to be returned in response to ?other? questions.


title: A Tibetan Component Representation Learning Method for Online Handwritten Tibetan Character Recognition

abstract: This paper presents a Tibetan component representation learning method for component-based online handwritten Tibetan character recognition. In conventional methods, we designed features manually for Tibetan components. The hand-crafted features are often incomplete and decrease the component recognition accuracy, which influences component-based character recognition performance. To overcome the deficiency, we use three layer deep belief networks to learn automatically representation features for components. Restricted Boltzmann machine is used to construct each hidden layer. The weight parameters of the networks are optimized by greedy layer-wise learning algorithm. Then we combine representation learning based component classifier into our previous integrated segmentation and recognition framework. Finally we add syllable association module to improve the handwriting input speed. Experimental results on MRG-OHTC database show that the component representation learning method gives the promising performance. The proposed method achieves the component-level and character-level recognition rates of 94.78% and 94.09%.


title: Have I Really Met Timing? -- Validating PrimeTime Timing Reports with Spice

abstract: At sign-off everybody is wondering about how good the accuracy of the static timing analysis timing reports generated with primetime/sup /spl reg// really is. Errors can be introduced by STA setup, interconnect modeling, library characterization etc. The claims that path timing calculated by primetime usually is within a few percent of spice don't help to ease your uncertainty. When the signal integrity features were introduced to primetime there was also a feature added that was hardly announced: primetime can write out timing paths for simulation with spice that can be used to validate the timing numbers calculated by primetime. By comparing the numbers calculated by primetime to a simulation with spice for selected paths the designers can verify the timing and build up confidence or identify errors. This paper will describe a validation flow for primetime timing reports that is based on extraction of the spice paths, starting the spice simulation, parsing the simulation results, and creating a report comparing primetime and spice timing. All these steps are done inside the TCL environment of primetime. It will describe this flow, what is needed for the spice simulation, how it can be set up, what can go wrong, and what kind of problems in the STA can be identified.


title: A survey of accuracy evaluation metrics of recommendation tasks

abstract: Recommender systems are now popular both commercially and in the research community, where many algorithms have been suggested for providing recommendations. These algorithms typically perform differently in various domains and tasks. Therefore, it is important from the research perspective, as well as from a practical view, to be able to decide on an algorithm that matches the domain and the task of interest. The standard way to make such decisions is by comparing a number of algorithms offline using some evaluation metric. Indeed, many evaluation metrics have been suggested for comparing recommendation algorithms. The decision on the proper evaluation metric is often critical, as each metric may favor a different algorithm. In this paper we review the proper construction of offline experiments for deciding on the most appropriate algorithm. We discuss three important tasks of recommender systems, and classify a set of appropriate well known evaluation metrics for each task. We demonstrate how using an improper evaluation metric can lead to the selection of an improper algorithm for the task of interest. We also discuss other important considerations when designing offline experiments.


title: Study of the one-dimensional off-lattice hot monomer reaction model

abstract: Hot monomers are particles having a transient mobility (a ballistic flight) prior to being definitely absorbed on a surface. After arriving at a surface, the excess energy coming from the kinetic energy in the gas phase is dissipated through degrees of freedom parallel to the surface plane. In this paper we study the hot monomer-monomer adsorption-reaction process on a continuum (off-lattice) one-dimensional space by means of Monte Carlo simulation. The system exhibits a second-order irreversible phase transition between reactive and saturated (absorbing) phases which belong to the directed percolation (DP) universality class. This result is interpreted by means of a coarse-grained Langevin description which allows us to extend the DP conjecture to transitions occurring in continuous media.


title: Kullback-Leibler divergence constrained distributionally robust optimization

abstract: In this paper we study distributionally robust optimization (DRO) problems where the ambiguity set of the probability distribution is defined by the Kullback-Leibler (KL) divergence. We consider DRO problems where the ambiguity is in the objective function, which takes a form of an expectation, and show that the resulted minimax DRO problems can be formulated as a one-layer convex minimization problem. We also consider DRO problems where the ambiguity is in the constraint. We show that ambiguous expectation-constrained programs may be reformulated as a one-layer convex optimization problem that takes the form of the Benstein approximation of Nemirovski and Shapiro (2006). We further consider distributionally robust probabilistic programs. We show that the optimal solution of a probability minimization problem is also optimal for the distributionally robust version of the same problem, and also show that the ambiguous chance-constrained programs (CCPs) may be reformulated as the original CCP with an adjusted confidence level. A number of examples and special cases are also discussed in the paper to show that the reformulated problems may take simple forms that can be solved easily. The main contribution of the paper is to show that the KL divergence constrained DRO problems are often of the same complexity as their original stochastic programming problems and, thus, KL divergence appears a good candidate in modeling distribution ambiguities in mathematical programming.


title: E-Mail Tracking in Online Marketing - Methods, Detection, and Usage

abstract: E-Mail tracking uses personalized links and pictures for gathering in- formation on user behavior, for example, where, when, on what kind of device, and how often an e-mail has been read. This information can be very useful for marketing purposes. On the other hand, privacy and security requirements of customers could be violated by tracking. This paper examines how e-mail track- ing works, how it can be detected automatically, and to what extent it is used in German e-commerce. We develop a detection model and software tool in order to collect and analyze more than 600 newsletter e-mails from companies of sev- eral different industries. The results show that the usage of e-mail tracking in Germany is prevalent but also varies depending on the industry.


title: Schema Conversion Model of SQL Database to NoSQL

abstract: With the increasing maturity of NoSQL databases as well as the situation of reading more than writing on large volumes of data, many applications turn to NoSQL and pick it as data storage system. Migrating from SQL database to NoSQL and providing efficient query become growing imperative. However, join operation is not supported in NoSQL database and read separately for multiple times which brings poor performance is unavoidable. This paper proposes a schema conversion model for transforming SQL database to NoSQL which can provide high performance of join query with nesting relevant tables, and a graph transforming algorithm for containing all required content of join query in a table by offering correctly nested sequence. A rigorous proof about the transform algorithm has been conducted and the experiment has verified the correctness of the conversion model and the high performance of join operation.


title: Speech Synthesis Based on Hidden Markov Models

abstract: This paper gives a general overview of hidden Markov model (HMM)-based speech synthesis, which has recently been demonstrated to be very effective in synthesizing speech. The main advantage of this approach is its flexibility in changing speaker identities, emotions, and speaking styles. This paper also discusses the relation between the HMM-based approach and the more conventional unit-selection approach that has dominated over the last decades. Finally, advanced techniques for future developments are described.


title: Linking dimensions of social media use to job performance: The role of social capital

abstract: Social use of technology positive indirect effect on employees' job performance.Cognitive use of technology positive indirect effect on employees' job performance.Hedonic use of the technology direct negative impact on routine performance.Hedonic use of the technology indirect positive impact on innovative performance. Organizations are increasingly adopting new technologies, such as social media, that afford employees a repertoire of uses not simply focused on work, but also on socialization and entertainment. Knowledge regarding the impact of such diverse technologies on job performance, however, is currently limited. This study adopts a technology use lens to study the effect of three categories of social media use - social, hedonic, and cognitive - on job performance, as mediated by three dimensions of social capital. The research was conducted via a large-scale survey within a multinational Information Technology company. Social and cognitive uses of technology were empirically shown to have a positive, albeit indirect, effect on employees' routine and innovative job performance. Hedonic use of the technology, while having a direct negative impact on routine performance was shown to positively contribute to the development of social ties, leading to a mitigating positive influence on innovative performance. This interesting positive side of hedonic use, along with all findings from our study, are discussed and used to offer insights to future research and practice.


