title: Gradient Flows for Optimizing Triangular Mesh-based Surfaces: Applications to 3D Reconstruction Problems Dealing with Visibility

abstract: This article tackles the problem of using variational methods for evolving 3D deformable surfaces. We give an overview of gradient descent flows when the shape is represented by a triangular mesh-based surface, and we detail the gradients of two generic energy functionals which embody a number of energies used in mesh processing and computer vision. In particular, we show how to rigorously account for visibility in the surface optimization process. We present different applications including 3D reconstruction from multiple views for which the visibility is fundamental. The gradient correctly takes into account the visibility changes that occur when a surface moves; this forces the contours generated by the reconstructed surface to match with the apparent contours in the input images.


title: Document-Level Machine Translation with Word Vector Models

abstract: In this paper we apply distributional semantic information to document-level machine translation. We train monolingual and bilingual word vector models on large corpora and we evaluate them first in a cross-lingual lexical substitution task and then on the final translation task. For translation, we incorporate the semantic information in a statistical document-level decoder (Docent), by enforcing translation choices that are semantically similar to the context. As expected, the bilingual word vector models are more appropriate for the purpose of translation. The final document-level translator incorporating ::: the semantic model outperforms the basic Docent (without semantics) and also ::: performs slightly over a standard sentence level SMT system in terms of ULC (the average of a set of standard automatic evaluation metrics for MT). Finally, we also present some manual analysis of the translations of some concrete documents


title: Passion and Pacing in Endurance Performance

abstract: Endurance sports are booming, with sports passionates of varying skills and expertise battering city streets and back roads on their weekly or daily exercise rounds. The investments required for performing in endurance exercise are nevertheless considerable, and passion for their sport might explain the efforts endurance athletes are willing to make. Passion may be defined as a strong motivational force and as such might be related to the neurophysiological basis underlying the drive to exercise. A complex relationship between the brain and other systems is responsible for athletes’ exercise behaviour and thus performance in sports. We anticipate important consequences of athletes’ short term choices, for example concerning risk taking actions,, on long term outcomes such as injuries, overtraining and burnout. We propose to consider athletes’ type of passion, in combination with neurophysiological parameters, as an explanatory factor inunderstanding the apparent disparity in the regulation of exercise intensity during endurance sports. Previous research has demonstrated that athletes can be passionate towards their sport in either a harmonious or an obsessive way. Although both lead to considerable investments and therefore often to successful performances, obsessive passion may affect athlete well-being and performance on the long run, due to the corresponding inflexible exercise behaviour. In this perspective we will thus examine the influence of passion in sport on athletes’ short term and long term decision-making and exercise behavior, in particular related to the regulation of exercise intensity, and discuss the expected long term effects of both types of passion for sport.


title: Selecting a characteristic set of reviews

abstract: Online reviews provide consumers with valuable information that guides their decisions on a variety of fronts: from entertainment and shopping to medical services. Although the proliferation of online reviews gives insights about different aspects of a product, it can also prove a serious drawback: consumers cannot and will not read thousands of reviews before making a purchase decision. This need to extract useful information from large review corpora has spawned considerable prior work, but so far all have drawbacks. Review summarization (generating statistical descriptions of review sets) sacrifices the immediacy and narrative structure of reviews. Likewise, review selection (identifying a subset of 'helpful' or 'important' reviews) leads to redundant or non-representative summaries. In this paper, we fill the gap between existing review-summarization and review-selection methods by selecting a small subset of reviews that together preserve the statistical properties of the entire review corpus. We formalize this task as a combinatorial optimization problem and show that it NP-hard both tosolve and approximate. We also design effective algorithms that prove to work well in practice. Our experiments with real review corpora on different types of products demonstrate the utility of our methods, and our user studies indicate that our methods provide a better summary than prior approaches.


title: DCSPARK: Virtualizing spark using Docker containers

abstract: As MapReduce has become a popular model for large-scale data procession in recent years, companies and researchers take advantage of this model to solve their problems. The applications may run on the same MapReduce cluster, with their own system-wide configure settings and library dependencies, respectively. Sometimes, their configure settings and library dependencies are conflicted with each other. How to ensure these applications to run together correctly without mutual interference and achieve high resources utilization gives a challenge to the researchers. In this paper, we propose DCSpark, a framework that leverages the power of Docker containers that allows users to run Spark applications which have conflicting configurations and library dependencies in one physical cluster. In addition, it's presented an implementation of our framework called DCM which is aimed at managing the physical cluster, processing scheduling problem and building the container-based Spark cluster images automatically according to the dependence environment of the applications. Our experimental evaluation shows that DCSpark introduces negligible overhead for CPU and memory performance compared with the native Spark cluster.


title: REST API Design Patterns for SDN Northbound API

abstract: REST architectural style gains increasing popularity in the networking protocol design, and it has become a prevalent choice for northbound API of Software-Defined Networking (SDN). This paper addresses many critical issues in RESTful networking protocol design, and presents a framework on how a networking protocol can be designed in a truly RESTful manner, making it towards a service oriented data networking. In particular, we introduce the HTTP content negotiation mechanism which allows clients to select different representation formats from the same resource URI. Most importantly, we present a hypertext-driven approach, so that hypertext links are defined between REST resources for the networking protocol to guide clients to identify the right resources rather than relying on fixed resource URIs. The advantages of our approach are verified in two folds. First, we show how to apply our approach to fix REST design problems in some existing northbound networking APIs, and then we show how to design a RESTful northbound API of SDN in the context of OpenStack. We implemented our proposed approach in the northbound REST API of SOX, a generalized SDN controller, and the benefits of the proposed approach are experimentally verified.


title: Abstract The effects of violent video games on adolescents The overlooked influence of development

abstract: Recent acts of extreme violence involving teens and associated links to violent video games have led to an increased interest in video game violence. Research suggests that violent video games influence aggressive behavior, aggressive affect, aggressive cognition, and physiological arousal. Anderson and Bushman [Annu. Rev. Psychol. 53 (2002) 27.] have posited a General Aggression Model (GAM) to explain the mechanism behind the link between violent video games and aggressive behavior. However, the influence of violent video games as a function of developmental changes across adolescence has yet to be addressed. The purpose of this review is to integrate the GAM with developmental changes that occur across adolescence.


