query: Colorization using optimization

positive: Scale-Space and Edge Detection Using Anisotropic Diffusion Abstracf-The scale-space technique introduced by Witkin involves generating coarser resolution images by convolving the original image with a Gaussian kernel. This approach has a major drawback: it is difficult to obtain accurately the locations of the “semantically meaningful” edges at coarse scales. In this paper we suggest a new definition of scale-space, and introduce a class of algorithms that realize it using a diffusion process. The diffusion coefficient is chosen to vary spatially in such a way as to encourage intraregion smoothing in preference to interregion smoothing. It is shown that the “no new maxima should be generated at coarse scales” property of conventional scale space is preserved. As the region boundaries in our approach remain sharp, we obtain a high quality edge detector which successfully exploits global information. Experimental results are shown on a number of images. The algorithm involves elementary, local operations replicated over the image making parallel hardware implementations feasible.

positive: Segmentation using Eigenvectors: A Unifying View Automatic grouping and segmentation of images remains a challenging problem in computer vision. Recently, a number of authors have demonstrated good performance on this task using methods that are based on eigenvectors of the a nity matrix. These approaches are extremely attractive in that they are based on simple eigendecomposition algorithms whose stability is well understood. Nevertheless, the use of eigendecompositions in the context of segmentation is far from well understood. In this paper we give a unied treatment of these algorithms, and show the close connections between them while highlighting their distinguishing features. We then prove results on eigenvectors of block matrices that allow us to analyze the performance of these algorithms in simple grouping settings. Finally, we use our analysis to motivate a variation on the existing methods that combines aspects from di erent eigenvector segmentation algorithms. We illustrate our analysis with results on real and synthetic images. Human perceiving a scene can often easily segment it into coherent segments or groups. There has been a tremendous amount of e ort devoted to achieving the same level of performance in computer vision. In many cases, this is done by associating with each pixel a feature vector (e.g. color, motion, texture, position) and using a clustering or grouping algorithm on these feature vectors. Perhaps the cleanest approach to segmenting points in feature space is based on mixture models in which one assumes the data were generated by multiple processes and estimates the parameters of the processes and the number of components in the mixture. The assignment of points to clusters can then be easily performed by calculating the posterior probability of a point belonging to a cluster. Despite the elegance of this approach, the estimation process leads to a notoriously di cult optimization. The frequently used EM algorithm [3] often converges to a local maximum that depends on the initial conditions.

positive: An Iterative Image Registration Technique with an Application to Stereo Vision Image registration finds a variety of applications in computer vision. Unfortunately, traditional image registration techniques tend to be costly. We present a new image registration technique that makes use of the spatial intensity gradient of the images to find a good match using a type of Newton-Raphson iteration. Our technique is faster because it examines far fewer potential matches between the images than existing techniques. Furthermore, this registration technique can be generalized to handle rotation, scaling and shearing. We show show our technique can be adapted for use in a stereo vision system.

positive: Normalized Cuts and Image Segmentation w e propose Q novel approach for solving the perceptual grouping problem in vision. Rather than focusing on local features and their consistencies in the amage data, our approach aims a t extracting the global impression of an image. We treat image segmentation QS (I graph partitioning problem and propose Q novel global criterion, the normalized cut, for segmenting the graph. The normalized cut craterion measures both the total dissimilarity between the different groups QS well as the total similarity within the groups. We show that an eficient computational technique based on a generaked eigenvalue problem can be used to optimize this criterion. w e have applied this approach to segmenting static images and found results very enco u raging.

positive: A Computational Approach to Edge Detection This paper describes a computational approach to edge detection. The success of the approach depends on the definition of a comprehensive set of goals for the computation of edge points. These goals must be precise enough to delimit the desired behavior of the detector while making minimal assumptions about the form of the solution. We define detection and localization criteria for a class of edges, and present mathematical forms for these criteria as functionals on the operator impulse response. A third criterion is then added to ensure that the detector has only one response to a single edge. We use the criteria in numerical optimization to derive detectors for several common image features, including step edges. On specializing the analysis to step edges, we find that there is a natural uncertainty principle between detection and localization performance, which are the two main goals. With this principle we derive a single operator shape which is optimal at any scale. The optimal detector has a simple approximate implementation in which edges are marked at maxima in gradient magnitude of a Gaussian-smoothed image. We extend this simple detector using operators of several widths to cope with different signal-to-noise ratios in the image. We present a general method, called feature synthesis, for the fine-to-coarse integration of information from operators at different scales. Finally we show that step edge detector performance improves considerably as the operator point spread function is extended along the edge.

negative: A First Look at a Telepresence System with Room-Sized Real-Time 3 D Capture and Life-Sized Tracked Display Wall This paper provides a first look at a telepresence system offering room-sized, fully dynamic real-time 3D scene capture and continuous-viewpoint head-tracked display on a life-sized tiled display wall. The system is an expansion of a previous system, based an array of commodity depth sensors. We describe adjustments and improvements made to camera calibration, sensor data processing, data merger, rendering, and display, as required to scale the earlier system to room-sized.

negative: Weighted extreme learning machine for imbalance learning Extreme learning machine (ELM) is a competitive machine learning technique, which is simple in theory and fast in implementation. The network types are ‘‘generalized’’ single hidden layer feedforward networks, which are quite diversified in the form of variety in feature mapping functions or kernels. To deal with data with imbalanced class distribution, a weighted ELM is proposed which is able (1) it is simple in theory and convenient in implementation; (2) a wide type of feature mapping functions or kernels are available for the proposed framework; (3) the proposed method can be applied directly into multiclass classification tasks. In addition, after integrating with the weighting scheme, (1) the weighted ELM is able to deal with data with imbalanced class distribution while maintain the good performance on well balanced data as unweighted ELM; (2) by assigning different weights for each example according to users’ needs, the weighted ELM can be generalized to cost sensitive learning. & 2012 Elsevier B.V. All rights reserved.

negative: Robust motion detection using histogram of oriented gradients for illumination variations This paper proposes a robust motion detection method for illumination variations which uses histogram of oriented gradients. The detection process is divided into two phases: coarse detection and refinement. In the coarse detection phase, first, a texture-based background model is built which implements a group of adaptive histograms of oriented gradients; then, by comparing the histogram of oriented gradients of each pixel between current frame and background model, a foreground is segmented based on texture feature which is not susceptible to illumination variations, whereas some missing foreground regions exist; finally, the result based on texture is optimized by combining the pixel-wise detection result produced by Gaussian Mixture Model (GMM) algorithm, which greatly improves the detection performance by incorporating efficient morphological operations. In the refinement phase, the above detection result is refined based on the distinction in color feature to eliminate errors like shadows, noises, redundant contour, etc. Experimental results show the effectiveness and robustness of our approach in detecting moving objects in varying illumination conditions.

negative: Renewable energy strategies for sustainable development This paper discusses the perspective of renewable energy (wind, solar, wave and biomass) in the making of strategies for a sustainable development. Such strategies typically involve three major technological changes: energy savings on the demand side, efficiency improvements in the energy production, and replacement of fossil fuels by various sources of renewable energy. Consequently, large-scale renewable energy implementation plans must include strategies of how to integrate the renewable sources in coherent energy systems influenced by energy savings and efficiency measures. Based on the case of Denmark, this paper discusses the problems and perspectives of converting present energy systems into a 100 percent renewable energy system. The conclusion is that such development will be possible. The necessary renewable energy sources are present, if further technological improvements of the energy system are achieved. Especially technologies of converting the transportation and the introduction of flexible energy system technologies are crucial.

negative: DeepSynth : Synthesizing A Musical Instrument With Video This paper introduces DeepSynth, an end-to-end neural network model for generating the sound of a musical instrument based on a silent video of it being played. We specifically focus on building a synthesizer for the piano, but the ideas proposed in this paper are applicable to a wide range of musical instruments. At a high level, the model consists of a convolutional neural network (CNN) to extract features from the raw video frames and two stacked neural autoregressive models, one to encode the spatiotemporal features of the video and the other to generate the raw audio waveform.

