title: A Short-Term Vehicular Density Prediction Scheme for Enhanced Beaconing Control

abstract: Channel congestion is a well-known problem in wireless networks in general and Vehicular Ad Hoc Networks (VANETs) in particular. Literature solutions propose to alleviate this problem by controlling the network load based on parameters like vehicle density or packet collision rate. In other words, each vehicle will observe the density of vehicles (or the packet collision rate) around itself in a certain time interval, and use this information to adjust its transmit parameters i.e. transmit rate and/or power, the next time it has a beacon to transmit (in the following time window). However, the information collected in the current time window might not still be valid in the next one. In fact, in a highly dynamic network like VANETs, vehicle density, and consequently the busy ratio and the collision rate, might vary a great deal even in the smallest time intervals. To cope with this newly identified problem, we propose a novel vehicle-centric short-term density prediction scheme that estimates the vehicular density around a given vehicle within the next time window allowing each vehicle to adapt its transmit parameters based on the current state of the network (as opposed to the previous state). The accuracy and the efficiency of our proposed scheme is demonstrated in a proof-of-concept case study, showing a significant improvement in terms of network performance.


title: Association Rule Mining for Suspicious Email Detection: A Data Mining Approach

abstract: Email has been an efficient and popular communication mechanism as the number of Internet user's increase. In many security informatics applications it is important to detect deceptive communication in email. This paper proposes to apply Association Rule Mining for Suspected Email Detection. (Emails about Criminal activities).Deception theory suggests that deceptive writing is characterized by reduced frequency of first person pronouns and exclusive words and elevated frequency of negative emotion words and action verbs . We apply this model of deception to the set of Email dataset, then applied Apriori algorithm to generate the rules The rules generated are used to test the email as deceptive or not. In particular we are interested in detecting emails about criminal activities. After classification we must be able to differentiate the emails giving information about past criminal activities(Informative email) and those acting as alerts(warnings) for the future criminal activities. This differentiation is done using the features considering the tense used in the emails. Experimental results show that simple Associative classifier provides promising detection rates.


title: Impact of Teen Depression on Academic, Social, and Physical Functioning

abstract: OBJECTIVE: This study aimed to determine the impact of teen depression on peer, family, school, and physical functioning and the burden on parents. METHODS: Patients participated in a longitudinal study of teens with and without probable depression, drawn from 11 primary care offices in Los Angeles, California, and Washington, DC. A total of 4856 teens completed full screening assessments; 4713 were eligible for the study, and 187 (4.0%) met the criteria for probable depression and were invited to participate, as were teens who were not depressed. A total of 184 baseline assessments for teens with probable depression and 184 for nondepressed teens were completed, as were 339 (90%) parent interviews. Follow-up interviews were conducted with 328 teens (89%) and 302 parents (82%). Measures included teen reports of peer and parent support, 2 measures of school functioning, grades, physical health, and days of impairment. Parent reports included peer, school, and family functioning and subjective and objective burdens on parents. RESULTS: Teens with depression and their parents reported more impairment in all areas, compared with teens without depression at baseline, and reported more coexisting emotional and behavioral problems. Both depression and coexisting problems were related to impairment. There was a lasting impact of depressive symptoms on most measures of peer, family, and school functioning 6 months later, but controlling for coexisting baseline emotional and behavioral problems attenuated this relationship for some measures. CONCLUSION: Improvements in teen depression might have benefits that extend beyond clinical symptoms, improving peer, family, and school functioning over time.


title: WritLarge: Ink Unleashed by Unified Scope, Action, & Zoom

abstract: WritLarge is a freeform canvas for early-stage design on electronic whiteboards with pen+touch input. The system aims to support a higher-level flow of interaction by 'chunking' the traditionally disjoint steps of selection and action into unified selection-action phrases. This holistic goal led us to address two complementary aspects: SELECTION, for which we devise a new technique known as the Zoom-Catcher that integrates pinch-to-zoom and selection in a single gesture for fluidly selecting and acting on content; plus: ACTION, where we demonstrate how this addresses the combined issues of navigating, selecting, and manipulating content. In particular, the designer can transform select ink strokes in flexible and easily-reversible representations via semantic, structural, and temporal axes of movement that are defined as conceptual 'moves' relative to the specified content. This approach dovetails zooming with lightweight specification of scope as well as the evocation of context-appropriate commands, at-hand, in a location-independent manner. This establishes powerful new primitives that can help to scaffold higher-level tasks, thereby unleashing the expressive power of ink in a compelling manner.


title: Energy Saving Additive Neural Network

abstract: In recent years, machine learning techniques based on neural networks for mobile computing become increasingly popular. Classical multi-layer neural networks require matrix multiplications at each stage. Multiplication operation is not an energy efficient operation and consequently it drains the battery of the mobile device. In this paper, we propose a new energy efficient neural network with the universal approximation property over space of Lebesgue integrable functions. This network, called, additive neural network, is very suitable for mobile computing. The neural structure is based on a novel vector product definition, called ef-operator, that permits a multiplier-free implementation. In ef-operation, the "product" of two real numbers is defined as the sum of their absolute values, with the sign determined by the sign of the product of the numbers. This "product" is used to construct a vector product in $R^N$. The vector product induces the $l_1$ norm. The proposed additive neural network successfully solves the XOR problem. The experiments on MNIST dataset show that the classification performances of the proposed additive neural networks are very similar to the corresponding multi-layer perceptron and convolutional neural networks (LeNet).


title: Birds of a feather don't always fly farthest: Similarity in Big Five personality predicts more negative marital satisfaction trajectories in long-term marriages.

abstract: Decades of research suggest that similarity in demographics, values, activities, and attitudes predicts higher marital satisfaction. The present study examined the relationship between similarity in Big Five personality factors and initial levels and 12-year trajectories of marital satisfaction in long-term couples, who were in their 40s and 60s at the beginning of the study. Across the entire sample, greater overall personality similarity predicted more negative slopes in marital satisfaction trajectories. In addition, spousal similarity on Conscientiousness and Extraversion more strongly predicted negative marital satisfaction outcomes among the midlife sample than among the older sample. Results are discussed in terms of the different life tasks faced by young, midlife, and older adults, and the implications of these tasks for the “ingredients” of marital satisfaction.


title: A game theoretic approach to vulnerability patching

abstract: Patching vulnerabilities is one of the key activities in security management. For most commercial systems however the number of relevant vulnerabilities is very high; as a consequence only a subset of them can be actually fixed: due to bounded resources, choosing them according to some optimal criterium is a critical challenge for the security manager. One has also to take into account, though, that even delivering attacks on vulnerabilities requires a non-negligible effort: also a potential attacker will always be constrained by bounded resources. Choosing which vulnerabilities to attack according to some optimality criterium is also a difficult challenge for a hacker. Here we argue that if both types of players are rational, wishing to maximize their ROI and aware of the two sides of the problem, their respective strategies can be discussed more naturally within a Game Theory (GT) framework. We develop the fact that the above described attack/defense scenario can be mapped onto a variant of GT models known as Search Games: we call this variant Enhanced Vulnerability Patching game. Under the hypothesis of rationality of the players, GT provides a prediction for their behavior in terms of a probability distribution over the possible choices: this result can help in supporting a semi-automatic choice of patch management with constrained resources. In this work we model and solve few prototypical instances of this class of games and outline the path towards more realistic and accurate GT models.


title: P-Sense: A participatory sensing system for air pollution monitoring and control

abstract: This article presents P-Sense, a participatory sensing application for air pollution monitoring and control. The paper describes in detail the system architecture and individual components of a successfully implemented application. In addition, the paper points out several other research-oriented problems that need to be addressed before these applications can be effectively implemented in practice, in a large-scale deployment. Security, privacy, data visualization and validation, and incentives are part of our work-in-progress activities


title: A bi-level view of inpainting - based image compression

abstract: Inpainting based image compression approaches, especially linear and non-linear diffusion models, are an active research topic for lossy image compression. The major challenge in these compression models is to find a small set of descriptive supporting points, which allow for an accurate reconstruction of the original image. It turns out in practice that this is a challenging problem even for the simplest Laplacian interpolation model. In this paper, we revisit the Laplacian interpolation compression model and introduce two fast algorithms, namely successive preconditioning primal dual algorithm and the recently proposed iPiano algorithm, to solve this problem efficiently. Furthermore, we extend the Laplacian interpolation based compression model to a more general form, which is based on principles from bi-level optimization. We investigate two different variants of the Laplacian model, namely biharmonic interpolation and smoothed Total Variation regularization. Our numerical results show that significant improvements can be obtained from the biharmonic interpolation model, and it can recover an image with very high quality from only 5% pixels.


title: Unsupervised 3D Reconstruction from a Single Image via Adversarial Learning

abstract: Recent advancements in deep learning opened new opportunities for learning a high-quality 3D model from a single 2D image given sufficient training on large-scale data sets. However, the significant imbalance between available amount of images and 3D models, and the limited availability of labeled 2D image data (i.e. manually annotated pairs between images and their corresponding 3D models), severely impacts the training of most supervised deep learning methods in practice. In this paper, driven by a novel design of adversarial networks, we have developed an unsupervised learning paradigm to reconstruct 3D models from a single 2D image, which is free of manually annotated pairwise input image and its associated 3D model. Particularly, the paradigm begins with training an adaption network via autoencoder with adversarial loss, which embeds unpaired 2D synthesized image domain with real world image domain to a shared latent vector space. Then, we jointly train a 3D deconvolutional network to transform the latent vector space to the 3D object space together with the embedding process. Our experiments verify our network's robust and superior performance in handling 3D volumetric object generation from a single 2D image.


