title: Multi-Level Video Frame Interpolation: Exploiting the Interaction Among Different Levels

abstract: This paper proposes a novel multi-level frame interpolation scheme by exploiting the interactions among different levels. The proposed scheme includes three major stages that work at block level, pixel level, and sequence level, respectively. Effective algorithms are designed for each stage, i.e., block-level motion estimation with dropping unreliable motion vectors, pixel-level motion vector-guided partial scale-invariant feature transform flow matching, and sequence-level 3-D total variation regularized completion. Compared to traditional methods that focus mostly at one single level, the proposed scheme manages to recognize and utilize the interactions among the three levels based on their distinct characteristics and intertwined relationships. With a proper exploitation of interactions, unique advantages for each level can be effectively preserved while inherent limitations of a given level can be overcome by utilizing information from other levels. Extensive experiments have confirmed its superior performance over several classical schemes, in both subjective visual quality and objective peak signal-to-noise ratio/structure similarity measurements, and typical artifacts can be significantly reduced.


title: Multiscale Categorical Object Recognition Using Contour Fragments

abstract: Psychophysical studies show that we can recognize objects using fragments of outline contour alone. This paper proposes a new automatic visual recognition system based only on local contour features, capable of localizing objects in space and scale. The system first builds a class-specific codebook of local fragments of contour using a novel formulation of chamfer matching. These local fragments allow recognition that is robust to within-class variation, pose changes, and articulation. Boosting combines these fragments into a cascaded sliding-window classifier, and mean shift is used to select strong responses as a final set of detection. We show how learning can be performed iteratively on both training and test sets to bootstrap an improved classifier. We compare with other methods based on contour and local descriptors in our detailed evaluation over 17 challenging categories and obtain highly competitive results. The results confirm that contour is indeed a powerful cue for multiscale and multiclass visual object recognition.


title: Dr. Android and Mr. Hide: fine-grained permissions in android applications

abstract: Google's Android platform includes a permission model that protects access to sensitive capabilities, such as Internet access, GPS use, and telephony. While permissions provide an important level of security, for many applications they allow broader access than actually required. In this paper, we introduce a novel framework that addresses this issue by adding finer-grained permissions to Android. Underlying our framework is a taxonomy of four major groups of Android permissions, each of which admits some common strategies for deriving sub-permissions. We used these strategies to investigate fine-grained versions of five of the most common Android permissions, including access to the Internet, user contacts, and system settings. We then developed a suite of tools that allow these fine-grained permissions to be inferred on existing apps; to be enforced by developers on their own apps; and to be retrofitted by users on existing apps. We evaluated our tools on a set of top apps from Google Play, and found that fine-grained permissions are applicable to a wide variety of apps and that they can be retrofitted to increase security of existing apps without affecting functionality.


title: [Botulinum toxin injection techniques in the lower third and middle of the face, the neck and the décolleté: the "Nefertiti lift"].

abstract: Although correction of the dynamic wrinkles of the upper part of the face is the major indication for botulinum toxin, there are also many possibilities for the middle and lower thirds of the face and neck. However, these injections are more delicate and require an experienced operator who has excellent knowledge of the muscles of these regions, their functions, the antagonist actions exercised on other muscles, particularly in terms of the complex equilibrium of the mouth. An excessive dose, an inappropriate injection point, or a centering mistake can all easily be responsible for undesirable side effects. However, the results obtained, often with lower doses than in the superior part of the face, can be highly satisfactory, notably in erasing bunny lines, improvement of marionette lines, peau d'orange chin, attenuation of peribuccal lines, melomental folds, correction of a gummy smile, and facial asymmetries. In the neck it is possible to reduce platysmal bands, horizontal lines, and diagonal lines of the neck and décolleté. The face contours can also be improved by the Nefertiti lift. In the mid and lower regions of the face, botulinum toxin is often a complement to other esthetic techniques, particularly filling procedures.


title: Scribe4Me: Evaluating a Mobile Sound Transcription Tool for the Deaf

abstract: People who are deaf or hard-of-hearing may have challenges communicating with others via spoken words and may have challenges being aware of audio events in their environments. This is especially true in public places, which may not have accessible ways of communicating announcements and other audio events. In this paper, we present the design and evaluation of a mobile sound transcription tool for the deaf and hard-of-hearing. Our tool, Scribe4Me, is designed to improve awareness of sound-based information in any location. When a button is pushed on the tool, a transcription of the last 30 seconds of sound is given to the user in a text message. Transcriptions include dialog and descriptions of environmental sounds. We describe a 2-week field study of an exploratory prototype, which shows that our approach is feasible, highlights particular contexts in which it is useful, and provides information about what should be contained in transcriptions.


title: Complications of Browlift Techniques: A Systematic Review

abstract: BACKGROUND ::: There is ongoing debate over which surgical technique is the safest for brow elevation. ::: ::: ::: OBJECTIVES ::: The authors outline complication rates for a variety of open and endoscopic browlift techniques based on the results of a literature review. ::: ::: ::: METHODS ::: The following databases were searched to capture relevant studies: MEDLINE, EMBASE, CINAHL, LILACS, Web of Science, Cochrane Libraries, controlled-trials.com, and clinicaltrials.gov. Eighty-two studies met the inclusion criteria. Assuming between-study heterogeneity due to the limitations and biases inherent to case series, a random-effects model was used to calculate weighted proportions. Pooled weighted proportions with 95% confidence intervals were determined. ::: ::: ::: RESULTS ::: All open and endoscopic procedures are associated with a variety of complications. Unacceptable scarring and paresthesia are the most common complications among all surgical browlifts. For anterior hairline incision with subcutaneous dissection, alopecia occurred in 8.5% of patients, paresthesia in 5.4%, unacceptable scarring in 2.1%, and skin necrosis in 1.8%. For coronal incision with subgaleal dissection, unacceptable scarring occurred in 3.6% of patients, hematoma in 0.5%, and infection in 0.2%. Endoscopic techniques with subperiosteal dissection had the highest complication rates: 6.2% for paresthesia, 3.6% for asymmetry, 3.0% for alopecia, and 2.7% for lagophthalmos. ::: ::: ::: CONCLUSIONS ::: Although complication rates vary with respect to incision site and plane of dissection, endoscopic techniques are associated with a larger variety of complications than open approaches. The findings should be interpreted with caution due to the limitations inherent to a case series. A well-designed comparative study is needed to evaluate the "true" rate of complications among the various browlift techniques.


title: Matrix manifold optimization for Gaussian mixtures

abstract: We take a new look at parameter estimation for Gaussian Mixture Model (GMMs). Specifically, we advance Riemannian manifold optimization (on the manifold of positive definite matrices) as a potential replacement for Expectation Maximization (EM), which has been the de facto standard for decades. An out-of-the-box invocation of Riemannian optimization, however, fails spectacularly: it obtains the same solution as EM, but vastly slower. Building on intuition from geometric convexity, we propose a simple reformulation that has remarkable consequences: it makes Riemannian optimization not only match EM (a nontrivial result on its own, given the poor record nonlinear programming has had against EM), but also outperforms it in many settings. To bring our ideas to fruition, we develop a well-tuned Riemannian LBFGS method that proves superior to known competing methods (e.g., Riemannian conjugate gradient). We hope that our results encourage a wider consideration of manifold optimization in machine learning and statistics.


title: Application of Morphological Bit Planes in Retinal Blood Vessel Extraction

abstract: The appearance of the retinal blood vessels is an important diagnostic indicator of various clinical disorders of the eye and the body. Retinal blood vessels have been shown to provide evidence in terms of change in diameter, branching angles, or tortuosity, as a result of ophthalmic disease. This paper reports the development for an automated method for segmentation of blood vessels in retinal images. A unique combination of methods for retinal blood vessel skeleton detection and multidirectional morphological bit plane slicing is presented to extract the blood vessels from the color retinal images. The skeleton of main vessels is extracted by the application of directional differential operators and then evaluation of combination of derivative signs and average derivative values. Mathematical morphology has been materialized as a proficient technique for quantifying the retinal vasculature in ocular fundus images. A multidirectional top-hat operator with rotating structuring elements is used to emphasize the vessels in a particular direction, and information is extracted using bit plane slicing. An iterative region growing method is applied to integrate the main skeleton and the images resulting from bit plane slicing of vessel direction-dependent morphological filters. The approach is tested on two publicly available databases DRIVE and STARE. Average accuracy achieved by the proposed method is 0.9423 for both the databases with significant values of sensitivity and specificity also; the algorithm outperforms the second human observer in terms of precision of segmented vessel tree.


title: Spatial decision support systems: an overview of technology and a test of efficacy

abstract: A laboratory experiment was used to investigate the effects on decision-maker performance of using geographic information system (GIS) technology as a spatial decision support system (SDSS). GIS are increasingly being used for decision-making, yet research about their contributions to the performance of decision-makers has been lacking. This study makes a contribution to that apparent void. Volunteer subjects completed a site location task that required decisions to be made based upon spatially referenced information. Performance was operationalized as elapsed time and accuracy. The task environment was manipulated in two dimensions. In one dimension, task complexity was varied on two levels. In the other dimension, some subjects were provided a geographic information system as a decision aid; the rest were not. Significant differences were found between task solutions developed by SDSS users and those developed by non-SDSS users. SDSS users experienced shorter solution times and fewer errors for both levels of task complexity. The study builds upon and extends image theory as a basis for explaining efficiency differences resulting from different graphical displays of spatial information.