Breadth-first crawling yields high-quality pages


Dynamic Poverty Prediction with Vegetation Index


title: The retinoblastoma protein and cell cycle control

abstract: A rigid rear axle for motor vehicles, especially for passenger motor vehicles, whose wheels are driven by way of drive shafts from a rear axle gear supported at a frame or the like; the axle member is thereby suspended by a vehicle part such as the frame or the like on four guide members arranged approximately at an angle of 45 DEG to the driving direction as viewed in plan view; all four guide members are of approximately equal length, of which the two lower ones point forwardly and the two upper ones point rearwardly; the points of pivotal connection of the four guide members at the axle member are disposed approximately one above the other outwardly in proximity of the wheel whereby the line of connection of the points of intersection of the upper and of the lower guide members, respectively, as viewed in side view, are arranged horizontally or sloping down forwardly and at least the upper guide members are arranged sloping down rearwardly.


title: How to Learn Klingon without a Dictionary: Detection and Measurement of Black Keywords Used by the Underground Economy

abstract: Online underground economy is an important channel that connects the merchants of illegal products and their buyers, which is also constantly monitored by legal authorities. As one common way for evasion, the merchants and buyers together create a vocabulary of jargons (called "black keywords" in this paper) to disguise the transaction (e.g., "smack" is one street name for "heroin" [1]). Black keywords are often "unfriendly" to the outsiders, which are created by either distorting the original meaning of common words or tweaking other black keywords. Understanding black keywords is of great importance to track and disrupt the underground economy, but it is also prohibitively difficult: the investigators have to infiltrate the inner circle of criminals to learn their meanings, a task both risky and time-consuming. In this paper, we make the first attempt towards capturing and understanding the ever-changing black keywords. We investigated the underground business promoted through blackhat SEO (search engine optimization) and demonstrate that the black keywords targeted by the SEOers can be discovered through a fully automated approach. Our insights are two-fold: first, the pages indexed under black keywords are more likely to contain malicious or fraudulent content (e.g., SEO pages) and alarmed by off-the-shelf detectors, second, people tend to query multiple similar black keywords to find the merchandise. Therefore, we could infer whether a search keyword is "black" by inspecting the associated search results and then use the related search queries to extend our findings. To this end, we built a system called KDES (Keywords Detection and Expansion System), and applied it to the search results of Baidu, China's top search engine. So far, we have already identified 478,879 black keywords which were clustered under 1,522 core words based on text similarity. We further extracted the information like emails, mobile phone numbers and instant messenger IDs from the pages and domains relevant to the underground business. Such information helps us gain better understanding about the underground economy of China in particular. In addition, our work could help search engine vendors purify the search results and disrupt the channel of the underground market. Our co-authors from Baidu compared our results with their blacklist, found many of them (e.g., long-tail and obfuscated keywords) were not in it, and then added them to Baidu's internal blacklist.


Suitable Employees Wanted? Find them with Semantic Techniques. Paper presented at the Workshop on Making Semantics Work For Business


title: Median Raphe Cyst with Ciliated Cells of the Penis

abstract: Median raphe cysts of the penis are uncommon benign lesions that affect young men. The cysts are lined with pseudostratified columnar or stratified squamous cell epithelium, mimicking the epithelial lining of the male urethra. Immunohistochemical study also indicates a histogenetic relationship with the urothelium. To our knowledge, there have been only 2 reports of ciliated epithelium in this kind of cyst published previously (1, 2). We describe here a rare case of median raphe cyst of the penis with ciliated cells, and discuss the histopatho-logical and immunohistochemical features.CASE REPORT


title: Effective 3D action recognition using EigenJoints

abstract: HighlightsEffective method to recognize human actions using 3D skeleton joints.New action feature descriptor, EigenJoints, for action recognition.Accumulated Motion Energy (AME) method to perform informative frames selection.Our proposed approach significantly outperforms the state-of-the-art methods on three public datasets. In this paper, we propose an effective method to recognize human actions using 3D skeleton joints recovered from 3D depth data of RGBD cameras. We design a new action feature descriptor for action recognition based on differences of skeleton joints, i.e., EigenJoints which combine action information including static posture, motion property, and overall dynamics. Accumulated Motion Energy (AME) is then proposed to perform informative frame selection, which is able to remove noisy frames and reduce computational cost. We employ non-parametric Naive-Bayes-Nearest-Neighbor (NBNN) to classify multiple actions. The experimental results on several challenging datasets demonstrate that our approach outperforms the state-of-the-art methods. In addition, we investigate how many frames are necessary for our method to perform classification in the scenario of online action recognition. We observe that the first 30-40% frames are sufficient to achieve comparable results to that using the entire video sequences on the MSR Action3D dataset.


title: A survey of computer systems for expressive music performance

abstract: We present a survey of research into automated and semiautomated computer systems for expressive performance of music. We will examine the motivation for such systems and then examine the majority of the systems developed over the last 25 years. To highlight some of the possible future directions for new research, the review uses primary terms of reference based on four elements: testing status, expressive representation, polyphonic ability, and performance creativity.


title: Interacting with a conversational agent system for educational purposes in online courses

abstract: Since distance education creates new opportunities for learners, the enrollment in online courses has been sharply increasing in higher education. However, the higher attrition rate is one of the more significant concerns in this field. Educational researchers have found that meaningful interactions play a significant role in learner persistence in online courses. Still, it is challenging for an individual instructor to promote learners' positive interaction experiences. The expectation of improved learners' interaction with conversational agent systems has received attention in the distance education field. Few conversational agent systems have been developed for educational purposes, and few systems are used in real online learning settings. This study aims at designing and developing a conversational agent system to promote the learner's meaningful interaction in online courses, and also exploring the feasibility of human interaction with the conversational agent system, or chatbot, in online courses in higher education. The primary findings of this study show that instant, content-related, and quality interactions between the learner and the conversational agent system is applicable to graduate-level online courses. Implications and future research are discussed.


title: Histograms as a side effect of data movement for big data

abstract: Histograms are a crucial part of database query planning but their computation is resource-intensive. As a consequence, generating histograms on database tables is typically performed as a batch job, separately from query processing. In this paper, we show how to calculate statistics as a side effect of data movement within a DBMS using a hardware accelerator in the data path. This accelerator analyzes tables as they are transmitted from storage to the processing unit, and provides histograms on the data retrieved for queries at virtually no extra performance cost. To evaluate our approach, we implemented this accelerator on an FPGA. This prototype calculates histograms faster and with similar or better accuracy than commercial databases. Moreover, the FPGA can provide various types of histograms such as Equi-depth, Compressed, or Max-diff on the same input data in parallel, without additional overhead.


