title: A security analysis of the OAuth protocol

abstract: The OAuth 2.0 authorization protocol standardises delegated authorization on the Web. Popular social networks such as Facebook, Google and Twitter implement their APIs based on the OAuth protocol to enhance user experience of social sign-on and social sharing. The intermediary authorization code can be potentially leaked during the transmission, which then may lead to its abuse. This paper uses an attacker model to study the security vulnerabilities of the OAuth 2.0 protocol. The experimental results show that common attacks such as replay attacks, impersonation attacks and forced-login CSRF attacks are capable of compromising the resources protected by the OAuth 2.0 protocol. The paper presents a systematic analysis of the potential root causes of the disclosed vulnerabilities.


title: MapReduce: Limitations, Optimizations and Open Issues

abstract: MapReduce has recently gained great popularity as a programming model for processing and analyzing massive data sets and is extensively used by academia and industry. Several implementations of the MapReduce model have emerged, the Apache Hadoop framework being the most widely adopted. Hadoop offers various utilities, such as a distributed file system, job scheduling and resource management capabilities and a Java API for writing applications. Hadoop's success has intrigued research interest and has led to various modifications and extensions to the framework. Implemented optimizations include performance improvements, programming model extensions, tuning automation and usability enhancements. In this paper, we discuss the current state of the Hadoop framework and its identified limitations. We present, compare and classify Hadoop/MapReduce variations, identify trends, open issues and possible future directions.


title: Homography-based 2D Visual Tracking and Servoing

abstract: The objective of this paper is to propose a new homography-based approach to image-based visual tracking and servoing. The visual tracking algorithm proposed in the paper is based on a new efficient second-order minimization method. Theoretical analysis and comparative experiments with other tracking approaches show that the proposed method has a higher convergence rate than standard first-order minimization techniques. Therefore, it is well adapted to real-time robotic applications. The output of the visual tracking is a homography linking the current and the reference image of a planar target. Using the homography, a task function isomorphic to the camera pose has been designed. A new image-based control law is proposed which does not need any measure of the 3D structure of the observed target (e.g. the normal to the plane). The theoretical proof of the existence of the isomorphism between the task function and the camera pose and the theoretical proof of the stability of the control law are provided. The experimental results, obtained with a 6 d.o.f. robot, show the advantages of the proposed method with respect to the existing approaches.


title: Bias Reduction via End-to-End Shift Learning: Application to Citizen Science

abstract: Citizen science projects are successful at gathering rich datasets for various applications. However, the data collected by citizen scientists are often biased --- in particular, aligned more with the citizens' preferences than with scientific objectives. We propose the Shift Compensation Network (SCN), an end-to-end learning scheme which learns the shift from the scientific objectives to the biased data while compensating for the shift by re-weighting the training data. Applied to bird observational data from the citizen science project eBird, we demonstrate how SCN quantifies the data distribution shift and outperforms supervised learning models that do not address the data bias. Compared with competing models in the context of covariate shift, we further demonstrate the advantage of SCN in both its effectiveness and its capability of handling massive high-dimensional data.


title: Automatic Table Detection in Document Images

abstract: In this paper, we propose a novel technique for automatic table detection in document images. Lines and tables are among the most frequent graphic, non-textual entities in documents and their detection is directly related to the OCR performance as well as to the document layout description. We propose a workflow for table detection that comprises three distinct steps: (i) image pre-processing; (ii) horizontal and vertical line detection and (iii) table detection. The efficiency of the proposed method is demonstrated by using a performance evaluation scheme which considers a great variety of documents such as forms, newspapers/magazines, scientific journals, tickets/bank cheques, certificates and handwritten documents.


title: Multi-task Learning of Facial Landmarks and Expression

abstract: Recently, deep neural networks have been shown to perform competitively on the task of predicting facial expression from images. Trained by gradient-based methods, these networks are amenable to "multi-task" learning via a multiple term objective. In this paper we demonstrate that learning representations to predict the position and shape of facial landmarks can improve expression recognition from images. We show competitive results on two large-scale datasets, the ICML 2013 Facial Expression Recognition challenge, and the Toronto Face Database.


title: GameBridge: Converging Toward a Transmedia Storytelling Experience through Gameplay

abstract: Transmedia storytelling enables a narrative to traverse various media platforms in order to create a richer storyworld. To achieve this goal, our group envisioned a product called GameBridge, which builds upon the concept of transmedia storytelling by implementing a cross-platform narrative. For our prototype, we decided to take the television show Game of Thrones and the corresponding book series, A Song of Ice and Fire, to create a game using content from both media to form our own storyline. By using both the television show and the book series, GameBridge creates a convergence point between the two media and allows the interactor to have agency of the story through gameplay. In the future, this model could be recreated with any storyworld that is told through various media, including movies.


title: External Calibration of Millimeter-Wave Atmospheric Radar Systems Using Corner Reflectors and Spheres

abstract: Millimeter-wave (MMW) radars have become standard cloud research tools because of their high sensitivity to small cloud particles, low prime power requirements, and compact size. However, MMW radars are more likely to suffer gain variations, so their calibration becomes crucial. Radar systems can be calibrated absolutely by various methods such as measurements of individual system parameters, comparison with in situ data, or external calibration using point targets. This paper focuses on this last method, which directly measures the combined response of all system components, and can be performed during a measurement campaign under field conditions.


title: Can the Production Network Be the Testbed?

abstract: A persistent problem in computer network research is validation. When deciding how to evaluate a new feature or bug fix, a researcher or operator must trade-off realism (in terms of scale, actual user traffic, real equipment) and cost (larger scale costs more money, real user traffic likely requires downtime, and real equipment requires vendor adoption which can take years). Building a realistic testbed is hard because "real" networking takes place on closed, commercial switches and routers with special purpose hardware. But if we build our testbed from software switches, they run several orders of magnitude slower. Even if we build a realistic network testbed, it is hard to scale, because it is special purpose and is in addition to the regular network. It needs its own location, support and dedicated links. For a testbed to have global reach takes investment beyond the reach of most researchers. ::: ::: In this paper, we describe a way to build a testbed that is embedded in--and thus grows with--the network. The technique--embodied in our first prototype, FlowVisor--slices the network hardware by placing a layer between the control plane and the data plane. We demonstrate that FlowVisor slices our own production network, with legacy protocols running in their own protected slice, alongside experiments created by researchers. The basic idea is that if unmodified hardware supports some basic primitives (in our prototype, Open-Flow, but others are possible), then a worldwide testbed can ride on the coat-tails of deployments, at no extra expense. Further, we evaluate the performance impact and describe how FlowVisor is deployed at seven other campuses as part of a wider evaluation platform.


title: Exploiting Passive RFID Technology for Activity Recognition in Smart Homes

abstract: The number of seniors and other people needing daily assistance continues to increase, but the current human resources available to achieve this in the coming years will certainly be insufficient. To remedy this situation, smart habitats have emerged as an innovative avenue for supporting needs of daily assistance. Smart homes aim to provide cognitive assistance in decision making by giving hints, suggestions, and reminders, with different kinds of effectors, to residents. To implement such technology, the first challenge to overcome is the recognition of ongoing activity. Some researchers have proposed solutions based on binary sensors or cameras, but these types of approaches infringed on residents' privacy. A new affordable activity-recognition system based on passive RFID technology can detect errors related to cognitive impairment. The entire system relies on an innovative model of elliptical trilateration with several filters, as well as on an ingenious representation of activities with spatial zones. The authors have deployed the system in a real smart-home prototype; this article renders the results of a complete set of experiments conducted on this new activity-recognition system with real scenarios.


Effect of Air Plasma Spray Parameters on the Properties of YSZ and CYSZ Thermal Barrier Coatings


title: A Distributed Decision Tree Algorithm and Its Implementation on Big Data Platforms

abstract: Decision tree algorithms are very popular in the field of data mining. This paper proposes a distributed decision tree algorithm and shows examples of its implementation on big data platforms. The major contribution of this paper is the novel KS-Tree algorithm which builds a decision tree in a distributed environment. KS-Tree is applied to some real world data mining problems and compared with state-of-the-art decision tree techniques that are implemented in R and Apache Spark. The results show that KS-Tree can achieve better results, especially with large data sets. Furthermore, we demonstrate that KS-Tree can be applied to various data mining tasks, such as variable selection.


title: Uranine: Real-time Privacy Leakage Monitoring without System Modification for Android

abstract: Mobile devices are becoming increasingly popular. One reason for their popularity is the availability of a wide range of third-party applications, which enrich the environment and increase usability. There are however privacy concerns centered around these applications – users do not know what private data is leaked by the applications. Previous works to detect privacy leakages are either not accurate enough or require operating system changes, which may not be possible due to users’ lack of skills or locked devices. We present Uranine (Uranine is a dye, which finds applications as a flow tracer in medicine and environmental studies.), a system that instruments Android applications to detect privacy leakages in real-time. Uranine does not require any platform modification nor does it need the application source code. We designed several mechanisms to overcome the challenges of tracking information flow across framework code, handling callback functions, and expressing all information-flow tracking at the bytecode level. Our evaluation of Uranine shows that it is accurate at detecting privacy leaks and has acceptable performance overhead.