title: The Visual Analogue WOMAC 3.0 scale - internal validity and responsiveness of the VAS version

abstract: BackgroundMany people suffer with Osteoarthritis (OA) and subsequent morbidity. Therefore, measuring outcome associated with OA is important. The Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) has been a widely used patient reported outcome in OA. However, there is relatively little evidence to support the use of the Visual Analogue Scale (VAS) version of the scale. We aimed to explore the internal validity and responsiveness of this VAS version of the WOMAC.MethodsPatients with chronic hip or knee pain of mechanical origin, waiting for a hip or knee joint replacement completed the WOMAC as part of a study to investigate the effects of acupuncture and placebo controls. Validity was tested using factor analysis and Rasch analysis, and responsiveness using standardised response means.ResultsTwo hundred and twenty one patients (mean age 66.8, SD 8.29, 58% female) were recruited. Factor and Rasch analysis confirmed unidimensional Pain and Physical Functioning scales, capable of transformation to interval scaling and invariant over time. Some Differential Item Functioning (DIF) was observed, but this cancelled out at the test level. The Stiffness scale fitted the Rasch model but adjustments for DIF could not be made due to the shortness of the scale. Using the interval transformed data, Standardised Response Means were smaller than when using the raw, ordinal data.ConclusionsThe WOMAC Pain and Physical Functioning subscales satisfied unidimensionality and ordinal scaling tests, and the ability to transform to an interval scale. Some Differential Item Functioning was observed, but this cancelled out at the test level and, by doing so, at the same time removed the disturbance of unidimensionality. The scaling characteristics of sets of items which use VAS require further analysis, as it would appear that they can lead to spurious levels of responsiveness and scale compression because they exaggerate the distortion of the ordinal scale.Trial numberUKCRN study ID: 4881ISRCTN78434638


title: Playing Go without Game Tree Search Using Convolutional Neural Networks

abstract: The game of Go has a long history in East Asian countries, but the field of Computer Go has yet to catch up to humans until the past couple of years. While the rules of Go are simple, the strategy and combinatorics of the game are immensely complex. Even within the past couple of years, new programs that rely on neural networks to evaluate board positions still explore many orders of magnitude more board positions per second than a professional can. We attempt to mimic human intuition in the game by creating a convolutional neural policy network which, without any sort of tree search, should play the game at or above the level of most humans. We introduce three structures and training methods that aim to create a strong Go player: non-rectangular convolutions, which will better learn the shapes on the board, supervised learning, training on a data set of 53,000 professional games, and reinforcement learning, training on games played between different versions of the network. Our network has already surpassed the skill level of intermediate amateurs simply using supervised learning. Further training and implementation of non-rectangular convolutions and reinforcement learning will likely increase this skill level much further.


title: Practical Processing of Mobile Sensor Data for Continual Deep Learning Predictions

abstract: We present a practical approach for processing mobile sensor time series data for continual deep learning predictions. The approach comprises data cleaning, normalization, capping, time-based compression, and finally classification with a recurrent neural network. We demonstrate the effectiveness of the approach in a case study with 279 participants. On the basis of sparse sensor events, the network continually predicts whether the participants would attend to a notification within 10 minutes. Compared to a random baseline, the classifier achieves a 40% performance increase (AUC of 0.702) on a withheld test set. This approach allows to forgo resource-intensive, domain-specific, error-prone feature engineering, which may drastically increase the applicability of machine learning to mobile phone sensor data.


title: Validation of single nucleotide polymorphisms associated with acute rejection in kidney transplant recipients using a large multi-center cohort.

abstract: There have been numerous reports proposing a statistically significant association between a genetic variant, usually in the form of a single nucleotide polymorphism (SNP), and acute rejection (AR). Unfortunately, there are additional publications reporting a lack of association with AR when a different cohort of recipients was analyzed for the same SNP. The objective of this report was to attempt replication of these published finding in our own kidney allograft recipient cohort. We analyzed 23 genetic variants, previously reported to have a significant association with AR, using a cohort of 969 clinically well-defined kidney transplant recipients. Only one SNP, rs6025 (Leiden mutation), within the coagulation factor V gene, showed a significant association with a P-value of 0.011 in a race-adjusted analysis and a P-value of 0.0003 in multiple variable analysis. An additional SNP, rs11706052 in IMPDH2, gave a modest P-value of 0.044 using multiple variable analysis, which is not significant when multiple testing is taken into consideration. Our results suggest that careful validation of previously reported associations with AR is necessary, and different strategies other than candidate gene studies can help to identify causative genetic variants associated with AR.


Incorporating Word Correlation Knowledge into Topic Modeling


title: Low-cost circuit design of EEG signal acquisition for the brain-computer interface system

abstract: Increasing number of research activities and different types of studies in brain-computer interface systems (BCIs) show potential in this young research area. However, BCIs have not become widely applied, most of them are still limited in the laboratory and off-line. One of the important reasons is that: EEG signal acquisition is completed by the professional medical equipments. They are expensive and the parameters of them cannot be flexiblely changed with the specific BCI experiment paradigm. In the paper, a single-channel low-cost circuit of EEG signal acquisition for the BCI system is designed. The circuit is composed of protection circuit, instrumentation amplifier, common mode rejection (CMR) circuit, gain adjustable amplifiers and filters. In order to test this circuit, the circuit simulation and the real-time EEG measurements are implemented. The experimental results show that the circuit is effective with good performance, it is very suitable for the online BCI system.


title: FPGA implementation of a support vector machine for classification and regression

abstract: We present a successful design for a high-performance, low-resource-consuming hardware for Support Vector Classification and Support Vector Regression. The system has been implemented on a low cost FPGA device and exploits the advantages of parallel processing to compute the feed forward phase in support vector machines. In this paper we show that the same hardware can be used for classification problems and regression problems, and we show satisfactory results on an image recognition problem by SV multiclass classification and on a function estimation problem by SV regression.


title: DOULION: counting triangles in massive graphs with a coin

abstract: Counting the number of triangles in a graph is a beautiful algorithmic problem which has gained importance over the last years due to its significant role in complex network analysis. Metrics frequently computed such as the clustering coefficient and the transitivity ratio involve the execution of a triangle counting algorithm. Furthermore, several interesting graph mining applications rely on computing the number of triangles in the graph of interest. In this paper, we focus on the problem of counting triangles in a graph. We propose a practical method, out of which all triangle counting algorithms can potentially benefit. Using a straightforward triangle counting algorithm as a black box, we performed 166 experiments on real-world networks and on synthetic datasets as well, where we show that our method works with high accuracy, typically more than 99% and gives significant speedups, resulting in even ≈ 130 times faster performance.


title: Pharmacologic management of neuropathic pain: Evidence-based recommendations

abstract: Patients with neuropathic pain (NP) are challenging to manage and evidence-based clinical recommendations for pharmacologic management are needed. Systematic literature reviews, randomized clinical trials, and existing guidelines were evaluated at a consensus meeting. Medications were considered for recommendation if their efficacy was supported by at least one methodologically-sound, randomized clinical trial (RCT) demonstrating superiority to placebo or a relevant comparison treatment. Recommendations were based on the amount and consistency of evidence, degree of efficacy, safety, and clinical experience of the authors. Available RCTs typically evaluated chronic NP of moderate to severe intensity. Recommended first-line treatments include certain antidepressants (i.e., tricyclic antidepressants and dual reuptake inhibitors of both serotonin and norepinephrine), calcium channel α2-δ ligands (i.e., gabapentin and pregabalin), and topical lidocaine. Opioid analgesics and tramadol are recommended as generally second-line treatments that can be considered for first-line use in select clinical circumstances. Other medications that would generally be used as third-line treatments but that could also be used as second-line treatments in some circumstances include certain antiepileptic and antidepressant medications, mexiletine, N-methyl-d-aspartate receptor antagonists, and topical capsaicin. Medication selection should be individualized, considering side effects, potential beneficial or deleterious effects on comorbidities, and whether prompt onset of pain relief is necessary. To date, no medications have demonstrated efficacy in lumbosacral radiculopathy, which is probably the most common type of NP. Long-term studies, head-to-head comparisons between medications, studies involving combinations of medications, and RCTs examining treatment of central NP are lacking and should be a priority for future research.


title: Thermal image human detection using Haar-cascade classifier

abstract: Haar-Cascade classifier method has been applied to detect the presence of a human on the thermal image. The evaluation was done on the performance of detection, represented by its precision and recall values. The thermal camera images were varied to obtain comprehensive results, which covered the distance of the object from the camera, the angle of the camera to the object, the number of objects, and the environmental conditions during image acquisition. The results showed that the greater the camera-object distance, the precision and recall of human detection results declined. Human objects would also be hard to detect if his/her pose was not facing frontally. The method was able to detect more than one human in the image with positions of in front of each other, side by side, or overlapped to one another. However, if there was any other object in the image that had characteristics similar to a human, the object would also be detected as a human being, resulting in a false detection. These other objects could be an infrared shadow formed from the reflection on glass or painted walls.


