Query: A Survey of Visualization Construction User Interfaces

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VisComplete: Automating Suggestions for Visualization Pipelines <=SEP=> Building visualization and analysis pipelines is a large hurdle in the adoption of visualization and workflow systems by domain scientists. In this paper, we propose techniques to help users construct pipelines by consensus-automatically suggesting completions based on a database of previously created pipelines. In particular, we compute correspondences between existing pipeline subgraphs from the database, and use these to predict sets of likely pipeline additions to a given partial pipeline. By presenting these predictions in a carefully designed interface, users can create visualizations and other data products more efficiently because they can augment their normal work patterns with the suggested completions. We present an implementation of our technique in a publicly-available, open-source scientific workflow system and demonstrate efficiency gains in real-world situations.

Integrality and Separability of Input Devices <=SEP=> Current input device taxonomies and other frameworks typically emphasize the mechanical structure of input devices. We suggest that selecting an appropriate input device for an interactive task requires looking beyond the physical structure of devices to the deeper perceptual structure of the task, the device, and the interrelationship between the perceptual structure of the task and the control properties of the device. We affirm that perception is key to understanding performance of multidimensional input devices on multidimensional tasks. We have therefore extended the theory of processing of percetual structure to graphical interactive tasks and to the control structure of input devices. This allows us to predict task and device combinations that lead to better performance and hypothesize that performance is improved when the perceptual structure of the task matches the control structure of the device. We conducted an experiment in which subjects performed two tasks with different perceptual structures, using two input devices with correspondingly different control structures, a three-dimensional tracker and a mouse. We analyzed both speed and accuracy, as well as the trajectories generated by subjects as they used the unconstrained three-dimensional tracker to perform each task. The result support our hypothesis and confirm the importance of matching the perceptual structure of the task and the control structure of the input device.

Visualization Techniques for Mining Large Databases: A Comparison <=SEP=> Visual data mining techniques ha ve proven to be of high v alue in exploratory data analysis and the y also have a high potential for mining lar ge databases. In this article, we describe and e valuate a ne w visualization-based approach to mining lar ge databases. The basic idea of our visual data mining techniques is to represent as man y d ta items as possible on the screen at the same time by mapping each data v alue o a pixel of the screen and arranging the pixels adequately . The major goal of this article is to e valuate our visual data mining techniques and to compare them to other well-kno wn visualization techniques for multidimensional data: the parallel coordinate and stick figure visualization techniques. F or the evaluation of visual data mining techniques, in the first place the perception of properties of the data counts, and only in the second place the CPU time and the number of secondary storage accesses are important. In addition to testing the visualization techniques using real data, we de velop d a testing environment for database visualizations similar to the benchmark approach used for comparing the performance of database systems. The testing en vironment allows the generation of test data sets with predefined data characteristics which are important for comparing the perceptual abilities of visual data mining techniques.

Angular Brushing of Extended Parallel Coordinates <=SEP=> In this paper we present angularbrushingfor parallel coordinates (PC) as a new approach to high-light rational dataproperties, i.e., features which depend on two data dimensions (instead of one). We also demonstrate smoothbrushing of PC as an intuitive tool to specify non-binary degreeof-interest functions (then used for F+C visualization). We also shortly describe our implementation as well as its application to the visualization of CFD data.

Inkjet-Configurable Gate Arrays (IGA) <=SEP=> Implementation of organic digital circuits (or printed electronic circuits) has been under an extensive investigation, facing some critical challenges such as process variability, device performance, cell design styles and circuit yield. Failure in any single Organic Thin Film Transistor (OTFT) often causes the whole circuit to fail since integration density is still low. For the same reason, the application of fault tolerant techniques is not that popular in these circuits. In this paper, we propose an approach for the direct mapping of digital functions on top of new prefabricated structures: Inkjet-Configurable Gate Arrays (IGA). This alternative has two main advantages. First, it helps to obtain high yield circuits out of mid-yield foils, and second, it allows implementing individual circuit personalization at a very low cost by using additive mask-less printing techniques thus avoiding the need for OTPROM-like (or E2PROM) devices. All along the design process of IGA cells and structures we used the scalability and technology-independent strategies provided by parameterizable cells (PCell) what helps dealing with current fast technology evolution.