title: A Routing Architecture for the Airborne Network

abstract: In this paper we present a candidate routing architecture for the future IP-based Airborne Network (AN). This routing architecture implements each tactical mobile network and each widebody platform as separate autonomous system (AS) domains internetworked via the Border Gateway Protocol (BGP). The advantages and disadvantages of this architecture are presented. The future AN will consist of multiple IP-enabled airborne platforms with wireless connections to each other and to multiple surface network domains. For example, Air Force platforms may connect to each other and may connect directly to Air Force domain ground sites. These Air Force platforms may wish to use Navy platforms as relays to access Navy ground sites and connect via SIPRNET to Air Force ground sites. Similarly, the best path may be to connect via an Army ground site to gain access to the Air Force Intranet via SIPRNET. Given the dynamically changing topology and the bandwidth-limited channel conditions corresponding to airborne networking versus terrestrial networking, it will be critical to develop effective link access protocols, routing protocols, and management strategies which can accommodate the unique characteristics of the Airborne Network. Various IP-enabled radios (e.g., TTNT) would be used to establish these connections. Military satellite links, as well as various commercial satellite links, such as INMARSAT and Iridium, would also be available. The specific IP-radios that will be available for both the air and the ground nodes of the AN certainly will influence the eventual choice of routing architecture.


title: A Runtime Assertion Checker for the Java Modeling Language (JML)

abstract: Debugging is made difficult by the need to precisely describe what each piece of the software is supposed to do, and to write code to defend modules against the errors of other modules; if this is not done it is difficult to assign blame to a small part of the program when things go wrong. Similarly, unit testing also needs precise descriptions of behavior, and is made difficult by the need to write test oracles. However, debugging and testing consume a significant fraction of the cost of software development and maintenance efforts. Inadequate debugging and testing also contribute to quality problems. We describe a runtime assertion checker for the Java Modeling Language (JML) that helps in assigning blame during debugging and in automatic generation of test oracles. It represents a significant advance over the current state of the art, because it can deal with very abstract specifications which hide representation details, and other features such as quantifiers, and inheritance of specifications. Yet JML specifications have a syntax that is easily understood by programmers. Thus, JML’s runtime assertion checker has the potential for decreasing the cost of debugging and testing.


title: High frequency active-clamp buck converter for low power automotive applications

abstract: Low power (5 W-25 W) automotive DC-DC converters have a very wide input voltage range from 4.5V to 42V, and it is usually operated at high switching frequency in order to comply with the strict CISPR-25 Standard for EMI performance. The conventional buck converter is currently employed for such applications owing to its simplicity and low cost, but it has low efficiency at high switching frequencies and high EMI emission due to hard switching. To solve these issues, an active-clamp buck converter is proposed in this paper, which features zero-voltage-switching (ZVS) and hence high efficiency and low EMI emission over a wide input voltage range, making it suitable for low power automotive applications. The operating principles including ZVS mechanism, detailed design considerations and experimental results from a 1 MHz prototype are presented.


title: Acted vs. natural frustration and delight: Many people smile in natural frustration

abstract: This work is part of research to build a system to combine facial and prosodic information to recognize commonly occurring user states such as delight and frustration. We create two experimental situations to elicit two emotional states: the first involves recalling situations while expressing either delight or frustration; the second experiment tries to elicit these states directly through a frustrating experience and through a delightful video. We find two significant differences in the nature of the acted vs. natural occurrences of expressions. First, the acted ones are much easier for the computer to recognize. Second, in 90% of the acted cases, participants did not smile when frustrated, whereas in 90% of the natural cases, participants smiled during the frustrating interaction, despite self-reporting significant frustration with the experience. This paper begins to explore the differences in the patterns of smiling that are seen under natural frustration and delight conditions, to see if there might be something measurably different about the smiles in these two cases, which could ultimately improve the performance of classifiers applied to natural expressions.


title: A matrix factorization technique with trust propagation for recommendation in social networks

abstract: Recommender systems are becoming tools of choice to select the online information relevant to a given user. Collaborative filtering is the most popular approach to building recommender systems and has been successfully employed in many applications. With the advent of online social networks, the social network based approach to recommendation has emerged. This approach assumes a social network among users and makes recommendations for a user based on the ratings of the users that have direct or indirect social relations with the given user. As one of their major benefits, social network based approaches have been shown to reduce the problems with cold start users. In this paper, we explore a model-based approach for recommendation in social networks, employing matrix factorization techniques. Advancing previous work, we incorporate the mechanism of trust propagation into the model. Trust propagation has been shown to be a crucial phenomenon in the social sciences, in social network analysis and in trust-based recommendation. We have conducted experiments on two real life data sets, the public domain Epinions.com dataset and a much larger dataset that we have recently crawled from Flixster.com. Our experiments demonstrate that modeling trust propagation leads to a substantial increase in recommendation accuracy, in particular for cold start users.


title: Streaming Hierarchical Video Segmentation

abstract: The use of video segmentation as an early processing step in video analysis lags behind the use of image segmentation for image analysis, despite many available video segmentation methods. A major reason for this lag is simply that videos are an order of magnitude bigger than images; yet most methods require all voxels in the video to be loaded into memory, which is clearly prohibitive for even medium length videos. We address this limitation by proposing an approximation framework for streaming hierarchical video segmentation motivated by data stream algorithms: each video frame is processed only once and does not change the segmentation of previous frames. We implement the graph-based hierarchical segmentation method within our streaming framework; our method is the first streaming hierarchical video segmentation method proposed. We perform thorough experimental analysis on a benchmark video data set and longer videos. Our results indicate the graph-based streaming hierarchical method outperforms other streaming video segmentation methods and performs nearly as well as the full-video hierarchical graph-based method.


title: Data Selection With Fewer Words

abstract: We present a method that improves data selection by combining a hybrid word/part-of-speech representation for corpora, with the idea of distinguishing between rare and frequent events. We validate our approach using data selection for machine translation, and show that it maintains or improves BLEU and TER translation scores while substantially improving vocabulary coverage and reducing data selection model size. Paradoxically, the coverage improvement is achieved by abstracting away over 97% of the total training corpus vocabulary using simple part-of-speech tags during the data selection process.


title: Could Compression Be of General Use? Evaluating Memory Compression across Domains

abstract: Recent proposals present compression as a cost-effective technique to increase cache and memory capacity and bandwidth. While these proposals show potentials of compression, there are several open questions to adopt these proposals in real systems including the following: (1) Do these techniques work for real-world workloads running for long time? (2) Which application domains would potentially benefit the most from compression? (3) At which level of memory hierarchy should we apply compression: caches, main memory, or both? In this article, our goal is to shed light on some main questions on applicability of compression. We evaluate compression in the memory hierarchy for selected examples from different application classes. We analyze real applications with real data and complete runs of several benchmarks. While simulators provide a pretty accurate framework to study potential performance/energy impacts of ideas, they mostly limit us to a small range of workloads with short runtimes. To enable studying real workloads, we introduce a fast and simple methodology to get samples of memory and cache contents of a real machine (a desktop or a server). Compared to a cycle-accurate simulator, our methodology allows us to study real workloads as well as benchmarks. Our toolset is not a replacement for simulators but mostly complements them. While we can use a simulator to measure performance/energy impact of a particular compression proposal, here with our methodology we can study the potentials with long running workloads in early stages of the design. Using our toolset, we evaluate a collection of workloads from different domains, such as a web server of CS department of UW—Madison for 24h, Google Chrome (watching a 1h-long movie on YouTube), and Linux games (playing for about an hour). We also use several benchmarks from different domains, including SPEC, mobile, and big data. We run these benchmarks to completion. Using these workloads and our toolset, we analyze different compression properties for both real applications and benchmarks. We focus on eight main hypotheses on compression, derived from previous work on compression. These properties (Table 2) act as foundation of several proposals on compression, so performance of those proposals depends very much on these basic properties. Overall, our results suggest that compression could be of general use both in main memory and caches. On average, the compression ratio is ≥2 for 64% and 54% of workloads, respectively, for memory and cache data. Our evaluation indicates significant potential for both cache and memory compression, with higher compressibility in memory due to abundance of zero blocks. Among application domains we studied, servers show on average the highest compressibility, while our mobile benchmarks show the lowest compressibility. For comparing benchmarks with real workloads, we show that (1) it is critical to run benchmarks to completion or considerably long runtimes to avoid biased conclusions, and (2) SPEC benchmarks are good representative of real Desktop applications in terms of compressibility of their datasets. However, this does not hold for all compression properties. For example, SPEC benchmarks have much better compression locality (i.e., neighboring blocks have similar compressibility) than real workloads. Thus, it is critical for designers to consider wider range of workloads, including real applications, to evaluate their compression techniques.


title: Impact of a contemplative end-of-life training program: being with dying.

abstract: Objective: Health care professionals report a lack of skills in the psychosocial and spiritual aspects of caring for dying people and high levels of moral distress, grief, and burnout. To address these concerns, the “Being with Dying: Professional Training Program in Contemplative End-of-Life Care” (BWD) was created. The premise of BWD, which is based on the development of mindfulness and receptive attention through contemplative practice, is that cultivating stability of mind and emotions enables clinicians to respond to others and themselves with compassion. This article describes the impact of BWD on the participants. Methods: Ninety-five BWD participants completed an anonymous online survey; 40 completed a confidential open-ended telephone interview. Results: Four main themes—the power of presence, cultivating balanced compassion, recognizing grief, and the importance of self-care—emerged in the interviews and were supported in the survey data. The interviewees considered BWD’s contemplative and reflective practices meaningful, useful, and valuable and reported that BWD provided skills, attitudes, behaviors, and tools to change how they worked with the dying and bereaved. Significance of results: The quality of presence has the potential to transform the care of dying people and the caregivers themselves. Cultivating this quality within themselves and others allows clinicians to explore alternatives to exclusively intellectual, procedural, and taskoriented approaches when caring for dying people. BWD provides a rare opportunity to engage in practices and methods that cultivate the stability of mind and emotions that may facilitate compassionate care of dying patients, families, and caregivers.