Evaluation of Urban Vehicle Routing Algorithms


title: Binocular eye tracking in VR for visual inspection training

abstract: This paper presents novel software techniques for binocular eye tracking within Virtual Reality and discusses their application to aircraft inspection training. The aesthetic appearance of the environment is driven by standard graphical techniques augmented by realistic texture maps of the physical environment. The user's gaze direction, as well as head position and orientation, are tracked to allow recording of the user's fixations within the environment. Methods are given for (1) integration of the eye tracker into a Virtual Reality framework, (2) stereo calculation of the user's 3D gaze vector, (3) a new 3D calibration technique developed to estimate the user's inter-pupillary distance post-facto, and (4) a new technique for eye movement analysis in 3-space. The 3D eye movement analysis technique is an improvement over traditional 2D approaches since it takes into account the 6 degrees of freedom of head movements and is resolution independent. Results indicate that although the current signal analysis approach is somewhat noisy and tends to underestimate the identified number of fixations, recorded eye movements provide valuable human factors process measures complementing performance statistics used to gauge training effectiveness.


title: Lipedema, a frequently unrecognized problem

abstract: Lipedema is characterized by symmetric lower extremity enlargement secondary to the deposition of fat. Lipedema is not rare, but it is commonly misdiagnosed as lymphedema. We describe a 20-year-old woman with massive lower extremity enlargement that did not respond to compression therapy. Magnetic resonance imaging of the lower extremities helped to confirm the diagnosis.


title: Efficient Normal-Form Parsing For Combinatory Categorial Grammar

abstract: Under categorial grammars that have powerful rules like composition, a simple n-word sentence can have exponentially many parses. Generating all parses is inefficient and obscures whatever true semantic ambiguities are in the input. This paper addresses the problem for a fairly general form of Combinatory Categorial Grammar, by means of an efficient, correct, and easy to implement normal-form parsing technique. The parser is proved to find exactly one parse in each semantic equivalence class of allowable parses; that is, spurious ambiguity (as carefully defined) is shown to be both safely and completely eliminated.


title: Gradient Boosting With Piece-Wise Linear Regression Trees

abstract: Gradient Boosted Decision Trees (GBDT) is a very successful ensemble learning algorithm widely used across a variety of applications. Recently, several variants of GBDT training algorithms and implementations have been designed and heavily optimized in some very popular open sourced toolkits including XGBoost, LightGBM and CatBoost. In this paper, we show that both the accuracy and efficiency of GBDT can be further enhanced by using more complex base learners. Specifically, we extend gradient boosting to use piecewise linear regression trees (PL Trees), instead of piecewise constant regression trees, as base learners. We show that PL Trees can accelerate convergence of GBDT and improve the accuracy. We also propose some optimization tricks to substantially reduce the training time of PL Trees, with little sacrifice of accuracy. Moreover, we propose several implementation techniques to speedup our algorithm on modern computer architectures with powerful Single Instruction Multiple Data (SIMD) parallelism. The experimental results show that GBDT with PL Trees can provide very competitive testing accuracy with comparable or less training time.


title: A BLE RSSI ranking based indoor positioning system for generic smartphones

abstract: Indoor navigation in physical retail type spaces aids the navigation of users to find physical items at known destinations. WiFi Fingerprinting using a mobile phone is perhaps the most widely used method. However, this is power-hungry, and its typical positioning accuracy (2.0 to 3.0 meters) is not enough to differentiate between adjacent narrow aisles to locate items. In this paper, we present a novel (Bluetooth Low Energy) BLE Received Signal Strength Indication (RSSI) ranking based fingerprinting method that uses Kendall Tau Correlation Coefficient (KTCC) to correlate a new signal position with the signal strength ranking of multiple low-power iBeacon devices situated in a retail space. This offers a higher positioning accuracy and is supported in recent smartphones. An important source of error is the RSSI, which varies depending on the model and orientation of the phone. We present a novel way to mitigate this. We validated our method in a retail-like indoor space of Queen Mary Library. We achieved an average positioning error of 0.87 meters which was sufficient to differentiate between physical space aisles.


Adaptive Background Estimation and Foreground Detection using Kalman-Filtering


title: What influences IT ethical behavior intentions - planned behavior, reasoned action, perceived importance, or individual characteristics?

abstract: This study extends IT ethics research by proposing an IT ethical behavioral model that includes attitude, perceived importance, subjective norms, situational factors, and individual characteristics. The proposed model integrates elements from the Theory of Planned Behavior (TPB) and Theory of Reasoned Action (TRA) as well as ethical decision-making models. It is hypothesized that behavioral intention is influenced by an individual's attitude (which in turn is influenced by consequences of the action and the environment), obligation, and personal characteristics. The results of the study show that some factors are consistently significant in affecting attitude and behavioral intention. Other factors are significant only in certain scenarios. From the results, organizations may be able to develop realistic training programs for IT professionals and managers and incorporate deterrent and preventive measures that can curb the rising tide of undesired misuse.


title: Iris recognition in non-ideal imaging conditions

abstract: This paper studies the iris recognition problem in the degraded iris images captured in non-ideal imaging conditions. In these circumstances iris recognition becomes challenging because of noisy factors such as the off-axis imaging, pose variation, image blurring, illumination change, occlusion, specular highlights and noise. We introduce a robust algorithm based on the Random Sample Consensus (RANSAC) for localization of non-circular iris boundaries. It can localize the iris boundaries more accurately than the methods based on the Hough transform. To account for iris pattern deformation, we describe an image registration method based on the Lucas-Kanade algorithm. Operating on the filtered iris images, this method divides one image into small sub-images and solves registration problem for every small sub-image. Under some reasonable assumptions this method becomes very efficient while maintaining its effectiveness. Finally, we investigate how to extract highly distinctive features in the degraded iris images. We present a sequential forward selection method for seeking a sub-optimal subset of filters from a family of Gabor filters. The recognition performance is greatly improved with a very small number of filters selected. Experiments were conducted on the UBIRIS.v2 iris database and promising results were obtained.


Money Laundering and Technology Enabled Crime: a cultural analysis


title: Automatic Bird Species Identification for Large Number of Species

abstract: In this paper we focus on the automatic identification of bird species from their audio recorded song. Bird monitoring is important to perform several tasks, such as to evaluate the quality of their living environment or to monitor dangerous situations to planes caused by birds near airports. We deal with the bird species identification problem using signal processing and machine learning techniques. First, features are extracted from the bird recorded songs using specific audio treatment, next the problem is performed according to a classical machine learning scenario, where a labeled database of previously known bird songs are employed to create a decision procedure that is used to predict the species of a new bird song. Experiments are conducted in a dataset of recorded songs of bird species which appear in a specific region. The experimental results compare the performance obtained in different situations, encompassing the complete audio signals, as recorded in the field, and short audio segments (pulses) obtained from the signals by a split procedure. The influence of the number of classes (bird species) in the identification accuracy is also evaluated.


title: Big five personality factors, hardiness, and social judgment as predictors of leader performance.

abstract: Purpose – The purpose of this paper is to evaluate the influence of psychological hardiness, social judgment, and “Big Five” personality dimensions on leader performance in US military academy cadets at West Point.Design/methodology/approach – Army cadets were studied in two different organizational contexts, i.e. summer field training and during academic semesters. Leader performance was measured with leadership grades (supervisor ratings) aggregated over four years at West Point.Findings – After controlling for general intellectual abilities, hierarchical regression results showed leader performance in the summer field training environment is predicted by Big Five extroversion, and hardiness, and a trend for social judgment. During the academic period context, leader performance is predicted by mental abilities, Big Five conscientiousness, and hardiness, with a trend for social judgment.Research limitations/implications – Results confirm the importance of psychological hardiness, extroversion, and consc...