A State-of-the-Art Survey on Real-Time Issues in Embedded Systems Virtualization <=SEP=> Virtualization has gained great acceptance in the server and cloud computing arena. In recent years, it has also been widely applied to real-time embedded systems with stringent timing constraints. We present a comprehensive survey on real-time issues in virtualization for embedded systems, covering popular virtualization systems including KVM, Xen, L4 and others.

Recent advances in deep learning for speech research at Microsoft <=SEP=> Deep learning is becoming a mainstream technology for speech recognition at industrial scale. In this paper, we provide an overview of the work by Microsoft speech researchers since 2009 in this area, focusing on more recent advances which shed light to the basic capabilities and limitations of the current deep learning technology. We organize this overview along the feature-domain and model-domain dimensions according to the conventional approach to analyzing speech systems. Selected experimental results, including speech recognition and related applications such as spoken dialogue and language modeling, are presented to demonstrate and analyze the strengths and weaknesses of the techniques described in the paper. Potential improvement of these techniques and future research directions are discussed.

An automatic vision-based malaria diagnosis system. <=SEP=> Malaria is a worldwide health problem with 225 million infections each year. A fast and easy-to-use method, with high performance is required to differentiate malaria from non-malarial fevers. Manual examination of blood smears is currently the gold standard, but it is time-consuming, labour-intensive, requires skilled microscopists and the sensitivity of the method depends heavily on the skills of the microscopist. We propose an easy-to-use, quantitative cartridge-scanner system for vision-based malaria diagnosis, focusing on low malaria parasite densities. We have used special finger-prick cartridges filled with acridine orange to obtain a thin blood film and a dedicated scanner to image the cartridge. Using supervised learning, we have built a Plasmodium falciparum detector. A two-step approach was used to first segment potentially interesting areas, which are then analysed in more detail. The performance of the detector was validated using 5,420 manually annotated parasite images from malaria parasite culture in medium, as well as using 40 cartridges of 11,780 images containing healthy blood. From finger prick to result, the prototype cartridge-scanner system gave a quantitative diagnosis in 16 min, of which only 1 min required manual interaction of basic operations. It does not require a wet lab or a skilled operator and provides parasite images for manual review and quality control. In healthy samples, the image analysis part of the system achieved an overall specificity of 99.999978% at the level of (infected) red blood cells, resulting in at most seven false positives per microlitre. Furthermore, the system showed a sensitivity of 75% at the cell level, enabling the detection of low parasite densities in a fast and easy-to-use manner. A field trial in Chittagong (Bangladesh) indicated that future work should primarily focus on improving the filling process of the cartridge and the focus control part of the scanner.

Geocoding location expressions in Twitter messages: A preference learning method <=SEP=> Resolving location expressions in text to the correct physical location, also known as geocoding or grounding, is complicated by the fact that so many places around the world share the same name. Correct resolution is made even more difficult when there is little context to determine which place is intended, as in a 140-character Twitter message, or when location cues from different sources conflict, as may be the case among different metadata fields of a Twitter message. We used supervised machine learning to weigh the different fields of the Twitter message and the features of a world gazetteer to create a model that will prefer the correct gazetteer candidate to resolve the extracted expression. We evaluated our model using the F1 measure and compared it to similar algorithms. Our method achieved results higher than state-of-the-art competitors.

FREEFORM ORIGAMI TESSELLATIONS BY GENERALIZING RESCH ’ S PATTERNS <=SEP=> In this research, we study a method to produce families of origami tessellations from given polyhedral surfaces. The resulting tessellated surfaces generalize the patterns proposed by Ron Resch and allow the construction of an origami tessellation that approximates a given surface. We will achieve these patterns by first constructing an initial configuration of the tessellated surfaces by separating each facets and inserting folded parts between them based on the local configuration. The initial configuration is then modified by solving the vertex coordinates to satisfy geometric constraints of developability, folding angle limitation, and local non-intersection. We propose a novel robust method for avoiding intersections between facets sharing vertices. Such generated polyhedral surfaces are not only applied to folding paper but also sheets of metal that does not allow 180◦ folding.