Generation Methods for the Story Cloze Test


title: Canny-VO: Visual Odometry With RGB-D Cameras Based on Geometric 3-D–2-D Edge Alignment

abstract: This paper reviews the classical problem of free-form curve registration and applies it to an efficient RGB-D visual odometry system called Canny-VO, as it efficiently tracks all Canny edge features extracted from the images. Two replacements for the distance transformation commonly used in edge registration are proposed: approximate nearest neighbor fields and oriented nearest neighbor fields. 3-D–2-D edge alignment benefits from these alternative formulations in terms of both efficiency and accuracy. It removes the need for the more computationally demanding paradigms of data-to-model registration, bilinear interpolation, and subgradient computation. To ensure robustness of the system in the presence of outliers and sensor noise, the registration is formulated as a maximum a posteriori problem and the resulting weighted least-squares objective is solved by the iteratively reweighted least-squares method. A variety of robust weight functions are investigated and the optimal choice is made based on the statistics of the residual errors. Efficiency is furthermore boosted by an adaptively sampled definition of the nearest neighbor fields. Extensive evaluations on public SLAM benchmark sequences demonstrate state-of-the-art performance and an advantage over classical Euclidean distance fields.


title: A Micro Inertial Energy Harvesting Platform With Self-Supplied Power Management Circuit for Autonomous Wireless Sensor Nodes

abstract: A 0.25 cm ::: 3 ::: autonomous energy harvesting micro-platform is realized to efficiently scavenge, rectify and store ambient vibration energy with batteryless cold start-up and zero sleep-mode power consumption. The fabricated compact system integrates a high-performance vacuum-packaged piezoelectric MEMS energy harvester with a power management IC and surface-mount components including an ultra-capacitor. The power management circuit incorporates a rectification stage with ~30 mV voltage drop, a bias-Ωip stage with a novel control system for increased harvesting efficiency, a trickle charger for permanent storage of harvested energy, and a low power supply-independent bias circuitry. The overall system weighs less than 0.6 grams, does not require a precharged battery, and has power consumption of 0.5 μW in active-mode and 10 pW in sleep-mode operation. While excited with 1 g vibration, the platform is tested to charge an initially depleted 70 mF ultra-capacitor to 1.85 V in 50 minutes (at 155 Hz vibration), and a 20 mF ultra-capacitor to 1.35 V in 7.5 min (at 419 Hz). The end-to-end rectification efficiency from the harvester to the ultra-capacitor is measured as 58-86%. The system can harvest from a minimum vibration level of 0.1 g.


title: Controllable procedural content generation via constrained multi-dimensional Markov chain sampling

abstract: Statistical models, such as Markov chains, have recently started to be studied for the purpose of Procedural Content Generation (PCG). A major problem with this approach is controlling the sampling process in order to obtain output satisfying some desired constraints. In this paper we present three approaches to constraining the content generated using multi-dimensional Markov chains: (1) a generate and test approach that simply resamples the content until the desired constraints are satisfied, (2) an approach that finds and resamples parts of the generated content that violate the constraints, and (3) an incremental method that checks for constraint violations during sampling. We test our approaches by generating maps for two classic video games, Super Mario Bros. and Kid Icarus.


title: Automated Phenotyping of Epicuticular Waxes of Grapevine Berries Using Light Separation and Convolutional Neural Networks

abstract: Abstract The epicuticular wax represents the outer layer of the grape berry skin and is known as trait that is significantly correlated to resilience towards Botrytis bunch rot. Traditionally this trait is classified using the OIV descriptor 227 (berry bloom) in a time consuming way resulting in subjective and error-prone phenotypic data. In the present study an objective, fast and sensor-based approach was developed to monitor epicuticular waxes. From the technical point-of-view, it is known that the measurement of different illumination components conveys important information about observed object surfaces. A Light-Separation-Lab is proposed in order to capture illumination-separated images of grapevine berries for phenotyping the distribution of epicuticular waxes (berry bloom). For image analysis, an efficient convolutional neural network approach is used to derive the uniformity and intactness of waxes on berries. Method validation over six grapevine cultivars shows accuracies up to 97.3%. In addition, electrical impedance of the cuticle and its epicuticular waxes (described as an indicator for the thickness of berry skin and its permeability) was correlated to the detected proportion of waxes with r = 0.76. This novel, fast and non-invasive phenotyping approach facilitates enlarged screenings within grapevine breeding material and genetic repositories regarding berry bloom characteristics and its impact on resilience towards Botrytis bunch rot.


title: The role of impulsive behavior in drug abuse

abstract: Background ::: Impulsivity is a multifaceted construct that has recently been recognized as a factor contributing to enhanced vulnerability to drug abuse.


title: Effect of seaweed liquid extract on growth and yield of Triticum aestivum var. Pusa Gold

abstract: The effect of seaweed liquid extract (SLE) of Sargassum wightii on germination, growth and yield of Triticum aestivum var. Pusa Gold was studied. Application of a lower concentration (20%) of SLE enhanced the percentage of seed germination, growth and yield, as measured by kernel number and seed dry weight. All growth and yield parameters were found to be highest at the 20% concentration SLE treatment. Total (100%) seed germination was observed for the 20% concentration SLE treatment, an 11% increase over the control. The present study demonstrated that seaweed liquid extract could serve as an alternative biofertilizer as is eco-friendly, cheaper, deliver substantial economic and environmental benefits to farmers.


title: Interface structure and strength of ultrasonically wedge bonded heavy aluminium wires in Si-based power modules

abstract: In this paper the microscopical structure of wedge bonded interfaces is investigated, with a focus on what effect the power in the ultrasonic bonding and the initial microscopical structure of the \(Al\) wire have on the quality of the bonding. The quality evaluation is based on mapping the microscopical restructuring of the wire grains during bonding and thereby assessing the effective bonding area. Three approaches are utilized in the interface characterization: mechanical shear test, optical microscopy combined with micro-sectioning, and scanning electron microscopy assisted by focused ion beam milling. The shear test is applied to quantify the strength of the bonded interfaces, while the other methods are used to map the grain reconstruction caused by the bonding. From the results it is possible to map a 3D image of the wire deformation, and the grain refinement region which is the dominating parameter with respect to fatigue related cracking of the interfaces. It is found that the bonding power, as well as the initial wire structure directly affects the refinement region and thereby the strength of the interface.


title: Vacancy-induced mechanical and thermodynamic properties of B2-RuAl

abstract: Abstract We apply the first-principles calculations to investigate the correlation between vacancy and the overall performances of RuAl. Three possible vacancies: Al -va , Ru -va and Ru-Al -va are considered. Al -va vacancy is more thermodynamic stability than that of Ru -va and Ru-Al -va vacancies. Al -va and Ru-Al -va vacancies improve the ductility of RuAl. However, Ru -va vacancy weakens the ductility. The calculated electronic structure shows that the removed atom weakens the localized hybridization between atoms, and forms the weak Ru-Al bond along the shear direction. Finally, we predict that these vacancies improve the heat capacity of RuAl due to the vibration of the removed atom.


title: Decision Support by Visual Incidence Anamneses for Increased Patient Safety

abstract: 1.1 How to get the right information? Decision support in Health Care is based on proper collection and evaluation of relevant information related to the patient and her/his medical history. In the process of Anamnesis the patient will provide individual testimony regarding her/his status in a dialogue with the physician. The diagnosis will then be conducted by complementing this information with written information from (electronic) Health Records (EHR) followed by clinical examination, laboratory testing and imaging. The quality and relevance of the information from Anamnesis is strongly dependant on the format and focus of the dialogue at hand. The dialogue itself is partly derived from analysing the patients EHR. As we know, the information found in EHRs is based on treatment of the patient by different hospital specialist departments. This structure of the EHR hampers the understanding of the treatment processes and their relevance in preparing the dialogue with the patient in the Anamnesis process. We argue in this chapter that tools supporting the Anamnesis Process, such as the Visual Incidence Anamneses (VIA), have potential to improve decision support and process transparency in diagnosing patients and hence increasing patient safety. The following two figures capture our main ideas. Figure 1 illustrates information collected by the physician at two separate anamnesis events. Situation N is the present situation. Situation N-1 refers to


title: Design of brushless permanent-magnet DC motors for racing motorcycles

abstract: The use of brushless permanent magnet DC drive motors in racing motorcycles is discussed in this paper. The application requirements are highlighted and the characteristics of the load demand and drive converter outlined. The possible topologies of the machine are investigated and a design for a internal permanent magnet is developed. This is a 6-pole machine with 18 stator slots and coils of one stator tooth pitch. The performance predictions are put forward and these are obtained from design software. Cooling is vital for these machines and this is briefly discussed.


title: Reducing Mutual Coupling of MIMO Antennas With Parasitic Elements for Mobile Terminals

abstract: Mutual coupling is a critical problem in the design of MIMO antennas because it deteriorates the performance of MIMO systems, which not only affects the antenna efficiency but also influences the correlation. Therefore, in this paper, using parasitic elements to reduce mutual coupling is studied. By adding parasitic elements a double-coupling path is introduced and it can create a reverse coupling to reduce mutual coupling. As an example, a dual-slot-element antenna with parasitic monopoles for mobile terminals is described. The discussion on channel capacity shows that the antenna can be considered as a good candidate for MIMO systems. Furthermore, based on the study of current distributions, it is concluded that the technique is sensitive to relative positions between parasitic elements, and relative positions between active element and parasitic element. Finally, we also extend the technique to a tri-element antenna.