negative: Implementation of page rank algorithm in Hadoop MapReduce framework One of the most popular algorithm in processing internet data i.e. webpages is page rank algorithm which is intended to decide the importance of a webpage by assigning a weighting value based on any incoming link to those webpage. However, the large amount of internet data may lead into computational burden in processing those page rank algorithm. To take into account those burden, in this paper we present a page rank processing algorithm over distributed system using Hadoop MapReduce framework called MR PageRank. Our algorithm can be decomposed into three processes, each of which is implemented in one Map and Reduce job. We first parse the raw webpage input to produce title of page and its outgoing links as key and value pair, respectively, as well as total dangling node's weight and total amount of pages. We next calculate the probability of each page and distribute this probability to each of outgoing link evenly. Each of the outgoing weight is shuffled and aggregated based on similarity of page title to update a new weighting value of each page. Notice that in the calculation we consider the dangling node and jumping factor. In the end, all of the page are descendingly sorted based on their weighting values. From the experimental result, we show that our implementation has output with reasonable ordering result. This electronic document is a “live” template and already defines the components of your paper.

negative: Big Data Analytics with Spark What’s more, Big Data Analytics with Spark provides an introduction to other big data technologies that are commonly used along with Spark, such as HDFS, Avro, Parquet, Ka a, Cassandra, HBase, Mesos, and so on. It also provides an introduction to machine learning and graph concepts. So the book is self-suffi cient; all the technologies that you need to know to use Spark are covered. The only thing that you are expected to have is some programming knowledge in any language.

negative:
Variation in the impact of social network characteristics on physical functioning in elderly persons: MacArthur Studies of Successful Aging. OBJECTIVES
Social support and social networks have been shown to exert significant effects on health and functioning among elderly persons. Although theorists have speculated that the strength of these effects may differ as a function of sociodemographic characteristics and prior health status, few studies have directly tested the moderating effects of these variables.

METHODS
Longitudinal data from the MacArthur Study of Successful Aging were used to examine the effects of structural and functional social support on changes in physical functioning over a 7-year period, measured by the Nagi scale, in a sample of initially high-functioning men and women aged 70 to 79 years. Multiple regression analyses were used to test the main effects of social support and social network variables, as well as their interactions with gender, income, and baseline physical performance.

RESULTS
After controlling for potential confounding effects, respondents with more social ties showed less functional decline. The beneficial effects of social ties were stronger for respondents who were male or had lower levels of baseline physical performance.

DISCUSSION
The effects of social support and social networks may vary according to the individual's gender and baseline physical capabilities. Studies of functional decline among elderly persons should not ignore this population variation in the effects of social networks.

negative: Design and Implementation of Autonomous Car using Raspberry Pi The project aims to build a monocular vision autonomous car prototype using Raspberry Pi as a processing chip. An HD camera along with an ultrasonic sensor is used to provide necessary data from the real world to the car. The car is capable of reaching the given destination safely and intelligently thus avoiding the risk of human errors. Many existing algorithms like lane detection, obstacle detection are combined together to provide the necessary control to the car.

negative: Routing and Wavelength Assignment in Optical WDM Networks This article discusses the routing and wavelength assignment (RWA) problem in optical networks employing wavelenength division multiplexing (WDM) technology. Two variants of the problem are studied: static RWA, whereby the traffic requirements are known in advance, and dynamic RWA in which connection requests arrive in some random fashion. Both point-topoint and multicast traffic demands are considered.

negative: Correcting a metacognitive error: feedback increases retention of low-confidence correct responses. Previous studies investigating posttest feedback have generally conceptualized feedback as a method for correcting erroneous responses, giving virtually no consideration to how feedback might promote learning of correct responses. Here, the authors show that when correct responses are made with low confidence, feedback serves to correct this initial metacognitive error, enhancing retention of low-confidence correct responses. In 2 experiments, subjects took an initial multiple-choice test on general knowledge facts and made a confidence judgment after each response. Feedback was provided for half of the questions, and retention was assessed by a final cued-recall test. Taking the initial test improved retention relative to not testing, and feedback further enhanced performance. Consistent with prior research, feedback improved retention by allowing subjects to correct initially erroneous responses. Of more importance, feedback also doubled the retention of correct low-confidence responses, relative to providing no feedback. The function of feedback is to correct both memory errors and metacognitive errors.

negative: Low-Power High-Efficiency Class D Audio Power Amplifiers The architecture, design, and implementation of two clock-free analog class D audio power amplifiers using 0.5 ¿m CMOS standard technology are introduced. The amplifiers are designed to consume significantly less power than former implementations. Both designs operate with a 2.7 V single voltage supply and deliver a maximum output power of 250 mW into an 8 ¿ speaker. The two class-D amplifiers are based on a hysteretic sliding mode controller, which avoids the complex task of generating the highly linear triangle carrier signal used in conventional architectures. The first design generates a two-level modulated signal, known as binary modulated amplifier (BMA); the second topology implements a three-level modulated signal, also called ternary modulated amplifier (TMA). The architectures and implementations are simple and compact, providing very low quiescent power consumption. Experimental results of the BMA/TMA yield an efficiency of 89/90% and a total harmonic distortion plus noise (THD+N) of 0.02/0.03%, respectively. The efficiency and linearity is comparable to state-of-the-art amplifiers but the static power consumption is less than one tenth of previous proposed architectures. Both class D amplifiers achieve a power supply rejection ratio greater than 75 dB at 217 Hz, and a signal-to-noise ratio (SNR) higher than 90 dB within the whole audio band. Each amplifier occupies less than 1.5 mm2.

negative: Incremental Clustering of Mobile Objects Moving objects are becoming increasingly attractive to the data mining community due to continuous advances in technologies like GPS, mobile computers, and wireless communication devices. Mining spatio-temporal data can benefit many different functions: marketing team managers for identifying the right customers at the right time, cellular companies for optimizing the resources allocation, web site administrators for data allocation matters, animal migration researchers for understanding migration patterns, and meteorology experts for weather forecasting. In this research we use a compact representation of a mobile trajectory and define a new similarity measure between trajectories. We also propose an incremental clustering algorithm for finding evolving groups of similar mobile objects in spatio-temporal data. The algorithm is evaluated empirically by the quality of object clusters (using Dunn and Rand indexes), memory space efficiency, execution times, and scalability (run time vs. number of objects).

negative: Filling Knowledge Base Gaps for Distant Supervision of Relation Extraction Distant supervision has attracted recent interest for training information extraction systems because it does not require any human annotation but rather employs existing knowledge bases to heuristically label a training corpus. However, previous work has failed to address the problem of false negative training examples mislabeled due to the incompleteness of knowledge bases. To tackle this problem, we propose a simple yet novel framework that combines a passage retrieval model using coarse features into a state-of-the-art relation extractor using multi-instance learning with fine features. We adapt the information retrieval technique of pseudorelevance feedback to expand knowledge bases, assuming entity pairs in top-ranked passages are more likely to express a relation. Our proposed technique significantly improves the quality of distantly supervised relation extraction, boosting recall from 47.7% to 61.2% with a consistently high level of precision of around 93% in the experiments.

negative: Common-Specific Multimodal Learning for Deep Belief Network Multimodal Deep Belief Network has been widely used to extract representations for multimodal data by fusing the high-level features of each data modality into common representations. Such straightforward fusion strategy can benefit the classification and information retrieval tasks. However, it may introduce noise in case the high-level features are not naturally common hence non-fusable for different modalities. Intuitively, each modality may have its own specific features and corresponding representation capabilities thus should not be simply fused. Therefore, it is more reasonable to fuse only the common features and represent the multimodal data by both the fused features and the modality-specific features. To distinguish common features from modal-specific features is a challenging task for traditional DBN models where all features are crudely mixed. This paper proposes the Common-Specific Multimodal Deep Belief Network (CSDBN) to solve the problem. CS-DBN automatically separates common features from modal-specific features and fuses only the common ones for data representation. Experimental results demonstrate the superiority of CS-DBN for classification tasks compared with the baseline approaches.