Spread: a new layer for profiled deep-learning side-channel attacks.


title: Optimal slot/pole and flux-barrier layer number combinations for synchronous reluctance machines

abstract: AC synchronous reluctance machine (SynRM) or permanent magnet assisted synchronous reluctance machine presently receives a great deal of interest, since there is less or even no rare earth permanent magnet in the rotor. Most of SynRM machines employ a stator that is originally designed for a standard squirrel cage induction motor for a similar output rating and application, or the SynRM machine with 24-slot, 4-pole are often directly chosen for investigation in most of the available literature. Therefore, it is necessary to investigate the influence of stator and rotor pole number combinations together with the flux-barrier layers number on the performance of SynRM machine with emphasis on output torque capability and torque ripple. In order to investigate the impact of slot/pole number combinations on performance of SynRM machine, the same ratio of rotor insulation width to the rotor iron width has been used for all the slot and pole number combinations machines. In addition, the effect of flux-barrier layer number of this machine on average torque and torque ripple has also been investigated.


title: Sentiment analysis of movie reviews: A new feature-based heuristic for aspect-level sentiment classification

abstract: This paper presents our experimental work on a new kind of domain specific feature-based heuristic for aspect-level sentiment analysis of movie reviews. We have devised an aspect oriented scheme that analyses the textual reviews of a movie and assign it a sentiment label on each aspect. The scores on each aspect from multiple reviews are then aggregated and a net sentiment profile of the movie is generated on all parameters. We have used a SentiWordNet based scheme with two different linguistic feature selections comprising of adjectives, adverbs and verbs and n-gram feature extraction. We have also used our SentiWordNet scheme to compute the document-level sentiment for each movie reviewed and compared the results with results obtained using Alchemy API. The sentiment profile of a movie is also compared with the document-level sentiment result. The results obtained show that our scheme produces a more accurate and focused sentiment profile than the simple document-level sentiment analysis.


title: 3-D Magnetic Field and Torque Analysis of a Novel Halbach Array Permanent-Magnet Spherical Motor

abstract: We present a three-dimensional (3-D) torque model based on spherical harmonic functions of a Halbach array permanent-magnet (PM) spherical motor. Using the torque model, we show how to realize three-degree-of-freedom motion. We compare the Halbach array PM spherical motor and the conventional parallel magnet array spherical motor in terms of spherical harmonic component and amplitude of the torque. We discuss our study of variations of air-gap magnetic field with parallel magnetization directions along different spatial positions in each magnet segment. We also discuss the parasitic effect of the proposed spherical Halbach array magnet. We have verified our results by finite-element method.


Schizotypic traits measured with O-LIFE and polymorphism of genes of dopaminergic system and circadian rhythms in bipolar affective illness


title: Supervised Bilingual Lexicon Induction with Multiple Monolingual Signals

abstract: Prior research into learning translations from source and target language monolingual texts has treated the task as an unsupervised learning problem. Although many techniques take advantage of a seed bilingual lexicon, this work is the first to use that data for supervised learning to combine a diverse set of signals derived from a pair of monolingual corpora into a single discriminative model. Even in a low resource machine translation setting, where induced translations have the potential to improve performance substantially, it is reasonable to assume access to some amount of data to perform this kind of optimization. Our work shows that only a few hundred translation pairs are needed to achieve strong performance on the bilingual lexicon induction task, and our approach yields an average relative gain in accuracy of nearly 50% over an unsupervised baseline. Large gains in accuracy hold for all 22 languages (low and high resource) that we investigate.


title: Impact of Hybrid Business Models in the Supply Chain Performance

abstract: Today’s manufacturing competition goes beyond single companies and becomes a battle fought between supply chains (Min, 2002), (Nyoman, 2004), meaning the focus has changed from the local manufacturing company to the international supply chain (Olhager, 2003), (Ismail, 2006). In this scenario, competitiveness becomes something holistic about the total supply chain system rather than just what the company entity at the end of the supply chain can offer (Duclos, 2003). Moreover, the service provided to the end customer is determined by the effectiveness and efficiency of the cooperation of all the companies in the supply chain (Terzi, 2004). This trend demands organizations to improve and optimize their supply chain strategies, in order to respond and satisfy customers’ demands (Sen, 2004), i.e. to provide end users with the right product at the right place, time, and price (Griffiths, 2000). As the degree of interaction between the members of the supply chain depends on the type of business models used by them (Browne, 1999), the fulfillment of the customer’s demands can be achieved through the use of different business models (Sen, 2004). A business model answers who the customer is and what the customer wants, so value can be delivered at an appropriate cost (Chung, 2004). Ngai (2005) offers a value chain-based classification of the different business models used in a supply chain environment: engineer-to-order (ETO), make-to-order (MTO), assembly-to-order (ATO), make-to-stock (MTS), and ship-to-stock (STS). Each typical business model has its advantages and limitations, and only fits for some certain supply chain scenarios (Wadhwa, 2002).


title: Real-time lane detection and forward collision warning system based on stereo vision

abstract: This paper presents a real-time and robust lane detection and forward collision warning technique based on stereo cameras. First, obstacles image is obtained through stereo matching and UV-disparity segmentation algorithm. Then, Inverse Perspective Mapping(IPM) and Sobel filtering are conducted to generate a low-noise top view of the road by fusing the obstacles image and the original image. Next, Hough Transformation for the top view map is completed and the extreme points(poles) are calculated as the detected lanes according to the traffic lanes model. Besides, the host lane is selected or supplemented among all the detected lanes and the nearest obstacle in this host lane is detected for the forward collision warning. Experimental results on the public data set indicate that our method can work effectively and real-timely in the normal structured environment.


title: Evaluating the past to improve the future - A qualitative study of ICU patients' experiences.

abstract: BACKGROUND ::: The recovery period for patients who have been in an intensive care unitis often prolonged and suboptimal. Anxiety, depression and post-traumatic stress disorder are common psychological problems. Intensive care staff offer various types of intensive aftercare. Intensive care follow-up aftercare services are not standard clinical practice in Norway. ::: ::: ::: OBJECTIVE ::: The overall aim of this study is to investigate how adult patients experience theirintensive care stay their recovery period, and the usefulness of an information pamphlet. ::: ::: ::: METHOD ::: A qualitative, exploratory research with semi-structured interviews of 29 survivors after discharge from intensive care and three months after discharge from the hospital. ::: ::: ::: RESULTS ::: Two main themes emerged: "Being on an unreal, strange journey" and "Balancing between who I was and who I am" Patients' recollection of their intensive care stay differed greatly. Continuity of care and the nurse's ability to see and value individual differences was highlighted. The information pamphlet helped intensive care survivors understand that what they went through was normal. ::: ::: ::: CONCLUSIONS ::: Continuity of care and an individual approach is crucial to meet patients' uniqueness and different coping mechanisms. Intensive care survivors and their families must be included when information material and rehabilitation programs are designed and evaluated.


title: Piecewise-Planar StereoScan:Structure and Motion from Plane Primitives

abstract: This article describes a pipeline that receives as input a sequence of images acquired by a calibrated stereo rig and outputs the camera motion and a Piecewise-Planar Reconstruction (PPR) of the scene. It firstly detects the 3D planes viewed by each stereo pair from semi-dense depth estimation. This is followed by estimating the pose between consecutive views using a new closed-form minimal algorithm that relies in point correspondences only when plane correspondences are insufficient to fully constrain the motion. Finally, the camera motion and the PPR are jointly refined, alternating between discrete optimization for generating plane hypotheses and continuous bundle adjustment. The approach differs from previous works in PPR by determining the poses from plane-primitives, by jointly estimating motion and piecewise-planar structure, and by operating sequentially, being suitable for applications of SLAM and visual odometry. Experiments are carried in challenging wide-baseline datasets where conventional point-based SfM usually fails.


title: Let’s Go All the Way: From Requirements via Colored Workflow Nets to a BPEL Implementation of a New Bank System

abstract: This paper describes use of the formal modeling language Colored Petri Nets (CPNs) in the development of a new bank system. As a basis for the paper, we present a requirements model, in the form of a CPN, which describes a new bank work process that must be supported by the new system. This model has been used to specify, validate, and elicit user requirements. The contribution of this paper is to describe two translation steps that go from the requirements CPN to an implementation of the new system. In the first translation step, a workflow model is derived from the requirements model. This model is represented in terms of a so-called Colored Workflow Net (CWN), which is a generalization of the classical workflow nets to CPN. In the second translation step, the CWN is translated into implementation code. The target implementation language is BPEL4WS deployed in the context of IBM WebSphere. A semi-automatic translation of the workflow model to BPEL4WS is possible because of the structural requirements imposed on CWNs.