Legal ethics : the law of professional responsibility


title: The Internet of Things Has a Gateway Problem

abstract: The vision of an Internet of Things (IoT) has captured the imagination of the world and raised billions of dollars, all before we stopped to deeply consider how all these Things should connect to the Internet. The current state-of-the-art requires application-layer gateways both in software and hardware that provide application-specific connectivity to IoT devices. In much the same way that it would be difficult to imagine requiring a new web browser for each website, it is hard to imagine our current approach to IoT connectivity scaling to support the IoT vision. The IoT gateway problem exists in part because today's gateways conflate network connectivity, in-network processing, and user interface functions. We believe that disentangling these functions would improve the connectivity potential for IoT devices. To realize the broader vision, we propose an architecture that leverages the increasingly ubiquitous presence of Bluetooth Low Energy radios to connect IoT peripherals to the Internet. In much the same way that WiFi access points revolutionized laptop utility, we envision that a worldwide deployment of IoT gateways could revolutionize application-agnostic connectivity, thus breaking free from the stove-piped architectures now taking hold. In this paper, we present our proposed architecture, show example applications enabled by it, and explore research challenges in its implementation and deployment.


title: Online VNF Scaling in Datacenters

abstract: Network Function Virtualization (NFV) is a promising technology that promises to significantly reduce the operational costs of network services by deploying virtualized network functions (VNFs) to commodity servers in place of dedicated hardware middleboxes. The VNFs are typically running on virtual machine instances in a cloud infrastructure, where the virtualization technology enables dynamic provisioning of VNF instances, to process the fluctuating traffic that needs to go through the network functions in a network service. In this paper, we target dynamic provisioning of enterprise network services - expressed as one or multiple service chains - in cloud datacenters, and design efficient online algorithms without requiring any information on future traffic rates. The key is to decide the number of instances of each VNF type to provision at each time, taking into consideration the server resource capacities and traffic rates between adjacent VNFs in a service chain. In the case of a single service chain, we discover an elegant structure of the problem and design an efficient randomized algorithm achieving a e/(e-1) competitive ratio. For multiple concurrent service chains, an online heuristic algorithm is proposed, which is O(1)-competitive. We demonstrate the effectiveness of our algorithms using solid theoretical analysis and trace-driven simulations.


title: Designing for interaction immediacy to enhance social skills of children with autism

abstract: Children with Autism Spectrum Disorder often require therapeutic interventions to support engagement in effective social interactions. In this paper, we present the results of a study conducted in three public schools that use an educational and behavioral intervention for the instruction of social skills in changing situational contexts. The results of this study led to the concept of interaction immediacy to help children maintain appropriate spatial boundaries, reply to conversation initiators, disengage appropriately at the end of an interaction, and identify potential communication partners. We describe design principles for Ubicomp technologies to support interaction immediacy and present an example design. The contribution of this work is twofold. First, we present an understanding of social skills in mobile and dynamic contexts. Second, we introduce the concept of interaction immediacy and show its effectiveness as a guiding principle for the design of Ubicomp applications.


title: RSA algorithm with a new approach encryption and decryption message text by ascii

abstract: In many research works, there has been an orientation to studying and developing many of the applications of public-key cryptography to secure the data while transmitting in the systems, In this paper we present an approach to encrypt and decrypt the message text according to the ASCII(American Standard Code for Information Interchange) and RSA algorithm by converting the message text into binary representation and dividing this representation to bytes(8s of 0s and 1s) and applying a bijective function between the group of those bytes and the group of characters of ASCII and then using this mechanism to be compatible with using RSA algorithm, finally, Java application was built to apply this approach directly.


title: Legislative prescriptions for controlling nonmedical vaccine exemptions.

abstract: This Viewpoint discusses types of exemptions from required vaccines and recent legislation to control numbers of nonmedical vaccine exemptions.


title: Semantic mapping at the schema level of XML documents to ontologies

abstract: XML is considered as the most widely used standard to represent, structure and share data via the Web. XML Schema and DTD ensure the content structuring of XML documents; these languages are used to determine the grammar to use in XML instances in order to describe a given domain. However, the requirements of the semantic Web are beyond what is syntactic and structural, and focus increasingly to the semantics, something that cannot be represented by XML and its derivatives and which is perfectly achievable by the ontology languages such as RDF and OWL. In this paper, we propose an automatic method for generating domain ontology from XML grammars by performing a mapping at the schema level.


title: Application-Oriented License Plate Recognition

abstract: We split the applications of vehicle license plate recognition (LPR) into three major categories and propose a solution with parameter settings that are adjustable for different applications. The three categories are access control (AC), law enforcement (LE), and road patrol (RP). Each application is characterized by variables of different variation scopes and thus requires different settings on the solution with which to deal. The proposed solution consists of three modules for plate detection, character segmentation, and recognition. Edge clustering is formulated for solving plate detection for the first time. It is also a novel application of the maximally stable extreme region (MSER) detector to character segmentation. A bilayer classifier, which is improved with an additional null class, is experimentally proven to be better than previous methods for character recognition. To assess the performance of the proposed solution, the application-oriented license plate (AOLP) database is composed and made available to the research community. Experiments show that the proposed solution outperforms many previous solutions, and LPR can be better solved by solutions with settings oriented for different applications.


title: A psychometric analysis of the trait emotional intelligence questionnaire-short form (TEIQue-SF) using item response theory

abstract: Trait emotional intelligence refers to a constellation of emotional self-perceptions located at the lower levels of personality hierarchies. In 2 studies, we sought to examine the psychometric properties of the Trait Emotional Intelligence Questionnaire-Short Form (TEIQue-SF; Petrides, 2009) using item response theory (IRT). Study 1 (N= 1,119, 455 men) showed that most items had good discrimination and threshold parameters and high item information values. At the global level, the TEIQue-SF showed very good precision across most of the latent trait range. Study 2 (N= 866, 432 men) used similar IRT techniques in a new sample based on the latest version of the TEIQue-SF (version 1.50). Results replicated Study 1, with the instrument showing good psychometric properties at the item and global level. Overall, the 2 studies suggest the TEIQue-SF can be recommended when a rapid assessment of trait emotional intelligence is required.


title: The Brain Binds Entities and Events by Multiregional Activation from Convergence Zones

abstract: The experience of reality, in both perception and recall, is spatially and temporally coherent and “in-register.” Features are bound in entities, and entities are bound in events. The properties of these entities and events, however, are represented in many different regions of the brain that are widely separated. The degree of neural parcellation is even greater when we consider that the perception of most entities and events also requires a motor interaction on the part of the perceiver (such as eye movements and hand movements) and often includes a recordable modification of the perceiver's somatic state. The question of how the brain achieves integration starting with the bits and pieces it has to work with, is the binding problem. Here we propose a new solution for this problem, at the level of neural systems that integrate functional regions of the telencephalon.


title: Designing Domain Specific Word Embeddings: Applications to Disease Surveillance.

abstract: Traditional disease surveillance can be augmented with a wide variety of realtime sources such as news and social media. However, these sources are in general unstructured and construction of surveillance tools such as taxonomical correlations and trace mapping involves considerable human supervision. In this paper, we motivate a disease vocabulary driven word2vec model (Dis2Vec) which we use to model diseases and constituent attributes as word embeddings from the HealthMap news corpus. We use these word embeddings to create disease taxonomies and evaluate our model accuracy against human annotated taxonomies. We compare our accuracies against several state-of-the art word2vec methods. Our results demonstrate that Dis2Vec outperforms traditional distributed vector representations in its ability to faithfully capture disease attributes and accurately forecast outbreaks.