negative: Pastry: Scalable, distributed object location and routing for large-scale peer-to- This paper presents the design and evaluation of Pastry, a sc al ble, distributed object location and routing scheme for wide-ar a peer-to-peer applications. Pastry performs application-level routing and ob ject location in a potentially very large overlay network of nodes connected via the Int rnet. It can be used to support a wide range of peer-to-peer applications li ke g obal data storage, global data sharing, and naming. An insert operation in Pastry stores an object at a user-defin ed number of diverse nodes within the Pastry network. A lookup operation reliabl y retrieves a copy of the requested object if one exists. Moreover, a lookup is usu ally routed to the node nearest the client issuing the lookup (by some measure of pro ximity), among the nodes storing the requested object. Pastry is completely de centralized, scalable, and self-configuring; it automatically adapts to the arriva l, departure and failure of nodes. Experimental results obtained with a prototype implementa tion on a simulated network of up to 100,000 nodes confirm Pastry’s scalability, its ability to selfconfigure and adapt to node failures, and its good network loc ality properties.

negative: Face-to-face transfer of wafer-scale graphene films Graphene has attracted worldwide interest since its experimental discovery, but the preparation of large-area, continuous graphene film on SiO2/Si wafers, free from growth-related morphological defects or transfer-induced cracks and folds, remains a formidable challenge. Growth of graphene by chemical vapour deposition on Cu foils has emerged as a powerful technique owing to its compatibility with industrial-scale roll-to-roll technology. However, the polycrystalline nature and microscopic roughness of Cu foils means that such roll-to-roll transferred films are not devoid of cracks and folds. High-fidelity transfer or direct growth of high-quality graphene films on arbitrary substrates is needed to enable wide-ranging applications in photonics or electronics, which include devices such as optoelectronic modulators, transistors, on-chip biosensors and tunnelling barriers. The direct growth of graphene film on an insulating substrate, such as a SiO2/Si wafer, would be useful for this purpose, but current research efforts remain grounded at the proof-of-concept stage, where only discontinuous, nanometre-sized islands can be obtained. Here we develop a face-to-face transfer method for wafer-scale graphene films that is so far the only known way to accomplish both the growth and transfer steps on one wafer. This spontaneous transfer method relies on nascent gas bubbles and capillary bridges between the graphene film and the underlying substrate during etching of the metal catalyst, which is analogous to the method used by tree frogs to remain attached to submerged leaves. In contrast to the previous wet or dry transfer results, the face-to-face transfer does not have to be done by hand and is compatible with any size and shape of substrate; this approach also enjoys the benefit of a much reduced density of transfer defects compared with the conventional transfer method. Most importantly, the direct growth and spontaneous attachment of graphene on the underlying substrate is amenable to batch processing in a semiconductor production line, and thus will speed up the technological application of graphene.

negative: A Simple and Accurate Syntax-Agnostic Neural Model for Dependency-based Semantic Role Labeling We introduce a simple and accurate neural model for dependency-based semantic role labeling. Our model predicts predicate-argument dependencies relying on states of a bidirectional LSTM encoder. The semantic role labeler achieves competitive performance on English, even without any kind of syntactic information and only using local inference. However, when automatically predicted partof-speech tags are provided as input, it substantially outperforms all previous local models and approaches the best reported results on the English CoNLL2009 dataset. We also consider Chinese, Czech and Spanish where our approach also achieves competitive results. Syntactic parsers are unreliable on out-of-domain data, so standard (i.e., syntactically-informed) SRL models are hindered when tested in this setting. Our syntax-agnostic model appears more robust, resulting in the best reported results on standard out-of-domain test sets.

negative: Memristor-Based Material Implication (IMPLY) Logic: Design Principles and Methodologies Memristors are novel devices, useful as memory at all hierarchies. These devices can also behave as logic circuits. In this paper, the IMPLY logic gate, a memristor-based logic circuit, is described. In this memristive logic family, each memristor is used as an input, output, computational logic element, and latch in different stages of the computing process. The logical state is determined by the resistance of the memristor. This logic family can be integrated within a memristor-based crossbar, commonly used for memory. In this paper, a methodology for designing this logic family is proposed. The design methodology is based on a general design flow, suitable for all deterministic memristive logic families, and includes some additional design constraints to support the IMPLY logic family. An IMPLY 8-bit full adder based on this design methodology is presented as a case study.

negative: Video Super-Resolution via Bidirectional Recurrent Convolutional Networks Super resolving a low-resolution video, namely video super-resolution (SR), is usually handled by either single-image SR or multi-frame SR. Single-Image SR deals with each video frame independently, and ignores intrinsic temporal dependency of video frames which actually plays a very important role in video SR. Multi-Frame SR generally extracts motion information, e.g., optical flow, to model the temporal dependency, but often shows high computational cost. Considering that recurrent neural networks (RNNs) can model long-term temporal dependency of video sequences well, we propose a fully convolutional RNN named bidirectional recurrent convolutional network for efficient multi-frame SR. Different from vanilla RNNs, 1) the commonly-used full feedforward and recurrent connections are replaced with weight-sharing convolutional connections. So they can greatly reduce the large number of network parameters and well model the temporal dependency in a finer level, i.e., patch-based rather than frame-based, and 2) connections from input layers at previous timesteps to the current hidden layer are added by 3D feedforward convolutions, which aim to capture discriminate spatio-temporal patterns for short-term fast-varying motions in local adjacent frames. Due to the cheap convolutional operations, our model has a low computational complexity and runs orders of magnitude faster than other multi-frame SR methods. With the powerful temporal dependency modeling, our model can super resolve videos with complex motions and achieve well performance.

negative: Semi-supervised time series classification The problem of time series classification has attracted great interest in the last decade. However current research assumes the existence of large amounts of labeled training data. In reality, such data may be very difficult or expensive to obtain. For example, it may require the time and expertise of cardiologists, space launch technicians, or other domain specialists. As in many other domains, there are often copious amounts of unlabeled data available. For example, the PhysioBank archive contains gigabytes of ECG data. In this work we propose a semi-supervised technique for building time series classifiers. While such algorithms are well known in text domains, we will show that special considerations must be made to make them both efficient and effective for the time series domain. We evaluate our work with a comprehensive set of experiments on diverse data sources including electrocardiograms, handwritten documents, and video datasets. The experimental results demonstrate that our approach requires only a handful of labeled examples to construct accurate classifiers.

negative: Directly Fabricating Soft Robotic Actuators With an Open-Source 3-D Printer 3-D printing silicone has been a long sought for goal by roboticists. Fused deposition manufacturing (FDM) is a readily accessible and simple 3-D printing scheme that could hold the key to printing silicone. This study details an approach to 3-D print silicone elastomer through use of a thickening additive and heat curing techniques. We fabricated an identical control actuator using molding and 3-D printing techniques for comparison. By comparing the free space elongation and fixed length force of both actuators, we were able to evaluate the quality of the print. We observed that the 3-D printed linear actuator was able to perform similarly to the molded actuator, with an average error of 5.08% in actuator response, establishing the feasibility of such a system. We envision that further development of this system would contribute to the way soft robotic systems are fabricated.

negative:
Luhn Revisited: Significant Words Language Models Users tend to articulate their complex information needs in only a few keywords, making underspecified statements of request the main bottleneck for retrieval effectiveness. Taking advantage of feedback information is one of the best ways to enrich the query representation, but can also lead to loss of query focus and harm performance in particular when the initial query retrieves only little relevant information when overfitting to accidental features of the particular observed feedback documents. Inspired by the early work of Luhn [23], we propose significant words language models of feedback documents that capture all, and only, the significant shared terms from feedback documents. We adjust the weights of common terms that are already well explained by the document collection as well as the weight of rare terms that are only explained by specific feedback documents, which eventually results in having only the significant terms left in the feedback model.
 Our main contributions are the following. First, we present significant words language models as the effective models capturing the essential terms and their probabilities. Second, we apply the resulting models to the relevance feedback task, and see a better performance over the state-of-the-art methods. Third, we see that the estimation method is remarkably robust making the models in- sensitive to noisy non-relevant terms in feedback documents. Our general observation is that the significant words language models more accurately capture relevance by excluding general terms and feedback document specific terms.