title: Reconfigurable multiband SAW filters for LTE applications

abstract: Reconfigurable surface acoustic wave (SAW) filters are presented for the 700 MHz frequency band currently allocated to Long Term Evolution (LTE). Unlike traditional SAW filters with a fixed center frequency, the proposed filters have a reconfigurable center frequency. First, a novel modular ladder type topology is proposed incorporating SAW resonators and GaAs Single Pole Single Throw (SPST) switches within the basic reconfigurable filter. The measured center frequency in the low band state is 696 MHz and the BW is 3.4%. In the high band state the measured center frequency is 718 MHz and the BW is 2.6%. The center frequency shift is 3.2%. Spurious responses caused by switches are an important design consideration and the root causes of the spurious responses in high band and low band are identified. Then a novel filter topology using Single Pole Double Throw (SPDT) absorptive switches is proposed to remove the spurious responses in the high band state. Finally the root cause of the remaining spurious response is analyzed and a solution is proposed.


title: Constructing elastic distinguishability metrics for location privacy

abstract: With the increasing popularity of hand-held devices, location-based applications and services have access to accurate and real-time location information, raising serious privacy concerns for their users. The recently introduced notion of geo-indistinguishability tries to address this problem by adapting the well-known concept of differential privacy to the area of location-based systems. Although geo-indistinguishability presents various appealing aspects, it has the problem of treating space in a uniform way, imposing the addition of the same amount of noise everywhere on the map. In this paper we propose a novel elastic distinguishability metric that warps the geometrical distance, capturing the different degrees of density of each area. As a consequence, the obtained mechanism adapts the level of noise while achieving the same degree of privacy everywhere. We also show how such an elastic metric can easily incorporate the concept of a "geographic fence" that is commonly employed to protect the highly recurrent locations of a user, such as his home or work. We perform an extensive evaluation of our technique by building an elastic metric for Paris' wide metropolitan area, using semantic information from the OpenStreetMap database. We compare the resulting mechanism against the Planar Laplace mechanism satisfying standard geo-indistinguishability, using two real-world datasets from the Gowalla and Brightkite location-based social networks. The results show that the elastic mechanism adapts well to the semantics of each area, adjusting the noise as we move outside the city center, hence offering better overall privacy.


title: Soft Water Level Sensors for Characterizing the Hydrological Behaviour of Agricultural Catchments

abstract: An innovative soft water level sensor is proposed to characterize the hydrological behaviour of agricultural catchments by measuring rainfall and stream flows. This sensor works as a capacitor coupled with a capacitance to frequency converter and measures water level at an adjustable time step acquisition. It was designed to be handy, minimally invasive and optimized in terms of energy consumption and low-cost fabrication so as to multiply its use on several catchments under natural conditions. It was used as a stage recorder to measure water level dynamics in a channel during a runoff event and as a rain gauge to measure rainfall amount and intensity. Based on the Manning equation, a method allowed estimation of water discharge with a given uncertainty and hence runoff volume at an event or annual scale. The sensor was tested under controlled conditions in the laboratory and under real conditions in the field. Comparisons of the sensor to reference devices (tipping bucket rain gauge, hydrostatic pressure transmitter limnimeter, Venturi channels…) showed accurate results: rainfall intensities and dynamic responses were accurately reproduced and discharges were estimated with an uncertainty usually acceptable in hydrology. Hence, it was used to monitor eleven small agricultural catchments located in the Mediterranean region. Both catchment reactivity and water budget have been calculated. Dynamic response of the catchments has been studied at the event scale through the rising time determination and at the annual scale by calculating the frequency of occurrence of runoff events. It provided significant insight into catchment hydrological behaviour which could be useful for agricultural management perspectives involving pollutant transport, flooding event and global water balance.


title: Dual Band Pentagonal Microstrip Antenna for Wi-Fi Applications

abstract: The proposed antenna is formed by a patch rectangular antenna with an inner pentagonal one, built on FR-4, due its low cost and availability. There were considered 2.4 and 5.8 GHz as the operation frequencies, considering linear polarization in order to apply them to Wi-Fi equipments. The pentagonal patches were designed considering them as equivalents with the corresponding circular resonators. The simulations are realized using CADFEKO, software based on the Moment Method (MoM). The proofs were realized considering the designed antennas as replacement ones on a router. Simulated input return loss show a good agreement with the operation frequencies design. It has been demonstrated that the dual antenna operate satisfactorily in the 2.4 and 5.8 GHz frequency ranges, showing its feasibility to use it for Wi-Fi applications. Experimental results are compared to predictions from a software package confirming monopolar radiation in two bands at 2.4 GHz and 5.8 GHz.


title: Nanoparticle lattices with bases: Fourier modal method and dipole approximation

abstract: The utilization of periodic structures such as photonic crystals and metasurfaces is common for light manipulation at nanoscales. One of the most widely used computational approaches to consider them and design effective optical devices is the Fourier modal method (FMM) based on Fourier decomposition of electromagnetic fields. Nevertheless, calculating of periodic structures with small inclusions is often a difficult task, since they induce lots of high-$k_\parallel$ harmonics that should be taken into account. In this paper, we consider small particle lattices with bases and construct their scattering matrices via discrete dipole approximation (DDA). Afterwards, these matrices are implemented in FMM for consideration of complicated layered structures. We show the performance of the proposed hybrid approach by its application to a lattice, which routes left and right circularly polarized incident light to guided modes propagating in opposite directions. We also demonstrate its precision by spectra comparison with finite element method (FEM) calculations. The high speed and precision of this approach enable the calculation of angle-dependent spectra with very high resolution in a reasonable time, which allows resolving narrow lines unobservable by other methods.


title: Storage-Efficient Edge Caching With Asynchronous User Requests

abstract: Edge caching has attracted great attention recently due to its potential for reducing service delays. One of the key performance metrics in caching is storage efficiency. To achieve high storage efficiency, we present an edge caching strategy with time-domain buffer sharing in this paper. More particularly, our scheme can determine not only which content items deserve pushing by the core network, but also how long the content items deserve caching in the buffer of the base station. To this end, we formulate a queueing model, in which the storage cost and the maximum caching time are bridged via Little’s Law. Based on this model, we present a probabilistic edge caching strategy with random maximum caching time to strike the optimal tradeoff between the storage cost and the overall hit ratio of content items. For different content items having different users’ demand preferences, we further formulate a nonconvex optimization problem to jointly allocate the transmission and storage resources. An efficient two-layer searching algorithm is presented to achieve an optimal solution. Moreover, we also present the analytical solution to the joint transmission and storage allocation problem in the special scenario where all content items have been cached in the core network.


title: Foundations of Transparency in Tactile Information Design

abstract: This paper places contemporary literature on the topic of unimodal, single-site display of information using complex tactile signals in the context of progress towards the achievement of transparent communication - placing minimal load on the user's attentional resources. We discuss recent evidence that more is possible with purely haptic display than is commonly believed, as well as procedural developments that support systematic design of transparent tactile information display; and we frame the advances required to realize significant benefits with the technology we have now. Examples used and objectives thus identified focus on establishing effective information representations, and outlining efficient tools and processes for perceptually guiding icon design. Our discussion is inspired by Weiser's vision of calm technology based on locatedness and seamless movement between center and periphery, and it is organized along the lines of potential utility, form and learning.


title: Visual Thinking In Action: Visualizations As Used On Whiteboards

abstract: While it is still most common for information visualization researchers to develop new visualizations from a data-or taskdriven perspective, there is growing interest in understanding the types of visualizations people create by themselves for personal use. As part of this recent direction, we have studied a large collection of whiteboards in a research institution, where people make active use of combinations of words, diagrams and various types of visuals to help them further their thought processes. Our goal is to arrive at a better understanding of the nature of visuals that are created spontaneously during brainstorming, thinking, communicating, and general problem solving on whiteboards. We use the qualitative approaches of open coding, interviewing, and affinity diagramming to explore the use of recognizable and novel visuals, and the interplay between visualization and diagrammatic elements with words, numbers and labels. We discuss the potential implications of our findings on information visualization design.