Heuristics for the Traveling Salesman Problem


title: A Novel 2.5-Dimensional Ultraminiaturized-Element Frequency Selective Surface

abstract: This paper proposes fabricating a new 2.5-dimensional ultraminiaturized element on a cost-effective printed circuit board to build a frequency selective surface (FSS). The proposed element consists of two main parts: a planar tapered meandering line (PTML) and a vertical via-based meandering line (VVML). Compared with previous published two-dimensional miniaturized elements, the proposed element is smaller (only 3.3% of the free space wavelength at the resonant frequency) and exhibits high resonant stability at various polarizations and incidence angles (only 0.4% deviation at the resonant frequency when the incident angle is as great as 75°). In particular, the introduction of VVML in the element substantially improves the FSS performance. A third-order equivalent circuit model was established to model the FSS performance precisely. This model can be reduced to a simpler first-order model, thus enabling the resonant frequency of the FSS to be predicted quickly. A prototype of the ultraminiaturized-element FSS was created and examined. The results of the proposed models, full-wave simulation, and measurement exhibited satisfactory consistency.


title: The Young Adult Human Brain: An MRI-based Morphometric Analysis

abstract: A potato planter having a plurality of feed tubes attached to at least one central hopper and a plurality of trailing wheels which support the planter during turning is disclosed. The novel planter is further characterized by having a pair of outboard hoppers one each attached to the outer most hopper of the central hoppers (which may be one large hopper with separated bottom bins) and outboard of the main support wheels. The potato planter preferaby has three trailing wheels wherein a pair of adjacent wheels is interconnected hydraulically through hydraulic cylinders to cause said wheels to interact mutually in regard to the terrain. The planter preferably contains an agitator for the main hopper and a secondary feed hopper which has a vibrator attached thereto for feeding potatoes to the main conveyor which transports the potatoes upwardly from the feed hopper and then downwardly to the planting position. The planter further utilizes a small spliced conveyor belt with internal metal inserts to transport potatoes from the main hopper to the smaller feed hopper. An alternative feature of the planter is the utilization of a recessed main bar which supports the forward main support wheels. Also, the trailing wheels are free to rotate, casterfashion, about an axle slightly inclining from the vertical.


title: Current Approaches to Tactical Performance Analyses in Soccer Using Position Data

abstract: Tactical match performance depends on the quality of actions of individual players or teams in space and time during match-play in order to be successful. Technological innovations have led to new possibilities to capture accurate spatio-temporal information of all players and unravel the dynamics and complexity of soccer matches. The main aim of this article is to give an overview of the current state of development of the analysis of position data in soccer. Based on the same single set of position data of a high-level 11 versus 11 match (Bayern Munich against FC Barcelona) three different promising approaches from the perspective of dynamic systems and neural networks will be presented: Tactical performance analysis revealed inter-player coordination, inter-team and inter-line coordination before critical events, as well as team-team interaction and compactness coefficients. This could lead to a multi-disciplinary discussion on match analyses in sport science and new avenues for theoretical and practical implications in soccer.


title: An all-digital ADC/TDC for sensor interface with TAD architecture in 0.18-µm digital CMOS

abstract: An analog-to-digital and time-to-digital converter using a common architecture (Time A/D converter TAD) is presented. Its resolutions for both analog-to-digital converter (ADC) mode and time-to-digital converter (TDC) mode are settable. The circuit structure is a completely digital circuit including a ring-shaped pulse-delay-line (RDL) driven by an input voltage V in , along with an RDL synchronous counter, latch, and encoder. A prototype TAD-IC core of 0.044 mm2 in a 0.18-µm digital CMOS achieved 3.1 mV/LSB (8-bit, 20-MS/s, 1.7 mW), 15 µV/LSB (16-bit, 100-kS/s, 1.3 mW) in ADC mode, and 126 ps/LSB (V in = 1.8 V, 25-bit), 368 ps/LSB (V in = 1.0 V, 25-bit) in TDC mode, respectively. As an actual example of high-resolution ADC, a radio-controlled clock (RCC) receiver IC prototype is implemented with a minimum detectable sensitivity of 0.7 µVrms.


title: Beyond Hellman’s Time-Memory Trade-Offs with Applications to Proofs of Space

abstract: Proofs of space (PoS) were suggested as more ecological and economical alternative to proofs of work, which are currently used in blockchain designs like Bitcoin. The existing PoS are based on rather sophisticated graph pebbling lower bounds. Much simpler and in several aspects more efficient schemes based on inverting random functions have been suggested, but they don’t give meaningful security guarantees due to existing time-memory trade-offs.


title: Beyond Gaussian Pyramid: Multi-skip Feature Stacking for action recognition

abstract: Most state-of-the-art action feature extractors involve differential operators, which act as highpass filters and tend to attenuate low frequency action information. This attenuation introduces bias to the resulting features and generates ill-conditioned feature matrices. The Gaussian Pyramid has been used as a feature enhancing technique that encodes scale-invariant characteristics into the feature space in an attempt to deal with this attenuation. However, at the core of the Gaussian Pyramid is a convolutional smoothing operation, which makes it incapable of generating new features at coarse scales. In order to address this problem, we propose a novel feature enhancing technique called Multi-skIp Feature Stacking (MIFS), which stacks features extracted using a family of differential filters parameterized with multiple time skips and encodes shift-invariance into the frequency space. MIFS compensates for information lost from using differential operators by recapturing information at coarse scales. This recaptured information allows us to match actions at different speeds and ranges of motion. We prove that MIFS enhances the learnability of differential-based features exponentially. The resulting feature matrices from MIFS have much smaller conditional numbers and variances than those from conventional methods. Experimental results show significantly improved performance on challenging action recognition and event detection tasks. Specifically, our method exceeds the state-of-the-arts on Hollywood2, UCF101 and UCF50 datasets and is comparable to state-of-the-arts on HMDB51 and Olympics Sports datasets. MIFS can also be used as a speedup strategy for feature extraction with minimal or no accuracy cost.


title: Location-based Mobile Augmented Reality Applications - Challenges, Examples, Lessons Learned

abstract: The technical capabilities of modern smart mobile devices more and more enable us to run desktop-like applications with demanding resource requirements in mobile environments. Along this trend, numerous concepts, techniques, and prototypes have been introduced, focusing on basic implementation issues of mobile applications. However, only little work exists that deals with the design and implementation (i.e., the engineering) of advanced smart mobile applications and reports on the lessons learned in this context. In this paper, we give profound insights into the design and implementation of such an ::: advanced mobile application, which enables location-based mobile augmented reality on two different mobile operating systems (i.e., iOS and Android). In particular, this kind of mobile application is characterized by high resource ::: demands since various sensors must be queried at run time and numerous virtual objects may have to be drawn in realtime on the screen of the smart mobile device (i.e., a high frame count per second be caused). We focus on the ::: efficient implementation of a robust mobile augmented reality engine, which provides location-based functionality, as well as the implementation of mobile business applications based on this engine. In the latter context, we also discuss the lessons learned when implementing mobile business applications with our mobile augmented reality engine.


" Wayfinding in Hospitals : Solving the Maze "


title: Crystallization Behavior of Mannitol in Frozen Aqueous Solutions

abstract: PURPOSE ::: To study the effect of cooling rate, the influence of phosphate buffers and polyvinylpyrrolidone (PVP) on the crystallization behavior of mannitol in frozen aqueous solutions. ::: ::: ::: METHODS ::: Low-temperature differential scanning calorimetry and powder X-ray diffractometry were used to characterize the frozen solutions. ::: ::: ::: RESULTS ::: Rapid cooling (20 degrees C/min) inhibited mannitol crystallization, whereas at slower cooling rates (10 degrees C and 5 degrees C/min) partial crystallization was observed. The amorphous freeze-concentrate was characterized by two glass transitions at -32 degrees C and -25 degrees C. When the frozen solutions were heated past the two glass transition temperatures, the solute crystallized as mannitol hydrate. An increase in the concentration of PVP increased the induction time for the crystallization of mannitol hydrate. At concentrations of > or =100 mM, the buffer salts significantly inhibited mannitol crystallization. ::: ::: ::: CONCLUSIONS ::: The crystallization behavior of mannitol in frozen solutions was influenced by the cooling rate and the presence of phosphate buffers and PVP.


title: Stimulus-Related Oscillatory Responses in the Auditory Cortex of Cats

abstract: The electroencephalographic oscillations in the higher frequency band have been the subject of intense scientific interest in the past few years. The higher frequency synchronous oscillation, called gamma band, has been extensively studied in the olfactory, visual, somatosensory and auditory system.


title: HARDNESS PROPERTIES OF PLASTIC OPTICAL FIBERS BY NANOINDENTATION NANOİZ TESTİ İLE PLASTİK OPTİK LİFLERİN ÖZELLİKLERİNİN İNCELENMESİ

abstract: Mechanical properties are significant for plastic optical fibers in weaving or knitting processes in textile applications. Both core and cladding in plastic optical fiber contribute to the mechanical properties. Nanoindentation is a promising method to investigate the mechanical properties (hardness, stiffness and Young's modulus) in nanoscale displacement and small load range. Nanoindentation creep as the highly time-dependent deformation has a significant effect on nanoindentation properties of polymeric materials. In present work, core and cladding in both latitudinal and longitudinal cross sections of plastic optical fibers with four diameters were investigated in different conditions (loading rates and holding times) under the maximum load of 0.3 mN. The results show that cladding is softer than core and both strong loading rate and holding time sensitivities on nanoindentation creep no matter in latitudinal or longitudinal cross sections. It is also found that the greater the fiber diameter, the higher the hardness and modulus.


title: Modeling of N-i-P Vs. P-i-N InGaN solar cells with ultrathin GaN interlayers for improved performance

abstract: P-i-N structure solar cells often provide improved performance over N-i-P devices because acceptors are easier to activate when the p-type layer is close to the surface. However, for strained InGaN solar cells on GaN, the polarization-induced electric field creates a barrier for photocurrent that impedes device performance. In this paper we show that for Ga-face growth, N-i-P structures can provide improved performance because the electric field from the junction is parallel to that formed from polarization induced sheet charges. Thus the fields complement each other to assist in creating photocurrent in N-i-P devices. Additionally we simulate an N-i-P cell using the recently demonstrated insertion of ultra-thin GaN interlayers to achieve thick strained layers with high material quality.