title: Enrich machine-to-machine data with semantic web technologies for cross-domain applications

abstract: The Internet of Things, more specifically, the Machine-to-Machine (M2M) standard enables machines and devices such as sensors to communicate with each other without human intervention. The M2M devices provide a great deal of M2M data, mainly used for specific M2M applications such as weather forecasting, healthcare or building automation. Existing applications are domain-specific and use their own descriptions of devices and measurements. A major challenge is to combine M2M data provided by these heterogeneous domains and by different projects. It is really a difficult task to understand the meaning of the M2M data to later reason about them. We propose a semantic-based approach to automatically combine, enrich and reason about M2M data to provide promising cross-domain M2M applications. A proof-of-concept to validate our approach is published online (http://sensormeasurement.appspot.com/).


title: LSHADE with semi-parameter adaptation hybrid with CMA-ES for solving CEC 2017 benchmark problems

abstract: To improve the optimization performance of LSHADE algorithm, an alternative adaptation approach for the selection of control parameters is proposed. The proposed algorithm, named LSHADE-SPA, uses a new semi-parameter adaptation approach to effectively adapt the values of the scaling factor of the Differential evolution algorithm. The proposed approach consists of two different settings for two control parameters F and Cr. The benefit of this approach is to prove that the semi-adaptive algorithm is better than pure random algorithm or fully adaptive or self-adaptive algorithm. To enhance the performance of our algorithm, we also introduced a hybridization framework named LSHADE-SPACMA between LSHADE-SPA and a modified version of CMA-ES. The modified version of CMA-ES undergoes the crossover operation to improve the exploration capability of the proposed framework. In LSHADE-SPACMA both algorithms will work simultaneously on the same population, but more populations will be assigned gradually to the better performance algorithm. In order to verify and analyze the performance of both LSHADE-SPA and LSHADE-SPACMA, Numerical experiments on a set of 30 test problems from the CEC2017 benchmark for 10, 30, 50 and 100 dimensions, including a comparison with LSHADE algorithm are executed. Experimental results indicate that in terms of robustness, stability, and quality of the solution obtained, of both LSHADE-SPA and LSHADE-SPACMA are better than LSHADE algorithm, especially as the dimension increases.


title: Binary Dragonfly Algorithm for Feature Selection

abstract: Wrapper feature selection methods aim to reduce the number of features from the original feature set to and improve the classification accuracy simultaneously. In this paper, a wrapper-feature selection algorithm based on the binary dragonfly algorithm is proposed. Dragonfly algorithm is a recent swarm intelligence algorithm that mimics the behavior of the dragonflies. Eighteen UCI datasets are used to evaluate the performance of the proposed approach. The results of the proposed method are compared with those of Particle Swarm Optimization (PSO), Genetic Algorithms (GAs) in terms of classification accuracy and number of selected attributes. The results show the ability of Binary Dragonfly Algorithm (BDA) in searching the feature space and selecting the most informative features for classification tasks.


title: Resource allocation for network-controlled device-to-device communications in LTE-Advanced

abstract: Network-controlled device-to-device (D2D) communication allows cellular users to communicate directly, i.e., without passing through the eNodeB, while the latter retains control over resource allocation. This allows the same time---frequency resources to be allocated to spatially separated D2D flows simultaneously, thus increasing the cell throughput. This paper presents a framework for: (1) selecting which communications should use the D2D mode, and when, and (2) allocating resources to D2D and non-D2D users, exploiting reuse for the former. We show that the two problems, although apparently similar, should be kept separate and solved at different timescales in order to avoid problems, such as excessive packet loss. We model both as optimization problems, and propose a heuristic solution to the second, which must be solved at millisecond timescales. Simulation results show that our framework is practically viable, it avoids the problem of packet losses, increases throughput and reduces delays.


title: An experimental evaluation of computer graphics imagery

abstract: Accurate simulation of light propagation within an environment and perceptually based imaging techniques are necessary for the creation of realistic images. A physical experiment that verifies the simulation of reflected light intensities for diffuse environments was conducted. Measurements of radiant energy flux densities are compared with predictions using the radiosity method for those physical environments. By using color science procedures the results of the light model simulation are then transformed to produce a color television image. The final image compares favorably with the original physical model. The experiment indicates that, when the physical model and the simulation were viewed through a view camera, subjects could not distinguish between them. The results and comparison of both test procedures are presented within this paper.


title: Efficient Metaheuristics for the Mixed Team Orienteering Problem with Time Windows

abstract: Given a graph whose nodes and edges are associated with a profit, a visiting (or traversing) time and an admittance time window, the Mixed Team Orienteering Problem with Time Windows (MTOPTW) seeks for a specific number of walks spanning a subset of nodes and edges of the graph so as to maximize the overall collected profit. The visit of the included nodes and edges should take place within their respective time window and the overall duration of each walk should be below a certain threshold. In this paper we introduce the MTOPTW, which can be used for modeling a realistic variant of the Tourist Trip Design Problem where the objective is the derivation of near-optimal multiple-day itineraries for tourists visiting a destination which features several points of interest (POIs) and scenic routes. Since the MTOPTW is a NP-hard problem, we propose the first metaheuristic approaches to tackle it. The effectiveness of our algorithms is validated through a number of experiments on POI and scenic route sets compiled from the city of Athens (Greece).


title: Trajectory Tracking of AR.Drone Quadrotor Using Fuzzy Logic Controller

abstract: In this paper, Fuzzy Logic Controller (FLC) is implemented in the AR.Drone quadrotor in order to make it follow a given trajectory reference. The distance between the position and angle of the AR.Drone to the reference point is used as the input of FLC. As for the output, pitch value and yaw rate will be the controlling signal for the AR.Drone. The navigation data of the AR.Drone are forward speed (v x ), sideward speed (v y ), and yaw. These navigation data are going to be used to estimate positions and orientation of the AR.Drone. To compensate the y-position drift, the value of v y is also use as a criterion to determine the roll compensation. The FLC algorithm is implemented to AR.Drone 2.0 Elite Edition using LabVIEW software. Also, the algorithm has been tested in various trajectories such as straight line, a straight line with a perpendicular turn, a rectangular trajectory, and a curved trajectory. The results have shown that AR.Drone can follow a given trajectory with various initial position and orientation quite well.