title: An inexpensive Arduino-based LED stimulator system for vision research

abstract: Abstract Light emitting diodes (LEDs) are being used increasingly as light sources in life sciences applications such as in vision research, fluorescence microscopy and in brain–computer interfacing. Here we present an inexpensive but effective visual stimulator based on light emitting diodes (LEDs) and open-source Arduino microcontroller prototyping platform. The main design goal of our system was to use off-the-shelf and open-source components as much as possible, and to reduce design complexity allowing use of the system to end-users without advanced electronics skills. The main core of the system is a USB-connected Arduino microcontroller platform designed initially with a specific emphasis on the ease-of-use creating interactive physical computing environments. The pulse-width modulation (PWM) signal of Arduino was used to drive LEDs allowing linear light intensity control. The visual stimulator was demonstrated in applications such as murine pupillometry, rodent models for cognitive research, and heterochromatic flicker photometry in human psychophysics. These examples illustrate some of the possible applications that can be easily implemented and that are advantageous for students, educational purposes and universities with limited resources. The LED stimulator system was developed as an open-source project. Software interface was developed using Python with simplified examples provided for Matlab and LabVIEW. Source code and hardware information are distributed under the GNU General Public Licence (GPL, version 3).


title: The Moderating Influence of Privacy Concern on the Efficacy of Privacy Assurance Mechanisms for Building Trust: A Multiple-Context Investigation Completed Research Paper

abstract: Privacy policy statements and privacy-assurance cues are among the most important website features that online providers use to alleviate web customers’ privacy concern. This study examines the moderating role of privacy concern on how the quality of privacy policy statements and privacy assurance cues contribute to increased trust, and the subsequent decision to disclose private information online. The results of this study show distinct behavioral differences between how individuals with high versus low privacy concern form their trust to disclose private information across different contexts. The paper adds to the trust literature by highlighting the influence of the customer’s level of privacy concern (as who) and of the context (as where). The paper also adds to the Elaboration Likelihood Model Theory (ELM) by demonstrating the combined moderating roles of context and degree of involvement (privacy concern).


title: Beyond Exchangeability: The Chinese Voting Process

abstract: Many online communities present user-contributed responses, such as reviews of products and answers to questions. User-provided helpfulness votes can highlight the most useful responses, but voting is a social process that can gain momentum based on the popularity of responses and the polarity of existing votes. We propose the Chinese Voting Process (CVP) which models the evolution of helpfulness votes as a self-reinforcing process dependent on position and presentation biases. We evaluate this model on Amazon product reviews and more than 80 StackExchange forums, measuring the intrinsic quality of individual responses and behavioral coefficients of different communities.


title: Accurate Non-Iterative O(n) Solution to the PnP Problem

abstract: We propose a non-iterative solution to the PnP problem-the estimation of the pose of a calibrated camera from n 3D-to-2D point correspondences-whose computational complexity grows linearly with n. This is in contrast to state-of-the-art methods that are O(n5) or even O(n8), without being more accurate. Our method is applicable for all nges4 and handles properly both planar and non-planar configurations. Our central idea is to express the n 3D points as a weighted sum of four virtual control points. The problem then reduces to estimating the coordinates of these control points in the camera referential, which can be done in O(n) time by expressing these coordinates as weighted sum of the eigenvectors of a 12 x12 matrix and solving a small constant number of quadratic equations to pick the right weights. The advantages of our method are demonstrated by thorough testing on both synthetic and real-data.


title: HotSpot : a Dynamic Compact Thermal Model at the Processor-Architecture Level

abstract: This paper describes a thermal-modeling approach that is easy to use and computationally efficient for modeling thermal effects and thermal-management techniques at the processor architecture level. Our approach is based on modeling thermal behavior of the microprocessor die and its package as a circuit of thermal resistances and capacitances that correspond to functional blocks at the architecture level. This yields a simple compact model, yet heat dissipation within all major functional blocks and the heat flow among blocks and through the package are accounted for. The model is parameterized, boundary- and initial-conditions independent, and is derived by a structure assembly approach. The architecture community has demonstrated growing interest in thermal management, but currently lacks a way to model on-chip temperatures in a tractable way. Our model can be used for initial exploration of the design space at the architecture level. The model can easily be integrated into popular power/performance simulators, can be used to determine how thermal stress is correlated to the architecture, and how architecture-level design decisions influence thermal behavior and related effects.


title: Recognition, indexing and retrieval of british broadcast news with the THISL system

abstract: This paper described the THISL spoken document retrieval system for British and North American Broadcast News. The system is based on the ABBOT large vocabulary speech recognizer and a probabilistic text retrieval system. We discuss the development of a realtime British English Broadcast News system, and its integration into a spoken document retrieval system. Detailed evaluation is performed using a similar North American Broadcast News system, to take advantage of the TREC SDR evaluation methodology. We report results on this evaluation, with particular reference to the effect of query expansion and of automatic segmentation algorithms.


A Software Defined Wireless Networking for Efficient Communication in Smart Cities


title: A Model and Algorithm for the Courier Delivery Problem with Uncertainty

abstract: We consider the courier delivery problem (CDP), a variant of the vehicle routing problem with time windows (VRPTW) in which customers appear probabilistically and their service times are uncertain. We use scenario-based stochastic programming with recourse to model the uncertainty in customers and robust optimization for the uncertainty in service times. Our proposed model generates a master plan and daily schedules by maximizing the coverage of customers and the similarity of routes in each scenario, while minimizing the total time spent by the couriers and the total earliness and lateness penalty. To solve large-scale problem instances, we develop an insertion-based solution heuristic, called master and daily scheduler (MADS), and a tabu search improvement procedure. The computational results show that our heuristic improves the similarity of routes and the lateness penalty at the expense of increased total time spent when compared to a solution of independently scheduling each day. Our experimental results also show improvements over current industry practice on two real-world data sets.


title: Supervised domain adaptation for I-vector based speaker recognition

abstract: In this paper, we present a comprehensive study on supervised domain adaptation of PLDA based i-vector speaker recognition systems. After describing the system parameters subject to adaptation, we study the impact of their adaptation on recognition performance. Using the recently designed domain adaptation challenge, we observe that the adaptation of the PLDA parameters (i.e. across-class and within-class co variances) produces the largest gains. Nonetheless, length-normalization is also important; whereas using an indomani UBM and T matrix is not crucial. For the PLDA adaptation, we compare four approaches. Three of them are proposed in this work, and a fourth one was previously published. Overall, the four techniques are successful at leveraging varying amounts of labeled in-domain data and their performance is quite similar. However, our approaches are less involved, and two of them are applicable to a larger class of models (low-rank across-class).


title: A Nested Attention Neural Hybrid Model for Grammatical Error Correction

abstract: Grammatical error correction (GEC) systems strive to correct both global errors in word order and usage, and local errors in spelling and inflection. Further developing upon recent work on neural machine translation, we propose a new hybrid neural model with nested attention layers for GEC. Experiments show that the new model can effectively correct errors of both types by incorporating word and character-level information,and that the model significantly outperforms previous neural models for GEC as measured on the standard CoNLL-14 benchmark dataset. Further analysis also shows that the superiority of the proposed model can be largely attributed to the use of the nested attention mechanism, which has proven particularly effective in correcting local errors that involve small edits in orthography.


title: Frequency Analysis of Temporal Graph Signals

abstract: This letter extends the concept of graph-frequency to graph signals that evolve with time. Our goal is to generalize and, in fact, unify the familiar concepts from time- and graph-frequency analysis. To this end, we study a joint temporal and graph Fourier transform (JFT) and demonstrate its attractive properties. We build on our results to create filters which act on the joint (temporal and graph) frequency domain, and show how these can be used to perform interference cancellation. The proposed algorithms are distributed, have linear complexity, and can approximate any desired joint filtering objective.