title: Very low-voltage fully differential amplifier for switched-capacitor applications

abstract: A fully differential opamp suitable for very-low voltage switched-capacitor circuits in standard CMOS technologies is introduced. The proposed two stage opamp needs a simple low voltage CMFB switched-capacitor circuit only for the second stage. Due to the reduced supply voltage, the CMFB circuit is implemented using boot-strapped switches. Minor modifications allow to use chopper stabilization for flicker noise reduction. Two different compensation schemes are discussed and compared using an example for 1 V operation of the amplifier.


title: Analysis of AC- gm dispersions due to traps in nitride charge trap layer and generated interface traps in 3-D NAND flash memory cells

abstract: The AC-g m dispersions are measured to analyze trap profiles in a cell of 3-D NAND flash cell string before and after P/E cycling. After 4k P/E cycling, the trap density (N t ) near the interface between the poly-Si channel and the tunneling oxide is increased according to the extracted N t profile. The temperature dependency of the AC-g m dispersion is also investigated.


title: Acute Supplementation with Nitrate-Rich Beetroot Juice Causes a Greater Increase in Plasma Nitrite and Reduction in Blood Pressure of Older Compared to Younger Adults

abstract: Nitrate-rich beetroot juice supplementation has been shown to improve cardiovascular and cognitive function in younger and older adults via increased nitric oxide production. However, it is unclear whether the level of effects differs between the two groups. We hypothesized that acute supplementation with nitrate-rich beetroot juice would improve cardiovascular and cognitive function in older and younger adults, with the potential for greater improvements in older adults. Thirteen younger (18–30 years) and 11 older (50–70 years) adults consumed either 150 mL of nitrate-rich beetroot juice (BR; 10.5 mmol nitrate) or placebo (PL; 1 mmol nitrate) in a double-blind, crossover design, 2.25 h prior to a 30-min treadmill walk. Plasma nitrate and nitrite concentrations, blood pressure (BP), heart rate (HR), cognitive function, mood and perceptual tests were performed throughout the trial. BR consumption significantly increased plasma nitrate (p < 0.001) and nitrite (p = 0.003) concentrations and reduced systolic BP (p < 0.001) in both age groups and reduced diastolic BP (p = 0.013) in older adults. Older adults showed a greater elevation in plasma nitrite (p = 0.038) and a greater reduction in diastolic BP (p = 0.005) following BR consumption than younger adults. Reaction time was improved in the Stroop test following BR supplementation for both groups (p = 0.045). Acute BR supplementation increased plasma nitrite concentrations and reduced diastolic BP to a greater degree in older adults; whilst systolic BP was reduced in both older and younger adults, suggesting nitrate-rich BR may improve cardiovascular health, particularly in older adults due to the greater benefits from reductions in diastolic BP.


title: A Wide-Angle E-Plane Scanning Linear Array Antenna With Wide Beam Elements

abstract: In order to improve the wide-angle scanning performance of the phased array antennas, a wide-beam microstrip antenna with metal walls is proposed in this letter. The beamwidth of the antenna is broadened by the horizontal current on the radiating patch and the vertical current on the metal walls. The half-power beamwidth of the E- and H-planes is 221° and 168° at 4.0 GHz. Furthermore, the wide-beam antenna element is employed in a nine-element E-plane linear array antenna. The main beam of the E-plane scanning linear array antenna can scan from −70° to +70° in the frequency band from 3.7 to 4.3 GHz with a gain fluctuation less than 2.7 dB and variation in maximum sidelobe level less than −5.8 dB. The E-plane scanning linear array antenna with nine elements is fabricated and tested. The measured results achieve a good agreement with the simulated results.


title: Requirements engineering: a roadmap

abstract: This paper presents an overview of the field of software systems requirements engineering (RE). It describes the main areas of RE practice, and highlights some key open research issues for the future.


title: Robota: Clever toy and educational tool

abstract: Abstract Therapeutic and educational applications of robots have created a demand for robots showing a number of social skills. These skills include the capacity to imitate, to learn from demonstration, to interpret gestures and to recognize speech. Robot toys are an ideal platform to investigate the potential and limitations of human–robot social interactions. This paper presents Robota, a mini-humanoid doll-shaped robot. Robota is used in an introductory robotics class at the undergraduate level. The class offers an introduction to different tools necessary for building human–robot social interactions. Through a series of hands-on projects, students learn how to use vision and speech processing and how to design learning algorithms. The goal of each project is to create an educational and entertaining game for normal and disabled children.


title: Cryptic diversity and deep divergence in an upper Amazonian leaflitter frog, Eleutherodactylus ockendeni

abstract: BackgroundThe forests of the upper Amazon basin harbour some of the world's highest anuran species richness, but to date we have only the sparsest understanding of the distribution of genetic diversity within and among species in this region. To quantify region-wide genealogical patterns and to test for the presence of deep intraspecific divergences that have been documented in some other neotropical anurans, we developed a molecular phylogeny of the wide-spread terrestrial leaflitter frog Eleutherodactylus ockendeni (Leptodactylidae) from 13 localities throughout its range in Ecuador using data from two mitochondrial genes (16S and cyt b; 1246 base pairs). We examined the relation between divergence of mtDNA and the nuclear genome, as sampled by five species-specific microsatellite loci, to evaluate indirectly whether lineages are reproductively isolated where they co-occur. Our extensive phylogeographic survey thus assesses the spatial distribution of E. ockendeni genetic diversity across eastern Ecuador.ResultsWe identified three distinct and well-supported clades within the Ecuadorean range of E. ockendeni: an uplands clade spanning north to south, a northeastern and central lowlands clade, and a central and southeastern clade, which is basal. Clades are separated by 12% to 15% net corrected p-distance for cytochrome b, with comparatively low sequence divergence within clades. Clades marginally overlap in some geographic areas (e.g., Napo River basin) but are reproductively isolated, evidenced by diagnostic differences in microsatellite PCR amplification profiles or DNA repeat number and coalescent analyses (in MDIV) best modelled without migration. Using Bayesian (BEAST) and net phylogenetic estimates, the Southeastern Clade diverged from the Upland/Lowland clades in the mid-Miocene or late Oligocene. Lowland and Upland clades speciated more recently, in the early or late Miocene.ConclusionOur findings uncover previously unsuspected cryptic species diversity within the common leaflitter frog E. ockendeni, with at least three different species in Ecuador. While these clades are clearly geographically circumscribed, they do not coincide with any existing landscape barriers. Divergences are ancient, from the Miocene, before the most dramatic mountain building in the Ecuadorean Andes. Therefore, this diversity is not a product of Pleistocene refuges. Our research coupled with other studies suggests that species richness in the upper Amazon is drastically underestimated by current inventories based on morphospecies.


title: emoji2vec: Learning Emoji Representations from their Description

abstract: Many current natural language processing applications for social media rely on representation learning and utilize pre-trained word embeddings. There currently exist several publicly-available, pre-trained sets of word embeddings, but they contain few or no emoji representations even as emoji usage in social media has increased. In this paper we release emoji2vec, pre-trained embeddings for all Unicode emoji which are learned from their description in the Unicode emoji standard. The resulting emoji embeddings can be readily used in downstream social natural language processing applications alongside word2vec. We demonstrate, for the downstream task of sentiment analysis, that emoji embeddings learned from short descriptions outperforms a skip-gram model trained on a large collection of tweets, while avoiding the need for contexts in which emoji need to appear frequently in order to estimate a representation.


title: Sample-efficient Actor-Critic Reinforcement Learning with Supervised Data for Dialogue Management

abstract: Deep reinforcement learning (RL) methods have significant potential for dialogue policy optimisation. However, they suffer from a poor performance in the early stages of learning. This is especially problematic for on-line learning with real users. Two approaches are introduced to tackle this problem. Firstly, to speed up the learning process, two sample-efficient neural networks algorithms: trust region actor-critic with experience replay (TRACER) and episodic natural actor-critic with experience replay (eNACER) are presented. For TRACER, the trust region helps to control the learning step size and avoid catastrophic model changes. For eNACER, the natural gradient identifies the steepest ascent direction in policy space to speed up the convergence. Both models employ off-policy learning with experience replay to improve sample-efficiency. Secondly, to mitigate the cold start issue, a corpus of demonstration data is utilised to pre-train the models prior to on-line reinforcement learning. Combining these two approaches, we demonstrate a practical approach to learn deep RL-based dialogue policies and demonstrate their effectiveness in a task-oriented information seeking domain.


title: Fine-grained attention for image caption generation

abstract: Despite the progress, generating natural language descriptions for images is still a challenging task. Most state-of-the-art methods for solving this problem apply existing deep convolutional neural network (CNN) models to extract a visual representation of the entire image, based on which the parallel structures between images and sentences are exploited using recurrent neural networks. However, there is an inherent drawback that their models may attend to a partial view of a visual element or a conglomeration of several concepts. In this paper, we present a fine-grained attention based model based on a deep recurrent architecture that combines recent advances in computer vision and machine translation. The model contains three sub-networks: a deep recurrent neural network for sentences, a deep convolutional network for images, and a region proposal network for nearly cost-free region proposals. Our model is able to automatically learn to fix its gaze on salient region proposals. The process of generating the next word, given the previously generated ones, is aligned with this visual perception experience. We validate the effectiveness of the proposed model on three benchmark datasets (Flickr 8K, Flickr 30K and MS COCO). The experimental results confirm the effectiveness of the proposed system.


title: IoT in Agriculture: Designing a Europe-Wide Large-Scale Pilot

abstract: The technologies associated with the Internet of Things have great potential for application in the domain of food and agriculture, especially in view of the societal and environmental challenges faced by this sector. From farm to fork, IoT technologies could transform the sector, contributing to food safety, and the reduction of agricultural inputs and food waste. A major step toward greater uptake of these technologies will be the execution of IoT-based large-scale pilots (LSPs) in the entire supply chain. This article outlines the challenges and constraints that an LSP deployment of IoT in this domain must consider. Sectoral and technological challenges are described in order to identify a set of technological and agrifood requirements. An architecture based on a system of systems approach is briefly presented, the importance of addressing the interoperability challenges faced by this sector is highlighted, and we elaborate on requirements for new business models, security, privacy, and data governance. A description of the technologies and solutions involved in designing pilots for four agrifood domains (dairy, fruit, arable, meat and vegetable supply chain) is eventually provided. In conclusion, it is noted that for IoT to be successful in this domain, a significant change of culture is needed.