title: A noiseaware filter for real-time depth upsampling

abstract: A new generation of active 3D range sensors, such as time-of-flight cameras, enables recording of full-frame depth maps at video frame rate. Unfortunately, the captured data are typically starkly contaminated by noise and the sensors feature only a rather limited image resolution. We therefore present a pipeline to enhance the quality and increase the spatial resolution of range data in real-time by upsampling the range information with the data from a high resolution video camera. Our algorithm is an adaptive multi-lateral upsampling filter that takes into account the inherent noisy nature of real-time depth data. Thus, we can greatly improve reconstruction quality, boost the resolution of the data to that of the video sensor, and prevent unwanted artifacts like texture copy into geometry. Our technique has been crafted to achieve improvement in depth map quality while maintaining high computational efficiency for a real-time application. By implementing our approach on the GPU, the creation of a real-time 3D camera with video camera resolution is feasible.


title: CANN: An intrusion detection system based on combining cluster centers and nearest neighbors

abstract: Abstract The aim of an intrusion detection systems (IDS) is to detect various types of malicious network traffic and computer usage, which cannot be detected by a conventional firewall. Many IDS have been developed based on machine learning techniques. Specifically, advanced detection approaches created by combining or integrating multiple learning techniques have shown better detection performance than general single learning techniques. The feature representation method is an important pattern classifier that facilitates correct classifications, however, there have been very few related studies focusing how to extract more representative features for normal connections and effective detection of attacks. This paper proposes a novel feature representation approach, namely the cluster center and nearest neighbor (CANN) approach. In this approach, two distances are measured and summed, the first one based on the distance between each data sample and its cluster center, and the second distance is between the data and its nearest neighbor in the same cluster. Then, this new and one-dimensional distance based feature is used to represent each data sample for intrusion detection by a k -Nearest Neighbor ( k -NN) classifier. The experimental results based on the KDD-Cup 99 dataset show that the CANN classifier not only performs better than or similar to k -NN and support vector machines trained and tested by the original feature representation in terms of classification accuracy, detection rates, and false alarms. I also provides high computational efficiency for the time of classifier training and testing (i.e., detection).


title: Scalable Clustering Algorithms with Balancing Constraints

abstract: Clustering methods for data-mining problems must be extremely scalable. In addition, several data mining applications demand that the clusters obtained be balanced, i.e., of approximately the same size or importance. In this paper, we propose a general framework for scalable, balanced clustering. The data clustering process is broken down into three steps: sampling of a small representative subset of the points, clustering of the sampled data, and populating the initial clusters with the remaining data followed by refinements. First, we show that a simple uniform sampling from the original data is sufficient to get a representative subset with high probability. While the proposed framework allows a large class of algorithms to be used for clustering the sampled set, we focus on some popular parametric algorithms for ease of exposition. We then present algorithms to populate and refine the clusters. The algorithm for populating the clusters is based on a generalization of the stable marriage problem, whereas the refinement algorithm is a constrained iterative relocation scheme. The complexity of the overall method is O(kN log N) for obtaining k balanced clusters from N data points, which compares favorably with other existing techniques for balanced clustering. In addition to providing balancing guarantees, the clustering performance obtained using the proposed framework is comparable to and often better than the corresponding unconstrained solution. Experimental results on several datasets, including high-dimensional (>20,000) ones, are provided to demonstrate the efficacy of the proposed framework.


title: Control Strategies for Self-Adaptive Software Systems

abstract: The pervasiveness and growing complexity of software systems are challenging software engineering to design systems that can adapt their behavior to withstand unpredictable, uncertain, and continuously changing execution environments. Control theoretical adaptation mechanisms have received growing interest from the software engineering community in the last few years for their mathematical grounding, allowing formal guarantees on the behavior of the controlled systems. However, most of these mechanisms are tailored to specific applications and can hardly be generalized into broadly applicable software design and development processes. This article discusses a reference control design process, from goal identification to the verification and validation of the controlled system. A taxonomy of the main control strategies is introduced, analyzing their applicability to software adaptation for both functional and nonfunctional goals. A brief extract on how to deal with uncertainty complements the discussion. Finally, the article highlights a set of open challenges, both for the software engineering and the control theory research communities.


title: Motion background modeling based on context-encoder

abstract: A background modeling method for motion-based background of a video made by moving camera is proposed in this paper. We utilize the recently proposed context-encoder to model the motion-based background from a dynamic foreground. This method aims to restore the overall scene of a video by removing the moving foreground objects and learning the feature of its context. An advantage of this method is that the performance of background modeling will not be affected when the camera is moving fast.


title: Detecting demeanor for healthcare with machine learning

abstract: This paper describes a new prototype system for detecting the demeanor of patients in emergency situations using the Intel RealSense camera system [1]. It describes how machine learning, a support vector machine (SVM) and the RealSense facial detection system can be used to track patient demeanour for pain monitoring. In a lab setting, the application has been trained to detect four different intensities of pain and provide demeanour information about the patient's eyes, mouth, and agitation state. Its utility as a basis for evaluating the condition of patients in situations using video, machine learning and 5G technology is discussed.


title: Yet another DSL for cross-platforms mobile development

abstract: With the growing success of mobility, mobile platforms (iOS, Android, WindowsPhone, etc.) multiply, each requiring specific development skills. Given this situation, it becomes very difficult for software developers to duplicate their apps accordingly. Meanwhile, web-based applications have evolved to be "mobile-friendly" but it appears that this is not a silver bullet: the user experience and the overall quality is still better with native applications. Of course, cross-platform mobile development tools have emerged in recent years. This paper provides a survey of these tools and points out that a full-fledged language for mobile development is highly desirable. Consequently, we present a preliminary work on Xmob, a technology-neutral DSL intended to be cross-compiled to produce native code for a variety of platforms.


title: UWB Omnidirectional Square Monopole Antenna for Use in Circular Cylindrical Microwave Imaging Systems

abstract: In this letter, a novel ultrawideband (UWB) printed monopole antenna (PMA) for use in a circular cylindrical microwave imaging system is presented. The proposed antenna consists of a square radiating patch and a ground plane with a pair of E-shaped slots and a rectangular slot with a pair of horizontal T-shaped strips protruded inside the slot, which provides a wide usable fractional bandwidth of more than 120% (2.97-12.83 GHz). By cutting two modified E-shaped slots with variable dimensions on the ground plane corners and also by inserting two T-shaped strips inside the rectangular slot located in the ground plane center, additional resonances are excited, and hence much wider impedance bandwidth can be produced, especially at the higher band. The proposed antenna has an ordinary square radiating patch, therefore displays a good omnidirectional radiation pattern even at higher frequencies, and also its radiation efficiency is greater than 86% across the entire radiating band. The designed antenna has a small size of 12 ×18 mm2. Simulated and measured results are presented to validate the usefulness of the proposed antenna structure for circular cylindrical microwave imaging system.