On Scalable and Robust Truth Discovery in Big Data Social Media Sensing Applications <=SEP=> Identifying trustworthy information in the presence of noisy data contributed by numerous unvetted sources from online social media (e.g., Twitter, Facebook, and Instagram) has been a crucial task in the era of big data. This task, referred to as truth discovery, targets at identifying the reliability of the sources and the truthfulness of claims they make without knowing either a priori. In this work, we identified three important challenges that have not been well addressed in the current truth discovery literature. The first one is “misinformation spread” where a significant number of sources are contributing to false claims, making the identification of truthful claims difficult. For example, on Twitter, rumors, scams, and influence bots are common examples of sources colluding, either intentionally or unintentionally, to spread misinformation and obscure the truth. The second challenge is “data sparsity” or the “long-tail phenomenon” where a majority of sources only contribute a small number of claims, providing insufficient evidence to determine those sources’ trustworthiness. For example, in the Twitter datasets that we collected during real-world events, more than 90% of sources only contributed to a single claim. Third, many current solutions are not scalable to large-scale social sensing events because of the centralized nature of their truth discovery algorithms. In this paper, we develop a Scalable and Robust Truth Discovery (SRTD) scheme to address the above three challenges. In particular, the SRTD scheme jointly quantifies both the reliability of sources and the credibility of claims using a principled approach. We further develop a distributed framework to implement the proposed truth discovery scheme using Work Queue in an HTCondor system. The evaluation results on three real-world datasets show that the SRTD scheme significantly outperforms the state-of-the-art truth discovery methods in terms of both effectiveness and efficiency.

Dense Multimodal Fusion for Hierarchically Joint Representation <=SEP=> Multiple modalities can provide more valuable information than single one by describing the same contents in various ways. Hence, it is highly expected to learn effective joint representation by fusing the features of different modalities. However, previous methods mainly focus on fusing the shallow features or high-level representations generated by unimodal deep networks, which only capture part of the hierarchical correlations across modalities. In this paper, we propose to densely integrate the representations by greedily stacking multiple shared layers between different modality-specific networks, which is named as Dense Multimodal Fusion (DMF). The joint representations in different shared layers can capture the correlations in different levels, and the connection between shared layers also provides an efficient way to learn the dependence among hierarchical correlations. These two properties jointly contribute to the multiple learning paths in DMF, which results in faster convergence, lower training loss, and better performance. We evaluate our model on three typical multimodal learning tasks, including audiovisual speech recognition, cross-modal retrieval, and multimodal classification. The noticeable performance in the experiments demonstrates that our model can learn more effective joint representation.

Hiding Behind the Shoulders of Giants: Abusing Crawlers for Indirect Web Attacks <=SEP=> It could be argued that without search engines, the web would have never grown to the size that it has today. To achieve maximum coverage and provide relevant results, search engines employ large armies of autonomous crawlers that continuously scour the web, following links, indexing content, and collecting features that are then used to calculate the ranking of each page. In this paper, we describe how autonomous crawlers can be abused by attackers to exploit vulnerabilities on thirdparty websites while hiding the true origin of the attacks. Moreover, we show how certain vulnerabilities on websites that are currently deemed unimportant, can be abused in a way that would allow an attacker to arbitrarily boost the rankings of malicious websites in the search results of popular search engines. Motivated by the potentials of these vulnerabilities, we propose a series of preventive and defensive countermeasures that website owners and search engines can adopt to minimize, or altogether eliminate, the effects of crawler-abusing attacks.