title: On-line fault detection of sensor measurements

abstract: On-line fault detection in sensor networks is of paramount importance due to the convergence of a variety of challenging technological, application, conceptual, and safety related factors. We introduce a taxonomy for classification of faults in sensor networks and the first on-line model-based testing technique. The approach is generic in the sense that it can be applied on an arbitrary system of heterogeneous sensors with an arbitrary type of fault model, while it provides a flexible tradeoff between accuracy and latency. The key idea is to formulate on-line testing as a set of instances of a non-linear function minimization and consequently apply nonparametric statistical methods to identify the sensors that have the highest probability to be faulty. The optimization is conducted using the Powell nonlinear function minimization method. The effectiveness of the approach is evaluated in the presence of random noise using a system of light sensors.


title: The effect of endurance training on parameters of aerobic fitness.

abstract: Endurance exercise training results in profound adaptations of the cardiorespiratory and neuromuscular systems that enhance the delivery of oxygen from the atmosphere to the mitochondria and enable a tighter regulation of muscle metabolism. These adaptations effect an improvement in endurance performance that is manifest as a rightward shift in the 'velocity-time curve'. This shift enables athletes to exercise for longer at a given absolute exercise intensity, or to exercise at a higher exercise intensity for a given duration. There are 4 key parameters of aerobic fitness that affect the nature of the velocity-time curve that can be measured in the human athlete. These are the maximal oxygen uptake (VO2max), exercise economy, the lactate/ventilatory threshold and oxygen uptake kinetics. Other parameters that may help determine endurance performance, and that are related to the other 4 parameters, are the velocity at VO2max (V-VO2max) and the maximal lactate steady state or critical power. This review considers the effect of endurance training on the key parameters of aerobic (endurance) fitness and attempts to relate these changes to the adaptations seen in the body's physiological systems with training. The importance of improvements in the aerobic fitness parameters to the enhancement of endurance performance is highlighted, as are the training methods that may be considered optimal for facilitating such improvements.


title: Three-Dimensional nand Flash Architecture Design Based on Single-Crystalline STacked ARray

abstract: Various critical issues related with 3-D stacked nand Flash memory are examined in this paper. Our single-crystalline STacked ARray (STAR) has many advantages such as better scalability, possibility of single-crystal channel, less sensitivity to 3-D interference, stable virtual source/drain characteristic, and more extendability over other stacked structures. With STAR, we proposed a unit 3-D structure, i.e., “building.” Then, using this new component, 3-D block and full chip architecture are successfully designed. For the first time, the structure and operation methods of the “full” array are considered. The fully designed 3-D nand Flash architecture will be the novel solution of reliable 3-D stacked nand Flash memory for terabit density.


title: Commonsense Knowledge Extraction Using Concepts Properties

abstract: This paper presents a semantically grounded method for extracting commonsense knowledge. First, commonsense rules are identified, e.g., one cannot see imaginary objects. Second, those rules are combined with a basic semantic representation in order to infer commonsense knowledge facts, e.g. one cannot see a flying carpet. Further combinations of semantic relations with inferred commonsense facts are proposed and analyzed. Results show that this novel method is able to extract thousands of commonsense facts with little human interaction and high accuracy.


title: Balancing the Criticisms: Validating Multi-Agent Models of Social Systems

abstract: Abstract Using multi-agent models to study social systems has attracted criticisms because of the challenges involved in their validation. Common criticisms that we have encountered are described, and for each one we attempt to give a balanced perspective of the criticism. A model of intra-state conflict is used to help demonstrate these points. We conclude that multi-agent models for social systems are most useful when (1) the connection between micro-behaviors and macro-behaviors are not well-understood and (2) when data collection from the real-world system is prohibitively expensive in terms of time or money or if it puts human lives at risk.


title: CAT: the CELCT Annotation Tool

abstract: This paper presents CAT CELCT Annotation Tool, a new general-purpose web-based tool for text annotation developed by CELCT (Center for the Evaluation of Language and Communication Technologies). The aim of CAT is to make text annotation an intuitive, easy and fast process. In particular, CAT was created to support human annotators in performing linguistic and semantic text annotation and was designed to improve productivity and reduce time spent on this task. Manual text annotation is, in fact, a time-consuming activity, and conflicts may arise with the strict deadlines annotation projects are frequently subject to. Thanks to its adaptability and user-friendly interface, CAT can positively contribute to improve time management in annotation project. Further, the tool has a number of features which make it an easy-to-use tool for many types of annotations. Even if the first prototype of CAT has been used to perform temporal and event annotation following the It-TimeML specifications, the tool is general enough to be used for annotating a broad range of linguistic and semantic phenomena. CAT is freely available for research purposes.


title: Design of a 3D printed hand prosthesis actuated by nylon 6-6 polymer based artificial muscles

abstract: The majority of arm amputees live in developing countries and cannot afford prostheses beyond cosmetic hands with simple grippers. Customized hand prostheses with high performance are too expensive for the average arm amputee. Currently, commercially available hand prostheses use costly and heavy DC motors for actuation. This paper presents an inexpensive hand prosthesis, which uses a 3D printable design to reduce the cost of customizable parts and novel electro-thermal actuator based on nylon 6-6 polymer muscles. The prosthetic hand was tested and found to be able to grasp a variety of shapes 100% of the time tested (sphere, cylinder, cube, and card) and other commonly used tools. Grip times for each object were repeatable with small standard deviations. With a low estimated material cost of $170 for actuation, this prosthesis could have a potential to be used for low-cost and high-performance system.


Queue Mining: Service Perspectives in Process Mining


title: Fungi—an unusual source for cosmetics

abstract: Fruiting bodies of some wild and cultivatable mushrooms contain medicinal compounds which are being used in traditional medicines and cosmetics. There are numerous potential medicinal products from mushrooms that could be used in cosmeceuticals (products applied topically, such as creams, lotions, and ointments) or nutricosmetics (products that are ingested orally). This paper provides a review of the fungi presently used in cosmeceuticals and nutricosmetics with some examples of cosmetic types and products. Species presently used, or patented to be used, in cosmeceuticals and nutricosmetics include Agaricus subrufescens (= A. blazei, A. brasiliensis) Choiromyces maeandriformis Cordyceps sinensis, Ganoderma lucidum, Grifola frondosa, Hypsizygus ulmarium, Inonotus obliquus, Lentinula edodes, Polyporus spp., Trametes versicolor, Tremella fuciformis, Tuber spp., Schizophyllum commune and many other lesser used taxa. Cosmetics incorporating fungi include those for skin care such as anti-aging, anti-oxidants, skin revitalizing, skin whitening and hair products. The mushrooms presently used are traditionally known to produce medicinal compounds and thus were the first to be incorporated in cosmetic applications. There are, however, numerous other mushroom species that are untested, undescribed or not yet cultivatable and that have huge potential for use in the cosmetic industry. Some fungi are also used in biotransformation and the products such as lactic acid and ceramides could potentially be used in cosmetics.


title: W.T.: SIFT flow: dense correspondence across different scenes. In: ECCV

abstract: While image registration has been studied in different areas of computer vision, aligning images depicting different scenes remains a challenging problem, closer to recognition than to image matching. Analogous to optical flow, where an image is aligned to its temporally adjacent frame, we propose SIFT flow, a method to align an image to its neighbors in a large image collection consisting of a variety of scenes. For a query image, histogram intersection on a bag-of-visual-words representation is used to find the set of nearest neighbors in the database. The SIFT flow algorithm then consists of matching densely sampled SIFT features between the two images, while preserving spatial discontinuities. The use of SIFT features allows robust matching across different scene/object appearances and the discontinuity-preserving spatial model allows matching of objects located at different parts of the scene. Experiments show that the proposed approach is able to robustly align complicated scenes with large spatial distortions. We collect a large database of videos and apply the SIFT flow algorithm to two applications: (i) motion field prediction from a single static image and (ii) motion synthesis via transfer of moving objects.


title: High Power Wideband Class-E Power Amplifier

abstract: This letter shows a high-power, high-efficiency, wideband Class-E RF power amplifier designed upon the load admittance synthesis concept and built using an uncomplicated low-loss load network with a low loss wideband admittance transformer as the main component. It uses a power Silicon LDMOS transistor to provide up to 145 W at 28 V peak power, up to 86% drain efficiency over 35% fractional bandwidth (from 85 to 120 MHz) and 15.6 dB gain at peak power without any adjustments. These are clear performance advantages over previous published works and commercially available amplifiers at a similar frequency band and power level. The amplifier applications include FM broadcast, aeronautical communications, nuclear, MRI, heating or RF power stage for Envelope Elimination and Restoration transmitters.


title: Towards Energy-Efficient Adaptive Mpeg-Dash Streaming Using Hevc

abstract: Green networking and communications have become important factors in nowadays energy-hungry video transmission, where users expect to gain high-quality video regardless of time and place. Modern processors, displays and high-speed mobile networks already enable high-definition video streaming to mobile devices that can decode and playback the stream successfully while still maintaining the device capacity for other tasks. However, user mobility and network congestion can affect even the best machinery and introduce the need for HTTP adaptive streaming, which has popularized among content providers. Still, its optimal utilization especially in terms of energy-efficiency is slightly investigated issue. In this paper, we tackle this issue and evaluate both the content production (headend) as well mobile user (terminal) energy consumption against different adaptation methods considering CPU vs. GPU based approaches. The results indicate that frame rate adaptation provides the lowest consumption in both ends and usage of GPUs can lead to further savings.