Information mining


title: Subspace Distribution Alignment for Unsupervised Domain Adaptation

abstract: We propose a novel method for unsupervised domain adaptation. Traditional machine learning algorithms often fail to generalize to new input distributions, causing reduced accuracy. Domain adaptation attempts to compensate for the performance degradation by transferring and adapting source knowledge to target domain. Existing unsupervised methods project domains into a lower-dimensional space and attempt to align the subspace bases, effectively learning a mapping from source to target points or vice versa. However, they fail to take into account the difference of the two distributions in the subspaces, resulting in misalignment even after adaptation. We present a unified view of existing subspace mapping based methods and develop a generalized approach that also aligns the distributions as well as the subspace bases. We provide a detailed evaluation of our approach on benchmark datasets and show improved results over published approaches.


title: Potential effect of physical activity based menu labels on the calorie content of selected fast food meals

abstract: In this study we examined the effect of physical activity based labels on the calorie content of meals selected from a sample fast food menu. Using a web-based survey, participants were randomly assigned to one of four menus which differed only in their labeling schemes (n=802): (1) a menu with no nutritional information, (2) a menu with calorie information, (3) a menu with calorie information and minutes to walk to burn those calories, or (4) a menu with calorie information and miles to walk to burn those calories. There was a significant difference in the mean number of calories ordered based on menu type (p=0.02), with an average of 1020 calories ordered from a menu with no nutritional information, 927 calories ordered from a menu with only calorie information, 916 calories ordered from a menu with both calorie information and minutes to walk to burn those calories, and 826 calories ordered from the menu with calorie information and the number of miles to walk to burn those calories. The menu with calories and the number of miles to walk to burn those calories appeared the most effective in influencing the selection of lower calorie meals (p=0.0007) when compared to the menu with no nutritional information provided. The majority of participants (82%) reported a preference for physical activity based menu labels over labels with calorie information alone and no nutritional information. Whether these labels are effective in real-life scenarios remains to be tested.


title: Fast and accurate sentiment classification using an enhanced Naive Bayes model

abstract: We have explored different methods of improving the accuracy of a Naive Bayes classifier for sentiment analysis. We observed that a combination of methods like effective negation handling, word n-grams and feature selection by mutual information results in a significant improvement in accuracy. This implies that a highly accurate and fast sentiment classifier can be built using a simple Naive Bayes model that has linear training and testing time complexities. We achieved an accuracy of 88.80% on the popular IMDB movie reviews dataset. The proposed method can be generalized to a number of text categorization problems for improving speed and accuracy.


title: Two-legged stair-climbing wheelchair and its stair dimension measurement using distance sensors

abstract: This paper presents a stair-climbing wheelchair mechanism and its stair dimension measurement method using distance sensors. The wheelchair climbs stairs using two legs having 7 degrees of freedom. The size of the proposed wheelchair is equal to or smaller than that of other commercially available wheelchairs. The proposed wheelchair can climb steep stairs (more than 30° slope), while the seat trajectory is smoothly controlled. Distance sensors are used for measuring the heights and widths of the stairs and the wheelchair direction error. Experiments were performed to test the stair-climbing mechanism and the sensing algorithm for the stair feature estimation.


title: Decentralized Consistent Network Updates in SDN with ez-Segway

abstract: We present ez-Segway, a decentralized mechanism to consistently and quickly update the network state while preventing forwarding anomalies (loops and black-holes) and avoiding link congestion. In our design, the centralized SDN controller only pre-computes information needed by the switches during the update execution. This information is distributed to the switches, which use partial knowledge and direct message passing to efficiently realize the update. This separation of concerns has the key benefit of improving update performance as the communication and computation bottlenecks at the controller are removed. Our evaluations via network emulations and large-scale simulations demonstrate the efficiency of ez-Segway, which compared to a centralized approach, improves network update times by up to 45% and 57% at the median and the 99th percentile, respectively. A deployment of a system prototype in a real OpenFlow switch and an implementation in P4 demonstrate the feasibility and low overhead of implementing simple network update functionality within switches.


title: Measurement of Integrated Circuit Conducted Emissions by Using a Transverse Electromagnetic Mode (TEM) Cell

abstract: This paper presents a new technique for the measurement of integrated circuit (IC) conducted emissions. In particular, the spectrum of interfering current flowing through an IC port is detected by using a transverse electromagnetic mode (TEM) cell. A structure composed of a microstrip line inserted in a matched TEM cell is considered. The structure is excited by an interfering source connected to one end of the transmission line. The relationship between the current spectrum of the source and the spectrum of the RF power delivered to the TEM mode of the cell is derived. This relationship is evaluated for one specific structure and the experimental validation is shown. Results of conducted emission measurement performed by using such a technique are shown as well and compared with those derived by using the magnetic probe method.


title: Framework for Computation Offloading in Mobile Cloud Computing

abstract: The inherently limited processing power and battery lifetime of mobile phones hinder the possible execution of computationally intensive applications like content-based video analysis or 3D modeling. Offloading of computationally intensive application parts from the mobile platform into a remote cloud infrastructure or nearby idle computers addresses this problem. This paper presents our Mobile Augmentation Cloud Services (MACS) middleware which enables adaptive extension of Android application execution from a mobile client into the cloud. Applications are developed by using the standard Android development pattern. The middleware does the heavy lifting of adaptive application partitioning, resource monitoring and computation offloading. These elastic mobile applications can run as usual mobile application, but they can also use remote computing resources transparently. Two prototype applications using the MACS middleware demonstrate the benefits of the approach. The evaluation shows that applications, which involve costly computations, can benefit from offloading with around 95% energy savings and significant performance gains compared to local execution only.


Customized Pricing


title: Spatiotemporal alignment for low-level asynchronous data fusion with radar sensors in grid-based tracking and mapping

abstract: Fusion of data from multiple sensors is often necessary to achieve an environment model, which meets the requirements of real-world applications, in particular those of autonomous vehicles. Sensor data fusion at a low-level yields potential advantages, as the data is fused before its interpretation with models and assumptions. However, spatiotemporal alignment, required for a precise fusion in dynamic environments, is difficult, as the sensors often cannot be synchronized. In this work, different approaches for spatiotemporal alignment of data from asynchronous sensors for low-level fusion are presented. Focus is given on radar sensors, as they allow measuring radial velocities in addition to range and bearing. The results are used to calculate fused measurement grids for grid-based tracking and mapping.