title: Probability Analysis of Crane Load and Load Combination Actions

abstract: This study presents probability models of crane load and load combination actions for reliability analysis of the industry buildings. Crane load values are simplified here only varying in time, and load combination actions only vary in space. With the feasible survey program and K-S test, the Gumbel distribution is chosen as the probability distribution of the arbitrary point-in-time crane load values with the statistical investigation data. With the simple stationary binomial random process model hypothesis and the block maxima method, it determined that the maximum value of crane load during the design reference period also follows the Gumbel distribution, with the peaks-over-threshold method evaluation. For multiple crane load combination actions, the modified Turkstra rule is improved to determine the probability distribution of the actions as the Gumbel distribution by considering the occurrence probability, numbers, location, and values of each crane load acting on the influence line, evaluated by the Monte Carlo simulations. Design standard values for crane loads and load combination actions, as specified in building codes, are evaluated based on these distributions in probability significance. The calculation results illustrate that the design standard values for crane loads are relatively safe and conservative.


title: Application of linearization via state feedback to heading control for Autonomous Underwater Vehicle

abstract: Most designs for heading controller adopted Taylor linearization technique and neglected its nonlinear factors. For this reason, the accuracy of heading control for autonomous underwater vehicle (AUV) was lower. In order to solve this problem and enhance the accuracy, robustness and stability for heading control system, a nonlinear model of AUV in horizontal plane was established, which include uncertainties. Based on linearization via state feedback, a linear model of AUV was obtained at the operating point, and a robust heading controller was designed with closed-loop gain shaping algorithm. The computer simulation was used to demonstrate the robust controller designed via state feedback, has a better performance than the controller, which based on a linear model using Taylor linearization technique.


title: Skill Progression Demonstrated by Users in the Scratch Animation Environment

abstract: The Scratch environment exemplifies a tool+community approach to teaching elementary programming skills, as it includes a website where users can publish, discuss, and organize animations that are programs. To explore this environment's effectiveness for helping people to develop programming skills, a quantitative analysis of 250 randomly selected users' data, including more than 1,000 of their animations, was performed. Skill based on 4 models that had proven useful in prior empirical studies was measured. Overall, mixed results about the environment's effectiveness were found. Among users who do not drop out, an increasing progression in social skills was found. However, an extremely high drop-out rate was also observed. Moreover, a flat or decreasing level of demonstrated skill was observed on virtually every measure. These results call into question whether simply combining an animation tool and an online community is sufficient for keeping people engaged long enough to learn elementary programming sk...


title: For whom is a picture worth a thousand words? Extensions of a dual-coding theory of multimedia learning.

abstract: In 2 experiments, high- and low-spatial ability students viewed a computer-gener ated animation and listened simultaneously (concurrent group) or successively (successive group) to a narration that explained the workings either of a bicycle tire pump (Experiment 1) or of the human respiratory system (Experiment 2). The concurrent group generated more creative solutions to subsequent transfer problems than did the successive group; this contiguity effect was strong for high- but not for low-spatial ability students. Consistent with a dual-coding theory, spatial ability allows high-spatial learners to devote more cognitive resources to building referential connections between visual and verbal representations of the presented material, whereas low-spatial ability learners must devote more cognitive resources to building representation connections between visually presented material and its visual representation.


title: A self-correcting projector

abstract: We describe a calibration and rendering technique for a projector that can render rectangular images under keystoned position. The projector utilizes a rigidly attached camera to form a stereo pair. We describe a very easy to use technique for calibration of the projector-camera pair using only black planar surfaces. We present an efficient rendering method to pre-warp images so that they appear correctly on the screen, and show experimental results.


title: An FPGA-based floating-point Jacobi iterative solver

abstract: Within the parallel computing domain, field programmable gate arrays (FPGA) are no longer restricted to their traditional role as substitutes for application-specific integrated circuits-as hardware "hidden" from the end user. Several high performance computing vendors offer parallel re configurable computers employing user-programmable FPGAs. These exciting new architectures allow end-users to, in effect, create reconfigurable coprocessors targeting the computationally intensive parts of each problem. The increased capability of contemporary FPGAs coupled with the embarrassingly parallel nature of the Jacobi iterative method make the Jacobi method an ideal candidate for hardware acceleration. This paper introduces a parameterized design for a deeply pipelined, highly parallelized IEEE 64-bit floating-point version of the Jacobi method. A Jacobi circuit is implemented using a Xilinx Virtex-II Pro as the target FPGA device. Implementation statistics and performance estimates are presented.


title: Articulated Billboards for Video-based Rendering

abstract: We present a novel representation and rendering method for free-viewpoint video of human characters based on multiple input video streams. The basic idea is to approximate the articulated 3D shape of the human body using a subdivision into textured billboards along the skeleton structure. Billboards are clustered to fans such that each skeleton bone contains one billboard per source camera. We call this representation articulated billboards. ::: ::: ::: ::: In the paper we describe a semi-automatic, data-driven algorithm to construct and render this representation, which robustly handles even challenging acquisition scenarios characterized by sparse camera positioning, inaccurate camera calibration, low video resolution, or occlusions in the scene. First, for each input view, a 2D pose estimation based on image silhouettes, motion capture data, and temporal video coherence is used to create a segmentation mask for each body part. Then, from the 2D poses and the segmentation, the actual articulated billboard model is constructed by a 3D joint optimization and compensation for camera calibration errors. The rendering method includes a novel way of blending the textural contributions of each billboard and features an adaptive seam correction to eliminate visible discontinuities between adjacent billboards textures. ::: ::: ::: ::: Our articulated billboards do not only minimize ghosting artifacts known from conventional billboard rendering, but also alleviate restrictions to the setup and sensitivities to errors of more complex 3D representations and multiview reconstruction techniques. Our results demonstrate the flexibility and the robustness of our approach with high quality free-viewpoint video generated from broadcast footage of challenging, uncontrolled environments.


title: Decoding signalling networks by mass spectrometry-based proteomics

abstract: Signalling networks regulate the biology of cells and organisms in normal and disease states. Large-scale 'precision proteomics' based on mass spectrometry now enables the system-wide characterization of signalling events, including the quantitative changes of thousands of proteins and their post-translational modifications, in response to any perturbation.


title: Cross-modality correlation propagation for cross-media retrieval

abstract: We consider the problem of cross-media retrieval, where the query and the retrieved results can be of different modalities. In this paper, we propose a novel cross-modality correlation propagation approach to simultaneously deal with positive correlation and negative correlation between media objects of different modalities, while existing works focus solely on the positive correlation. Negative correlation is very important because it provides the effective exclusive information. The correlation is modeled as must-link constraints and cannot-link constraints respectively. Furthermore, our approach is able to propagate the correlation between heterogeneous modalities. Experiments on the wikipedia dataset show the effectiveness of our cross-modality correlation propagation approach, compared with state-of-the-art methods.


title: Bayesian Rose Trees

abstract: Hierarchical structure is ubiquitous in data across many domains. There are many hierarchical clustering methods, frequently used by domain experts, which strive to discover this structure. However, most of these methods limit discoverable hierarchies to those with binary branching structure. This limitation, while computationally convenient, is often undesirable. In this paper we explore a Bayesian hierarchical clustering algorithm that can produce trees with arbitrary branching structure at each node, known as rose trees. We interpret these trees as mixtures over partitions of a data set, and use a computationally efficient, greedy agglomerative algorithm to find the rose trees which have high marginal likelihood given the data. Lastly, we perform experiments which demonstrate that rose trees are better models of data than the typical binary trees returned by other hierarchical clustering algorithms.


title: HideM: Protecting the Contents of Userspace Memory in the Face of Disclosure Vulnerabilities

abstract: Memory disclosure vulnerabilities have become a common component for enabling reliable exploitation of systems by leaking the contents of executable data. Previous research towards protecting executable data from disclosure has failed to gain popularity due to large performance penalties and required architectural changes. Other research has focused on protecting application data but fails to consider a vulnerable application that leaks its own executable data. In this paper we present HideM, a practical system for protecting against memory disclosures in contemporary commodity systems. HideM addresses limitations in existing advanced security protections (e.g., fine-grained ASLR, CFI) wherein an adversary discloses executable data from memory, reasons about protection weaknesses, and builds corresponding exploits. HideM uses the split-TLB architecture, commonly found in CPUs, to enable fine-grained execute and read permission on memory. HideM enforces fine-grained permission based on policy generated from binary structure thus enabling protection of Commercial-Off-The-Shelf (COTS) binaries. In our evaluation of HideM, we find application overhead ranges from a 6.5% increase to a 2% reduction in runtime and observe runtime memory overhead ranging from 0.04% to 25%. HideM requires adversaries to guess ROP gadget locations making exploitation unreliable. We find adversaries have less than a 16% chance of correctly guessing a single gadget across all 28 evaluated applications. Thus, HideM is a practical system for protecting vulnerable applications which leak executable data.


title: Training Neural Network Language Models On Very Large Corpora

abstract: During the last years there has been growing interest in using neural networks for language modeling. In contrast to the well known back-off n-gram language models, the neural network approach attempts to overcome the data sparseness problem by performing the estimation in a continuous space. This type of language model was mostly used for tasks for which only a very limited amount of in-domain training data is available.In this paper we present new algorithms to train a neural network language model on very large text corpora. This makes possible the use of the approach in domains where several hundreds of millions words of texts are available. The neural network language model is evaluated in a state-of-the-art real-time continuous speech recognizer for French Broadcast News. Word error reductions of 0.5% absolute are reported using only a very limited amount of additional processing time.