negative:
Performance Engineering for Microservices: Research Challenges and Directions Microservices complement approaches like DevOps and continuous delivery in terms of software architecture. Along with this architectural style, several important deployment technologies, such as container-based virtualization and container orchestration solutions, have emerged. These technologies allow to efficiently exploit cloud platforms, providing a high degree of scalability, availability, and portability for microservices.
 Despite the obvious importance of a sufficient level of performance, there is still a lack of performance engineering approaches explicitly taking into account the particularities of microservices. In this paper, we argue why new solutions to performance engineering for microservices are needed. Furthermore, we identify open issues and outline possible research directions with regard to performance-aware testing, monitoring, and modeling of microservices.

negative: ABF + + : Fast and Robust Angle Based Flattening Temporary version-In print Conformal parameterization of mesh models has numerous applications in geometry processing. Conformality is desirable for remeshing, surface reconstruction, and many other mesh processing applications. Subject to the conformality requirement, these applications typically bene t from parameterizations with smaller stretch. The Angle Based Flattening (ABF) method, presented a few years ago, generates provably valid conformal parameterizations with low stretch. However, it is quite time consuming and becomes error prone for large meshes due to numerical error accumulation. This work presents ABF++, a highly e cient extension of the ABF method that overcomes these drawbacks, while maintaining all the advantages of ABF. ABF++ robustly parameterizes meshes of hundreds of thousands and millions of triangles within minutes. It is based on two main components: (1) a new numerical solution technique that dramatically reduces the dimension of the linear systems solved at each iteration, speeding up the solution; (2) an e cient hierarchical solution technique. The speedup with (1) does not come at the expense of greater distortion. The hierarchical technique (2) enables parameterization of models with millions of faces in seconds, at the expense of a minor increase in parametric distortion. The parameterization computed by ABF++ are provably valid, i.e. they contain no ipped triangles. As a result of these extensions, the ABF++ method is extremely suitable for robustly and e ciently parameterizing models for geometry processing applications.

negative: Color spaces for computer graphics Normal human color perception is a product of three independent sensory systems. By mirroring this mechanism, full-color display devices create colors as mixtures of three primaries. Any displayable color can be described by the corresponding values of these primaries. Frequently it is more convenient to define various other color spaces, or coordinate systems, for color representation or manipulation. Several such color spaces are presented which are suitable for applications involving user specification of color, along with the defining equations and illustrations. The use of special color spaces for particular kinds of color computations is discussed.

negative: CAPTCHA Design: Color, Usability, and Security Most user interfaces use color, which can greatly enhance their design. Because the use of color is typically a usability issue, it rarely causes security failures. However, using color when designing CAPTCHAs, a standard security technology that many commercial websites apply widely, can have an impact on usability and interesting but critical implications for security. Here, the authors examine some CAPTCHAs to determine whether their use of color negatively affects their usability, security, or both.

negative: Skin Segmentation Using YCBCR and RGB Color Models Face detection is one of the challenging problems in image processing. This report proposes a novel technique for detecting faces in color images using skin color model algorithm combined with skin likely-hood, skin Segmentation, Morphological operation and Template matching. Color images with skin color in the chromatic and pure color space YCrCb, which separates luminance and chrominance components. A Gaussian probability density is estimated from skin samples, collected from different ethnic groups, via the maximum-likelihood criterion. Adaptive thresholding for segmentation to localize the faces within the detected skin regions. Further, mathematical morphological operators are used to remove noisy regions and fill holes in the skin-color region, so that candidate human face regions can be extracted. These systems can achieve high detection accuracy, high detection speed and reduce the false detecting rate. The two methodology used for the Skin Segmentation is YCbCr and RGB Model. In YCbCr model both the skin colour and texture of the image can be used to identify the particular object in the image,where as in RGB model only the skin color has to be used for identification of the person.Hence the YCbCr model is better than the RGB model . From our analysis we conclude that the new approach in modeling skin color can achieve good detection success rate. The algorithm gives computationally a very efficient as well as an accurate approach for skin detection. The performance of different color space may be dependent on the method used to model the color for skin pixel. Keywords—Gaussian probability, YCrCb,RGB Model

negative: 2D Color Barcodes for Mobile Phones We propose a new high capacity color barcode, named HCC2D (High Capacity Colored 2-Dimensional), which use colors to increase the barcode data density. The introduction and recognition of colored modules poses some new and non-trivial computer vision challenges, such as handling the color distortions introduced by the hardware equipment that realizes the Print&Scan process. We developed a prototype for generating and reading the HCC2D code format, both on desktops (Linux and Windows platforms) and on mobile phones (Android platforms). We tested this prototype in many experiments considering different operating scenarios and data densities, and compared it to known 2-dimensional barcodes.

negative: Joint effects of emotion and color on memory. Numerous studies have shown that memory is enhanced for emotionally negative and positive information relative to neutral information. We examined whether emotion-induced memory enhancement is influenced by low-level perceptual attributes such as color. Because in everyday life red is often used as a warning signal, whereas green signals security, we hypothesized that red might enhance memory for negative information and green memory for positive information. To capture the signaling function of colors, we measured memory for words standing out from the context by color, and manipulated the color and emotional significance of the outstanding words. Making words outstanding by color strongly enhanced memory, replicating the well-known von Restorff effect. Furthermore, memory for colored words was further increased by emotional significance, replicating the memory-enhancing effect of emotion. Most intriguingly, the effects of emotion on memory additionally depended on color type. Red strongly increased memory for negative words, whereas green strongly increased memory for positive words. These findings provide the first evidence that emotion-induced memory enhancement is influenced by color and demonstrate that different colors can have different functions in human memory.

negative: CSIFT: A SIFT Descriptor with Color Invariant Characteristics SIFT has been proven to be the most robust local invariant feature descriptor. SIFT is designed mainly for gray images. However, color provides valuable information in object description and matching tasks. Many objects can be misclassified if their color contents are ignored. This paper addresses this problem and proposes a novel colored local invariant feature descriptor. Instead of using the gray space to represent the input image, the proposed approach builds the SIFT descriptors in a color invariant space. The built Colored SIFT (CSIFT) is more robust than the conventional SIFT with respect to color and photometrical variations. The evaluation results support the potential of the proposed approach.

negative: Color texture classification by integrative Co-occurrence matrices Integrative Co-occurrence matrices are introduced as novel features for color texture classi"cation. The extended Co-occurrence notation allows the comparison between integrative and parallel color texture concepts. The information pro"t of the new matrices is shown quantitatively using the Kolmogorov distance and by extensive classi"cation experiments on two datasets. Applying them to the RGB and the LUV color space the combined color and intensity textures are studied and the existence of intensity independent pure color patterns is demonstrated. The results are compared with two baselines: gray-scale texture analysis and color histogram analysis. The novel features improve the classi"cation results up to 20% and 32% for the "rst and second baseline, respectively. ? 2003 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.


query: FaCT++ Description Logic Reasoner: System Description

positive: The Description Logic Handbook: Theory, Implementation, and Applications Like modal logic, temporal logic, or description logic, separation logic has become a Description Logic Handbook: Theory, Implementation and Applications,”. The Description Logic Handbook: Theory, Implementation and Applications (Franz Baader, Diego Calvanese, Deborah. McGuinness, Daniele Nardi, Peter. To formalize knowledge, we employ various logic-based formalisms, such as The Description Logic Handbook: Theory, Implementation and Applications.