Image Enhancement of Medical Images using Gabor Filter Bank on Hexagonal Sampled Grids


title: Temporal Ontology And Temporal Reference

abstract: A semantics of temporal categories in language and a theory of their use in defining the temporal relations between events both require a more complex structure on the domain underlying the meaning representations than is commonly assumed. This paper proposes an ontology based on such notions as causation and consequence, rather than on purely temporal primitives. A central notion in the ontology is that of an elementary event-complex called a "nucleus." A nucleus can be thought of as an association of a goal event, or "culmination," with a "preparatory process" by which it is accomplished, and a "consequent state," which ensues. Natural-language categories like aspects, futurates, adverbials, and when-clauses are argued to change the temporal/aspectual category of propositions under the control of such a nucleic knowledge representation structure. The same concept of a nucleus plays a central role in a theory of temporal reference, and of the semantics of tense, which we follow McCawley, Partee, and Isard in regarding as an anaphoric category. We claim that any manageable formalism for natural-language temporal descriptions will have to embody such an ontology, as will any usable temporal database for knowledge about events which is to be interrogated using natural language.


title: A fuzzy-logic-based approach to qualitative modeling

abstract: This paper discusses a general approach to quali- tative modeling based on fuzzy logic. The method of qualitative modeling is divided into two parts: fuzzy modeling and linguistic approximation. It proposes to use a fuzzy clustering method (fuzzy c-means method) to identify the structure of a fuzzy model. To clarify the advantages of the proposed method, it also shows some examples of modeling, among them a model of a dynamical process and a model of a human operator's control action.


title: Sentiment Prediction using Collaborative Filtering

abstract: Learning sentiment models from short texts such as tweets is a notoriously challenging problem due to very strong noise and data sparsity. This paper presents a novel, collaborative filtering-based approach for sentiment prediction in twitter conversation threads. Given a set of sentiment holders and sentiment targets, we assume we know the true sentiments for a small fraction of holder-target pairs. This information is then used to predict the sentiment of a previously unknown user towards another user or an entity using collaborative filtering algorithms. We validate our model on two Twitter datasets using different collaborative filtering techniques. Our preliminary results demonstrate that the proposed approach can be effectively used in twitter sentiment prediction, thus mitigating the data sparsity problem.


title: Auditory Scene Analysis

abstract: Auditory scene analysis (ASA) is the process by which the auditory system separates the individual sounds in natural-world situations, in which these sounds are usually interleaved and overlapped in time and their components interleaved and overlapped in frequency. ASA is difficult because the ear has access only to the single pressure wave that is the sum of the pressure waves coming from all the individual sound sources (such as human voices, or foot steps). To construct a separate mental description for each source, ASA must analyze the incoming signal by the use of heuristic processes. These are based on regularities in the incoming sound that result from the fact that it is really the sum of some underlying sounds. For example, the incoming sound is built up of many frequency components. If a set, A, of these frequency components begin together at exactly the same time, whereas another set B all begin together, but at a different time from those of A, then the components of A will be considered as parts of one sound, and those of B as parts of a different sound. This grouping is justified by a regularity in the environment, namely that all the frequency components of a single sound tend to start at the same time. Other heuristic analyses are based on different regularities in how sound is produced. The grouping of these components can determine the perceived pitch, timbre, loudness, and spatial position of the resulting sounds.


title: A 0.18-μm CMOS shock wave generator with an on-chip antenna and a digitally programmable delay circuit

abstract: A 9–11-GHz fully integrated shock wave generator using a 0.18-μm CMOS process for in-door active imaging applications is presented. This chip includes an on-chip wideband meandering dipole antenna, a shock wave generator and a 5-bit digitally programmable delay circuit. The pulse generator simulation produces a 0.4-V peak–peak (p-p) pulse amplitude with a 45.86-ps monopulse cycle in simulation. The integrated dipole antenna's bandwidth −10 dB is 25.3 GHz in EMDS, ADS 2008 simulation. A 20-dB standard horn antenna with distance of 38 mm to this chip is used for the measurement. Measurement results show that 9–11-GHz pulse frequency response with 1.1-mVp-p pulse output voltage received from the horn antenna and 3-ps delay control resolution are suitable for fully on-chip pulse beam forming purpose with integrated antenna array.


title: Rib-reinforced Shell Structure

abstract: Shell structures are extensively used in engineering due to their efficient load-carrying capacity relative to material volume. However, large-span shells require additional supporting structures to strengthen fragile regions. The problem of designing optimal stiffeners is therefore becoming a major challenge for shell applications. To address it, we propose a computational framework to design and optimize rib layout on arbitrary shell to improve the overall structural stiffness and mechanical performance. The essential of our method is to place ribs along the principal stress lines which reflect paths of material continuity and indicates trajectories of internal forces. Given a surface and user-specified external loads, we perform a Finite Element Analysis. Using the resulting principal stress field, we generate a quad-mesh whose edges align with this cross field. Then we extract an initial rib network from the quad-mesh. After simplifying rib network by removing ribs with little contribution, we perform a rib flow optimization which allows ribs to swing on surface to further adjust rib distribution. Finally, we optimize rib cross-section to maximally reduce material usage while achieving certain structural stiffness requirements. We demonstrate that our rib-reinforced shell structures achieve good static performances. And experimental results by 3D printed objects show the effectiveness of our method.


title: Wireless Coexistence and EMC of Bluetooth and 802.11b Devices in Controlled Laboratory Settings

abstract: This paper presents experimental testing that has been performed on wireless communication devices as victims of electromagnetic interference (EMI). Wireless victims included universal serial bus (USB) network adapters and personal digital assistants (PDAs) equipped with IEEE 802.11b and Bluetooth technologies. The experimental data in this paper was gathered in an anechoic chamber and a gigahertz transverse electromagnetic (GTEM) cell to ensure reliable and repeatable results. This testing includes: Electromagnetic compatibility (EMC) testing performed in accordance with IEC 60601-1-2, an in-band sweep of EMC testing, and coexistence testing. The tests in this study show that a Bluetooth communication was able to coexist with other Bluetooth devices with no decrease in throughput and no communication breakdowns. However, testing revealed a significant decrease in throughput and increase in communication breakdowns when an 802.11b source is near an 802.11b victim. In a hospital setting decreased throughput and communication breakdowns can cause wireless medical devices to fail. It is therefore vital to have an understanding of the effect EMI can have on wireless communication devices.