Current advances in the fabrication of microneedles for transdermal delivery. <=SEP=> The transdermal route is an excellent site for drug delivery due to the avoidance of gastric degradation and hepatic metabolism, in addition to easy accessibility. Although offering numerous attractive advantages, many available transdermal systems are not able to deliver drugs and other compounds as desired. The use of hypodermic needles, associated with phobia, pain and accidental needle-sticks has been used to overcome the delivery limitation of macromolecular compounds. The means to overcome the disadvantages of hypodermic needles has led to the development of microneedles for transdermal delivery. However, since the initial stages of microneedle fabrication, recent research has been conducted integrating various fabrication techniques for generating sophisticated microneedle devices for transdermal delivery including progress on their commercialization. A concerted effort has been made within this review to highlight the current advances of microneedles, and to provide an update of pharmaceutical research in the field of microneedle-assisted transdermal drug delivery systems.

Evaluation of mobile app paradigms <=SEP=> The explosion of mobile applications both in number and variety raises the need of shedding light on their architecture, composition and quality. Indeed, it is crucial to understand which mobile application paradigm fits better to what type of application and usage. Such understanding has direct consequences on the user experience, the development cost and sale revenues of mobile apps. In this paper, we identified four main mobile application paradigms and evaluated them from the developer, user and service provider viewpoints. To ensure objectivity and soundness we start by defining high level criteria and then breaking down in finer-grained criteria. After a theoretical evaluation an implementation was carried out as a practical verification. The selected application is object recognition app, which is both exciting and challenging to develop.

Applications of data mining in robotics <=SEP=> From a high-level viewpoint, robotics is all about data. In other words, each robot agent for performing its designated tasks should deal with data in some form - from collecting data from the environment to generating internal data. For this reason and due to broad range of possible applications, managing and dealing with such data is an important issue in the field. On the other side, data - in its different types - is known as an invaluable treasure of knowledge and insight within any information system. In this regard, extracting such hidden knowledge through data mining has been in the center of focus for more than two decades. In fact, data mining could be considered as a super-influential phenomenon on all scientific fields and robotics is not an exception. Over the recent years, many studies have been conducted on different aspects of leveraging data mining techniques in the robotic domain to improve performance of robots in various ways. However, despite the current disparate and unorganized studies in different directions, there are some potential areas that have not received enough attention. In this paper, we provide a high-level overview of the field and try to organize the current studies as a reference point and road map for future works. Moreover, challenges and future possible directions will be studied.

A carpet cloak for visible light. <=SEP=> We report an invisibility carpet cloak device, which is capable of making an object undetectable by visible light. The cloak is designed using quasi conformal mapping and is fabricated in a silicon nitride waveguide on a specially developed nanoporous silicon oxide substrate with a very low refractive index (n<1.25). The spatial index variation is realized by etching holes of various sizes in the nitride layer at deep subwavelength scale creating a local effective medium index. The fabricated device demonstrates wideband invisibility throughout the visible spectrum with low loss. This silicon nitride on low index substrate can also be a general scheme for implementation of transformation optical devices at visible frequencies.

Tag suggestion and localization in user-generated videos based on social knowledge <=SEP=> Nowadays, almost any web site that provides means for sharing user-generated multimedia content, like Flickr, Facebook, YouTube and Vimeo, has tagging functionalities to let users annotate the material that they want to share. The tags are then used to retrieve the uploaded content, and to ease browsing and exploration of these collections, e.g. using tag clouds. However, while tagging a single image is straightforward, and sites like Flickr and Facebook allow also to tag easily portions of the uploaded photos, tagging a video sequence is more cumbersome, so that users just tend to tag the overall content of a video. Moreover, the tagging process is completely manual, and often users tend to spend as few time as possible to annotate the material, resulting in a sparse annotation of the visual content. A semi-automatic process, that helps the users to tag a video sequence would improve the quality of annotations and thus the overall user experience. While research on image tagging has received a considerable attention in the latest years, there are still very few works that address the problem of automatically assigning tags to videos, locating them temporally within the video sequence. In this paper we present a system for video tag suggestion and temporal localization based on collective knowledge and visual similarity of frames. The algorithm suggests new tags that can be associated to a given keyframe exploiting the tags associated to videos and images uploaded to social sites like YouTube and Flickr and visual features.