title: A Spectral Regularizer for Unsupervised Disentanglement

abstract: Generative models that learn to associate variations in the output along isolated attributes with coordinate directions in latent space are said to possess disentangled representations. This has been a recent topic of great interest, but remains poorly understood. We show that even for GANs that do not possess disentangled representations, one can find paths in latent space over which local disentanglement occurs. These paths are determined by the leading right-singular vectors of the Jacobian of the generator with respect to its input. We describe an efficient regularizer that aligns these vectors with the coordinate axes, and show that it can be used to induce high-quality, disentangled representations in GANs, in a completely unsupervised manner.


title: Improved Person Detection on Omnidirectional Images with Non-maxima Suppression

abstract: We propose a person detector on omnidirectional images, an accurate method to generate minimal enclosing rectangles of persons. The basic idea is to adapt the qualitative detection performance of a convolutional neural network based method, namely YOLOv2 to fish-eye images. The design of our approach picks up the idea of a state-of-the-art object detector and highly overlapping areas of images with their regions of interests. This overlap reduces the number of false negatives. Based on the raw bounding boxes of the detector we fine-tuned overlapping bounding boxes by three approaches: the non-maximum suppression, the soft non-maximum suppression and the soft non-maximum suppression with Gaussian smoothing. The evaluation was done on the PIROPO database and an own annotated Flat dataset, supplemented with bounding boxes on omnidirectional images. We achieve an average precision of 64.4 % with YOLOv2 for the class person on PIROPO and 77.6 % on Flat. For this purpose we fine-tuned the soft non-maximum suppression with Gaussian smoothing.


title: Minimizing Walking Length in Map Matching

abstract: In this paper, we propose a geometric algorithm for a map matching problem. More specifically, we are given a planar graph, H, with a straight-line embedding in a plane, a directed polygonal curve, T, and a distance value \(\varepsilon >0\). The task is to find a path, P, in H, and a parameterization of T, that minimize the sum of the length of walks on T and P whereby the distance between the entities moving along P and T is at most \(\varepsilon \), at any time during the walks. It is allowed to walk forwards and backwards on T and edges of H. We propose an algorithm with \(\mathcal {O}\left( mn \left( m+n\right) \log (mn)\right) \) time complexity and \(\mathcal {O}\left( mn \left( m+n\right) \right) \) space complexity, where m (n, respectively) is the number of edges of H (of T, respectively). As we show, the algorithm can be generalized to work also for weighted non-planar graphs within the same time and space complexities.


title: Optimal design of an axial flux switched reluctance motor with grain oriented electrical steel

abstract: This paper proposes using grain oriented electrical steel (GOES) in axial flux switched reluctance motors (AFSRM) to improve the torque characteristic employing the material's excellent magnetic properties in the rolling direction. Based on the studying the parameter and switching characteristics of an AFSRM, GOES can be divided into pieces to be arranged in the rotor and stator tooth core so that the main magnetic flux flows can follow the rolling direction of the material during the phase conduction period. Three dimensional and quasi three dimensional finite element methods are employed to analyze and optimize AFSRM with different shapes and deviation angles between the magnetic flux and rolling direction of the GOES; the maximum electromagnetic torque of the proposed design shows a 21.4% increase in comparison with the one of conventional non-oriented electrical steel (NOES) motors under identical conditions.


title: The Marx generator as an ultra wideband source

abstract: Summary form only given. Traditional uses of the Marx generator have been limited to energy storage and delivery systems, such as charging capacitors or pulse forming lines. However, low energy, high peak power Marx generators are finding applications in Ultra Wideband radar and high power microwave systems. This paper presents the results of a new compact, high powered Marx generator designed for these applications. The pulse parameters of the generator are discussed, as well as radiation measurements made with TEM horn antennas.


title: Image Sensor Based Visible Light Communication and Its Application to Pose, Position, and Range Estimations

abstract: SUMMARY This study introduces an image sensor based visible light communication (VLC) and its application to pose, position, and range estimations. There are two types of visible-light receiver: a photodiode and an image sensor. A photodiode is usually used as a reception device of VLC, and an image sensor consisting of a large number of pixels can also be used as a VLC reception device. A photodiode detects the signal intensity of incoming light, while an image sensor not only detects the incoming signal intensity but also an accurate angle of arrival of light emitted from a visible light transmitter such as a white LED light. After angles of arrival of light are detected by an image sensor, positioning and data reception can be performed. The ability of an image sensor to detect an accurate angle of arrival will provide attractive applications of VLC such as pose, position calculation, and range estimation. Furthermore, because the image sensor has the ability to spatially separate sources, outdoor positioning even with strong


Perfect cryptographic security from partially independent channels


title: Age, executive function, and social decision making: A dorsolateral prefrontal theory of cognitive aging

abstract: Current neuropsychological models propose that some age-related cognitive changes are due to frontal-lobe deterioration. However, these models have not considered the possible subdivision of the frontal lobes into the dorsolateral and ventromedial regions. This study assessed the age effects on 3 tasks of executive function and working memory, tasks dependent on dorsolateral prefrontal dysfunction; and 3 tasks of emotion and social decision making, tasks dependent on ventromedial prefrontal dysfunction. Age-related differences in performance were found on all tasks dependent on dorsolateral prefrontal dysfunction. In contrast, age-related differences were not found on the majority of the tasks dependent on ventromedial prefrontal dysfunction. The results support a specific dorsolateral prefrontal theory of cognitive changes with age, rather than a global decline in frontal-lobe function.


Inserting Spoken Language in Grammar Teaching Material Based on Interactive Mulimedia


title: Big Linked Cancer Data : Integrating Linked TCGA and PubMed

abstract: The amount of bio-medical data available on the Web grows exponentially with time. The resulting large volume of data makes manual exploration very tedious. Moreover, the velocity at which this data changes and the variety of formats in which bio-medical data is published makes it difficult to access them in an integrated form. Finally, the lack of an integrated vocabulary makes querying this data more difficult. In this paper, we advocate the use of Linked Data to integrate, query and visualize bio-medical data. The resulting Big Linked Data allows discovering knowledge distributed across manifold sources, making it viable for the serendipitous discovery of novel knowledge. We present the concept of Big Linked Data by showing how the constant stream of new bio-medical publications can be integrated with the Linked Cancer Genome Atlas dataset (TCGA) within a virtual integration scenario. We ensure the scalability of our approach through the novel TopFed federated query engine, which we evaluate by comparing the query execution time of our system with that of FedX on Linked TCGA. Then, we show how we can harness the value hidden in the underlying integrated data by making it easier to explore through a user-friendly interface. We evaluate the usability of the interface by using the standard system usability questionnaire as well as a customized questionnaire designed for the users of our system. Our overall result of 77 suggests that our interface is easy to use and can thus lead to novel insights.


title: Usability Evaluation of Laboratory and Radiology Information Systems Integrated into a Hospital Information System

abstract: This study was conducted to evaluate the usability of widely used laboratory and radiology information systems. Three usability experts independently evaluated the user interfaces of Laboratory and Radiology Information Systems using heuristic evaluation method. They applied Nielsen's heuristics to identify and classify usability problems and Nielsen's severity rating to judge their severity. Overall, 116 unique heuristic violations were identified as usability problems. In terms of severity, 67 % of problems were rated as major and catastrophic. Among 10 heuristics, "consistency and standards" was violated most frequently. Moreover, mean severity of problems concerning "error prevention" and "help and documentation" heuristics was higher than of the others. Despite widespread use of specific healthcare information systems, they suffer from usability problems. Improving the usability of systems by following existing design standards and principles from the early phased of system development life cycle is recommended. Especially, it is recommended that the designers design systems that inhibit the initiation of erroneous actions and provide sufficient guidance to users.


title: High-rate codes that are linear in space and time

abstract: Multiple-antenna systems that operate at high rates require simple yet effective space-time transmission schemes to handle the large traffic volume in real time. At rates of tens of bits per second per hertz, Vertical Bell Labs Layered Space-Time (V-BLAST), where every antenna transmits its own independent substream of data, has been shown to have good performance and simple encoding and decoding. Yet V-BLAST suffers from its inability to work with fewer receive antennas than transmit antennas-this deficiency is especially important for modern cellular systems, where a base station typically has more antennas than the mobile handsets. Furthermore, because V-BLAST transmits independent data streams on its antennas there is no built-in spatial coding to guard against deep fades from any given transmit antenna. On the other hand, there are many previously proposed space-time codes that have good fading resistance and simple decoding, but these codes generally have poor performance at high data rates or with many antennas. We propose a high-rate coding scheme that can handle any configuration of transmit and receive antennas and that subsumes both V-BLAST and many proposed space-time block codes as special cases. The scheme transmits substreams of data in linear combinations over space and time. The codes are designed to optimize the mutual information between the transmitted and received signals. Because of their linear structure, the codes retain the decoding simplicity of V-BLAST, and because of their information-theoretic optimality, they possess many coding advantages. We give examples of the codes and show that their performance is generally superior to earlier proposed methods over a wide range of rates and signal-to-noise ratios (SNRs).