title: Posted Price Model Based on GRS and Its Optimization Using in Grid Resource Allocation

abstract: Grid computing is a promising technology for future computing platforms. Economy model is a different approach to realize resource allocation in grid computing. In the economy model of grid resource allocation such as commodity or posted price model, sharing resource is based on negotiating about the flat fee, usage duration or time, QoS and some other items between the provider and the consumer of grid resource. But it is impossible task for there is a great deal of grid resources and users in grid even in a virtual organization, and it will be unsatisfactory for its sharing efficiency. In this paper, the idea of GRS (grid resource supermarket) and its pricing strategy are introduced. An improvement of posted price model based on GRS and optimization based on MOGAP (multi-objects generalized assignment problem) are proposed. Our scheme can help to solve the problem in the previous description. In the end an example is given to show the feasibility of this scheme.


title: The Shock of Aging: Molecular Chaperones and the Heat Shock Response in Longevity and Aging – A Mini-Review

abstract: BACKGROUND ::: Aging can be thought of as the collision between destructive processes that act on cells and organs over the lifetime and the responses that promote homeostasis, vitality and longevity. However, the precise mechanisms that determine the rates of aging in organisms are not known. ::: ::: ::: OBJECTIVE ::: Macromolecules such as proteins are continuously exposed to potential damaging agents that can cause loss of molecular function and depletion of cell populations over the lifetime of essential organs. One of the key homeostatic responses involved in maintaining longevity is the induction of heat shock proteins (HSPs), a conserved reaction to damaged intracellular proteins. We aim to discuss how the interplay between protein damage and its repair or removal from the cell may influence longevity and aging. ::: ::: ::: METHODS ::: We have reviewed experiments carried out in mammalian and non-mammalian organisms on molecular chaperones and the transcription factor (heat shock factor 1, HSF1) responsible for their expression. We have discussed mechanisms through which these molecules are regulated in cells, respond to stimuli that enhance longevity and become impaired during aging. ::: ::: ::: RESULTS ::: The transcription factor HSF1 initiates the prolific induction of HSP when cells are exposed to protein damage. HSPs are molecular chaperones that protect the proteome by folding denatured polypeptides and promoting the degradation of severely damaged proteins. Activation of HSF1 is coupled functionally to fundamental pathways of longevity and orchestrates the evasion of aging through HSP induction and antagonism of protein aggregation. In addition to mediating protein quality control, some HSPs such as Hsp27 and Hsp70 directly protect cells against damage-induced entry into death pathways. However, the heat shock response declines in potency over the lifetime, and enfeeblement of the response contributes to aging by permitting the emergence of protein aggregation diseases, reduction in cellular vigor and decreased longevity. ::: ::: ::: CONCLUSIONS ::: Molecular chaperones play an important role in the deterrence of protein damage during aging and their expression is required for longevity. Chemical stimulation of HSP synthesis might therefore be a significant strategy in future design of antiaging pharmaceuticals.


title: Diversity and isolation of rare actinomycetes: an overview.

abstract: A renewed interest in the development of new antimicrobial agents is urgently needed to combat the increasing number of antibiotic-resistant strains of pathogenic microorganisms. Actinomycetes continue to be the mainstream supplier of antibiotics used in industry. The likelihood of discovering a new compound with novel chemical structure can be increased with intensive efforts in isolating and screening of rare genera of microorganisms to include in natural-product-screening collections. An unexpected variety of rare actinomycetes is now being isolated worldwide from previously uninvestigated diverse natural habitats, using different selective isolation methods. These isolation efforts include methods to enhance growth (enrichment) of rare actinomycetes, and eliminate unwanted microorganisms (pretreatment). To speed up the strain isolation process, knowledge about the distribution of such unexploited groups of microorganisms must also be augmented. This is a summary of using these microorganisms as new potential biological resources, and a review of almost all of the selective isolation methods, including pretreatment and enrichment techniques that have been developed to date for the isolation of rare actinomycetes.


title: Question Answering with Knowledge Base, Web and Beyond

abstract: In this tutorial, we give the audience a coherent overview of the research of question answering (QA). We first introduce a variety of QA problems proposed by pioneer researchers and briefly describe the early efforts. By contrasting with the current research trend in this domain, the audience can easily comprehend what technical problems remain challenging and what the main breakthroughs and opportunities are during the past half century. For the rest of the tutorial, we select three categories of the QA problems that have recently attracted a great deal of attention in the research community, and present the tasks with the latest technical survey. We conclude the tutorial by discussing the new opportunities and future directions of QA research.


title: SNOPT: An SQP algorithm for large-scale constrained optimization

abstract: Sequential quadratic programming (SQP) methods have proved highly effective for solving constrained optimization problems with smooth nonlinear functions in the objective and constraints. Here we consider problems with general inequality constraints (linear and nonlinear). We assume that first derivatives are available and that the constraint gradients are sparse. ::: We discuss an SQP algorithm that uses a smooth augmented Lagrangian merit function and makes explicit provision for infeasibility in the original problem and the QP subproblems. SNOPT is a particular implementation that makes use of a semidefinite QP solver. It is based on a limited-memory quasi-Newton approximation to the Hessian of the Lagrangian and uses a reduced-Hessian algorithm (SQOPT) for solving the QP subproblems. It is designed for problems with many thousands of constraints and variables but a moderate number of degrees of freedom (say, up to 2000). An important application is to trajectory optimization in the aerospace industry. Numerical results are given for most problems in the CUTE and COPS test collections (about 900 examples).


title: EFFECT OF SACCHAROMYCES CEREVISIAE ON SURVIVAL, GROWTH, BIOCHEMICAL CONSTITUENTS AND ENERGY UTILIZATION IN THE PRAWN MACROBRACHIUM ROSENBERGII

abstract: A probiotic yeast, Saccharomyces cerevisiae was incorporated in basal diet prepared with fish meal, soybean meal, groundnut oil cake, corn flour, tapioca flour, egg albumin, cod liver oil and vitamin Bcomplex, at four different concentrations (10g, 20g, 30g and 40g kg-1) and fed to Macrobrachium rosenbergii post larvae (PL) for 90 days. The effect of this probiotic incorporation on the growth and survival performances, concentration of protein, amino acid, carbohydrate and lipid, and energy utilization was found to be significantly (P<0.05) higher at 40g kg-1 followed by 30g, 20g and 10g kg-1. 40g kg-1 S. cerevisiae incorporation was found to be established the highest rate of colony formation, 234x10-4 cfu (colony formation units). Actually, presence of Bacillus spp., Bacillus cereus, Pseudomonas spp., Escherichia coli, Streptococcus spp., and Klebsiella pneumoniae were deducted in water medium and the PL gut of control group. There is a general belief that Pseudomonas spp. and K. pneumonia are pathogenic to prawns. The establishment of S. cerevisiae colony in the gut of experimental PL has eradicated these pathogenic bacteria. Therefore, it is suggested that establishment of S. cerevisiae colony has led to better growth, survival and biochemical constituents in M. rosenbergii PL. Thus, S. cerevisiae could be taken as a useful probiotic in M. rosenbergii culture.


title: Hypergraph-Based Image Representation

abstract: An appropriate image representation induces some good image treatment algorithms. Hypergraph theory is a theory of finite combinatorial sets, modeling a lot of problems of operational research and combinatorial optimization. Hypergraphs are now used in many domains such as chemistry, engineering and image processing. We present an overview of a hypergraph-based picture representation giving much application in picture manipulation, analysis and restoration: the Image Adaptive Neighborhood Hypergraph (IANH). With the IANH it is possible to build powerful noise detection an elimination algorithm, but also to make some edges detection or some image segmentation. IANH has various applications and this paper presents a survey of them.


title: Combating Online In-Auction Fraud: Clues, Techniques and Challenges

abstract: The number of Internet auction shoppers is rapidly growing. However, online auction customers may suffer from auction fraud, sometimes without even noticing it. In-auction fraud differs from pre- and post-auction fraud in that it happens in the bidding period of an active auction. Since the in-auction fraud strategies are subtle and complex, it makes the fraudulent behavior more difficult to discover. Researchers from disciplines such as computer science and economics have proposed a number of methods to deal with in-auction fraud. In this paper, we summarize commonly seen indicators of in-auction fraud, provide a review of significant contributions in the literature of Internet in-auction fraud, and identify future challenging research tasks.