title: Echo state networks with filter neurons and a delay&sum readout

abstract: Abstract Echo state networks (ESNs) are a novel approach to recurrent neural network training with the advantage of a very simple and linear learning algorithm. It has been demonstrated that ESNs outperform other methods on a number of benchmark tasks. Although the approach is appealing, there are still some inherent limitations in the original formulation. Here we suggest two enhancements of this network model. First, the previously proposed idea of filters in neurons is extended to arbitrary infinite impulse response (IIR) filter neurons. This enables such networks to learn multiple attractors and signals at different timescales, which is especially important for modeling real-world time series. Second, a delay&sum readout is introduced, which adds trainable delays in the synaptic connections of output neurons and therefore vastly improves the memory capacity of echo state networks. It is shown in commonly used benchmark tasks and real-world examples, that this new structure is able to significantly outperform standard ESNs and other state-of-the-art models for nonlinear dynamical system modeling.


title: Personality and risk-taking: common biosocial factors

abstract: The first part of this article describes a study of the relationships between personality and risk-taking in six areas: smoking, drinking, drugs, sex, driving, and gambling. The participants, 260 college students, were given self-report measures of risky behaviors in each of the six areas and the Zuckerman- Kuhlman five-factor personality questionnaire. Generalized risk-taking (across all six areas) was related to scales for impulsive sensation seeking, aggression, and sociability, but not to scales for neuroticism or activity. Gender differences on risk-taking were mediated by differences on impulsive sensation seeking. The second part discusses biological traits associated with both risk-taking and personality, particularly sensation seeking, such as the D4 dopamine receptor gene, the enzyme monoamine oxidase, and augmenting or reducing of the cortical evoked potential. Comparative studies show relationships between biological markers shared with other species and correlated behaviors similar to sensation seeking in humans. A biosocial model of the traits underlying risk-taking is presented. Many of life's decisions involve a balance between anticipated reward and risk. The wild rodent venturing into an open field balances the possibility of finding food against the chance of being devoured by a predator on the ground or from the sky. The married man or woman who


title: UIMA: an architectural approach to unstructured information processing in the corporate research environment

abstract: IBM Research has over 200 people working on Unstructured Information Management (UIM) technologies with a strong focus on Natural Language Processing (NLP). These researchers are engaged in activities ranging from natural language dialog, information retrieval, topic-tracking, named-entity detection, document classification and machine translation to bioinformatics and open-domain question answering. An analysis of these activities strongly suggested that improving the organization's ability to quickly discover each other's results and rapidly combine different technologies and approaches would accelerate scientific advance. Furthermore, the ability to reuse and combine results through a common architecture and a robust software framework would accelerate the transfer of research results in NLP into IBM's product platforms. Market analyses indicating a growing need to process unstructured information, specifically multilingual, natural language text, coupled with IBM Research's investment in NLP, led to the development of middleware architecture for processing unstructured information dubbed UIMA. At the heart of UIMA are powerful search capabilities and a data-driven framework for the development, composition and distributed deployment of analysis engines. In this paper we give a general introduction to UIMA focusing on the design points of its analysis engine architecture and we discuss how UIMA is helping to accelerate research and technology transfer.


title: Fixed point semantics and partial recursion in Coq

abstract: We propose to use Knaster-Tarski least fixed point theorem as a basis to define recursive functions in the Calculus of Inductive Constructions. This widens the class of functions that can be modeled in type-theory based theorem proving tools to potentially non-terminating functions. This is only possible if we extend the logical framework by adding some axioms of classical logic. We claim that the extended framework makes it possible to reason about terminating or non-terminating computations and we show that extraction can also be extended to handle the new functions.


title: A Novel Reconfigurable Microgrid Architecture With Renewable Energy Sources and Storage

abstract: This paper proposes a novel reconfigurable microgrid architecture comprising photovoltaic, wind, microhydro, and fuel cell based renewable energy sources. Transient and extended power backup are provided with ultra-capacitor (UC) and battery storage, respectively. A distinguishing feature of this microgrid is that its various control layers can switch roles during emergency. As renewable energy sources with significantly different dynamic behavior and dissimilar generating capacities are involved, disruptive effects such as voltage dips and fluctuations, frequency variation, and harmonic distortion are likely. This is handled through centralized monitoring in conjunction with hierarchical control. The reliability and sustainability of the resulting complex microgrid architecture is ensured through the proposed reconfigurable control and power network of the microgrid, supported by a hybrid communication layer comprising CAN, RS-485, and MODBUS protocols. The proposed architecture is strengthened by an additional “advisory” layer comprising an advisory controller that supports long-term optimization of microgrid operation under normal conditions and governs interim role assignments to control layers during crisis. All these special features ensure continuity of power with sustained performance or gracefully degraded performance even under various kinds of faults and abnormal events. Design of various controllers, protection, and reconfigurability features with relevant analysis and experimental results are presented.


title: Towards 3D point cloud based object maps for household environments. Robotics and Autonomous Systems

abstract: This article investigates the problem of acquiring 3D object maps of indoor household environments, in particular kitchens. The objects modeled in these maps include cupboards, tables, drawers and shelves, which are of particular importance for a household robotic assistant. Our mapping approach is based on PCD (point cloud data) representations. Sophisticated interpretation methods operating on these representations eliminate noise and resample the data without deleting the important details, and interpret the improved point clouds in terms of rectangular planes and 3D geometric shapes. We detail the steps of our mapping approach and explain the key techniques that make it work. The novel techniques include statistical analysis, persistent histogram features estimation that allows for a consistent registration, resampling with additional robust fitting techniques, and segmentation of the environment into meaningful regions.


Bayesian Network Classifiers


title: PROCESS MINING IN HEALTHCARE A Case

abstract: To gain competitive advantage, hospitals try to streamline their processes. In order to do so, it is essential to have an accurate view of the “careflows” under consideration. In this paper, we apply process mining techniques to obtain meaningful knowledge about these flows, e.g., to discover typical paths followed by particular groups of patients. This is a non-trivial task given the dynamic nature of healthcare processes. The paper demonstrates the applicability of process mining using a real case of a gynecological oncology process in a Dutch hospital. Using a variety of process mining techniques, we analyzed the healthcare process from three different perspectives: (1) the control flow perspective, (2) the organizational perspective and (3) the performance perspective. In order to do so we extracted relevant event logs from the hospitals information system and analyzed these logs using the ProM framework. The results show that process mining can be used to provide new insights that facilitate the improvement of existing careflows.


title: Generating Better Queries for Systematic Reviews

abstract: Systematic reviews form the cornerstone of evidence based medicine, aiming to answer complex medical questions based on all evidence currently available. Key to the effectiveness of a systematic review is an (often large) Boolean query used to search large publication repositories. These Boolean queries are carefully crafted by researchers and information specialists, and often reviewed by a panel of experts. However, little is known about the effectiveness of the Boolean queries at the time of formulation. In this paper we investigate whether a better Boolean query than that defined in the protocol of a systematic review, can be created, and we develop methods for the transformation of a given Boolean query into a more effective one. Our approach involves defining possible transformations of Boolean queries and their clauses. It also involves casting the problem of identifying a transformed query that is better than the original into: (i) a classification problem; and (ii) a learning to rank problem. Empirical experiments are conducted on a real set of systematic reviews. Analysis of results shows that query transformations that are better than the original queries do exist, and that our approaches are able to select more effective queries from the set of possible transformed queries so as to maximise different target effectiveness measures.


title: Wi-Sleep: Contactless Sleep Monitoring via WiFi Signals

abstract: Is it possible to leverage WiFi signals collected in bedrooms to monitor a person's sleep? In this paper, we show that with off-the-shelf WiFi devices, fine-grained sleep information like a person's respiration, sleeping postures and rollovers can be successfully extracted. We do this by introducing Wi-Sleep, the first sleep monitoring system based on WiFi signals. Wi-Sleep adopts off-the-shelf WiFi devices to continuously collect the fine-grained wireless channel state information (CSI) around a person. From the CSI, Wi-Sleep extracts rhythmic patterns associated with respiration and abrupt changes due to the body movement. Compared to existing sleep monitoring systems that usually require special devices attached to human body (i.e. Probes, head belt, and wrist band), Wi-Sleep is completely contact less. In addition, different from many vision-based sleep monitoring systems, Wi-Sleep is robust to low-light environments and does not raise privacy concerns. Preliminary testing results show that the Wi-Sleep can reliably track a person's respiration and sleeping postures in different conditions.