1 Bayesian Logic Programming: Theory and Tool


title: IGCV3: Interleaved Low-Rank Group Convolutions for Efficient Deep Neural Networks

abstract: In this paper, we are interested in building lightweight and efficient convolutional neural networks. Inspired by the success of two design patterns, composition of structured sparse kernels, e.g., interleaved group convolutions (IGC), and composition of low-rank kernels, e.g., bottle-neck modules, we study the combination of such two design patterns, using the composition of structured sparse low-rank kernels, to form a convolutional kernel. Rather than introducing a complementary condition over channels, we introduce a loose complementary condition, which is formulated by imposing the complementary condition over super-channels, to guide the design for generating a dense convolutional kernel. The resulting network is called IGCV3. We empirically demonstrate that the combination of low-rank and sparse kernels boosts the performance and the superiority of our proposed approach to the state-of-the-arts, IGCV2 and MobileNetV2 over image classification on CIFAR and ImageNet and object detection on COCO.


title: Online Multi-Object Tracking with Historical Appearance Matching and Scene Adaptive Detection Filtering

abstract: In this paper, we propose the methods to handle temporal errors during multi-object tracking. Temporal error occurs when objects are occluded or noisy detections appear near the object. In those situations, tracking may fail and various errors like drift or ID-switching occur. It is hard to overcome temporal errors only by using motion and shape information. So, we propose the historical appearance matching method and joint-input siamese network which was trained by 2-step process. It can prevent tracking failures although objects are temporally occluded or last matching information is unreliable. We also provide useful technique to remove noisy detections effectively according to scene condition. Tracking performance, especially identity consistency, is highly improved by attaching our methods.


The Adoption of Cloud Computing by Irish SMEs – an Exploratory Study


title: The implications of megatrends in information and communication technology and transportation for changes in global physical activity

abstract: Summary Physical inactivity accounts for more than 3 million deaths per year, most from non-communicable diseases in low-income and middle-income countries. We used reviews of physical activity interventions and a simulation model to examine how megatrends in information and communication technology and transportation directly and indirectly affect levels of physical activity across countries of low, middle, and high income. The model suggested that the direct and potentiating effects of information and communication technology, especially mobile phones, are nearly equal in magnitude to the mean effects of planned physical activity interventions. The greatest potential to increase population physical activity might thus be in creation of synergistic policies in sectors outside health including communication and transportation. However, there remains a glaring mismatch between where studies on physical activity interventions are undertaken and where the potential lies in low-income and middle-income countries for population-level effects that will truly affect global health.


title: Have things changed now?: an empirical study of bug characteristics in modern open source software

abstract: Software errors are a major cause for system failures. To effectively design tools and support for detecting and recovering from software failures requires a deep understanding of bug characteristics. Recently, software and its development process have significantly changed in many ways, including more help from bug detection tools, shift towards multi-threading architecture, the open-source development paradigm and increasing concerns about security and user-friendly interface. Therefore, results from previous studies may not be applicable to present software. Furthermore, many new aspects such as security, concurrency and open-source-related characteristics have not well studied. Additionally, previous studies were based on a small number of bugs, which may lead to non-representative results.To investigate the impacts of the new factors on software errors, we analyze bug characteristics by first sampling hundreds of real world bugs in two large, representative open-source projects. To validate the representativeness of our results, we use natural language text classification techniques and automatically analyze around 29, 000 bugs from the Bugzilla databases of the software.Our study has discovered several new interesting characteristics: (1) memory-related bugs have decreased because quite a few effective detection tools became available recently; (2) surprisingly, some simple memory-related bugs such as NULL pointer dereferences that should have been detected by existing tools in development are still a major component, which indicates that the tools have not been used with their full capacity; (3) semantic bugs are the dominant root causes, as they are application specific and difficult to fix, which suggests that more efforts should be put into detecting and fixing them; (4) security bugs are increasing, and the majority of them cause severe impacts.


title: Modeling and Regulator Design for Three-Input Power Systems with Decoupling Control

abstract: In hybrid renewable power systems, the use of a multiple-input dc/dc converter (MIC) leads to simpler circuit and lower cost, when compared to the conventional use of several single-input converters. This paper proposed a novel three-input buck/boost/buck-boost converter, which can be used in applications with various values of input voltage. The energy sources in this converter can deliver power to the load either simultaneously or individually in one switching period. The steady relationship, the power management strategy and the small-signal circuit model of this converter have been derived. With decoupling technology, modeling and regulator design can be obtained under multi-loop control modes. Finally, three generating methods of a multiple-input buck/boost/buck-boost converter is given, and this method can be extended to the other multiple-input dc/dc converters.


title: Current Concepts in the Rehabilitation of the Overhead Throwing Athlete

abstract: The overhead throwing motion is an extremely skillful and intricate movement that is very stressful on the shoulder joint complex. The overhead throwing athlete places extraordinary demands on this complex. Excessively high stresses are applied to the shoulder joint because of the tremendous forces generated by the thrower. The thrower's shoulder must be lax enough to allow excessive external rotation, but stable enough to prevent symptomatic humeral head subluxations, thus requiring a delicate balance between mobility and functional stability. We refer to this as the “thrower's paradox.” This balance is frequently compromised, which leads to injury. Numerous types of injuries may occur to the surrounding tissues during overhead throwing. Frequently, injuries can be successfully treated with a well-structured and carefully implemented nonoperative rehabilitation program. The key to successful nonoperative treatment is a thorough clinical examination and accurate diagnosis. Athletes often exhibit numerous ...


title: A New Unbiased and Efficient Class of LSH-Based Samplers and Estimators for Partition Function Computation in Log-Linear Models

abstract: Log-linear models are arguably the most successful class of graphical models for large-scale applications because of their simplicity and tractability. Learning and inference with these models require calculating the partition function, which is a major bottleneck and intractable for large state spaces. Importance Sampling (IS) and MCMC-based approaches are lucrative. However, the condition of having a"good"proposal distribution is often not satisfied in practice. In this paper, we add a new dimension to efficient estimation via sampling. We propose a new sampling scheme and an unbiased estimator that estimates the partition function accurately in sub-linear time. Our samples are generated in near-constant time using locality sensitive hashing (LSH), and so are correlated and unnormalized. We demonstrate the effectiveness of our proposed approach by comparing the accuracy and speed of estimating the partition function against other state-of-the-art estimation techniques including IS and the efficient variant of Gumbel-Max sampling. With our efficient sampling scheme, we accurately train real-world language models using only 1-2% of computations.


title: Interactive multiresolution mesh editing

abstract: We describe a multiresolution representation for meshes based on subdivision, which is a natural extension of the existing patch-based surface representations. Combining subdivision and the smoothing algorithms of Taubin [26] allows us to construct a set of algorithms for interactive multiresolution editing of complex hierarchical meshes of arbitrary topology. The simplicity of the underlying algorithms for refinement and coarsification enables us to make them local and adaptive, thereby considerably improving their efficiency. We have built a scalable interactive multiresolution editing system based on such algorithms.


title: ForcePhone: Software Lets Smartphones Sense Touch Force

abstract: To enhance the input capability on the severely limited space of a phone's screen, touch force has been a vital way to expand user-phone interactions. For example, users can get an options menu by hard-pressing a button or can surf the previous webpage by squeezing the phone. Researchers have shown the usefulness and importance of this expressive input interface (especially for the one-hand operation), but this advanced function has not yet been realized and deployed in most state-of-the-art smartphones. Unlike many other proposals, which require the addition of specialized hardware, the authors propose a software-based solution called ForcePhone. Their solution exploits a well-known physical property, called structure-borne sound propagation, to enable commodity phones to recognize the force applied to their touchscreen and body.


title: Motion planning based on learning models of pedestrian and driver behaviors

abstract: Autonomous driving has shown the capability of providing driver convenience and enhancing safety. While introducing autonomous driving into our current traffic system, one significant issue is to make the autonomous vehicle be able to react in the same way as real human drivers. In order to ensure that an autonomous vehicle of the future will perform like human drivers, this paper proposes a vehicle motion planning model, which can represent how drivers control vehicles based on the assessment of traffic environments in the real signalized intersection. The proposed motion planning model comprises functions of pedestrian intention detection, gap detection and vehicle dynamic control. The three functions are constructed based on the analysis of actual data collected from real traffic environments. Finally, this paper demonstrates the performance of the proposed method by comparing the behaviors of our model with the behaviors of real pedestrians and human drivers. The experimental results show that our proposed model can achieve 85% recognition rate for the pedestrian crossing intention. Moreover, the vehicle controlled by the proposed motion planning model and the actual human-driven vehicle are highly similar with respect to the gap acceptance in intersections.