MBLLEN: Low-Light Image/Video Enhancement Using CNNs


title: Clothoid-Based Global Path Planning for Autonomous Vehicles in Urban Scenarios

abstract: Intelligent vehicles require an efficient way to compute a feasible path that connects its current localization to a destination point. To achieve that, the knowledge about the road network, including its geometry, is important since connections between roads can be used in the path planning. This work consists on computing a feasible global path for autonomous vehicles with kinematic constraints. Piecewise linear continuous-curvature paths composed of clothoids, circular arcs, and straight lines are used for this purpose. Low curvature derivatives provide comfort to passengers. This approach provides a compact road network representation as only the parameters of the curves are stored. A real urban scenario with straight and curved roads, multiple lanes, intersections, and roundabouts is modeled and the proposed approach is validated. As a result of the proposed approaches, door-to-door global continuous-curvature paths are generated considering the shortest traveled distance.


title: TCP FAIRNESS ISSUES IN IEEE 802.11 WIRELESS LANS

abstract: In this paper, we study several TCP fairness issues in wireless local networks, both experimentally and via simulation. We illustrate the unfairness problems caused by the 802.11 MAC protocol, as well as by cross-layer interactions. Two possible solutions, namely SPM-AF queue management and LAS queue management, are proposed in the paper. These two queue management approaches are designed to alleviate the unfairness among competing TCP flows. The evaluation of these approaches uses the ns-2 network simulator. The simulation results show that the proposed approaches provide better sharing of the limited wireless network bandwidth among TCP flows.


title: Performing edge detection by difference of Gaussians using q-Gaussian kernels

abstract: In image processing, edge detection is a valuable tool to perform the extraction of features from an image. This detection reduces the amount of information to be processed, since the redundant information (considered less relevant) can be disconsidered. The technique of edge detection consists of determining the points of a digital image whose intensity changes sharply. This changes are, for example, due to the discontinuities of the orientation on a surface. A well known method of edge detection is the Difference of Gaussians (DoG). The method consists of subtracting two Gaussians, where a kernel has a standard deviation smaller than the previous one. The convolution between the subtraction of kernels and the input image results in the edge detection of this image. This paper introduces a method of extracting edges using DoG with kernels based on the q-Gaussian probability distribution, derived from the q-statistic proposed by Constantino Tsallis. To demonstrate the method's potential, we compare the introduced method with the tradicional DoG using Gaussians kernels. The results showed that the proposed method can extract edges with more accurate details.


Simulation modeling of plants and plant ecosystems


title: Broadside Radiation From a Planar 2-D Leaky-Wave Antenna by Practical Surface-Wave Launching

abstract: A planar 2-D leaky-wave (LW) antenna, capable of broadside radiation, is presented for millimeter wave applications. A directive surface-wave launcher (SWL) is utilized as the antenna feed exciting cylindrical surface-waves (SWs) on a grounded dielectric slab (GDS). With the addition of a segmented circular strip grating, cylindrical LWs can be excited on the antenna aperture. Measurements illustrate maximum gain at broadside at 19.48 GHz in both the E and H planes with a 10deg half power beamwidth. Specifically, a directive pencil beam is observed just at the edge of the TE1 SW mode cuttoff frequency of the slab (19.47 GHz), suggesting maximum radiation at the edge of a TE stopband.


title: Input-Admittance Calculation and Shaping for Controlled Voltage-Source Converters

abstract: A controlled power electronic converter can cause local instabilities when interacting with other dynamic subsystems in a power system. Oscillations at a certain frequency cannot, however, build up if the converter differential input admittance has a positive conductance (real part) at that frequency, since power is then dissipated. In this paper, input-admittance expressions for a voltage-source converter are derived. It is seen how the admittance can be shaped in order to get a positive real part in the desired frequency regions by adjusting the controller parameters.


title: Data-Driven Composition for Service-Oriented Situational Web Applications

abstract: The convergence of Services Computing and Web 2.0 gains a large space of opportunities to compose “situational” web applications from web-delivered services. However, the large number of services and the complexity of composition constraints make manual composition difficult to application developers, who might be non-professional programmers or even end-users. This paper presents a systematic data-driven approach to assisting situational application development. We first propose a technique to extract useful information from multiple sources to abstract service capabilities with a set tags. This supports intuitive expression of user’s desired composition goals by simple queries, without having to know underlying technical details. A planning technique then exploits composition solutions which can constitute the desired goals, even with some potential new interesting composition opportunities. A browser-based tool facilitates visual and iterative refinement of composition solutions, to finally come up with the satisfying outputs. A series of experiments demonstrate the efficiency and effectiveness of our approach.


title: Partially Blind Deblurring of Barcode from Out-of-Focus Blur

abstract: This paper addresses the nonstationary out-of-focus (OOF) blur removal in the application of barcode reconstruction. We propose a partially blind deblurring method when partial knowledge of the clean barcode is available. In particular, we consider an image formation model based on geometrical optics, which involves the point-spread function (PSF) for the OOF blur. With the known information, we can estimate a low-dimensional representation of the PSF using the Levenberg--Marquardt algorithm. Once the PSF is obtained, the deblurred image is computed by solving a quadratic program. We find that imposing a [0,1] box constraint is often good enough to enforce binary signal. Experiments on real data demonstrate that the forward model is physically realistic and our partially blind deblurring method can yield good reconstructions.


A New Approach to DNA Cryptography Using 8 x 8 Playfair Cipher and Ramanujam Square Matrix


title: Local Derivative Pattern Versus Local Binary Pattern: Face Recognition With High-Order Local Pattern Descriptor

abstract: This paper proposes a novel high-order local pattern descriptor, local derivative pattern (LDP), for face recognition. LDP is a general framework to encode directional pattern features based on local derivative variations. The nth-order LDP is proposed to encode the (n-1)th -order local derivative direction variations, which can capture more detailed information than the first-order local pattern used in local binary pattern (LBP). Different from LBP encoding the relationship between the central point and its neighbors, the LDP templates extract high-order local information by encoding various distinctive spatial relationships contained in a given local region. Both gray-level images and Gabor feature images are used to evaluate the comparative performances of LDP and LBP. Extensive experimental results on FERET, CAS-PEAL, CMU-PIE, Extended Yale B, and FRGC databases show that the high-order LDP consistently performs much better than LBP for both face identification and face verification under various conditions.