positive: Attributive Concept Descriptions with Complements Schmidt-SchaulL M. and G. Smolka, Attributive concept descriptions with complements, Artificial Intelligence 48 (1991) 1-26. We investigate the consequences of adding unions and complements to attributive concept descriptions employed in terminological knowledge representation languages. It is shown that deciding coherence and subsumption of such descriptions are PSPACE-complete problems that can be decided with linear space.

positive: Racer: A Core Inference Engine for the Semantic Web In this paper we describe Racer, which can be considered as a core inference engine for the semantic web. The Racer inference server offers two APIs that are already used by at least three different network clients, i.e., the ontology editor OilEd, the visualization tool RICE, and the ontology development environment Protege 2. The Racer server supports the standard DIG protocol via HTTP and a TCP based protocol with extensive query facilities. Racer currently supports the web ontology languages DAML+OIL, RDF, and OWL.

positive: Ontology Development 101 : A Guide to Creating Your First Ontology 1 Why develop an ontology? In recent years the development of ontologies—explicit formal specifications of the terms in the domain and relations among them (Gruber 1993)—has been moving from the realm of ArtificialIntelligence laboratories to the desktops of domain experts. Ontologies have become common on the World-Wide Web. The ontologies on the Web range from large taxonomies categorizing Web sites (such as on Yahoo!) to categorizations of products for sale and their features (such as on Amazon.com). The WWW Consortium (W3C) is developing the Resource Description Framework (Brickley and Guha 1999), a language for encoding knowledge on Web pages to make it understandable to electronic agents searching for information. The Defense Advanced Research Projects Agency (DARPA), in conjunction with the W3C, is developing DARPA Agent Markup Language (DAML) by extending RDF with more expressive constructs aimed at facilitating agent interaction on the Web (Hendler and McGuinness 2000). Many disciplines now develop standardized ontologies that domain experts can use to share and annotate information in their fields. Medicine, for example, has produced large, standardized, structured vocabularies such as SNOMED (Price and Spackman 2000) and the semantic network of the Unified Medical Language System (Humphreys and Lindberg 1993). Broad general-purpose ontologies are emerging as well. For example, the United Nations Development Program and Dun & Bradstreet combined their efforts to develop the UNSPSC ontology which provides terminology for products and services (www.unspsc.org). An ontology defines a common vocabulary for researchers who need to share information in a domain. It includes machine-interpretable definitions of basic concepts in the domain and relations among them. Why would someone want to develop an ontology? Some of the reasons are:

positive: The Semantic Web" in Scientific American

negative: Hand Gesture Recognition System The task of gesture recognition is highly challenging due to complex background, presence of nongesture hand motions, and different illumination environments. In this paper, a new technique is proposed which begins by detecting the hand and determining its canter, tracking the hands trajectory and analyzing the variations in the hand locations, and finally recognizing the gesture. The proposed technique overcomes background complexity and distance from camera which is up to 2.5 meters. Experimental results show that the proposed technique can recognize 12 gestures with rate above 94%. Keywords— Hand gesture recognition, skin detection, Hand tracking.

negative: A review of split-bolus single-pass CT in the assessment of trauma patients The purpose of this study was to review and compare the image quality and radiation dose of split-bolus single-pass computed tomography(CT) in the assessment of trauma patients in comparison to standard multi-phase CT techniques. An online electronic database was searched using the MESH terms “split-bolus,” “dual phase,” and “single pass.” Inclusion criteria required the research article to compare a split contrast bolus protocol in a single-pass scan in the assessment of trauma patients. Studies using split-bolus CT technique in non-traumatic injury assessment were excluded. Six articles met the inclusion criteria. Parenchymal and vascular image qualities, as well as subjective image quality assessments, were equal or superior in comparison to non-split-bolus multi-phase trauma CT protocols. Split-bolus single-pass CT decreased radiation exposure in all studies. Further research is required to determine the superior split-bolus protocol and the specificity and sensitivity of detecting blunt cerebrovascular injury screening, splenic parenchymal vascular lesions, and characterization of pelvic vascular extravasation.

negative: Dual-Band Microstrip Bandpass Filter Using Stepped-Impedance Resonators With New Coupling Schemes A microstrip bandpass filter using stepped-impedance resonators is designed in low-temperature co-fired ceramic technology for dual-band applications at 2.4 and 5.2 GHz. New coupling schemes are proposed to replace the normal counterparts. It is found that the new coupling scheme for the interstages can enhance the layout compactness of the bandpass filter; while the new coupling scheme at the input and output can improve the performance of the bandpass filter. To validate the design and analysis, a prototype of the bandpass filter was fabricated and measured. It is shown that the measured and simulated performances are in good agreement. The prototype of the bandpass filter achieved insertion loss of 1.25 and 1.87 dB, S11 of -29 and -40 dB, and bandwidth of 21% and 12.7% at 2.4 and 5.2 GHz, respectively. The bandpass filter is further studied for a single-package solution of dual-band radio transceivers. The bandpass filter is, therefore, integrated into a ceramic ball grid array package. The integration is analyzed with an emphasis on the connection of the bandpass filter to the antenna and to the transceiver die

negative: A Survey on the Security of Stateful SDN Data Planes Software-defined networking (SDN) emerged as an attempt to introduce network innovations faster, and to radically simplify and automate the management of large networks. SDN traditionally leverages OpenFlow as device-level abstraction. Since OpenFlow permits the programmer to “just” abstract a static flow-table, any stateful control and processing intelligence is necessarily delegated to the network controller. Motivated by the latency and signaling overhead that comes along with such a two-tiered SDN programming model, in the last couple of years several works have proposed innovative switch-level (data plane) programming abstractions capable to deploy some smartness directly inside the network switches, e.g., in the form of localized stateful flow processing. Furthermore, the possible inclusion of states and state maintenance primitives inside the switches is currently being debated in the OpenFlow standardization community itself. In this paper, after having provided the reader with a background on such emerging stateful SDN data plane proposals, we focus our attention on the security implications that data plane programmability brings about. Also via the identification of potential attack scenarios, we specifically highlight possible vulnerabilities specific to stateful in-switch processing (including denial of service and saturation attacks), which we believe should be carefully taken into consideration in the ongoing design of current and future proposals for stateful SDN data planes.

negative: Prosthetic gingival reconstruction in a fixed partial restoration. Part 1: introduction to artificial gingiva as an alternative therapy. The Class III defect environment entails a vertical and horizontal deficiency in the edentulous ridge. Often, bone and soft tissue surgical procedures fall short of achieving a natural esthetic result. Alternative surgical and restorative protocols for these types of prosthetic gingival restorations are presented in this three-part series, which highlights the diagnostic and treatment aspects as well as the lab and maintenance challenges. A complete philosophical approach involves both a biologic understanding of the limitations of the hard and soft tissue healing process as well as that of multiple adjacent implants in the esthetic zone. These limitations may often necessitate the use of gingiva-colored "pink" restorative materials and essential preemptive planning via three-dimensional computer-aided design/computer-assisted manufacture to achieve the desired esthetic outcome. The present report outlines a rationale for consideration of artificial gingiva when planning dental prostheses. Prosthetic gingiva can overcome the limitations of grafting and should be a consideration in the initial treatment plan. (Int J Periodontics Restorative Dent 2009;29:471-477.).

negative: Car monitoring, alerting and tracking model: Enhancement with mobility and database facilities Statistics show that the number of cars is increasing rapidly and so is the number of car theft attempts, locally and internationally. Although there are a lot of car security systems that had been produced lately, but the result is still disappointing as the number of car theft cases still increases. The thieves are inventing cleverer and stronger stealing techniques that need more powerful security systems. This project “Car Monitoring and Tracking System” is being proposed to solve the issue. It introduces the integration between monitoring and tracking system. Both elements are very crucial in order to have a powerful security system. The system can send SMS and MMS to the owner to have fast response especially if the car is nearby. This paper focuses on using MMS and database technology, the picture of the intruder will be sent via local GSM/GPRS service provider to user (and/or) police. The Database offers the required information about car and owner, which will help the police or security authorities in their job. Moreover, in the event of theft local police and user can easily track the car using GPS system that can be link to Google Earth and other mapping software. The implementation and testing results show the success of prototype in sending MMS to owner within 40 seconds and receiving acknowledgment to the database (police or security unit) within 4 minutes. The timing and results are suitable to owner and police to take suitable action against intruder.