title: A Clinical Comparison of Variable-Damping and Mechanically Passive Prosthetic Knee Devices

abstract: OBJECTIVE ::: Although variable-damping knee prostheses offer some improvements over mechanically passive prostheses to transfemoral amputees, there is insufficient evidence that such prostheses provide advantages at self-selected walking speeds. In this investigation, we address this question by comparing two variable-damping knees, the hydraulic-based Otto Bock C-leg and the magnetorheological-based Ossur Rheo, with the mechanically passive, hydraulic-based Mauch SNS. ::: ::: ::: DESIGN ::: For each prosthesis, metabolic data were collected on eight unilateral amputees walking at self-selected speeds across an indoor track. Furthermore, kinetic, kinematic, and electromyographic data were collected while walking at self-selected speeds across a 10-m walkway in a laboratory. ::: ::: ::: RESULTS ::: When using the Rheo, metabolic rate decreases by 5% compared with the Mauch and by 3% compared with the C-leg. Furthermore, for the C-leg and Rheo knee devices, we observe biomechanical advantages over the mechanically passive Mauch. These advantages include an enhanced smoothness of gait, a decrease in hip work production, a lower peak hip flexion moment at terminal stance, and a reduction in peak hip power generation at toe-off. ::: ::: ::: CONCLUSION ::: The results of this study indicate that variable-damping knee prostheses offer advantages over mechanically passive designs for unilateral transfemoral amputees walking at self-selected ambulatory speeds, and the results further suggest that a magnetorheological-based system may have advantages over hydraulic-based designs.


title: Beliefs about beliefs: Representation and constraining function of wrong beliefs in young children's understanding of deception

abstract: ‘A travelling salesman found himself spending the night at home with his wife when one of his trips was unexpectedly cancelled. The two of them were sound asleep, when in the middle of the night there was a loud knock at the front door. The wife woke up with a start and cried out, ‘Oh, my God! It’s my husband!* Whereupon the husband leapt out from the bed, ran across the room and jumped out the window.’ Schank and Abelson, 1977, p. 59.


title: Commissioned Paper Airline Schedule Planning: Accomplishments and Opportunities

abstract: Plagued by high labor costs, low profitability margins, airspace and airport congestion, high capital and operating costs, security and safety concerns, and complex and large-scale management and operations decisions, the airline industry has armed its planners with sophisticated optimization tools to improve decision making and increase airline profits. In this paper, we describe optimization approaches for airline schedule planning, demonstrating how optimization can facilitate the management of a diverse and finite set of expensive, highly constrained resources. We focus on the art and science of modeling and solving these problems, providing illustrative examples of the associated impacts and challenges, and highlighting effective techniques that might be applicable to problems arising in other industries.


Sparse plus low-rank graphical models of time series for functional connectivity in MEG


title: Asymmetrical Body Perception A Possible Role for Neural Body Representations

abstract: Perception of one's body is related not only to the physical appearance of the body, but also to the neural representation of the body. The brain contains many body maps that systematically differ between right- and left-handed people. In general, the cortical representations of the right arm and right hand tend to be of greater area in the left hemisphere than in the right hemisphere for right-handed people, whereas these cortical representations tend to be symmetrical across hemispheres for left-handers. We took advantage of these naturally occurring differences and examined perceived arm length in right- and left-handed people. When looking at each arm and hand individually, right-handed participants perceived their right arms and right hands to be longer than their left arms and left hands, whereas left-handed participants perceived both arms accurately. These experiments reveal a possible relationship between implicit body maps in the brain and conscious perception of the body.


Vehicle Telematics at an Italian Insurer: New Auto Insurance Products and a New Industry Ecosystem


Building and Leveraging Category Hierarchies for Large-scale Image Classification


title: Path planning using Matlab-ROS integration applied to mobile robots

abstract: In this paper the possibilities that Matlab provides to design, implementation and monitoring programs of autonomous navigation for mobile robots, on both simulated and real platforms, through its new toolbox for robotics will be explored. Robotics System Toolbox has established itself as a solid tool of integration between Matlab and robot operating under ROS environment. It will be studied the tools available in this toolbox that allow achieving the connection between the two platforms, in addition to the generation of algorithms of location, path planning, mapping and autonomous navigation.


title: A simple continuum regression approach

abstract: A continuum regression approach based on two parameters, namely the number of latent variables to be introduced in the model and a tuning parameter is proposed. A noteworthy feature of this approach is that it is straightforward and simple. It brings several procedures under the same umbrella. These procedures encompass Ordinary least squares (OLS) regression, Partial least squares regression (PCR) and Ridge regression. Interesting properties related to the method are also discussed. This makes it possible to highlight the rationale behind the method of analysis by showing that it aims at realizing a compromise between achieving a good fit and setting up a stable model. Copyright © 2006 John Wiley & Sons, Ltd.


title: Perceptual optimization of DCT color quantization matrices

abstract: The JPEG baseline standard for image compression employs a block discrete cosine transform (DCT) and uniform quantization. For a monochrome image, a single quantization matrix is allowed, while for a color image, distinct matrices are allowed for each color channel. We describe a method, called DCTune, for the design of color quantization matrices that is based on a model of the visibility of quantization artifacts. The model describes the artifact visibility as a function of DCT frequency, color channel, and display resolution and brightness. The model also describes the summation of artifacts over space and frequency, and masking of artifacts by the image itself. The DCTune matrices are different from the de facto JPEG matrices, and appear to provide superior visual quality at equal bit-rates. >


title: Ensemble-Vis: A Framework for the Statistical Visualization of Ensemble Data

abstract: Scientists increasingly use ensemble data sets to explore relationships present in dynamic systems. Ensemble data sets combine spatio-temporal simulation results generated using multiple numerical models, sampled input conditions and perturbed parameters. While ensemble data sets are a powerful tool for mitigating uncertainty, they pose significant visualization and analysis challenges due to their complexity. In this article, we present Ensemble-Vis, a framework consisting of a collection of overview and statistical displays linked through a high level of interactivity. Ensemble-Vis allows scientists to gain key scientific insight into the distribution of simulation results as well as the uncertainty associated with the scientific data. In contrast to methods that present large amounts of diverse information in a single display, we argue that combining multiple linked displays yields a clearer presentation of the data and facilitates a greater level of visual data analysis. We demonstrate our framework using driving problems from climate modeling and meteorology and discuss generalizations to other fields.


title: Scalable co-optimization of morphology and control in embodied machines

abstract: Evolution sculpts both the body plans and nervous systems of agents together over time. By contrast, in artificial intelligence and robotics, a robot's body plan is usually designed by hand, and control policies are then optimized for that fixed design. The task of simultaneously co-optimizing the morphology and controller of an embodied robot has remained a challenge. In psychology, the theory of embodied cognition posits that behaviour arises from a close coupling between body plan and sensorimotor control, which suggests why co-optimizing these two subsystems is so difficult: most evolutionary changes to morphology tend to adversely impact sensorimotor control, leading to an overall decrease in behavioural performance. Here, we further examine this hypothesis and demonstrate a technique for 'morphological innovation protection', which temporarily reduces selection pressure on recently morphologically changed individuals, thus enabling evolution some time to 'readapt' to the new morphology with subsequent control policy mutations. We show the potential for this method to avoid local optima and converge to similar highly fit morphologies across widely varying initial conditions, while sustaining fitness improvements further into optimization. While this technique is admittedly only the first of many steps that must be taken to achieve scalable optimization of embodied machines, we hope that theoretical insight into the cause of evolutionary stagnation in current methods will help to enable the automation of robot design and behavioural training-while simultaneously providing a test bed to investigate the theory of embodied cognition.


title: Sequence Discriminative Training for Deep Learning based Acoustic Keyword Spotting

abstract: Speech recognition is a sequence prediction problem. Besides employing various deep learning approaches for framelevel classification, sequence-level discriminative training has been proved to be indispensable to achieve the state-of-the-art performance in large vocabulary continuous speech recognition (LVCSR). However, keyword spotting (KWS), as one of the most common speech recognition tasks, almost only benefits from frame-level deep learning due to the difficulty of getting competing sequence hypotheses. The few studies on sequence discriminative training for KWS are limited for fixed vocabulary or LVCSR based methods and have not been compared to the state-of-the-art deep learning based KWS approaches. In this paper, a sequence discriminative training framework is proposed for both fixed vocabulary and unrestricted acoustic KWS. Sequence discriminative training for both sequence-level generative and discriminative models are systematically investigated. By introducing word-independent phone lattices or non-keyword blank symbols to construct competing hypotheses, feasible and efficient sequence discriminative training approaches are proposed for acoustic KWS. Experiments showed that the proposed approaches obtained consistent and significant improvement in both fixed vocabulary and unrestricted KWS tasks, compared to previous frame-level deep learning based acoustic KWS methods.


title: The Role of Syntax in Vector Space Models of Compositional Semantics

abstract: Modelling the compositional process by which the meaning of an utterance arises from the meaning of its parts is a fundamental task of Natural Language Processing. In this paper we draw upon recent advances in the learning of vector space representations of sentential semantics and the transparent interface between syntax and semantics provided by Combinatory Categorial Grammar to introduce Combinatory Categorial Autoencoders. This model leverages the CCG combinatory operators to guide a non-linear transformation of meaning within a sentence. We use this model to learn high dimensional embeddings for sentences and evaluate them in a range of tasks, demonstrating that the incorporation of syntax allows a concise model to learn representations that are both effective and general.


title: Using Verbs to Characterize Noun-Noun Relations

abstract: We present a novel, simple, unsupervised method for characterizing the semantic relations that hold between nouns in noun-noun compounds. The main idea is to discover predicates that make explicit the hidden relations between the nouns. This is accomplished by writing Web search engine queries that restate the noun compound as a relative clause containing a wildcard character to be filled in with a verb. A comparison to results from the literature suggest this is a promising approach.


title: Feature Extraction and Classification of EEG Signals Using Wavelet Transform, SVM and Artificial Neural Networks for Brain Computer Interfaces

abstract: Brain Computer Interface one of hopeful interface technologies between humans and machines. Electroencephalogram-based Brain Computer Interfaces have become a hot spot in the research of neural engineering, rehabilitation, and brain science. The artifacts are disturbance that can occur during the signal acquisition and that can alter the analysis of the signals themselves. Detecting artifacts produced in electroencephalography data by muscle activity, eye blinks and electrical noise is a common and important problem in electroencephalography research. In this research, we used five different methods for detecting trials containing artifacts. Finally we used two different neural networks, and support vector machine to classify features that are extracted by wavelet transform.