Evaluating visual analytics with eye tracking <=SEP=> The application of eye tracking for the evaluation of humans' viewing behavior is a common approach in psychological research. So far, the use of this technique for the evaluation of visual analytics and visualization is less prominent. We investigate recent scientific publications from the main visualization and visual analytics conferences and journals that include an evaluation by eye tracking. Furthermore, we provide an overview of evaluation goals that can be achieved by eye tracking and state-of-the-art analysis techniques for eye tracking data. Ideally, visual analytics leads to a mixed-initiative cognitive system where the mechanism of distribution is the interaction of the user with visualization environments. Therefore, we also include a discussion of cognitive approaches and models to include the user in the evaluation process. Based on our review of the current use of eye tracking evaluation in our field and the cognitive theory, we propose directions of future research on evaluation methodology, leading to the grand challenge of developing an evaluation approach to the mixed-initiative cognitive system of visual analytics.

Constructing long short-term memory based deep recurrent neural networks for large vocabulary speech recognition <=SEP=> Long short-term memory (LSTM) based acoustic modeling methods have recently been shown to give state-of-the-art performance on some speech recognition tasks. To achieve a further performance improvement, in this research, deep extensions on LSTM are investigated considering that deep hierarchical model has turned out to be more efficient than a shallow one. Motivated by previous research on constructing deep recurrent neural networks (RNNs), alternative deep LSTM architectures are proposed and empirically evaluated on a large vocabulary conversational telephone speech recognition task. Meanwhile, regarding to multi-GPU devices, the training process for LSTM networks is introduced and discussed. Experimental results demonstrate that the deep LSTM networks benefit from the depth and yield the state-of-the-art performance on this task.

ESD protection circuit schemes for DDR3 DQ drivers <=SEP=> The high-speed interface of DQ pins in DDR3 DRAM requires special ESD considerations. During ESD characterization testing, high voltages from the power rail could pass through the pre-driver PMOSFET to damage the pull-down NMOSFET gate oxide. Special circuit design of the pre-driver circuit is required to eliminate this ESD failure mechanism.

2 A Physically Unclonable Function with BER < 10-8 for Robust Chip Authentication Using Oscillator Collapse in 40 nm CMOS <=SEP=> Security is a key concern in today’s mobile devices and a number of hardware implementations of security primitives have been proposed, including true random number generators, differential power attack avoidance, and chip-ID generators [1-4]. Recently, physically unclonable functions (PUFs) were proposed as a secure method for chip authentication in unsecure environments [5-7]. A PUF is a function that maps an input code (“challenge”) to an output code (“response”) in a manner that is unique for every chip. PUFs are increasingly used for IC authentication to offer protection against identity theft, cloning, and counterfeit components [2-4].

Bootstrap Confidence Intervals <=SEP=> This article surveys bootstrap methods for producing good approximate confidence intervals. The goal is to improve by an order of magnitude upon the accuracy of the standard intervals θ̂ ± zασ̂ , in a way that allows routine application even to very complicated problems. Both theory and examples are used to show how this is done. The first seven sections provide a heuristic overview of four bootstrap confidence interval procedures: BCa, bootstrap-t, ABC and calibration. Sections 8 and 9 describe the theory behind these methods, and their close connection with the likelihood-based confidence interval theory developed by Barndorff-Nielsen, Cox and Reid and others.

Efficient and Scalable IoT Service Delivery on Cloud <=SEP=> Nowadays, IoT services are typically delivered as physically isolated vertical solutions, in which all system components ranging from sensory devices to applications are customized and tightly coupled for the requirements of each specific project. The efficiency and scalability of such service delivery model are intrinsically limited, posing significant challenges to IoT solution providers. Therefore, we propose a novel PaaS framework that provides essential platform services for IoT solution providers to efficiently deliver and continuously extend their services. This paper first introduces the IoT PaaS architecture, on which IoT solutions can be delivered as virtual verticals by leveraging computing resources and middleware services on cloud. Then we present the detailed mechanism and implementation of domain mediation, which helps solution providers to efficiently provide domain-specific control applications. The proposed approaches are demonstrated through the implementation of a domain mediator for building management and two use cases using the mediator.