title: Visualization by Demonstration: An Interaction Paradigm for Visual Data Exploration

abstract: Although data visualization tools continue to improve, during the data exploration process many of them require users to manually specify visualization techniques, mappings, and parameters. In response, we present the Visualization by Demonstration paradigm, a novel interaction method for visual data exploration. A system which adopts this paradigm allows users to provide visual demonstrations of incremental changes to the visual representation. The system then recommends potential transformations ( Visual Representation, Data Mapping, Axes, and View Specification transformations ) from the given demonstrations. The user and the system continue to collaborate, incrementally producing more demonstrations and refining the transformations, until the most effective possible visualization is created. As a proof of concept, we present VisExemplar, a mixed-initiative prototype that allows users to explore their data by recommending appropriate transformations in response to the given demonstrations.


title: A behavioural model for product families

abstract: In this paper we propose a behavioural model, namely the Extended Modal Labeled Transition Systems, as a basis for the formalization of the different notions of variability usually present in product families definitions. In particular, an EMLTS is able to define a family of products by telling at any state of the system whether transitions are optional or compulsory for the products of the family. Based on this model, verification that a product belongs to a family can be carried out by means of automatic tools.


title: LS-Net: Learning to Solve Nonlinear Least Squares for Monocular Stereo

abstract: Sum-of-squares objective functions are very popular in computer vision algorithms. However, these objective functions are not always easy to optimize. The underlying assumptions made by solvers are often not satisfied and many problems are inherently ill-posed. In this paper, we propose LS-Net, a neural nonlinear least squares optimization algorithm which learns to effectively optimize these cost functions even in the presence of adversities. Unlike traditional approaches, the proposed solver requires no hand-crafted regularizers or priors as these are implicitly learned from the data. We apply our method to the problem of motion stereo ie. jointly estimating the motion and scene geometry from pairs of images of a monocular sequence. We show that our learned optimizer is able to efficiently and effectively solve this challenging optimization problem.


title: Robust Face Recognition via Sparse Representation

abstract: We consider the problem of automatically recognizing human faces from frontal views with varying expression and illumination, as well as occlusion and disguise. We cast the recognition problem as one of classifying among multiple linear regression models and argue that new theory from sparse signal representation offers the key to addressing this problem. Based on a sparse representation computed by C1-minimization, we propose a general classification algorithm for (image-based) object recognition. This new framework provides new insights into two crucial issues in face recognition: feature extraction and robustness to occlusion. For feature extraction, we show that if sparsity in the recognition problem is properly harnessed, the choice of features is no longer critical. What is critical, however, is whether the number of features is sufficiently large and whether the sparse representation is correctly computed. Unconventional features such as downsampled images and random projections perform just as well as conventional features such as eigenfaces and Laplacianfaces, as long as the dimension of the feature space surpasses certain threshold, predicted by the theory of sparse representation. This framework can handle errors due to occlusion and corruption uniformly by exploiting the fact that these errors are often sparse with respect to the standard (pixel) basis. The theory of sparse representation helps predict how much occlusion the recognition algorithm can handle and how to choose the training images to maximize robustness to occlusion. We conduct extensive experiments on publicly available databases to verify the efficacy of the proposed algorithm and corroborate the above claims.


title: Dynamic MOS sigmoid array folding analog-to-digital conversion

abstract: A dynamic, saturating difference circuit for large-scale parallel folding analog-to-digital conversion is presented. The circuit comprises a subthreshold nMOS transistor source-coupled to a capacitor, implementing a log-domain integrator. The output current is a logistic sigmoidal function of the change in voltage on the gate. Offset and gain of the differential sigmoid are controlled by timing of global clock signals and are independent of transistor mismatch. Folding operation for analog-to-digital conversion is obtained by differentially combining and integrating currents from a bank of sigmoid units. A 128-channel parallel bank of 4-bit Gray-code folding analog-to-digital converters measures 0.75 mm/spl times/2 mm in 0.5 /spl mu/m CMOS and delivers 768 Msps at 82-mW power dissipation.


title: Boltzmann machines for time-series

abstract: We review Boltzmann machines extended for time-series. These models often have recurrent structure, and back propagration through time (BPTT) is used to learn their parameters. The per-step computational complexity of BPTT in online learning, however, grows linearly with respect to the length of preceding time-series (i.e., learning rule is not local in time), which limits the applicability of BPTT in online learning. We then review dynamic Boltzmann machines (DyBMs), whose learning rule is local in time. DyBM's learning rule relates to spike-timing dependent plasticity (STDP), which has been postulated and experimentally confirmed for biological neural networks.


title: Distributed agile: project management in a global environment

abstract: Agile methods have been gaining acceptance in the mainstream software development community. At the same time, globally distributed software development is another trend delivering high-quality software to global users at lower costs. Little is published about the adoption and adaption of Agile methods in a distributed team and software globalization/localization project environment. The overall performance and satisfaction with the international deployment of the latest version of My Yahoo! increased by more than 30% after the global product team, distributed over three continents, adopted Agile methods. Our objective is to highlight successful practices and challenges that have been overcome by the globalization project, and suggest a framework for software globalization project management using a distributed Agile approach.


IMPROVING SOIL OXYGENATION WITH HYDROGEN PEROXIDE INJECTION INTO HEAVY CLAY LOAM SOIL: EFFECT ON PLANT WATER STATUS, CO2 ASSIMILATION AND BIOMASS OF AVOCADO TREES


title: We can learn your #hashtags: Connecting tweets to explicit topics

abstract: In Twitter, users can annotate tweets with hashtags to indicate the ongoing topics. Hashtags provide users a convenient way to categorize tweets. From the system's perspective, hashtags play an important role in tweet retrieval, event detection, topic tracking, and advertising, etc. Annotating tweets with the right hashtags can lead to a better user experience. However, two problems remain unsolved during an annotation: (1) Before the user decides to create a new hashtag, is there any way to help her/him find out whether some related hashtags have already been created and widely used? (2) Different users may have different preferences for categorizing tweets. However, few work has been done to study the personalization issue in hashtag recommendation. To address the above problems, we propose a statistical model for personalized hashtag recommendation in this paper. With millions of pairs being published everyday, we are able to learn the complex mappings from tweets to hashtags with the wisdom of the crowd. Two questions are answered in the model: (1) Different from traditional item recommendation data, users and tweets in Twitter have rich auxiliary information like URLs, mentions, locations, social relations, etc. How can we incorporate these features for hashtag recommendation? (2) Different hashtags have different temporal characteristics. Hashtags related to breaking events in the physical world have strong rise-and-fall temporal pattern while some other hashtags remain stable in the system. How can we incorporate hashtag related features to serve for hashtag recommendation? With all the above factors considered, we show that our model successfully outperforms existing methods on real datasets crawled from Twitter.


title: INFORMATION SECURITY BEHAVIOR: FACTORS AND RESEARCH DIRECTIONS

abstract: This study presents an extensive literature review on information security behavior in the context of factors affecting security behavior of users in organizational environments. The study critically analyzes articles in the information security behavior and brings forward 18 themes for security practitioners and researchers to consider in implementing information security initiatives. The findings of this review can be used by researchers and practitioners as a roadmap to guide further research in information security behavior. Also, the various factors identified in this paper can be used to improve information security programs in organizations.


title: Identifying the Information Structure of Scientific Abstracts: An Investigation of Three Different Schemes

abstract: Many practical tasks require accessing specific types of information in scientific literature; e.g. information about the objective, methods, results or conclusions of the study in question. Several schemes have been developed to characterize such information in full journal papers. Yet many tasks focus on abstracts instead. We take three schemes of different type and granularity (those based on section names, argumentative zones and conceptual structure of documents) and investigate their applicability to biomedical abstracts. We show that even for the finest-grained of these schemes, the majority of categories appear in abstracts and can be identified relatively reliably using machine learning. We discuss the impact of our results and the need for subsequent task-based evaluation of the schemes.


A New Fuzzing Technique for Software Vulnerability Mining