AEGIS: A Fast Authenticated Encryption Algorithm ⋆ (Full Version)


title: You Are Known by How You Vlog: Personality Impressions and Nonverbal Behavior in YouTube

abstract: An increasing interest in understanding human perception in social media has led to the study of the processes of personality self-presentation and impression formation based on user profiles and text blogs. However, despite the popularity of online video, we do not know of any attempt to study personality impressions that go beyond the use of text and still photos. In this paper, we analyze one facet of YouTube as a repository of brief behavioral slices in the form of personal conversational vlogs, which are a unique medium for selfpresentation and interpersonal perception. We investigate the use of nonverbal cues as descriptors of vloggers’ behavior and find significant associations between automatically extracted nonverbal cues for several personality judgments. As one notable result, audio and visual cues together can be used to predict 34% of the variance of the Extraversion trait of the Big Five model. In addition, we explore the associations between vloggers’ personality scores and the level of social attention that their videos received in YouTube. Our study is conducted on a dataset of 442 YouTube vlogs and 2,210 annotations collected using Amazon’s Mechanical Turk.


title: Market Index and Stock Price Direction Prediction using Machine Learning Techniques: An empirical study on the KOSPI and HSI.

abstract: The prediction of a stock market direction may serve as an early recommendation system for short-term investors and as an early financial distress warning system for long-term shareholders. In this paper, we propose an empirical study on the Korean and Hong Kong stock market with an integrated machine learning framework that employs Principal Component Analysis (PCA) and Support Vector Machine (SVM). We try to predict the upward or downward direction of stock market index and stock price. In the proposed framework, PCA, as a feature selection method, identifies principal components in the stock market movement and SVM, as a classifier for future stock market movement, processes them along with other economic factors in training and forecasting. We present the results of an extensive empirical study of the proposed method on the Korean composite stock price index (KOSPI) and Hangseng index (HSI), as well as the individual constituents included in the indices. In our experiment, ten years data (from January 1st, 2002 to January 1st, 2012) are collected and schemed by rolling windows to predict one-day-ahead directions. The experimental results show notably high hit ratios in predicting the movements of the individual constituents in the KOSPI and HSI. The results also varify the \textit{co-movement} effect between the Korean (Hong Kong) stock market and the American stock market.


title: Acute Syphilitic Balanitis and Gross Penile Edema in an HIV- Infected Man

abstract: Syphilis presenting with balanitis and penile edema is rare, and in most cases has been considered to be a manifestation of primary syphilis. We report the case of an HIV-infected man who presented with penile edema and balanitis concomitant with an eruption on his buttocks, lower abdomen and thighs. Histologic examination and serology were diagnostic of syphilis, and the patient was successfully treated with benzathine-penicillin. Our case represents an unusual, infrequently reported presentation of secondary syphilis.


title: Data Preprocessing and Easy Access Retrieval of Data through Data Ware House

abstract: The World Wide Web (WWW) provides a simple yet effective media for users to search, browse, and retrieve information in the Web. Web Usage Mining is the application of data mining techniques to click stream data in order to extract usage patterns. These patterns are analyzed to determine user's behavior which is an important and challenging research topic in web usage mining. In order to determine which pages of the web site were accessed and how various web pages were reached, requires examining the raw data recorded in the log files created by the web server. An important fundamental task for Web log mining is data


title: Group awareness in distributed software development

abstract: Open-source software development projects are almost always collaborative and distributed. Despite the difficulties imposed by distance, these projects have managed to produce large, complex, and successful systems. However, there is still little known about how open-source teams manage their collaboration. In this paper we look at one aspect of this issue: how distributed developers maintain group awareness. We interviewed developers, read project communication, and looked at project artifacts from three successful open source projects. We found that distributed developers do need to maintain awareness of one another, and that they maintain both a general awareness of the entire team and more detailed knowledge of people that they plan to work with. Although there are several sources of information, this awareness is maintained primarily through text-based communication (mailing lists and chat systems). These textual channels have several characteristics that help to support the maintenance of awareness, as long as developers are committed to reading the lists and to making their project communication public.


title: Cloud-Based Big Data Management and Analytics for Scholarly Resources: Current Trends, Challenges and Scope for Future Research

abstract: With the shifting focus of organizations and governments towards digitization of academic and technical documents, there has been an increasing need to use this reserve of scholarly documents for developing applications that can facilitate and aid in better management of research. In addition to this, the evolving nature of research problems has made them essentially interdisciplinary. As a result, there is a growing need for scholarly applications like collaborator discovery, expert finding and research recommendation systems. This research paper reviews the current trends and identifies the challenges existing in the architecture, services and applications of big scholarly data platform with a specific focus on directions for future research.


title: StackGhost: Hardware Facilitated Stack Protection

abstract: Conventional Security have relied on overwriting the saved return pointer on the stack to hijack the path of execution. Under Sun Microsystem's Sparc processor architecture, we were able to implement a kernel modification to transparently and automatically guard application's return pointers. ::: ::: Our implementation called Stack Ghost Open-BSD 2.8 acts as a ghost in the machine. Stack-Ghost advances exploit prevention in that it protects every application run on the system without their knowledge nor does it require their source or binary modification. ::: ::: We will document several of the methods devised to preserve the sanctity of the system and will explore the performance ramifications of Stack Ghost.


title: Design of a dual-band printed slot antenna with utilizing a band rejection element for the 5G wireless applications

abstract: A design and analysis of a proximity-fed printed slot antenna with 28/38 GHz dual band characteristics for future 5G wireless applications are presented. To obtain a dual-band at 28/38 GHz that covers 5G applications, an elliptical patch is etched non-concentrically inside a ground plane elliptical slot. The antenna is proximity-fed by a microstrip on the other side of the substrate. Results indicate that the proposed antenna yields a broadband impedance bandwidth greater than 20 GHz (from 20 to more than 40 GHz) defined by S 11 < −10 dB that covers both 5G bands. To reduce the interference between the 5G system and other applications, L-shaped slot is etched off in the feed line to create a notched band of 30–35 GHz. Simulated results show that the designed antenna has dual band at 28 and 38 GHz for 5G system with band notched at 33 GHz. The antenna provides almost omni-directional patterns, relatively flat gain, and high radiation efficiency through the frequency band excluding the rejected band.


title: Torque ripple reduction in fractional-slot Interior PM machines optimizing the flux-barrier geometries

abstract: As far as the permanent magnet (PM) machine design is concerned, there are two recent trends. The first deals with the adoption of fractional-slot (FS) non-overlapped coil windings for the stator, which minimizes the length of the end- windings with a consequent benefit on the reduction of copper cost and losses. The second regards the choice of rotor structures with Interior PM (IPM rotors), which exhibits a reluctance torque components and allows a low-cost PM to be used. However, the adoption of FS winding (characterized by a high MMF harmonic content) together with an IPM rotor (which is an anisotropic structure) is not devoid of drawbacks. Certainly the biggest one is the high ripple in the torque behaviour. An analytical procedure to analyze and minimize such a torque ripple is presented in this paper. Some solutions are compared, taking the PM heights and the flux-barrier end angles as design variables. Some finite element results are also presented to integrate the analysis, including the effect of stator saturation and the presence of open slots.