title: A LED micro-display with 90×90 pixels on a 80 μm pitch

abstract: We report on the flip-chip packaging of 90×90 arrays of addressable microLEDs onto CMOS circuitry by a solder reflow process. The substrate emitting microLEDs were parabolically shaped and fabricated on free-standing GaN. The individual emitters were 15 μm in diameter with an array pitch of 80 μm where all bond pads were given a gold finish. The solder balls were dispensed onto the CMOS pads with an automated PacTech solder dispenser that dispensed 50 μm diameter solder spheres with a composition of Sn (96.5%)-Ag-(3%)-Cu (0.5%). After alignment of the LED array on top of the CMOS chip, the solder was reflowed at a temperature of 260 C in nitrogen ambient. The completed assemblies form micro-displays that can display computer generated patterns designed for the stimulation of neural cells. The displays were designed to be mounted on the camera port of a microscope to project the emitted light onto neural tissue.


title: Swarm Intelligence in Optimization

abstract: Optimization techniques inspired by swarm intelligence have become increasingly popular during the last decade. They are characterized by a decentralized way of working that mimics the behavior of swarms of social insects, flocks of birds, or schools of fish. The advantage of these approaches over traditional techniques is their robustness and flexibility. These properties make swarm intelligence a successful design paradigm for algorithms that deal with increasingly complex problems. In this chapter we focus on two of the most successful examples of optimization techniques inspired by swarm intelligence: ant colony optimization and particle swarm optimization. Ant colony optimization was introduced as a technique for combinatorial optimization in the early 1990s. The inspiring source of ant colony optimization is the foraging behavior of real ant colonies. In addition, particle swarm optimization was introduced for continuous optimization in the mid-1990s, inspired by bird flocking.


title: Data Mining for Improving a Cleaning Process in the Semiconductor Industry

abstract: As device geometry continues to shrink, micro-contaminants have an increasingly negative impact on yield. By diminishing the contamination problem, semiconductor manufacturers will significantly improve wafer yield. This paper presents a comprehensive and successful application of data mining methodologies to the refinement of a new dry cleaning technology that utilizes a laser beam for the removal of micro-contaminants. Experiments with three classification-based data mining methods (decision tree induction, neural networks, and composite classifiers) have been conducted. The composite classifier architecture has been shown to yield higher accuracy than the accuracy of each individual classifier on its own. The paper suggests that data mining methodologies may be particularly useful when data is scarce, and the various physical and chemical parameters that affect the process exhibit highly complex interactions. Another implication is that on-line monitoring of the cleaning process using data mining may be highly effective.


0 Wireless sensor networks : modeling and simulation


title: Unsupervised Clustering Approach for Network Anomaly Detection

abstract: This paper describes the advantages of using the anomaly detection approach over the misuse detection technique in detecting unknown network intrusions or attacks. It also investigates the performance of various clustering algorithms when applied to anomaly detection. Five different clustering algorithms: k-Means, improved k-Means, k-Medoids, EM clustering and distance-based outlier detection algorithms are used. Our experiment shows that misuse detection techniques, which implemented four different classifiers (naive Bayes, rule induction, decision tree and nearest neighbour) failed to detect network traffic, which contained a large number of unknown intrusions; where the highest accuracy was only 63.97% and the lowest false positive rate was 17.90%. On the other hand, the anomaly detection module showed promising results where the distance-based outlier detection algorithm outperformed other algorithms with an accuracy of 80.15%. The accuracy for EM clustering was 78.06%, for k-Medoids it was 76.71%, for improved k-Means it was 65.40% and for k-Means it was 57.81%. Unfortunately, our anomaly detection module produces high false positive rate (more than 20%) for all four clustering algorithms. Therefore, our future work will be more focus in reducing the false positive rate and improving the accuracy using more advance machine learning techniques.


title: Scapular winging: anatomical review, diagnosis, and treatments

abstract: Scapular winging is a rare debilitating condition that leads to limited functional activity of the upper extremity. It is the result of numerous causes, including traumatic, iatrogenic, and idiopathic processes that most often result in nerve injury and paralysis of either the serratus anterior, trapezius, or rhomboid muscles. Diagnosis is easily made upon visible inspection of the scapula, with serratus anterior paralysis resulting in medial winging of the scapula. This is in contrast to the lateral winging generated by trapezius and rhomboid paralysis. Most cases of serratus anterior paralysis spontaneously resolve within 24 months, while conservative treatment of trapezius paralysis is less effective. A conservative course of treatment is usually followed for rhomboid paralysis. To allow time for spontaneous recovery, a 6–24 month course of conservative treatment is often recommended, after which if there is no recovery, patients become candidates for corrective surgery.


title: Sequence to Sequence -- Video to Text

abstract: Real-world videos often have complex dynamics, methods for generating open-domain video descriptions should be senstive to temporal structure and allow both input (sequence of frames) and output (sequence of words) of variable length. To approach this problem we propose a novel end-to-end sequence-to-sequence model to generate captions for videos. For this we exploit recurrent neural networks, specifically LSTMs, which have demonstrated state-of-the-art performance in image caption generation. Our LSTM model is trained on video-sentence pairs and learns to associate a sequence of video frames to a sequence of words in order to generate a description of the event in the video clip. Our model naturally is able to learn the temporal structure of the sequence of frames as well as the sequence model of the generated sentences, i.e. a language model. We evaluate several variants of our model that exploit different visual features on a standard set of YouTube videos and two movie description datasets (M-VAD and MPII-MD).


title: The curvelet transform for image denoising

abstract: Summary form only given, as follows. We present approximate digital implementations of two new mathematical transforms, namely, the ridgelet transform and the curvelet transform. Our implementations offer exact reconstruction, stability against perturbations, ease of implementation, and low computational complexity. We apply these digital transforms to the denoising of some standard images embedded in white noise. In the tests reported here, simple thresholding of the curvelet coefficients is very competitive with 'state of the art' techniques based on wavelets, including thresholding of decimated or undecimated wavelet transforms and also including tree-based Bayesian posterior mean methods. Moreover, the curvelet reconstructions exhibit higher perceptual quality than wavelet-based reconstructions, offering visually sharper images and, in particular, higher quality recovery of edges and of faint linear and curvilinear features.


title: Intrinsic Brain Activity in Altered States of Consciousness -- How Conscious Is the Default Mode of Brain Function?

abstract: Spontaneous brain activity has recently received increasing interest in the neuroimaging community. However, the value of resting-state studies to a better understanding of brain–behavior relationships has been challenged. That altered states of consciousness are a privileged way to study the relationships between spontaneous brain activity and behavior is proposed, and common resting-state brain activity features observed in various states of altered consciousness are reviewed. Early positron emission tomography studies showed that states of extremely low or high brain activity are often associated with unconsciousness. However, this relationship is not absolute, and the precise link between global brain metabolism and awareness remains yet difficult to assert. In contrast, voxel-based analyses identified a systematic impairment of associative frontoparieto–cingulate areas in altered states of consciousness, such as sleep, anesthesia, coma, vegetative state, epileptic loss of consciousness, and somnambulism. In parallel, recent functional magnetic resonance imaging studies have identified structured patterns of slow neuronal oscillations in the resting human brain. Similar coherent blood oxygen level–dependent (BOLD) systemwide patterns can also be found, in particular in the default-mode network, in several states of unconsciousness, such as coma, anesthesia, and slow-wave sleep. The latter results suggest that slow coherent spontaneous BOLD fluctuations cannot be exclusively a reflection of conscious mental activity, but may reflect default brain connectivity shaping brain areas of most likely interactions in a way that transcends levels of consciousness, and whose functional significance remains largely in the dark.


title: Electronic personal maternity records: Both web and smartphone services

abstract: This study develops an antenatal care information system to assist women during pregnancy. We designed and implemented the system as both a web-based service and a multi-platform application for smartphones and tablets. The proposed system has three novel features: (1) web-based maternity records, which contains concise explanations of various antenatal screening and diagnostic tests; (2) self-care journals, which allow pregnant women to keep track of their gestational weight gains, blood pressure, fetal movements, and contractions; and (3) health education, which automatically presents detailed information on antenatal care and other pregnancy-related knowledge according to the women's gestational age. A survey was conducted among pregnant women to evaluate the usability and acceptance of the proposed system. In order to prove that the antenatal care was effective, clinical outcomes should be provided and the results are focused on a usability evaluation.


Autophagy regulates odontoblast differentiation by suppressing NF-κB activation in an inflammatory environment


title: Single image dehazing

abstract: In this paper we present a new method for estimating the optical transmission in hazy scenes given a single input image. Based on this estimation, the scattered light is eliminated to increase scene visibility and recover haze-free scene contrasts. In this new approach we formulate a refined image formation model that accounts for surface shading in addition to the transmission function. This allows us to resolve ambiguities in the data by searching for a solution in which the resulting shading and transmission functions are locally statistically uncorrelated. A similar principle is used to estimate the color of the haze. Results demonstrate the new method abilities to remove the haze layer as well as provide a reliable transmission estimate which can be used for additional applications such as image refocusing and novel view synthesis.


title: Achieving high coverage for floating-point code via unconstrained programming

abstract: Achieving high code coverage is essential in testing, which gives us confidence in code quality. Testing floating-point code usually requires painstaking efforts in handling floating-point constraints, e.g., in symbolic execution. This paper turns the challenge of testing floating-point code into the opportunity of applying unconstrained programming --- the mathematical solution for calculating function minimum points over the entire search space. Our core insight is to derive a representing function from the floating-point program, any of whose minimum points is a test input guaranteed to exercise a new branch of the tested program. This guarantee allows us to achieve high coverage of the floating-point program by repeatedly minimizing the representing function. We have realized this approach in a tool called CoverMe and conducted an extensive evaluation of it on Sun's C math library. Our evaluation results show that CoverMe achieves, on average, 90.8% branch coverage in 6.9 seconds, drastically outperforming our compared tools: (1) Random testing, (2) AFL, a highly optimized, robust fuzzer released by Google, and (3) Austin, a state-of-the-art coverage-based testing tool designed to support floating-point code.