negative: Data Governance Framework for Big Data Implementation with a Case of Korea Big Data governance requires a data governance that can satisfy the needs for corporate governance, IT governance, and ITA/EA. While the existing data governance focuses on the processing of structured data, Big Data governance needs to be established in consideration of a broad sense of Big Data services including unstructured data. To achieve the goals of Big Data, strategies need to be established together with goals that are aligned with the vision and objective of an organization. In addition to the preparation of the IT infrastructure, a proper preparation of the components is required to effectively implement the strategy for Big Data services. We propose the Big Data Governance Framework in this paper. The Big Data governance framework presents criteria different from existing criteria at the data quality level. It focuses on timely, reliable, meaningful, and sufficient data services, focusing on what data attributes should be achieved based on the data attributes of Big Data services. In addition to the quality level of Big Data, the personal information protection strategy and the data disclosure/accountability strategy are also needed to achieve goals and to prevent problems. This paper performed case analysis based on the Big Data Governance Framework with the National Pension Service of South Korea. Big Data services in the public sector are an inevitable choice to improve the quality of people's life. Big Data governance and its framework are the essential components for the realization of Big Data service.

negative: Numeric data frames and probabilistic judgments in complex real-world environments This thesis investigates human probabilistic judgment in complex real-world settings to identify processes underpinning biases across groups which relate to numerical frames and formats. Experiments are conducted replicating real-world environments and data to test judgment performance based on framing and format. Regardless of background skills and experience, people in professional and consumer contexts show a strong tendency to perceive the world from a linear perspective, interpreting information in concrete, absolute terms and making judgments based on seeking and applying linear functions. Whether predicting sales, selecting between financial products, or forecasting refugee camp data, people use minimal cues and systematically apply additive methods amidst non-linear trends and percentage points to yield linear estimates in both rich and sparse informational contexts. Depending on data variability and temporality, human rationality and choice may be significantly helped or hindered by informational framing and format. The findings deliver both theoretical and practical contributions. Across groups and individual differences, the effects of informational format and the tendency to linearly extrapolate are connected by the bias to perceive values in concrete terms and make sense of data by seeking simple referent points. People compare and combine referents using additive methods when inappropriate and adhere strongly to defaults when applied in complex numeric environments. The practical contribution involves a framing manipulation which shows that format biases (i.e., additive processing) and optimism (i.e., associated with intertemporal effects) can be counteracted in judgments involving percentages and exponential growth rates by using absolute formats and positioning defaults in future event context information. This framing manipulation was highly effective in improving loan choice and repayment judgments compared to information in standard finance industry formats. There is a strong potential to increase rationality using this data format manipulation in other financial settings and domains such as health behaviour change in which peoples’ erroneous interpretation of percentages and non-linear relations negatively impact choice and behaviours in both the short and long-term.

negative: A reconfigurable three- and single-phase AC/DC non-isolated bi-directional converter for multiple worldwide voltages AC/DC converters for worldwide deployment or multiple applications are often required to operate with single-or three-phase AC connections at a variety of voltages. Electric vehicle charging is an excellent example: a typical AC/DC converter optimized for a 400 V three-phase European connection will only produce about 35% of rated power when operating on a 240 V single-phase connection in North America. This paper proposes a reconfigurable AC-DC converter with integral DC-DC stage that allows the single-phase AC power to roughly double compared to a standard configuration, up to 2/3 of the DC/DC stage rating. The system is constructed with two conventional back-to-back three-phase two-level bridges. When operating in single-phase mode, rather than leaving the DC side under-utilized, our proposed design repurposes one of the DC phase legs and the unused AC phase to create two parallel single-phase connections. This roughly doubles the AC output current and hence doubles the single phase AC power.

negative: Instructional video in e-learning: Assessing the impact of interactive video on learning effectiveness Interactive video in an e-learning system allows proactive and random access to video content. Our empirical study examined the influence of interactive video on learning outcome and learner satisfaction in e-learning environments. Four different settings were studied: three were e-learning environments—with interactive video, with non-interactive video, and without video. The fourth was the traditional classroom environment. Results of the experiment showed that the value of video for learning effectiveness was contingent upon the provision of interactivity. Students in the e-learning environment that provided interactive video achieved significantly better learning performance and a higher level of learner satisfaction than those in other settings. However, students who used the e-learning environment that provided non-interactive video did not improve either. The findings suggest that it may be important to integrate interactive instructional video into e-learning systems. # 2005 Elsevier B.V. All rights reserved.

negative: IoT based monitoring and control of appliances for smart home The recent technology in home automation provides security, safety and comfortable life at home. That is why in the competitive environment and fast world, home automation technology is required for every person. This purposed home automation technology provides smart monitoring and control of the home appliances as well as door permission system for interaction between the visitor and home/office owner. The control and monitoring the status (ON/OFF of the appliances) have been implemented using multiple ways such as The Internet, electrical switch, and Graphical User Interface (GUI) interface. The system has low-cost design, user-friendly interface, and easy installation in home or multi-purpose building. Using this technology, the consumer can reduce the wastage of electrical power by regular monitoring of home appliances or the proper ON/OFF scheduling of the devices.

negative: 2.1 28Gb/s 560mW multi-standard SerDes with single-stage analog front-end and 14-tap decision-feedback equalizer in 28nm CMOS A high-speed SerDes must meet multiple challenges including high-speed operation, intensive equalization technique, low power consumption, small area and robustness. In order to meet new standards, such a OIF CEI-25G-LR, CEI-28G-MR/SR/VSR, IEEE802.3bj and 32G-FC, data-rates are increased to 25 to 28Gb/s, which is more than 75% higher than the previous generation of SerDes. For SerDes applications with several hundreds of lanes integrated in single chip, power consumption is very important factor while maintaining high performance. There are several previous works at 28Gb/s or higher data-rate [1-2]. They use an unrolled DFE to meet the critical timing margin, but the unrolled DFE structure increases the number of DFE slicers, increasing the overall power and die area. In order to tackle these challenges, we introduce several circuits and architectural techniques. The analog front-end (AFE) uses a single-stage architecture and a compact on-chip passive inductor in the transimpedance amplifier (TIA), providing 15dB boost. The boost is adaptive and its adaptation loop is decoupled from the decision-feedback equalizer (DFE) adaptation loop by the use of a group-delay adaptation (GDA) algorithm. DFE has a half-rate 1-tap unrolled structure with 2 total error latches for power and area reduction. A two-stage sense-amplifier-based slicer achieves a sensitivity of 15mV and DFE timing closure. We also develop a high-speed clock buffer that uses a new active-inductor circuit. This active-inductor circuit has the capability to control output-common-mode voltage to optimize circuit operating points.

negative: Understanding Human Motion and Gestures for Underwater Human-Robot Collaboration In this paper, we present a number of robust methodologies for an underwater robot to visually detect, follow, and interact with a diver for collaborative task execution. We design and develop two autonomous diver-following algorithms, the first of which utilizes both spatialand frequency-domain features pertaining to human swimming patterns in order to visually track a diver. The second algorithm uses a convolutional neural network-based model for robust tracking-by-detection. In addition, we propose a hand gesture-based human-robot communication framework that is syntactically simpler and computationally more efficient than the existing grammar-based frameworks. In the proposed interaction framework, deep visual detectors are used to provide accurate hand gesture recognition; subsequently, a finite-state machine performs robust and efficient gesture-to-instruction mapping. The distinguishing feature of this framework is that it can be easily adopted by divers for communicating with underwater robots without using artificial markers or requiring memorization of complex language rules. Furthermore, we validate the performance and effectiveness of the proposed methodologies through extensive field experiments in closedand open-water environments. Finally, we perform a user interaction study to demonstrate the usability benefits of our proposed interaction framework compared to existing methods.