title: CENTENNIAL FORUM GIScience, Geography, Form, and Process

abstract: he map has evolved over the past few centuries as humanity's primary method for storing and communicating knowledge of the Earth's surface. Topographic maps portray the general form of the surface and its primary physical and cultural features; thematic maps show the variation of more specialized properties such as soil type or population density; and bathymetric maps and hydrographic charts show the characteristics of the sea floor. Maps serve as one of the most important repositories of both the raw data and the results of geographic inquiry, and mapmaking has always figured prominently in the skill set of geographers or their supporting staff. Maps are thus important and indispensable tools in the geographer's search for understanding of how human and physical processes act and interact on the Earth's surface: of how the world works. Geographic information systems (GIS) were devised in the 1960s as computer applications for handling large volumes of information obtained from maps and for performing operations that would be too tedious, expensive, or inaccurate to perform by hand. The Canada Geographic Information System, widely recognized as the first GIS, was built for the purpose of making vast numbers of calculations of area, reporting the results in tables. Over time, the range of functions performed by GIS has grown exponentially, and today it is reasonable to think of a GIS as able to perform virtually any conceivable operation on data obtained from maps (Longley et al. 2001). Geographers have adopted GIS enthusiastically, seeing it as a powerful device for storing, analyzing, and visualizing map information and thus as a much more effective substitute for the paper map (Goodchild 1988). Over the past decade numerous journals, conferences, academic positions, and programs have adopted titles that combine information with spatial or geographic and with science or theory. In what follows I will use the term geographic information science (GIScience) for simplicity and not enquire into the subtle differences between, for example, spatial and geographic information theory (Goodchild 2001). Geographers have been associated with many of these changes-and, in many cases, have been at the forefront-and many of the new programs and positions are found in departments of geography. But there has been relatively little general commentary on these trends, or on what they might mean for the discipline of geography as a whole. The first centennial of the Association of American Geographers is an appropriate occasion to reflect on the nature of GIScience and its relationship, if any, to the discipline of geography. I begin with a discussion of the nature of GIScience, of its relationship to GIS and of its links to the traditional sciences of geographic information. This leads to a discussion of whether GIScience is a natural science, concerned with discovering empirical principles and law-like statements about the world; or whether it is a design science, concerned with identifying practical principles for achieving human ends, or both. In the third major section I examine how GIScience is positioned with respect to the historic tension in geography between form and process and whether the growth of interest in GIScience has tended to favor form over process. The final section examines a future for GIScience that places greater emphasis on process and discusses the steps that will be needed to make such a future possible.


title: Benefits and challenges for platform-based design

abstract: Platforms have become an important concept in the design of electronic systems. We present here the motivations behind the interest shown and the challenges that we have to face to make the Platform-based Design method a standard. As a generic term, platforms have meant different things to different people. The main challenges are to distill the essence of the method, to formalize it and to provide a framework to support its use in areas that go beyond the original domain of application.


title: A Bidirectional Battery Charger With Modular Integrated Charge Equalization Circuit

abstract: High-voltage battery pack and charger structure are the main concerns of the electric vehicle (EV) developments. To achieve required voltage and power in propulsion systems, cascading battery stacks is essential. However, due to small discrepancies between different cells, charge imbalance occurs. Charge imbalance reduces battery cells life cycle, accessible energy, and system performance. Hence, charge equalization among battery stacks is necessary. Furthermore, EVs should have some essential requirements to provide desired contributions in future networks. Therefore, bidirectional power flow and reactive power control are other important capabilities of the battery charger system. In this paper, a bidirectional structure with two operation modes for a modular battery stack is developed. The developed structure has active and reactive control capabilities during grid to vehicle and vehicle to grid operations. To improve the battery system performance, a modular charge equalizer circuit (CEC) for both operation modes is developed. The modular configuration of the battery stack improves the system reliability and flexibility. Moreover, the overall system performance for the CEC and converter is improved by utilizing the integrated control strategy. Performance of the proposed structure is evaluated using simulations and laboratory experiments. The results confirm the capability of the proposed structure.


title: Please take over! An analysis and strategy for a driver take over request during autonomous driving

abstract: During autonomous driving, in particular conditional or highly automated driving, a critical part of the system is the driver take over request. Little focus has been given to this important aspect in an automated driving journey. A driver take over request, or TOR, can happen for various reasons and under varying circumstances. Once a TOR occurs, as defined in conditional or highly automated driving, the driver has a finite amount of time in order to take over manual control of the vehicle before the automated driving system deactivates. This paper presents a detailed analysis of why a TOR can occur, how the automated driving system should react during the TOR phase and what should happen at the end of a TOR in order to realize a safe and comfortable TOR for the driver. Various driving strategies during a TOR are presented and evaluated for a single-lane highway scenario.


title: Microglia communication: Parallels between aging and Alzheimer's disease

abstract: Aging alters the functional integrity of the adult brain, driving cognitive impairments and susceptibility to neurodegenerative disorders in healthy individuals. In fact, aging remains the most dominant risk factor for Alzheimer's disease (AD). Recent findings have expanded our understanding of microglia function in the normal aging and AD brain, provoking an appreciation for microglia involvement in remodeling neuronal connections and maintaining brain integrity. This homeostatic function of microglia is achieved in part through the ability of microglia to interact extensively with and rapidly respond to changes in the brain microenvironment to enable adequate phenotypic transformations. Here, we discuss pro-inflammatory drivers of microglia transformation in aging and AD by focusing on the immune-modulatory functions of secreted factors, such as cytokines, complement factors and extracellular vesicles. We highlight the involvement of these secreted factors in aging and AD-associated cellular changes in microglia immune activation, surveillance function, and phagocytosis. Finally, we discuss how pro-inflammatory phenotypic changes associated with altered immune communication could both facilitate and exacerbate impairments in synaptic plasticity and cognitive function observed in the aged and AD brain.


title: Modeling and predictive capacity adjustment for job shop systems with RMTs

abstract: Demand fluctuations along with the requirements of cost-effectiveness, high customization, high product quality lead to a significant increase in manufacturing dynamics and complexity. These fluctuations disturb the work plan and lead to performance deterioration. Capacity adjustment is one of the major approaches to react on the fluctuations in demand. In this paper, we focus on improving the potential of current processes to react flexibly on these fluctuations on short term level by means of reconfigurable machine tools (RMTs). Furthermore, we propose a closed-loop feedback control system based on model predictive control (MPC) to align uncertainties and dynamics in terms of logistic objectives. The proposed method provides a new solution for manufacturers to optimize resources and improve the production performance in a dynamically changing environment. We demonstrate the effectiveness of the proposed method by simulation of a three-product four-workstation job shop system.


title: The model minority as a shared reality and its implication for interracial perceptions.

abstract: Despite the disparities of the life experiences among Asian Americans, the model minority stereotype continues to propagate in the United States. Taking a shared reality theory perspective, we demonstrate that the model minority image of being diligent, high achieving, and submissive is a characteristic representation of Asian Americans that is widely shared among Americans (Study 1). In addition, Americans assume that most people in the United States expect Asian Americans to conform to the model minority image (Study 2). Taken together, these results suggest that the model minority representation is a shared reality in the United States. Furthermore, results from an experimental study (Study 3) confirms that media exposure to Asian-American successes can strengthen European Americans’ belief in the model minority as a shared reality, broadening the difference between the perceived acceptance of Asian Americans and African Americans in the community. Such exposure also strengthens European Americans’ inclination to align their personal attitudes toward Asian Americans with their perceived shared attitude in the community.


title: The implementation of strategic information systems planning methodologies

abstract: Strategic information systems planning (SISP) is the process of deciding the objectives for organizational computing and identifying potential computer applications which the organization should implement. This article gives a thorough definition of SISP and then illustrates it with three methodologies.A survey of 80 organizations examined the problems faced by information systems managers when they attempt to implement such a methodology. The subjects' overall satisfaction with the methodology, its resource requirements, process, output, and final execution were not particularly high. The tow problems rated most severe were the difficulty in securing top management commitment for implementing the plan and the need for substantial further analysis in order to carry out the plan.The survey also investigated some potential cause of the problems. Survey results suggest that the SISP methodologies may often produce satisfactory plans but that organizations lack the management commitment and control mechanisms to ensure that they follow the plans.


title: Graph-Structured Multi-task Regression and an Efficient Optimization Method for General Fused Lasso

abstract: We consider the problem of learning a structured multi-task regression, where the output consists of multiple responses that are related by a graph and the correlated response variables are dependent on the common inputs in a sparse but synergistic manner. Previous methods such as l1/l2-regularized multi-task regression assume that all of the output variables are equally related to the inputs, although in many real-world problems, outputs are related in a complex manner. In this paper, we propose graph-guided fused lasso (GFlasso) for structured multi-task regression that exploits the graph structure over the output variables. We introduce a novel penalty function based on fusion penalty to encourage highly correlated outputs to share a common set of relevant inputs. In addition, we propose a simple yet efficient proximal-gradient method for optimizing GFlasso that can also be applied to any optimization problems with a convex smooth loss and the general class of fusion penalty defined on arbitrary graph structures. By exploiting the structure of the non-smooth ''fusion penalty'', our method achieves a faster convergence rate than the standard first-order method, sub-gradient method, and is significantly more scalable than the widely adopted second-order cone-programming and quadratic-programming formulations. In addition, we provide an analysis of the consistency property of the GFlasso model. Experimental results not only demonstrate the superiority of GFlasso over the standard lasso but also show the efficiency and scalability of our proximal-gradient method.


title: Towards Better Analysis of Deep Convolutional Neural Networks

abstract: Deep convolutional neural networks (CNNs) have achieved breakthrough performance in many pattern recognition tasks such as image classification. However, the development of high-quality deep models typically relies on a substantial amount of trial-and-error, as there is still no clear understanding of when and why a deep model works. In this paper, we present a visual analytics approach for better understanding, diagnosing, and refining deep CNNs. We formulate a deep CNN as a directed acyclic graph. Based on this formulation, a hybrid visualization is developed to disclose the multiple facets of each neuron and the interactions between them. In particular, we introduce a hierarchical rectangle packing algorithm and a matrix reordering algorithm to show the derived features of a neuron cluster. We also propose a biclustering-based edge bundling method to reduce visual clutter caused by a large number of connections between neurons. We evaluated our method on a set of CNNs and the results are generally favorable.


title: Lightweight road manager: smartphone-based automatic determination of road damage status by deep neural network

abstract: Citizens in various locations can report local infrastructure issues to the government by posting reports on certain websites, such as Chiba Report and FixMyStreet. Recently, these systems have begun operating worldwide. In these systems, a large volume of information is collected on infrastructure problems that are identified by citizens (e.g., broken paving slabs, fly tipping, graffiti, potholes). This information is expected to be utilized for infrastructure maintenance. However, local problems (especially road defection) identified by citizens are sometimes not deemed an urgent matter for road managers. This is because it is difficult for an average person to determine road damage status. Furthermore, non-critical reports may be a burden for local government because each report requires visual confirmation. We therefore propose a smartphone application based on a deep neural network that can determine road damage status using only photographs of the road. This application is based on a deep neural network model trained by citizen reports and road manager inspection results, which are gathered daily on a government server. The application updates the model parameters each time it launches and thereby becomes increasingly more intelligent and effective. The proposed system enables average citizens to easily determine road damage status using only a smartphone application. In addition, because not only expert road managers but also local government officials without expert knowledge can inspect the road, the proposed system can be useful for local governments that lack expert road managers.