An Algorithm For Object Removal And Image Completion Using Exemplar-Based Image Inpainting


title: Artificial General Intelligence : a Gentle Introduction

abstract: Artificial General Intelligence (AGI) treats "intelligence" as a whole, therefore it is different from most of the traditional AI works. This talk outline presents an overview of AGI, by summarizing its historical development, fundamental problems, as well as some representative projects. Additional materials are linked in the text.


title: A Framework for Emotion Mining from Text in Online Social Networks

abstract: Online Social Networks are so popular nowadays that they are a major component of an individual’s social interaction. They are also emotionally-rich environments where close friends share their emotions, feelings and thoughts. In this paper, a new framework is proposed for characterizing emotional interactions in social networks, and then using these characteristics to distinguish friends from acquaintances. The goal is to extract the emotional content of texts in online social networks. The interest is in whether the text is an expression of the writer’s emotions or not. For this purpose, text mining techniques are performed on comments retrieved from a social network. The framework includes a model for data collection, database schemas, data processing and data mining steps. The informal language of online social networks is a main point to consider before performing any text mining techniques. This is why the framework includes the development of special lexicons. In general, the paper presents a new perspective for studying friendship relations and emotions’ expression in online social networks where it deals with the nature of these sites and the nature of the language used. It considers Lebanese Face book users as a case study. The technique adopted is unsupervised, it mainly uses the k-means clustering algorithm. Experiments show high accuracy for the model in both determining subjectivity of texts and predicting friendship.


title: Concentrated versus distributed winding in permanent magnet synchronous motors

abstract: This paper deals with performances of the two cylindrical permanent magnet synchronous motors (PMSM) identical from the point of view of geometrical dimensions but with different stator windings and PM materials. Motor A has fractional slot concentrated winding and rotor with ferrite magnets and motor B has distributed windings and rotor with NdFeB magnets. Parameters and performances of both motors are investigated by calculations, measurements and FEM and differences of both motors are discussed.


title: Communication protocols for underwater data collection using a robotic sensor network

abstract: We examine the problem of collecting data from an underwater sensor network using an autonomous underwater vehicle (AUV). The sensors in the network are equipped with acoustic modems that provide noisy, range-limited communication to the AUV. One challenge in this scenario is to plan paths that maximize the information collected and minimize travel time. While executing a path, the AUV can improve performance by communicating with multiple nodes in the network at once. Such multi-node communication requires a scheduling protocol that is robust to channel variations and interference. To solve this problem, we develop and test a multiple access control protocol for the underwater data collection scenario. We perform simulated experiments that utilize a realistic model of acoustic communication taken from experimental test data. These simulations demonstrate that properly designed scheduling protocols are essential for choosing the appropriate path planning algorithms for data collection.


title: Using the Kinect for detecting tremors: Challenges and opportunities

abstract: The Kinect sensor is an attachment for the Xbox gaming console which allows players to interact with games through body movement. This paper explores the possibility of using the Kinect in a clinical setting for detection of Parkinson's, postural, flapping, titubation and voice tremors. All of the physical tremors were simulated and the ability of the Kinect to capture them was evaluated. The Kinect was also used to record voice data from real voice tremors patients. Physical and voice data were gathered from healthy persons for comparison. The results showed that the Kinect could reliably detect voice, postural and Parkinson's tremors. A very consistent graph could be obtained repeatedly for both Parkinson's and postural tremors. For voice tremors there was also a consistent pattern that differentiated a normal voice from one with a tremor. We have therefore proven that the Kinect can consistently record Parkinson's, postural and voice tremors.


What Should Be in an XAI Explanation? What IFT Reveals


title: Interpretable conditions for identifying direct and indirect effects

abstract: Abstract : This paper translates the conditions necessary for the identification of natural direct and indirect effects into a transparent language, thus permitting a more informed judgment of the plausibility of these conditions. We show that the conditions usually cited in the literature are overly restricted, and can be relaxed substantially, without compromising identification. In particular, we show that natural effects can be identified by methods other than adjustment. The identification conditions can be further relaxed in parametric models with interactions, and permit us to compare the relative importance of several pathways, mediated by interdependent variables.


title: Crowdsourcing New Product Ideas over Time: An Analysis of the Dell IdeaStorm Community

abstract: Several organizations have developed ongoing crowdsourcing communities that repeatedly collect ideas for new products and services from a large, dispersed “crowd” of nonexperts consumers over time. Despite its promises, little is known about the nature of an individual's ideation efforts in such an online community. Studying Dell's IdeaStorm community, serial ideators are found to be more likely than consumers with only one idea to generate an idea the organization finds valuable enough to implement, but they are unlikely to repeat their early success once their ideas are implemented. As ideators with past success attempt to again come up with ideas that will excite the organization, they instead end up proposing ideas similar to their ideas that were already implemented i.e., they generate less diverse ideas. The negative effects of past success are somewhat mitigated for ideators with diverse commenting activity on others' ideas. These findings highlight some of the challenges in maintaining an ongoing supply of quality ideas from the crowd over time. ::: ::: This paper was accepted by Kamalini Ramdas, entrepreneurship and innovation.


title: Process Mining Extension to SCAMPI

abstract: Existing process assessment methods, such as SCAMPI-Standard CMMI Appraisal Method for Process Improvement, do not use contemporary data collection and analysis techniques like processes mining, text mining or data mining. On the contrary, they use traditional ones: questionnaires, document review, interviews and demonstrations. Process mining is a technique that can be used to aid process assessments, aiming to conduct them with greater deepness and coverage, while keeping similar level of effort. The purpose of the PhD work is to develop a framework (structure and content) to apply process mining techniques in SCAMPI assessments.


title: A Metric for Covariance Matrices

abstract: The paper presents a metric for positive definite covariance matrices. It is a natural expression involving traces and joint eigenvalues of the matrices. It is shown to be the distance coming from a canonical invariant Riemannian metric on the space Sym + (n, ℝ) of real symmetric positive definite matrices


title: The effects of a mindfulness meditation-based stress reduction program on mood and symptoms of stress in cancer outpatients: 6-month follow-up

abstract: Abstract The goals of this work were to assess the effects of participation in a mindfulness meditation-based stress reduction program on mood disturbance and symptoms of stress in cancer outpatients immediately after and 6 months after program completion. A convenience sample of eligible cancer patients were enrolled after they had given informed consent. All patients completed the Profile of Mood States (POMS) and Symptoms of Stress Inventory (SOSI) both before and after the intervention and 6 months later. The intervention consisted of a mindfulness meditation group lasting 1.5 h each week for 7 weeks, plus daily home meditation practice. A total of 89 patients, average age 51, provided pre-intervention data. Eighty patients provided post-intervention data, and 54 completed the 6-month follow-up The participants were heterogeneous with respect to type and stage of cancer. Patients' scores decreased significantly from before to after the intervention on the POMS and SOSI total scores and most subscales, indicating less mood disturbance and fewer symptoms of stress, and these improvements were maintained at the 6-month follow-up. More advanced stages of cancer were associated with less initial mood disturbance, while more home practice and higher initial POMS scores predicted improvements on the POMS between the pre- and post-intervention scores. Female gender and more education were associated with higher initial SOSI scores, and improvements on the SOSI were predicted by more education and greater initial mood disturbance. This program was effective in decreasing mood disturbance and stress symptoms for up to 6 months in both male and female patients with a wide variety of cancer diagnoses, stages of illness, and educational background, and with disparate ages.


title: Evolve or Die: High-Availability Design Principles Drawn from Googles Network Infrastructure

abstract: Maintaining the highest levels of availability for content providers is challenging in the face of scale, network evolution and complexity. Little, however, is known about failures large content providers are susceptible to, and what mechanisms they employ to ensure high availability. From a detailed analysis of over 100 high-impact failure events in a global-scale content provider encompassing several data centers and two WANs, we quantify several dimensions of availability failures. We find that failures are evenly distributed across different network types and planes, but that a large number of failures happen when a management operation is in progress within the network. We discuss some of these failures in detail, and also describe our design principles for high availability motivated by these failures, including using defense in depth, maintaining consistency across planes, failing open on large failures, carefully preventing and avoiding failures, and assessing root cause quickly. Our findings suggest that, as networks become more complicated, failures lurk everywhere, and, counter-intuitively, continuous incremental evolution of the network can, when applied together with our design principles, result in a more robust network.


title: Analysis on data mining models for Internet Of Things

abstract: This paper's aim is to establish the relationship between Data Mining and the Internet of Things. Four models for data mining have been discussed indicating some challenges of the data for Internet of Things which are multi-layer model, distributed model, grid based model and big data model. The key issues about the models like collection of data, data abstraction and aggregation, event filtering etc. are also discussed. After reviewing the key issues, a new model has been proposed. In addition, a tweet sentiment analyzer project has been made on the concept of data mining using the python language.