Wireless Body Area Network Security and Privacy Issue in E-Healthcare <=SEP=> Wireless Body Area Network (WBAN) is a collection of wireless sensor nodes which can be placed within the body or outside the body of a human or a living person which in result observes or monitors the functionality and adjoining situations of the body. Utilizing a Wireless Body Area Network, the patient encounters a greater physical versatility and is never again constrained to remain in the hospital. As the Wireless Body Area Network sensor devices is being utilized for gathering the sensitive data and possibly will run into antagonistic situations, they require complicated and very secure security medium or structure to avoid the vitriolic communications within the system. These devices represent various security and privacy protection of touchy and private patient medical data. The medical records of patients are a significant and an unsolved situation due to which a changing or exploitation to the system is possible. In this research paper, we first present an overview of WBAN, how they utilized for healthcare monitoring, its architecture then highlight major security and privacy requirements and assaults at different network layer in a WBAN and we finally talk about various cryptographic algorithms and laws for providing solution of security and privacy in WBAN. Keywords—E-Health; privacy; security; wireless body area networks

Self-Compassion and Body Dissatisfaction in Women : A Randomized Controlled Trial of a Brief Meditation Intervention <=SEP=> Body dissatisfaction is a major source of suffering among women of all ages. One potential factor that could mitigate body dissatisfaction is self-compassion, a construct that is garnering increasing research attention due to its strong association with psychological health. This study investigated whether a brief 3-week period of self-compassion meditation training would improve body satisfaction in a multigenerational group of women. Participants were randomized either to the meditation intervention group (N=98; M age=38.42) or to a waitlist control group (N=130; M age=36.42). Results suggested that compared to the control group, intervention participants experienced significantly greater reductions in body dissatisfaction, body shame, and contingent self-worth based on appearance, as well as greater gains in self-compassion and body appreciation. All improvements were maintained when assessed 3 months later. Self-compassion meditation may be a useful and cost-effective means of improving body image in

How Affective Is a "Like"?: The Effect of Paralinguistic Digital Affordances on Perceived Social Support <=SEP=> A national survey asked 323 U.S. adults about paralinguistic digital affordances (PDAs) and how these forms of lightweight feedback within social media were associated with their perceived social support. People perceived PDAs (e.g., Likes, Favorites, and Upvotes) as socially supportive both quantitatively and qualitatively, even without implicit meaning associated with them. People who are highly sensitive about what others think of them and have high self-esteem are more likely to perceive higher social support from PDAs.

Tweet Sarcasm Detection Using Deep Neural Network <=SEP=> Sarcasm detection has been modeled as a binary document classification task, with rich features being defined manually over input documents. Traditional models employ discrete manual features to address the task, with much research effect being devoted to the design of effective feature templates. We investigate the use of neural network for tweet sarcasm detection, and compare the effects of the continuous automatic features with discrete manual features. In particular, we use a bi-directional gated recurrent neural network to capture syntactic and semantic information over tweets locally, and a pooling neural network to extract contextual features automatically from history tweets. Results show that neural features give improved accuracies for sarcasm detection, with different error distributions compared with discrete manual features.

Stable Proportional-Derivative Controllers <=SEP=> In computer animation, the proportional-derivative (PD) controller is a common technique for tracking characters' motion. A new formulation of the PD controller-stable PD (SPD)-allows arbitrarily high gains, even at large time steps. The key is to determine joint forces and torques while taking into account the character's positions and velocities in the next time step. SPD is stable even when combined with a physics simulator that uses simple Euler integration. SPD controllers have a variety of uses, including motion tracking in a physics simulator, keyframe interpolation with secondary motion, and constraint satisfaction for simulation.