Short Text Classification on Complaint Documents


title: Lifted graphical models: a survey

abstract: Lifted graphical models provide a language for expressing dependencies between different types of entities, their attributes, and their diverse relations, as well as techniques for probabilistic reasoning in such multi-relational domains. In this survey, we review a general form for a lifted graphical model, a par-factor graph, and show how a number of existing statistical relational representations map to this formalism. We discuss inference algorithms, including lifted inference algorithms, that efficiently compute the answers to probabilistic queries over such models. We also review work in learning lifted graphical models from data. There is a growing need for statistical relational models (whether they go by that name or another), as we are inundated with data which is a mix of structured and unstructured, with entities and relations extracted in a noisy manner from text, and with the need to reason effectively with this data. We hope that this synthesis of ideas from many different research groups will provide an accessible starting point for new researchers in this expanding field.


title: High Bit Encoding Chipless RFID Tag Using Multiple E-Shaped Microstrip Resonators

abstract: Encoding a large number of bits within a narrow band is an important factor in the development of chipless RFID tags. A novel 8 bit chipless RFID tag with a limited bandwidth of 650 MHz is proposed here. The proposed tag comprises a multi-resonating circuit with eight E-shaped microstrip resonators in the frequency band of 3.12 to 3.77 GHz and two cross-polarized transmitting and receiving monopole antennas. The unique feature of the proposed tag is that a different set of frequencies can be derived by changing a single parameter of the structure. The prototype of the tag is fabricated on a substrate C-MET LK4.3 of dielectric constant 4.3 and loss tangent 0.0018. Different tag combinations are designed and tested using bistatic measurement setup. Measurement results on realized prototypes are provided to ensure the reliability of the proposed design.


title: Shape-from-silhouette of articulated objects and its use for human body kinematics estimation and motion capture

abstract: Shape-from-silhouette (SFS), also known as visual hull (VH) construction, is a popular 3D reconstruction method, which estimates the shape of an object from multiple silhouette images. The original SFS formulation assumes that the entire silhouette images are captured either at the same time or while the object is static. This assumption is violated when the object moves or changes shape. Hence the use of SFS with moving objects has been restricted to treating each time instant sequentially and independently. Recently we have successfully extended the traditional SFS formulation to refine the shape of a rigidly moving object over time. We further extend SFS to apply to dynamic articulated objects. Given silhouettes of a moving articulated object, the process of recovering the shape and motion requires two steps: (1) correctly segmenting (points on the boundary of) the silhouettes to each articulated part of the object, (2) estimating the motion of each individual part using the segmented silhouette. In this paper, we propose an iterative algorithm to solve this simultaneous assignment and alignment problem. Once we have estimated the shape and motion of each part of the object, the articulation points between each pair of rigid parts are obtained by solving a simple motion constraint between the connected parts. To validate our algorithm, we first apply it to segment the different body parts and estimate the joint positions of a person. The acquired kinematic (shape and joint) information is then used to track the motion of the person in new video sequences.


title: Balance control of a unicycle robot

abstract: This paper presents the design and implementation of balance control for a unicycle robot. The robot consists of a wheel, a body frame, and a reaction wheel. The wheel enables the robot to move forward or backward to obtain longitudinal stability, while the reaction wheel is used for obtaining lateral stability. Control of this system is a challenging task because of inherent nonlinearity, instability, and underactuation. A detailed dynamic model of the system is derived for control design. By retaining the predominant nonlinear terms and neglecting the high-order coupling terms, the system model is simplified to two decoupled systems. Sliding mode control and feedback linearization are then used to design the stabilizing controllers for the simplified models. The robot is constructed and the designed control schemes are realized through a digital signal processor. The effectiveness of the control schemes is demonstrated through experimental studies.


title: TRUNK MUSCLE ACTIVATION DURING DYNAMIC WEIGHT-TRAINING EXERCISES AND ISOMETRIC INSTABILITY ACTIVITIES

abstract: Hamlyn, N., D.G. Behm, and W.B. Young. Trunk muscle activation during dynamic weight-training exercises and isometric instability activities. J. Strength Cond. Res. 21(4): 1108-1112. 2007.-The purpose of this study was to examine the extent of activation in various trunk muscles during dynamic weight-training and isometric instability exercises. Sixteen subjects performed squats and deadlifts with 80% 1 repetition maximum (1RM), as well as with body weight as resistance and 2 unstable calisthenic-type exercises (superman and sidebridge). Electromyographic (EMG) activity was measured from the lower abdominals (LA), external obliques (EO), upper lumbar erector spinae (ULES), and lumbar-sacral erector spinae (LSES) muscle groups. Results indicated that the LSES EMG activity during the 80% 1RM squat significantly exceeded 80% 1RM deadlift LSES EMG activity by 34.5%. The LSES EMG activity of the 80% 1RM squat also exceeded the body weight squat, deadlift, superman, and sidebridge by 56, 56.6, 65.5, and 53.1%, respectively. The 80% 1RM deadlift ULES EMG activity significantly exceeded the 80% 1RM squat exercise by 12.9%. In addition, the 80% 1RM deadlift ULES EMG activity also exceeded the body weight squat, deadlift, superman, and sidebridge exercises by 66.7, 65.5, 69.3, and 68.6%, respectively. There were no significant changes in EO or LA activity. Therefore, the augmented activity of the LSES and ULES during 80% 1RM squat and deadlift resistance exercises exceeded the activation levels achieved with the same exercises performed with body weight and selected instability exercises. Individuals performing upright, resisted, dynamic exercises can achieve high trunk muscle activation and thus may not need to add instability device exercises to augment core stability training.


title: Identifying influential nodes in complex networks

abstract: Identifying influential nodes that lead to faster and wider spreading in complex networks is of theoretical and practical significance. The degree centrality method is very simple but of little relevance. Global metrics such as betweenness centrality and closeness centrality can better identify influential nodes, but are incapable to be applied in large-scale networks due to the computational complexity. In order to design an effective ranking method, we proposed a semi-local centrality measure as a tradeoff between the low-relevant degree centrality and other time-consuming measures. We use the Susceptible–Infected–Recovered (SIR) model to evaluate the performance by using the spreading rate and the number of infected nodes. Simulations on four real networks show that our method can well identify influential nodes.


The 10^13 first zeros of the Riemann Zeta function, . . .


title: A 3-D-Point-Cloud System for Human-Pose Estimation

abstract: This paper focuses on human-pose estimation using a stationary depth sensor. The main challenge concerns reducing the feature ambiguity and modeling human poses in high- dimensional human-pose space because of the curse of dimen- sionality. We propose a 3-D-point-cloud system that captures the geometric properties (orientation and shape) of the 3-D point cloud of a human to reduce the feature ambiguity, and use the result from action classification to discover low-dimensional manifolds in human-pose space in estimating the underlying probability distribution of human poses. In the proposed sys- tem, a 3-D-point-cloud feature called viewpoint and shape feature histogram (VISH) is proposed to extract the 3-D points from a human and arrange them into a tree structure that preserves the global and local properties of the 3-D points. A nonparametric action-mixture model (AMM) is then proposed to model human poses using low-dimensional manifolds based on the concept of distributed representation. Since human poses estimated using the proposed AMM are in discrete space, a kinematic model is added in the last stage of the proposed system to model the spatial relationship of body parts in continuous space to reduce the quan- tization error in the AMM. The proposed system has been trained and evaluated on a benchmark dataset. Computer-simulation results showed that the overall error and standard deviation of the proposed 3-D-point-cloud system were reduced compared with some existing approaches without action classification.


title: An Open-Switch Fault Diagnosis Method for Single-Phase PWM Rectifier Using a Model-Based Approach in High-Speed Railway Electrical Traction Drive System

abstract: The converter with a single-phase rectifier, a dc-link circuit and a three-phase inverter is widely applied in high-speed railway electrical traction drive system. The fault frequency of single-phase rectifier is higher than that of three-phase inverter. Thus, this paper presents a new and fast model-based approach for open-switch fault diagnosis of the single-phase pulse width modulation rectifier, based on the mixed logical dynamic model and residual generation. It requires no additional hardware but only some measurements and command signals which are available in control system. This diagnosis method is quite suitable for electrical traction application due to the fast diagnosis time, simple structure and high reliability. Experimental results confirm the effectiveness and accuracy of the proposed algorithm. It is shown that such diagnosis method can locate the faulty switch in a few milliseconds which is important to avoid catastrophic consequences.


title: The impact of website content dimension and e-trust on e-marketing effectiveness: The case of Iranian commercial saffron corporations

abstract: Abstract By considering the problems that commercial saffron companies have faced in international markets, the aim of this study is to investigate the impact of website content, including informational and design dimensions, on the effectiveness of e-marketing and e-trust as mediator variables. These aspects are examined with reference to sales and marketing division managers in a sample of 100 commercial saffron corporations in the Khorasan province. The findings support the ideas that website content has an effect on e-marketing and e-trust and that e-trust plays a mediating role in the relationship between e-trust and e-marketing effectiveness.