Vulval Varicosities in Pregnancy


title: Learning Nonlinear Generative Models of Time Series With a Kalman Filter in RKHS

abstract: This paper presents a novel generative model for time series based on the Kalman filter algorithm in a reproducing kernel Hilbert space (RKHS) using the conditional embedding operator. The end result is a nonlinear model that quantifies the hidden state uncertainty and propagates its probability distribution forward as in the Kalman algorithm. The embedded dynamics can be described by the estimated conditional embedding operator constructed directly from the training measurement data. Using this operator as the counterpart of the state transition matrix, we reformulate the Kalman filter algorithm in RKHS. For the state model, the hidden states are the estimated embeddings of the measurement distribution, while the measurement model serves to connect the estimated measurement embeddings with the current mapped measurements in the RKHS. This novel algorithm is applied to noisy time-series estimation and prediction, and simulation results show that it outperforms other existing algorithms. In addition, improvements are proposed to reduce the size of the operator and reduce the computation complexity.


title: Electromagnetic simulation of 3D stacked ICs: Full model vs. S-parameter cascaded based model

abstract: Three-dimensional electromagnetic simulation models are often simplified and/or segmented in order to reduce the simulation time and memory requirements without sacrificing the accuracy of the results. This paper investigates the difference between full model and S-parameter cascaded based model of 3D stacked ICs with the presence of Through Silicon Vias. It is found that the simulation of the full model is required for accurate results, however, a divide and conquers (segmentation) approach can be used for preliminary post layout analysis. Modeling guidelines are discussed and details on the proper choice of ports, boundary conditions, and solver technology are highlighted. A de-embedding methodology is finally explored to improve the accuracy of the cascaded/segmented results.


title: Network Traffic Classification with Improved Random Forest

abstract: Accurate network traffic classification is significant to numerous network activities, such as QoS and network management etc. While port-based or payload-based classification methods are becoming more and more difficult, Machine Learning methods are promising in many aspects. In this paper, we improve the standard Random Forest by setting the variable selection probability according to the importance of the corresponding variable to classify network traffic. Our test results show that the Improved Random Forest has better classification performance. And it takes less time to build the model.


title: Elie Metchnikoff: Father of natural immunity

abstract: We celebrate the centenary of the Nobel award to Elie Metchnikoff in 2008, shared with Paul Ehrlich, as respective pioneers of cellular and humoral immunology. Metchnikoff is rightly famous for his recognition of the biological significance of leukocyte recruitment and phagocytosis of microbes in host defence against infection, inflammation and immunity. As a comparative zoologist he utilised a broad range of model organisms for microscopic studies in vivo and in vitro. His work prefigures much of contemporary research in innate immunity. In this brief review, I cover some details of his Europe-wide life in Russia (mainly Odessa) and at the Pasteur Institute in Paris. He had a complex personality, yet his creative genius, imagination and insights justify the title of “Father of natural immunity”.


How Well Do Change Sequences Predict Defects? Sequence Learning from Software Changes


title: 3-D low-loss coplanar waveguide transmission lines in multilayer MMICs

abstract: Newly developed transmission-line structures using the great flexibility of three-dimensional multilayer technology have been designed and fabricated. In this paper, we demonstrate that monolithic microwave integrated circuit (MMIC) coplanar waveguide transmission lines with a wide range of characteristic impedances can easily be designed using the multilayer technique. Furthermore, this implementation can avoid the well-known current crowding effects on the conductor edges minimizing dissipation loss. The system of three layers of metals and two layers of sandwich polyimide as dielectrics was employed. The fabricated transmission lines have been characterized providing a wide range of impedances from 10 to 70 Ω. In addition, the effects of unintentional horizontal and vertical coupling in multilayer MMICs have been investigated. The results indicated that an optimum separation of 75 μm is necessary for negligible coupling (∼-30 dB).


title: Adapting attributes by selecting features similar across domains

abstract: Attributes are semantic visual properties shared by objects. They have been shown to improve object recognition and to enhance content-based image search. While attributes are expected to cover multiple categories, e.g. a dalmatian and a whale can both have "smooth skin", we find that the appearance of a single attribute varies quite a bit across categories. Thus, an attribute model learned on one category may not be usable on another category. We show how to adapt attribute models towards new categories. We ensure that positive transfer can occur between a source domain of categories and a novel target domain, by learning in a feature subspace found by feature selection where the data distributions of the domains are similar. We demonstrate that when data from the novel domain is limited, regularizing attribute models for that novel domain with models trained on an auxiliary domain (via Adaptive SVM) improves the accuracy of attribute prediction.


LAYOUTGAN: GENERATING GRAPHIC LAYOUTS


title: Effectiveness of Animation in Trend Visualization

abstract: Animation has been used to show trends in multi-dimensional data. This technique has recently gained new prominence for presentations, most notably with Gapminder Trendalyzer. In Trendalyzer, animation together with interesting data and an engaging presenter helps the audience understand the results of an analysis of the data. It is less clear whether trend animation is effective for analysis. This paper proposes two alternative trend visualizations that use static depictions of trends: one which shows traces of all trends overlaid simultaneously in one display and a second that uses a small multiples display to show the trend traces side-by-side. The paper evaluates the three visualizations for both analysis and presentation. Results indicate that trend animation can be challenging to use even for presentations; while it is the fastest technique for presentation and participants find it enjoyable and exciting, it does lead to many participant errors. Animation is the least effective form for analysis; both static depictions of trends are significantly faster than animation, and the small multiples display is more accurate.


title: Infrastructure as a Service and Cloud Technologies

abstract: To choose the most appropriate cloud-computing model for your organization, you must analyze your IT infrastructure, usage, and needs. To help with this, this article describes cloud computing's current status.


title: Shaping in Practice: Training Wheels to Learn Fast Hopping Directly in Hardware

abstract: Learning instead of designing robot controllers can greatly reduce engineering effort required, while also emphasizing robustness. Despite considerable progress in simulation, applying learning directly in hardware is still challenging, in part due to the necessity to explore potentially unstable parameters. We explore the concept of shaping the reward landscape with training wheels; temporary modifications of the physical hardware that facilitate learning. We demonstrate the concept with a robot leg mounted on a boom learning to hop fast. This proof of concept embodies typical challenges such as instability and contact, while being simple enough to empirically map out and visualize the reward landscape. Based on our results we propose three criteria for designing effective training wheels for learning in robotics. A video synopsis can be found at https://youtu.be/6iH5E3LrYh8.


title: Robotic Arm Based 3D Reconstruction Test Automation

abstract: The 3-D reconstruction involves the construction of a 3-D model from a set of images. The 3-D reconstruction has varied uses that include 3-D printing, the generation of 3-D models that can be shared through social media, and more. The 3-D reconstruction involves complex computations in mobile phones that must determine the pose estimation. The pose estimation involves the process of transforming a 2-D object into 3-D space. Once the pose estimation is done, then the mesh generation is performed using the graphics processing unit. This helps render the 3-D object. The competitive advantages of using hardware processors are to accelerate the intensive computation using graphics processors and digital signal processors. The stated problem that this technical paper addresses is the need for a reliable automated test for the 3-D reconstruction feature. The solution to this problem involved the design and development of an automated test system using a programmable robotic arm and rotor for precisely testing the quality of 3-D reconstruction features. The 3-D reconstruction testing involves using a robotic arm lab to accurately test the algorithmic integrity and end-to-end validation of the generated 3-D models. The robotic arm can move the hardware at different panning speeds, specific angles, fixed distances from the object, and more. The ability to reproduce the scanning at a fixed distance and the same panning speed helps to generate test results that can be benchmarked by different software builds. The 3-D reconstruction also requires a depth sensor to be mounted onto the device under examination. We use this robotic arm lab for functional, high performance, and stable validation of the 3-D reconstruction feature. This paper addresses the computer vision use case testing for 3-D reconstruction features and how we have used the robotic arm lab for automating these use cases.


title: Learning Deep Generative Models of Graphs

abstract: Graphs are fundamental data structures which concisely capture the relational structure in many important real-world domains, such as knowledge graphs, physical and social interactions, language, and chemistry. Here we introduce a powerful new approach for learning generative models over graphs, which can capture both their structure and attributes. Our approach uses graph neural networks to express probabilistic dependencies among a graph's nodes and edges, and can, in principle, learn distributions over any arbitrary graph. In a series of experiments our results show that once trained, our models can generate good quality samples of both synthetic graphs as well as real molecular graphs, both unconditionally and conditioned on data. Compared to baselines that do not use graph-structured representations, our models often perform far better. We also explore key challenges of learning generative models of graphs, such as how to handle symmetries and ordering of elements during the graph generation process, and offer possible solutions. Our work is the first and most general approach for learning generative models over arbitrary graphs, and opens new directions for moving away from restrictions of vector- and sequence-like knowledge representations, toward more expressive and flexible relational data structures.


title: transition, and level flight control design for a single-propeller indoor airplane

abstract: This paper presents vehicle models and test flight results for an autonomous fixed-wing aircraft with the capability to take off, hover, transition to and from level-flight, and perch on a vertical landing platform. These maneuvers are all demonstrated in the highly space constrained environment of the Real-time indoor Autonomous Vehicle test ENvironment (RAVEN) at MIT. RAVEN promotes the rapid prototyping of UAV planning and control technologies by allowing the use of unmodified commercially available model aircraft for autonomous flight. Experimental results of several hover tests, transition maneuvers, and perch landings are presented. By enabling a fixed-wing UAV to achieve these feats, we demonstrate that the desirable speed and range performance of an autonomous fixed-wing aircraft in level flight can be complimented by hover capabilities that are typically limited to rotary-wing vehicles. This combination has the potential to significantly ease support and maintenance of operational autonomous vehicles.