negative: mTOR regulation of autophagy. Nutrient starvation induces autophagy in eukaryotic cells through inhibition of TOR (target of rapamycin), an evolutionarily-conserved protein kinase. TOR, as a central regulator of cell growth, plays a key role at the interface of the pathways that coordinately regulate the balance between cell growth and autophagy in response to nutritional status, growth factor and stress signals. Although TOR has been known as a key regulator of autophagy for more than a decade, the underlying regulatory mechanisms have not been clearly understood. This review discusses the recent advances in understanding of the mechanism by which TOR regulates autophagy with focus on mammalian TOR (mTOR) and its regulation of the autophagy machinery.

negative: EyeTell: Video-Assisted Touchscreen Keystroke Inference from Eye Movements Keystroke inference attacks pose an increasing threat to ubiquitous mobile devices. This paper presents EyeTell, a novel video-assisted attack that can infer a victim's keystrokes on his touchscreen device from a video capturing his eye movements. EyeTell explores the observation that human eyes naturally focus on and follow the keys they type, so a typing sequence on a soft keyboard results in a unique gaze trace of continuous eye movements. In contrast to prior work, EyeTell requires neither the attacker to visually observe the victim's inputting process nor the victim device to be placed on a static holder. Comprehensive experiments on iOS and Android devices confirm the high efficacy of EyeTell for inferring PINs, lock patterns, and English words under various environmental conditions.

negative: Rigid-Foldable Thick Origami In this paper, a method is proposed for geometrically constructing thick panel structures that follow the kinetic behavior of rigid origami by using tapered or two-ply panels and hinges located at their edges. The proposed method can convert generalized pattern of rigid-foldable origami into thick panels structure with kinetic motion, which leads to novel designs of origami for various engineering purposes including architecture.

negative: Moving ERP Systems to the Cloud-Data Security Issues This paper brings to light data security issues and concerns for organizations by moving their Enterprise Resource Planning (ERP) systems to the cloud. Cloud computing has become the new trend of how organizations conduct business and has enabled them to innovate and compete in a dynamic environment through new and innovative business models. The growing popularity and success of the cloud has led to the emergence of cloud-based Software-as-a-Service (SaaS) ERP systems, a new alternative approach to traditional on-premise ERP systems. Cloud-based ERP has a myriad of benefits for organizations. However, infrastructure engineers need to address data security issues before moving their enterprise applications to the cloud. Cloud-based ERP raises specific concerns about the confidentiality and integrity of the data stored in the cloud. Such concerns that affect the adoption of cloud-based ERP are based on the size of the organization. Small to medium enterprises (SMEs) gain the maximum benefits from cloud-based ERP as many of the concerns around data security are not relevant to them. On the contrary, larger organizations are more cautious in moving their mission critical enterprise applications to the cloud. A hybrid solution where organizations can choose to keep their sensitive applications on-premise while leveraging the benefits of the cloud is proposed in this paper as an effective solution that is gaining momentum and popularity for large organizations.

negative: Implementation of RSA 2048-bit and AES 256-bit with digital signature for secure electronic health record application This research addresses the implementation of encryption and digital signature technique for electronic health record to prevent cybercrime problem such as robbery, modification and unauthorized access. In this research, RSA 2048-bit algorithm, AES 256-bit and SHA 256 will be implemented in Java programming. Secure Electronic Health Record Information (SEHR) application design is intended to combine given services, such as confidentiality, integrity, authentication, and non-repudiation. Cryptography is used to ensure the file records and electronic documents for detailed information on the medical past, present and future forecasts that have been given only for the patients. The document will be encrypted using an encryption algorithm based on NIST Standard. In the application, there are two schemes, namely the protection and verification scheme. This research uses black-box testing and white-box testing to test the software input, output, and code without testing the process and design that occurs in the system. We demonstrated the implementation of cryptography in Secure Electronic Health Record Information (SEHR). The implementation of encryption and digital signature in this research can prevent archive thievery which is shown on implementation and is proven on the test.

negative: An automotive radar network based on 77 GHz FMCW sensors Automotive radar sensors will be used in many future applications to increase comfort and safety. Adaptive cruise control (ACC), parking aid, collision warning or avoidance and pre crash are some automotive applications, which are based on high performance radar sensors. This paper describes results gained by an European Commission funded research project and a new radar network based on four individual monostatic 77 GHz FMCW radar sensors. Each sensor measures target range and velocity simultaneously and extremely accurate even in multiple target situations with a maximum range of 40 m. The azimuth angle is estimated by multilateration techniques in a radar network architecture. The system design and the signal processing of this network are described in detail in this paper. Due to the high range resolution performance of each radar sensor ordinary targets cannot be considered any longer as point but as extended targets. System capability, performance and reliability can be improved essentially if the single sensor FMCW signal processing, the target position processing in the radar network and the tracking scheme are jointly optimized.

negative: Sex differences in intrinsic aptitude for mathematics and science?: a critical review. This article considers 3 claims that cognitive sex differences account for the differential representation of men and women in high-level careers in mathematics and science: (a) males are more focused on objects from the beginning of life and therefore are predisposed to better learning about mechanical systems; (b) males have a profile of spatial and numerical abilities producing greater aptitude for mathematics; and (c) males are more variable in their cognitive abilities and therefore predominate at the upper reaches of mathematical talent. Research on cognitive development in human infants, preschool children, and students at all levels fails to support these claims. Instead, it provides evidence that mathematical and scientific reasoning develop from a set of biologically based cognitive capacities that males and females share. These capacities lead men and women to develop equal talent for mathematics and science.

negative: Reality mining based on Social Network Analysis Data Mining is the extraction of hidden predictive information from large databases. The process of discovering interesting, useful, nontrivial patterns from large spatial datasets is called spatial data mining. When time gets associated it becomes spatio temporal data mining. The study of spatio temporal data mining is of great concern for the study of mobile phone sensed data. Reality Mining is defined as the study of human social behavior based on mobile phone sensed data. It is based on the data collected by sensors in mobile phones, security cameras, RFID readers, etc. All allow for the measurement of human physical and social activity. In this paper Netviz, Gephi and Weka tools have been used to convert and analyze the Facebook. Further, analysis of a reality mining dataset is also presented.

negative: Improving Restaurants by Extracting Subtopics from Yelp Reviews In this paper, we describe latent subtopics discovered from Yelp restaurant reviews by running an online Latent Dirichlet Allocation (LDA) algorithm. The goal is to point out demand of customers from a large amount of reviews, with high dimensionality. These topics can provide meaningful insights to restaurants about what customers care about in order to increase their Yelp ratings, which directly affects their revenue. We used the open dataset from the Yelp Dataset Challenge with over 158,000 restaurant reviews. To find latent subtopics from reviews, we adopted Online LDA, a generative probabilistic model for collections of discrete data such as text corpora. We present the breakdown of hidden topics over all reviews, predict stars per hidden topics discovered, and extend our findings to that of temporal information regarding restaurants peak hours. Overall, we have found several interesting insights and a method which could definitely prove useful to restaurant owners.