Hybrid Project Management: Agile with Discipline.


title: Disease Comorbidity Network Guides the Detection of Molecular Evidence for the Link Between Colorectal Cancer and Obesity

abstract: Epidemiological studies suggested that obesity increases the risk of colorectal cancer (CRC). The genetic connection between CRC and obesity is multifactorial and inconclusive. In this study, we hypothesize that the study of shared comorbid diseases between CRC and obesity can offer unique insights into common genetic basis of these two diseases. We constructed a comorbidity network based on mining health data for millions of patients. We developed a novel approach and extracted the diseases that play critical roles in connecting obesity and CRC in the comorbidity network. Our approach was able to prioritize metabolic syndrome and diabetes, which are known to be associated with obesity and CRC through insulin resistance pathways. Interestingly, we found that osteoporosis was highly associated with the connection between obesity and CRC. Through gene expression meta-analysis, we identified novel genes shared among CRC, obesity and osteoporosis. Literature evidences support that these genes may contribute in explaining the genetic overlaps between obesity and CRC.


title: Energy Management of Microgrid in Grid-Connected and Stand-Alone Modes

abstract: There are two operation modes of microgrids: grid-connected mode and stand-alone mode. Normally, a microgrid will be connected to the main grid for the majority of time, i.e., operates in the grid-connected mode. In the stand-alone mode, a microgrid is isolated from the main grid; the highest priority for microgrids is to keep a reliable power supply to customers instead of economic benefits. So, the objectives and energy management strategies are different in two modes. In this paper, a novel double-layer coordinated control approach for microgrid energy management is proposed, which consists of two layers: the schedule layer and the dispatch layer. The schedule layer obtains an economic operation scheme based on forecasting data, while the dispatch layer provides power of controllable units based on real-time data. Errors between the forecasting and real-time data are resolved through coordination control of the two layers by reserving adequate active power in the schedule layer, then allocating that reserve in the dispatch layer to deal with the indeterminacy of uncontrollable units. A typical-structure microgrid is studied as an example, and simulation results are presented to demonstrate the performance of the proposed double-layer coordination control method in both grid-connected mode and stand-alone mode.


title: Multi-view surface reconstruction using polarization

abstract: A new technique for surface reconstruction is developed that uses polarization information from two views. One common problem arising from many multiple view techniques is that of finding correspondences between pixels on each image. In the new method, these correspondences are found by exploiting the spontaneous polarization of light caused by reflection to recover surface normals. These normals are then used to recover surface height. The similarity between reconstructed surface regions determines whether or not a pair of points correspond to each other. The technique is thus able to overcome the convex/concave ambiguity found in many single view techniques. Because the technique relies on smooth surface regions to detect correspondences, rather than feature detection, it is applicable to objects normally inaccessible to stereo vision. Also due to this fact, it is possible to remove noise without causing oversmoothing problems.


title: Estimating the incremental effects of interactions for marketing attribution

abstract: Assigning credit to different marketing activities has long been an important but challenging goal for a marketer. With the advent of digital marketing, the marketer can now potentially record each interaction with a prospective customer. With this development it is possible to measure and assign credit for each marketing interaction. We propose an econometric model to estimate the true incremental number of purchases that can be attributed to a given marketing channel. We extend our model to attribute credit for revenue realization. We also propose an approach to automatically identify audience segments where attribution models differ. We build and test our model on a real world data set belonging to a travel and experience industry organization's web data. The results show that our approach improves upon industry standard rule-based approaches, by correcting for the biases inherent to these model.


title: Nurses’ assessments and patients’ perceptions: development of the night nursing care instrument (NNCI), measuring nursing care at night

abstract: Background: Nursing care provided at night has a different purpose and objective to that provided during the day. A review of the literature does not reveal any scientifically tested research instr ...


Fast Robust Invariant Feature


title: A generative theory of similarity

abstract: A Generative Theory of Similarity Charles Kemp, Aaron Bernstein & Joshua B. Tenenbaum {ckemp, aaronber, jbt}@mit.edu Department of Brain and Cognitive Sciences Massachusetts Institute of Technology Abstract We propose that similarity judgments are inferences about generative processes, and that two objects appear similar when they are likely to have been generated by the same process. We present a formal model based on this idea, and suggest that it may be particularly useful for explaining high-level judgments of similarity. We compare our model to featural and transformational accounts, and describe an experiment where it outper- forms a transformational model. Keywords: similarity; generative processes; computational theory Every object is the outcome of a generative process. An animal grows from a fertilized egg into an adult, a city develops from a settlement into a metropolis, and an artifact is assembled from a pile of raw materials ac- cording to the plan of its designer. Observations like these motivate the generative approach, which proposes that an object may be understood by thinking about the process that generated it. The promise of the approach is that apparently complex objects may be produced by simple processes, an insight that has proved productive across disciplines including biology (Thompson, 1961), physics (Wolfram, 2002), and architecture (Alexander, 1979). To give two celebrated examples from biology, the shape of a pinecone and the markings on a cheetah’s tail can be generated by remarkably simple processes of growth. These patterns can be characterized much more compactly by describing their causal history than by at- tempting to describe them directly. Leyton has argued that the generative approach pro- vides a general framework for understanding cognition. Applications of the approach can be found in generative theories of memory (Leyton, 1992), categorization (An- derson, 1991; Feldman, 1997; Rehder, 2003), visual perception (Leyton, 1992), speech perception (Liber- man et al., 1967), syntax (Chomsky, 1965) 1 , and mu- sic (Lehrdahl and Jackendoff, 1996). This paper offers a generative theory of similarity, a notion often invoked by models of high-level cognition. We argue that two objects are similar to the extent that they seem to have been generated by the same underlying process. The literature on similarity covers settings that ex- tend from the comparison of simple stimuli like tones and colored patches to the comparison of highly-structured objects like narratives. The generative approach is rele- vant to the entire spectrum of applications, but we are particularly interested in high-level similarity. In par- ticular, we are interested in how similarity judgments draw on intuitive theories, or systems of rich conceptual knowledge (Murphy and Medin, 1985). Intuitive theories and generative processes are intimately linked: Murphy (1993), for example, defines a theory as “a set of causal relations that collectively generate or explain the phe- nomena in a domain.” Our generative framework should therefore help to explain how similarity judgments are guided by intuitive theories. Others have recognized the importance of this issue: Murphy and Medin (1985) sug- gest, for example, that “the notion of similarity must be extended to include theoretical knowledge.” We develop a formal theory of similarity and compare it to two existing theories. The featural account (Tver- sky, 1977) suggests that the similarity of two objects is a function of their common and distinctive features, and the transformation account suggests that similarity de- pends on the number of operations required to transform one object into the other (Hahn et al., 2003). We show that versions of both approaches emerge as special cases of our model, and present an experiment that directly compares our model with the transformation account. Generative processes and similarity Before introducing our formal model, we describe several cases where the assessment of similarity relies on infer- ences about generative processes. Suppose we are shown The approach we have described should be distinguished from two usages of “generative” that are found in the linguis- tics literature. Generativity sometimes refers to the infinite use of finite means: for us, a generative process need not meet this criterion, although many interesting processes will. The second (and more central) usage refers to a grammar’s abil- ity to generate the set of grammatical sentences: Chomsky (1965) defines a generative grammar as “a system of rules that in some explicit and well-defined way assigns structural descriptions to sentences.” A system of this sort need not be generative in our sense — in particular, it need not as- sume that structural descriptions have derivational histories. Generative grammars, however, are typically expressed us- ing formalisms that do assign derivational histories to struc- tural descriptions, and theories that assume the psychological reality of these histories are instances of the generative ap- proach. Chomsky (1995) has rejected theories of this sort: “the ordering of operations [in grammatical theory] is ab- stract, expressing postulated properties of the language fac- ulty of the brain, with no temporal interpretation implied.” Others, however, argue for linguistic theories that are gener- ative in our sense (Marantz, To appear)


title: Animal Hematotoxicology: A Practical Guide for Toxicologists and Biomedical Researchers

abstract: Introduction and Developing Areas of Interest Hemopoiesis, Blood Cell Types, and Bone Marrow Examination Erythrocytes, Anemias, and Polycythemias Additional Erythrocytic Measurements Iron, Associated Proteins, and Vitamins Leukocytes Immunotoxicology Hemostasis Appendices: References for Laboratory Animal Hematology Data SI Units and Conversions Expectable Ranges General Abbreviations Some Common Biochemical Abbreviations Compounds Causing Reductions and Destruction of Erythrocytes Compounds Causing Reductions of Neutrophil Numbers Compounds Affecting Numbers, Function, and Aggregation of Platelets Leukocyte-Cluster of Differentiation