title: Droiddetector: android malware characterization and detection using deep learning

abstract: Smartphones and mobile tablets are rapidly becoming indispensable in daily life. Android has been the most popular mobile operating system since 2012. However, owing to the open nature of Android, countless malwares are hidden in a large number of benign apps in Android markets that seriously threaten Android security. Deep learning is a new area of machine learning research that has gained increasing attention in artificial intelligence. In this study, we propose to associate the features from the static analysis with features from dynamic analysis of Android apps and characterize malware using deep learning techniques. We implement an online deep-learning-based Android malware detection engine (DroidDetector) that can automatically detect whether an app is a malware or not. With thousands of Android apps, we thoroughly test DroidDetector and perform an indepth analysis on the features that deep learning essentially exploits to characterize malware. The results show that deep learning is suitable for characterizing Android malware and especially effective with the availability of more training data. DroidDetector can achieve 96.76% detection accuracy, which outperforms traditional machine learning techniques. An evaluation of ten popular anti-virus softwares demonstrates the urgency of advancing our capabilities in Android malware detection.


title: Effect of Sweeteners on Quality of Yogurt

abstract: The fermentation time for the production of yogurts made by varying 3 sweeteners (honey, sucrose and fructose) at 2, 4 and 6% levels (by weight basis) was initially studied. The yogurt with honey added had a significantly shorter fermentation time (p < 0.05) than that with either glucose or fructose. The yogurt with sucrose added has more overall acceptance than with fructose, whereas honey yogurt has the lowest overall acceptance. The 4% honey yogurt showed a significant decrease (p < 0.05) in pH, but an increase in lactic acid production and Lactobacilli counts among the three yogurts (p < 0.05). With respect to storage time at 4 oC for 4 weeks, all yogurts with the three sweeteners showed a decrease in pH and an increase in lactic acid production whereas the syneresis rate was found to increase remarkably in the first week of storage, before changing slightly in later weeks. Also, the viability retention of Lactobacilli was found to range between 8.40-8.47 logCFU/g after the fourth week of storage.


title: Using gamification to incentivize sustainable urban mobility

abstract: Sustainable urban mobility is an important dimension in a Smart City, and one of the key issues for city sustainability. However, innovative and often costly mobility policies and solutions introduced by cities are liable to fail, if not combined with initiatives aimed at increasing the awareness of citizens, and promoting their behavioural change. This paper explores the potential of gamification mechanisms to incentivize voluntary behavioural changes towards sustainable mobility solutions. We present a service-based gamification framework, developed within the STREETLIFE EU Project, which can be used to develop games on top of existing services and systems within a Smart City, and discuss the empirical findings of an experiment conducted in the city of Rovereto on the effectiveness of gamification to promote sustainable urban mobility.


title: Development ofTheileria mutans (Theiler, 1906) in the gut and the haemolymph of the tickAmblyomma variegatum (Fabricius, 1794)

abstract: The development of Theileria mutans in the gut and haemolymph of its tick vector, Amblyomma variegatum, was studied by Giemsa-stained smears after the nymphal tick had completed engorgement on parasitaemic calves. Sexual stages occurred in the gut between five and seven days after repletion. The zygotes were observed in the gut from day 29 and the kinetes from day 30, i.e. three and four days after the nymphs moulted to adults. From day 34 kinetes were observed in the haemolymph. The developmental stages of T. mutans in the tick were compared with those of other Theileria spp. of cattle.


title: AI techniques for optimizing multi-objective reservoir operation upon human and riverine ecosystem demands

abstract: Summary Flow regime is the key driver of the riverine ecology. This study proposes a novel hybrid methodology based on artificial intelligence (AI) techniques for quantifying riverine ecosystems requirements and delivering suitable flow regimes that sustain river and floodplain ecology through optimizing reservoir operation. This approach addresses issues to better fit riverine ecosystem requirements with existing human demands. We first explored and characterized the relationship between flow regimes and fish communities through a hybrid artificial neural network (ANN). Then the non-dominated sorting genetic algorithm II (NSGA-II) was established for river flow management over the Shihmen Reservoir in northern Taiwan. The ecosystem requirement took the form of maximizing fish diversity, which could be estimated by the hybrid ANN. The human requirement was to provide a higher satisfaction degree of water supply. The results demonstrated that the proposed methodology could offer a number of diversified alternative strategies for reservoir operation and improve reservoir operational strategies producing downstream flows that could meet both human and ecosystem needs. Applications that make this methodology attractive to water resources managers benefit from the wide spread of Pareto-front (optimal) solutions allowing decision makers to easily determine the best compromise through the trade-off between reservoir operational strategies for human and ecosystem needs.


title: Dynamic solid waste collection and management system based on sensors, elevator and GSM

abstract: Solid waste management is one of major aspect which has to be considered in terms of making urban area environment healthier. The common dustbins placed by the municipal corporation are leading no. of health, environmental and social issues. Various causes are there like improper dustbin placement in city, improper system of collecting waste by City Corporation, and more specifically people are not aware enough to use dustbins in proper way. These various major causes are leading serious problems like, an unhygienic condition, air pollution, and unhealthy environment creating health disease. Up till now, research has been carried out by developing a Software Applications for indicating dustbin status, another by Shortest path method for garbage collecting vehicles by integrating RFID, GSM, GIS system; but no any active efforts has been taken paying attention towards managing such waste in atomized way. Considering all these major factors, a smart solid waste management system is designed that will check status and give alert of dustbin fullness and more significantly system has a feature to literate people to use dustbin properly and to automatically sense and clean garbage present outside the dustbin. Thus presented solution achieves smart solid waste management satisfying goal of making Indian cities clean, healthy and hygienic.


title: ConceptDoppler: a weather tracker for internet censorship

abstract: The text of this paper has passed across many Internet routers on its way to the reader, but some routers will not pass it along unfettered because of censored words it contains. We present two sets of results: 1) Internet measurements of keyword filtering by the Great “Firewall” of China (GFC); and 2) initial results of using latent semantic analysis as an efficient way to reproduce a blacklist of censored words via probing. Our Internet measurements suggest that the GFC’s keyword filtering is more a panopticon than a firewall, i.e., it need not block every illicit word, but only enough to promote self-censorship. China’s largest ISP, ChinaNET, performed 83.3% of all filtering of our probes, and 99.1% of all filtering that occurred at the first hop past the Chinese border. Filtering occurred beyond the third hop for 11.8% of our probes, and there were sometimes as many as 13 hops past the border to a filtering router. Approximately 28.3% of the Chinese hosts we sent probes to were reachable along paths that were not filtered at all. While more tests are needed to provide a definitive picture of the GFC’s implementation, our results disprove the notion that GFC keyword filtering is a firewall strictly at the border of China’s Internet. While evading a firewall a single time defeats its purpose, it would be necessary to evade a panopticon almost every time. Thus, in lieu of evasion, we propose ConceptDoppler, an architecture for maintaining a censorship “weather report” about what keywords are filtered over time. Probing with potentially filtered keywords is arduous due to the GFC’s complexity and can be invasive if not done efficiently. Just as an understanding of the mixing of gases preceded effective weather reporting, understanding of the relationship between keywords and concepts is essential for tracking Internet censorship. We show that LSA can effectively pare down a corpus of text and cluster filtered keywords for efficient probing, present 122 keywords we discovered by probing, and underscore the need for tracking and studying censorship blacklists by discovering some surprising blacklisted keywords such as l�‡ (conversion rate), �„K— (Mein Kampf), and ýE0(NfT�� (International geological scientific federation (Beijing)).


title: Gender identification from E-mails

abstract: In this paper, we investigate the topic of gender identification for short length, multi-genre, content-free e-mails. We introduce for the first time (to our knowledge), psycholinguistic and gender-linked cues for this problem, along with traditional stylometric features. Decision tree and Support Vector Machines learning algorithms are used to identify the gender of the author of a given e-mail. The experiment results show that our approach is promising with an average accuracy of 82.2%.


title: High-resolution mapping of global surface water and its long-term changes

abstract: The location and persistence of surface water (inland and coastal) is both affected by climate and human activity and affects climate, biological diversity and human wellbeing. Global data sets documenting surface water location and seasonality have been produced from inventories and national descriptions, statistical extrapolation of regional data and satellite imagery, but measuring long-term changes at high resolution remains a challenge. Here, using three million Landsat satellite images, we quantify changes in global surface water over the past 32 years at 30-metre resolution. We record the months and years when water was present, where occurrence changed and what form changes took in terms of seasonality and persistence. Between 1984 and 2015 permanent surface water has disappeared from an area of almost 90,000 square kilometres, roughly equivalent to that of Lake Superior, though new permanent bodies of surface water covering 184,000 square kilometres have formed elsewhere. All continental regions show a net increase in permanent water, except Oceania, which has a fractional (one per cent) net loss. Much of the increase is from reservoir filling, although climate change is also implicated. Loss is more geographically concentrated than gain. Over 70 per cent of global net permanent water loss occurred in the Middle East and Central Asia, linked to drought and human actions including river diversion or damming and unregulated withdrawal. Losses in Australia and the USA linked to long-term droughts are also evident. This globally consistent, validated data set shows that impacts of climate change and climate oscillations on surface water occurrence can be measured and that evidence can be gathered to show how surface water is altered by human activities. We anticipate that this freely available data will improve the modelling of surface forcing, provide evidence of state and change in wetland ecotones (the transition areas between biomes), and inform water-management decision-making.


title: Selfish supernumerary chromosome reveals its origin as a mosaic of host genome and organellar sequences.