negative: Ontology Based SMS Controller for Smart Phones Text analysis includes lexical analysis of the text and has been widely studied and used in diverse applications. In the last decade, researchers have proposed many efficient solutions to analyze / classify large text dataset, however, analysis / classification of short text is still a challenge because 1) the data is very sparse 2) It contains noise words and 3) It is difficult to understand the syntactical structure of the text. Short Messaging Service (SMS) is a text messaging service for mobile/smart phone and this service is frequently used by all mobile users. Because of the popularity of SMS service, marketing companies nowadays are also using this service for direct marketing also known as SMS marketing.In this paper, we have proposed Ontology based SMS Controller which analyze the text message and classify it using ontology aslegitimate or spam. The proposed system has been tested on different scenarios and experimental results shows that the proposed solution is effective both in terms of efficiency and time. Keywords—Short Text Classification; SMS Spam; Text Analysis; Ontology based SMS Spam; Text Analysis and Ontology

negative: BP-STDP: Approximating backpropagation using spike timing dependent plasticity The problem of training spiking neural networks (SNNs) is a necessary precondition to understanding computations within the brain, a field still in its infancy. Previous work has shown that supervised learning in multi-layer SNNs enables bio-inspired networks to recognize patterns of stimuli through hierarchical feature acquisition. Although gradient descent has shown impressive performance in multi-layer (and deep) SNNs, it is generally not considered biologically plausible and is also computationally expensive. This paper proposes a novel supervised learning approach based on an event-based spike-timing-dependent plasticity (STDP) rule embedded in a network of integrate-and-fire (IF) neurons. The proposed temporally local learning rule follows the backpropagation weight change updates applied at each time step. This approach enjoys benefits of both accurate gradient descent and temporally local, efficient STDP. Thus, this method is able to address some open questions regarding accurate and efficient computations that occur in the brain. The experimental results on the XOR problem, the Iris data, and the MNIST dataset demonstrate that the proposed SNN performs as successfully as the traditional NNs. Our approach also compares favorably with the state-of-the-art multi-layer SNNs.

negative: A Requirements-to-Implementation Mapping Tool for Requirements Traceability Software quality is a major concern in software engineering, particularly when we are dealing with software services that must be available 24 hours a day, please the customer and be kept permanently. In software engineering, requirements management is one of the most important tasks that can influence the success of a software project. Maintain traceability information is a fundamental aspect in software engineering and can facilitate the process of software development and maintenance. This information can be used to support various activities such as analyzing the impact of changes and software verification and validation. However, traceability techniques are mainly used between requirements and software test cases. This paper presents a prototype of a tool that supports the mapping of functional requirements with the pages and HTML elements of a web site. This tool helps maintaining requirements traceability information updated and, ultimately, increasing the efficiency of requirements change management, which may contribute to the overall quality of the software service.

negative: Towards Extracting Faithful and Descriptive Representations of Latent Variable Models Methods that use latent representations of data, such as matrix and tensor factorization or deep neural methods, are becoming increasingly popular for applications such as knowledge base population and recommendation systems. These approaches have been shown to be very robust and scalable but, in contrast to more symbolic approaches, lack interpretability. This makes debugging such models difficult, and might result in users not trusting the predictions of such systems. To overcome this issue we propose to extract an interpretable proxy model from a predictive latent variable model. We use a socalled pedagogical method, where we query our predictive model to obtain observations needed for learning a descriptive model. We describe two families of (presumably more) descriptive models, simple logic rules and Bayesian networks, and show how members of these families provide descriptive representations of matrix factorization models. Preliminary experiments on knowledge extraction from text indicate that even though Bayesian networks may be more faithful to a matrix factorization model than the logic rules, the latter are possibly more useful for interpretation and debugging.

negative: An MPC/hybrid system approach to traction control This paper describes a hybrid model and a model predictive control (MPC) strategy for solving a traction control problem. The problem is tackled in a systematic way from modeling to control synthesis and implementation. The model is described first in the Hybrid Systems Description Language to obtain a mixed-logical dynamical (MLD) hybrid model of the open-loop system. For the resulting MLD model, we design a receding horizon finite-time optimal controller. The resulting optimal controller is converted to its equivalent piecewise affine form by employing multiparametric programming techniques, and finally experimentally tested on a car prototype. Experiments show that good and robust performance is achieved in a limited development time by avoiding the design of ad hoc supervisory and logical constructs usually required by controllers developed according to standard techniques.

negative: From Non-Functional Requirements to Design through Patterns Design patterns aid in documenting and communicating proven design solutions to recurring problems. They describe not only how to solve design problems, but also why a solution is chosen over others and what trade-offs are made. Non-functional requirements (NFRs) are pervasive in descriptions of design patterns. They play a crucial role in understanding the problem being addressed, the trade-offs discussed, and the design solution proposed. However, since design patterns are mostly expressed as informal text, the structure of the design reasoning is not systematically organised. This paper proposes a systematic treatment of NFRs in descriptions of patterns and when applying patterns during design. The approach organises, analyses and refines non-functional requirements, and provides guidance and reasoning support when applying patterns during the design of a software system. Three design patterns taken from the literature are used to illustrate this approach.

negative: Generating Legal Arguments and Predictions from Case Texts In this paper, we present methods for automatically finding abstract, legally relevant concepts in case texts and demonstrate how they can be used to make predictions of case outcomes, given the texts as inputs.In a set of experiments to test these methods, we focus on the open question of how best to represent legal text for finding abstract concepts. We compare different ways of representing legal case texts in order to test whether adding domain knowledge and some linguistic information can improve performance.We found that replacing individual names by roles in the case texts led to better indexing, and that adding certain syntactic and semantic information, in the form of Propositional Patterns that capture a sense of "who did what", led to better prediction. Our experiments also showed that of three learning algorithms, Nearest Neighbor worked best in learning how to identify indexing concepts in texts.In these experiments, we introduced a prototype system that can reason with text cases; it analyzes a case, predicts its outcome considering other cases in the database, and explains the prediction, all starting with a textual description of the case's facts as input.

negative: Logic-Based Models for the Analysis of Cell Signaling Networks† Computational models are increasingly used to analyze the operation of complex biochemical networks, including those involved in cell signaling networks. Here we review recent advances in applying logic-based modeling to mammalian cell biology. Logic-based models represent biomolecular networks in a simple and intuitive manner without describing the detailed biochemistry of each interaction. A brief description of several logic-based modeling methods is followed by six case studies that demonstrate biological questions recently addressed using logic-based models and point to potential advances in model formalisms and training procedures that promise to enhance the utility of logic-based methods for studying the relationship between environmental inputs and phenotypic or signaling state outputs of complex signaling networks.

negative: Semantics and implementation of continuous sliding window queries over data streams In recent years the processing of continuous queries over potentially infinite data streams has attracted a lot of research attention. We observed that the majority of work addresses individual stream operations and system-related issues rather than the development of a general-purpose basis for stream processing systems. Furthermore, example continuous queries are often formulated in some declarative query language without specifying the underlying semantics precisely enough. To overcome these deficiencies, this article presents a consistent and powerful operator algebra for data streams which ensures that continuous queries have well-defined, deterministic results. In analogy to traditional database systems, we distinguish between a logical and a physical operator algebra. While the logical algebra specifies the semantics of the individual operators in a descriptive but concrete way over temporal multisets, the physical algebra provides efficient implementations in the form of stream-to-stream operators. By adapting and enhancing research from temporal databases to meet the challenging requirements in streaming applications, we are able to carry over the conventional transformation rules from relational databases to stream processing. For this reason, our approach not only makes it possible to express continuous queries with a sound semantics, but also provides a solid foundation for query optimization, one of the major research topics in the stream community. Since this article seamlessly explains the steps from query formulation to query execution, it outlines the innovative features and operational functionality implemented in our state-of-the-art stream processing infrastructure.

negative: Modeling by shortest data description It is known that the usual criteria resulting from such, should we dare to say, ad hoc principles as the least squares, the minimum prediction error, and the maximum likelihood principles, cannot be used to estimate the order nor other structure parameters. This very fact seems to suggest that these time-honored principles are successful only insofar as they happen to coincide with one or another facet of some more powerful and comprehensive principle. The shortest description length of individual recursively definable objects was studied by Kolmogorov[1] and others, and it gives rise to the algorithmic notion of entropy. In statistical estimation, however, the data always includes a random element, and the original notion of entropy of random variables turns out to be the appropriate one to use. In a peripheral way we also invoke a simple combinatorial entropy notion which, however, need not cause any con-