title: MPIIGaze: Real-World Dataset and Deep Appearance-Based Gaze Estimation

abstract: Learning-based methods are believed to work well for unconstrained gaze estimation, i.e. gaze estimation from a monocular RGB camera without assumptions regarding user, environment, or camera. However, current gaze datasets were collected under laboratory conditions and methods were not evaluated across multiple datasets. Our work makes three contributions towards addressing these limitations. First, we present the MPIIGaze dataset, which contains 213,659 full face images and corresponding ground-truth gaze positions collected from 15 users during everyday laptop use over several months. An experience sampling approach ensured continuous gaze and head poses and realistic variation in eye appearance and illumination. To facilitate cross-dataset evaluations, 37,667 images were manually annotated with eye corners, mouth corners, and pupil centres. Second, we present an extensive evaluation of state-of-the-art gaze estimation methods on three current datasets, including MPIIGaze. We study key challenges including target gaze range, illumination conditions, and facial appearance variation. We show that image resolution and the use of both eyes affect gaze estimation performance, while head pose and pupil centre information are less informative. Finally, we propose GazeNet, the first deep appearance-based gaze estimation method. GazeNet improves on the state of the art by 22 percent (from a mean error of 13.9 degrees to 10.8 degrees) for the most challenging cross-dataset evaluation.


title: Dual-band rectenna for ambient RF energy harvesting at GSM 900 MHz and 1800 MHz

abstract: This paper presents a novel dual-band rectenna for RF energy harvesting system. This rectenna is created from a dual-band antenna and a dual-band rectifier which operates at GSM bands (900 MHz and 1800 MHz). The printed monopole antenna is miniaturized by two meander-lines. The received signal from the receiving antenna is rectified by a voltage doubler using Schottky diode SMS-7630. The rectifier is optimized for low input power level of −20dBm using harmonic balance. Prototype is designed and fabricated. The simulation is validated by measurement with power conversion efficiency of 20% and 40.8% (in measurement) at the input power level of −20dBm. The proposed rectenna has output voltage from 183–415 mV. From the measured results, this rectenna provides the possibility to harvest the ambient electromagnetic energy for powering low-power electronic devices.


title: Reviving the past: cultural heritage meets virtual reality

abstract: The use of immersive virtual reality (VR) systems in museums is a recent trend, as the development of new interactive technologies has inevitably impacted the more traditional sciences and arts. This is more evident in the case of novel interactive technologies that fascinate the broad public, as has always been the case with virtual reality. The increasing development of VR technologies has matured enough to expand research from the military and scientific visualization realm into more multidisciplinary areas, such as education, art and entertainment. This paper analyzes the interactive virtual environments developed at an institution of informal education and discusses the issues involved in developing immersive interactive virtual archaeology projects for the broad public.


title: Economic Attention Networks: Associative Memory and Resource Allocation for General Intelligence

abstract: A novel method for simultaneously storing memories and allocating resources in AI systems is presented. The method, Economic Attention Networks (ECANs), bears some resemblance to the spread of activation in attractor neural networks, but differs via explicitly differentiating two kinds of “activation” (Short Term Importance, related to processor allocation; and Long Term Importance, related to memory allocation), and in using equations that are based on ideas from economics rather than approximative neural modeling. Here we explain the basic ideas of ECANs, and then investigate the functionality of ECANs as associative memories, via mathematical analysis and the reportage of experimental results obtained from the implementation of ECANs in the OpenCog integrative AGI system.


title: Dynamic units of visual speech

abstract: We present a new method for generating a dynamic, concatenative, unit of visual speech that can generate realistic visual speech animation. We redefine visemes as temporal units that describe distinctive speech movements of the visual speech articulators. Traditionally visemes have been surmized as the set of static mouth shapes representing clusters of contrastive phonemes (e.g. /p, b, m/, and /f, v/). In this work, the motion of the visual speech articulators are used to generate discrete, dynamic visual speech gestures. These gestures are clustered, providing a finite set of movements that describe visual speech, the visemes. Dynamic visemes are applied to speech animation by simply concatenating viseme units. We compare to static visemes using subjective evaluation. We find that dynamic visemes are able to produce more accurate and visually pleasing speech animation given phonetically annotated audio, reducing the amount of time that an animator needs to spend manually refining the animation.


title: Multi-class blue noise sampling

abstract: Sampling is a core process for a variety of graphics applications. Among existing sampling methods, blue noise sampling remains popular thanks to its spatial uniformity and absence of aliasing artifacts. However, research so far has been mainly focused on blue noise sampling with a single class of samples. This could be insufficient for common natural as well as man-made phenomena requiring multiple classes of samples, such as object placement, imaging sensors, and stippling patterns. ::: We extend blue noise sampling to multiple classes where each individual class as well as their unions exhibit blue noise characteristics. We propose two flavors of algorithms to generate such multi-class blue noise samples, one extended from traditional Poisson hard disk sampling for explicit control of sample spacing, and another based on our soft disk sampling for explicit control of sample count. Our algorithms support uniform and adaptive sampling, and are applicable to both discrete and continuous sample space in arbitrary dimensions. We study characteristics of samples generated by our methods, and demonstrate applications in object placement, sensor layout, and color stippling.


title: LARGE-SCALE KALMAN FILTERING USING THE LIMITED MEMORY BFGS METHOD ∗

abstract: The standard formulations of the Kalman filter (KF) and exten ded Kalman filter (EKF) require the storage and multiplication of matrices of size , where is the size of the state space, and the inversion of matrices of size , where is the size of the observation space. Thus when both and are large, implementation issues arise. In this paper, we advocate the use of the limited memory BFGS method (LBFGS) to address these issues. A detailed description of how to use LBFGS within both the KF and EKF methods is given. The methodology is then tested on two examples: the first is la rge-scale and linear, and the second is small scale and nonlinear. Our results indicate that the resulting methods , which we will denote LBFGS-KF and LBFGS-EKF, yield results that are comparable with those obtained using KF and EKF, respectively, and can be used on much larger scale problems.


title: Crowdsourcing, Attention and Productivity

abstract: We show through an analysis of a massive data set from YouTube that the productivity exhibited in crowdsourcing exhibits a strong positive dependence on attention, measured by the number of downloads. Conversely, a lack of attention leads to a decrease in the number of videos uploaded and the consequent drop in productivity, which in many cases asymptotes to no uploads whatsoever. Moreover, short-term contributors compare their performance to the average contributor's performance while long-term contributors compare it to their own media.


title: Nightmares in crisis: clinical applications of lucid dreaming techniques.

abstract: A patient in crisis was offered treatment with a major focus on alleviating nightmares using lucid dreaming (dreaming while knowing that one is dreaming). Of sixty-eight non-psychotic patients seen consecutively in a psychiatry emergency room, she was one of 16 (23.5%) found to have a concurrent complaint of nightmares (dream anxiety disorder). The benefits of the skills developed with lucid dreaming extended into areas other than nightmares as the patient entered psychotherapy. The techniques appeared to play a role in the reduction of nightmare frequency, intensity, and distress, and to enhance ego growth and personal development. Further research in lucid dreaming as an adjunctive treatment for patients with nightmares and as a useful technique in psychotherapy is suggested.


title: RESERVOIR COMPUTING: A POWERFUL BLACK-BOX FRAMEWORK FOR NONLINEAR AUDIO PROCESSING

abstract: This paper proposes reservoir computing as a general framework for nonlinear audio processing. Reservoir computing is a novel approach to recurrent neural network training with the advantage of a very simple and linear learning algorithm. It can in theory approximate arbitrary nonlinear dynamical systems with arbitrary precision, has an inherent temporal processing capability and is therefore well suited for many nonlinear audio processing problems. Always when nonlinear relationships are present in the data and time information is crucial, reservoir computing can be applied. Examples from three application areas are presented: nonlinear system identification of a tube amplifier emulator algorithm, nonlinear audio prediction, as necessary in a wireless transmission of audio where dropouts may occur, and automatic melody transcription out of a polyphonic audio stream, as one example from the big field of music information retrieval. Reservoir computing was able to outperform state-of-the-art alternative models in all studied tasks.


title: Neural computing for scientific computing applications: more than just machine learning

abstract: Neural computing has been identified as a computing alternative in the post-Moore's Law era, however much of its attention has been directed at specialized applications such as machine learning. For scientific computing applications, particularly those that often depend on supercomputing, it is not clear that neural machine learning is the exclusive contribution to be made by neuromorphic platforms. In our presentation, we will discuss ways that looking to the brain as a whole and neurons specifically can provide new sources for inspiration for computing beyond current machine learning applications. Particularly for scientific computing, where approximate methods for computation introduce additional challenges, the development of non-approximate methods for neural computation is potentially quite valuable. In addition, the brain's dramatic ability to utilize context at many different scales and incorporate information from many different modalities is a capability currently poorly realized by neural machine learning approaches yet offers considerable potential impact on scientific applications.


title: Acquiring forensic evidence from infrastructure-asa-service cloud computing : Exploring and evaluating tools , trust , and techniques

abstract: Abstract We expose and explore technical and trust issues that arise in acquiring forensic evidence from infrastructure-as-a-service cloud computing and analyze some strategies for addressing these challenges. First, we create a model to show the layers of trust required in the cloud. Second, we present the overarching context for a cloud forensic exam and analyze choices available to an examiner. Third, we provide for the first time an evaluation of popular forensic acquisition tools including Guidance EnCase and AccesData Forensic Toolkit, and show that they can successfully return volatile and non-volatile data from the cloud. We explain, however, that with those techniques judge and jury must accept a great deal of trust in the authenticity and integrity of the data from many layers of the cloud model. In addition, we explore four other solutions for acquisition—Trusted Platform Modules, the management plane, forensics-as-a-service, and legal solutions, which assume less trust but require more cooperation from the cloud service provider. Our work lays a foundation for future development of new acquisition methods for the cloud that will be trustworthy and forensically sound. Our work also helps forensic examiners, law enforcement, and the court evaluate confidence in evidence from the cloud.