abstract: Supernumerary B chromosomes are optional additions to the basic set of A chromosomes, and occur in all eukaryotic groups. They differ from the basic complement in morphology, pairing behavior, and inheritance and are not required for normal growth and development. The current view is that B chromosomes are parasitic elements comparable to selfish DNA, like transposons. In contrast to transposons, they are autonomously inherited independent of the host genome and have their own mechanisms of mitotic or meiotic drive. Although B chromosomes were first described a century ago, little is known about their origin and molecular makeup. The widely accepted view is that they are derived from fragments of A chromosomes and/or generated in response to interspecific hybridization. Through next-generation sequencing of sorted A and B chromosomes, we show that B chromosomes of rye are rich in gene-derived sequences, allowing us to trace their origin to fragments of A chromosomes, with the largest parts corresponding to rye chromosomes 3R and 7R. Compared with A chromosomes, B chromosomes were also found to accumulate large amounts of specific repeats and insertions of organellar DNA. The origin of rye B chromosomes occurred an estimated ∼1.1-1.3 Mya, overlapping in time with the onset of the genus Secale (1.7 Mya). We propose a comprehensive model of B chromosome evolution, including its origin by recombination of several A chromosomes followed by capturing of additional A-derived and organellar sequences and amplification of B-specific repeats.


title: Combining Data-driven and Model-driven Methods for Robust Facial Landmark Detection

abstract: Facial landmark detection is an important yet challenging task for real-world computer vision applications. This paper proposes an effective and robust approach for facial landmark detection by combining data- and model-driven methods. First, a fully convolutional network (FCN) is trained to compute response maps of all facial landmark points. Such a data-driven method could make full use of holistic information in a facial image for global estimation of facial landmarks. After that, the maximum points in the response maps are fitted with a pre-trained point distribution model (PDM) to generate the initial facial shape. This model-driven method is able to correct the inaccurate locations of outliers by considering the shape prior information. Finally, a weighted version of regularized landmark mean-shift (RLMS) is employed to fine-tune the facial shape iteratively. This estimation-correction-tuning process perfectly combines the advantages of the global robustness of the data-driven method (FCN), outlier correction capability of the model-driven method (PDM), and non-parametric optimization of RLMS. Results of extensive experiments demonstrate that our approach achieves state-of-the-art performances on challenging data sets, including 300W, AFLW, AFW, and COFW. The proposed method is able to produce satisfying detection results on face images with exaggerated expressions, large head poses, and partial occlusions.


title: Single-band PIFA MIMO antenna system design for future 5G wireless communication applications

abstract: A three-element single-band printed inverted F-antenna (PIFA) system operating in the 28 GHz band for the future millimeter-wave (mmWave) fifth generation (5G) wireless communications is presented. The multiple-input multiple-output (MIMO) antenna system consists of three single-band PIFA antenna elements. The size of a single PIFA antenna element is 0.85 × 0.85 at 28 GHz. The antenna array shows good isolation characteristics between closely spaced antenna elements. A minimum isolation of 13 dB is found between its antenna elements. Results show that the proposed antenna has a bandwidth of 807 MHz (27.73 GHz–28.54 GHz). Other important parameters in any wireless communication system such as envelop correlation coefficient (ECC), diversity gain (DG), mean effective gain (MEG), and multiplexing efficiency (ME), have been calculated and discussed. Results show that the proposed antenna array is a good candidate for the future 5G MIMO communication applications.


title: Extracting Team Mental Models Through Textual Analysis

abstract: Summary An approach, called map analysis, for extracting, analyzing and combining representations of individual's mental models as cognitive maps is presented. This textual analysis technique allows the researcher to extract cognitive maps, locate similarities across maps, and combine maps to generate a team map. Using map analysis the researcher can address questions about the nature of team mental models and the extent to which sharing is necessary for effective teamwork. This technique is illustrated using data drawn from a study of software engineering teams. The impact of critical coding choices on the resultant findings is examined. It is shown that various coding choices have systematic effects on the complexity of the coded maps and their similarity. Consequently, a thorough analysis requires analyzing the data several times under different coding choices. For example, re-analysis under different coding scenarios revealed that although members of successful teams tend to have more elaborate, more widely shared maps than members of non-successful teams, this difference is significant only when the data is unfiltered. Thus a better interpretation of this result is that all teams have comparable models, but successful teams are able to describe their models in more ways than are non-successful teams. ? 1997 John Wiley & Sons, Ltd.


title: CyberC3: A Prototype Cybernetic Transportation System for Urban Applications

abstract: In this paper, a prototype cybernetic transportation system called cybernetic technologies for cars in Chinese cities (CyberC3) is introduced. This system contains as many modules as necessary to evaluate the feasibility for its potential mass application in cities, including a central control room, five stations, three on-road monitoring cameras, three intelligent vehicles, and a green-energy power system. The entire system is centrally controlled and runs in two different modes - the ?shuttle mode? and the ?on-demand mode.? The control algorithm is divided into three logical layers: scheduling, planning, and executing. The scheduling layer manages the entire system, the planning layer navigates the vehicle, and the executing layer controls the vehicle in real time. The vehicle is powered by supercapacitor batteries. This system is a demonstration system, as well as a research platform, and has been open to the public in Shanghai, China, since May 2007, which has helped to evaluate the entire system and spread the concept of ?cybernetic transportation systems? in China.


title: Quantifying Lexical Novelty in Song Lyrics

abstract: Novelty is an important psychological construct that affects both perceptual and behavioral processes. Here, we propose a lexical novelty score (LNS) for a song’s lyric, based on the statistical properties of a corpus of 275,905 lyrics (available at www.smcnus.org/lyrics/). A lyric-level LNS was derived as a function of the inverse document frequencies of its unique words. An artist-level LNS was then computed using the LNSs of lyrics uniquely associated with each artist. Statistical tests were performed to determine whether lyrics and artists on Billboard Magazine’s lists of “All-Time Top 100” songs and artists had significantly lower LNSs than “non-top” songs and artists. An affirmative and highly consistent answer was found in both cases. These results highlight the potential utility of the LNS as a feature for MIR.


title: Fitting B-spline curves to point clouds by curvature-based squared distance minimization

abstract: Computing a curve to approximate data points is a problem encountered frequently in many applications in computer graphics, computer vision, CAD/CAM, and image processing. We present a novel and efficient method, called squared distance minimization (SDM), for computing a planar B-spline curve, closed or open, to approximate a target shape defined by a point cloud, that is, a set of unorganized, possibly noisy data points. We show that SDM significantly outperforms other optimization methods used currently in common practice of curve fitting. In SDM, a B-spline curve starts from some properly specified initial shape and converges towards the target shape through iterative quadratic minimization of the fitting error. Our contribution is the introduction of a new fitting error term, called the squared distance (SD) error term, defined by a curvature-based quadratic approximant of squared distances from data points to a fitting curve. The SD error term faithfully measures the geometric distance between a fitting curve and a target shape, thus leading to faster and more stable convergence than the point distance (PD) error term, which is commonly used in computer graphics and CAGD, and the tangent distance (TD) error term, which is often adopted in the computer vision community. To provide a theoretical explanation of the superior performance of SDM, we formulate the B-spline curve fitting problem as a nonlinear least squares problem and conclude that SDM is a quasi-Newton method which employs a curvature-based positive definite approximant to the true Hessian of the objective function. Furthermore, we show that the method based on the TD error term is a Gauss-Newton iteration, which is unstable for target shapes with high curvature variations, whereas optimization based on the PD error term is the alternating method that is known to have linear convergence.


title: Automatic Classification of Acquisition Problems Affecting Fingerprint Images in Automated Border Controls

abstract: Automated Border Control (ABC) systems are technologies designed to increase the speed and accuracy of the identity verifications performed at international borders. A great number of ABCs deployed in different countries use fingerprint recognition techniques because of their high accuracy and user acceptability. However, the accuracy of fingerprint recognition methods can drastically decrease in this application context due to user-sensor interaction factors. This paper presents two main contributions. The first of them consists in an experimental evaluation performed to search the main negative aspects that could affect the usability and accuracy in ABCs based on fingerprint biometrics. The mainly considered aspects consists in the presence of luggage and cleanness of the finger skin. The second contribution consists in a novel approach for automatically identifying the type of user-sensor interaction that caused quality degradations in fingerprint samples. This method uses a specific feature set and computational intelligence techniques to detect non-idealities in the acquisition process and to suggest corrective actions to travelers and border guards. To the best of our knowledge, this is the first method in the literature designed to detect problems in user-sensor interaction different from improper pressures on the acquisition surface. We validated the proposed approach using a dataset of 2880 images simulating different scenarios typical of ABCs. Results shown that the proposed approach is feasible and can obtain satisfactory performance, with a classification error of 0.098.


Theorizing Corruption through Agent-Based Modeling


title: Properties of multiwinner voting rules

abstract: A committee selection rule (or, multiwinner voting rule) is a mapping that takes a collection of strict preference rankings and a positive integer k as input, and outputs one or more subsets of candidates of size k. In this paper we consider committee selection rules that can be viewed as generalizations of single-winner scoring rules, including SNTV, Bloc, k-Borda, STV, as well as several variants of the Chamberlin-Courant rule and the Monroe rule and their approximations. We identify two natural broad classes of committee selection rules, and show that many of the existing rules belong to one or both of these classes. We then formulate a number of desirable properties of committee selection rules, and evaluate the rules we consider with respect to these properties.