title: Accelerating SQL database operations on a GPU with CUDA

abstract: Prior work has shown dramatic acceleration for various database operations on GPUs, but only using primitives that are not part of conventional database languages such as SQL. This paper implements a subset of the SQLite command processor directly on the GPU. This dramatically reduces the effort required to achieve GPU acceleration by avoiding the need for database programmers to use new programming languages such as CUDA or modify their programs to use non-SQL libraries. This paper focuses on accelerating SELECT queries and describes the considerations in an efficient GPU implementation of the SQLite command processor. Results on an NVIDIA Tesla C1060 achieve speedups of 20-70X depending on the size of the result set.


title: The Impact of Layout-Dependent STI Stress and Effective Width on Low-Frequency Noise and High-Frequency Performance in Nanoscale nMOSFETs

abstract: The impact of channel width scaling on low-frequency noise (LFN) and high-frequency performance in multifinger MOSFETs is reported in this paper. The compressive stress from shallow trench isolation (STI) cannot explain the lower LFN in extremely narrow devices. STI top corner rounding (TCR)-induced ΔW is identified as an important factor that is responsible for the increase in transconductance Gm and the reduction in LFN with width scaling to nanoscale regime. A semi-empirical model was derived to simulate the effective mobility (μeff) degradation from STI stress and the increase in effective width (Weff) from ΔW due to STI TCR. The proposed model can accurately predict width scaling effect on Gm based on a tradeoff between μeff and Weff. The enhanced STI stress may lead to an increase in interface traps density (Nit), but the influence is relatively minor and can be compensated by the Weff effect. Unfortunately, the extremely narrow devices suffer fT degradation due to an increase in Cgg. The investigation of impact from width scaling on μeff, Gm, and LFN, as well as the tradeoff between LFN and high-frequency performance, provides an important layout guideline for analog and RF circuit design.


title: Applications of SAT Solvers to AES Key Recovery from Decayed Key Schedule Images

abstract: Cold boot attack is a side channel attack which exploits the data remanence property of random access memory (RAM) to retrieve its contents which remain readable shortly after its power has been removed. Given the nature of the cold boot attack, only a corrupted image of the memory contents will be available to the attacker. In this paper, we investigate the use of an off-the-shelf SAT solver, CryptoMinSat, to improve the key recovery of the AES-128 key schedules from its corresponding decayed memory images. By exploiting the asymmetric decay of the memory images and the redundancy of key material inherent in the AES key schedule, rectifying the faults in the corrupted memory images of the AES-128 key schedule is formulated as a Boolean satisfiability problem which can be solved efficiently for relatively very large decay factors. Our experimental results show that this approach improves upon the previously known results.


title: Deep Learning for Robust Normal Estimation in Unstructured Point Clouds

abstract: Normal estimation in point clouds is a crucial first step for numerous algorithms, from surface reconstruction and scene understanding to rendering. A recurrent issue when estimating normals is to make appropriate decisions close to sharp features, not to smooth edges, or when the sampling density is not uniform, to prevent bias. Rather than resorting to manually-designed geometric priors, we propose to learn how to make these decisions, using ground-truth data made from synthetic scenes. For this, we project a discretized Hough space representing normal directions onto a structure amenable to deep learning. The resulting normal estimation method outperforms most of the time the state of the art regarding robustness to outliers, to noise and to point density variation, in the presence of sharp edges, while remaining fast, scaling up to millions of points.


title: Detecting Emergency Events and Geo-Location Awareness from Twitter Streams

abstract: the rapidly increasing number of messages on twitter is quite interesting. Through twitter streaming, this paper is capable of delivering tweets for any keywords from clients all around the world or Hashtag in real-time. However, semantic topic extraction and tracking the userinterested news events from messages on twitter can be considered as a challenging task. In this paper focused on detecting unusual behaviors and geo-social events in Twitter streams. The proposed method is able to provide/manage notifications and awareness (i.e., alerts) for users. In general, there are 4 steps in the process of identifying alerts and current awareness as follows: 1) Applying the set of realtime streaming APIs offered by Twitter for retrieving the public live stream of Twitter messages, 2) Utilizing an online clustering method to form the group of similar messages based on a corpus of text messages, 3) Applying an algorithm to compare the newly extracted event-term candidate with the list of event-term based on timestamp and calculate ranking indicating real-world events by frequent re-tweets, and 4) Identifying the alerts and current awareness based on the user's geo-location. Into the bargain, the proposed method is evaluated using high-volume Twitter data and as a result the findings are reported.


Poetic Machine: Computational Creativity for Automatic Poetry Generation in Bengali


title: Profanity use in online communities

abstract: As user-generated Web content increases, the amount of inappropriate and/or objectionable content also grows. Several scholarly communities are addressing how to detect and manage such content: research in computer vision focuses on detection of inappropriate images, natural language processing technology has advanced to recognize insults. However, profanity detection systems remain flawed. Current list-based profanity detection systems have two limitations. First, they are easy to circumvent and easily become stale - that is, they cannot adapt to misspellings, abbreviations, and the fast pace of profane slang evolution. Secondly, they offer a one-size fits all solution; they typically do not accommodate domain, community and context specific needs. However, social settings have their own normative behaviors - what is deemed acceptable in one community may not be in another. In this paper, through analysis of comments from a social news site, we provide evidence that current systems are performing poorly and evaluate the cases on which they fail. We then address community differences regarding creation/tolerance of profanity and suggest a shift to more contextually nuanced profanity detection systems.


title: Coherent noise for non-photorealistic rendering

abstract: A wide variety of non-photorealistic rendering techniques make use of random variation in the placement or appearance of primitives. In order to avoid the "shower-door" effect, this random variation should move with the objects in the scene. Here we present coherent noise tailored to this purpose. We compute the coherent noise with a specialized filter that uses the depth and velocity fields of a source sequence. The computation is fast and suitable for interactive applications like games.


title: Deep Belief Networks using discriminative features for phone recognition

abstract: Deep Belief Networks (DBNs) are multi-layer generative models. They can be trained to model windows of coefficients extracted from speech and they discover multiple layers of features that capture the higher-order statistical structure of the data. These features can be used to initialize the hidden units of a feed-forward neural network that is then trained to predict the HMM state for the central frame of the window. Initializing with features that are good at generating speech makes the neural network perform much better than initializing with random weights. DBNs have already been used successfully for phone recognition with input coefficients that are MFCCs or filterbank outputs [1, 2]. In this paper, we demonstrate that they work even better when their inputs are speaker adaptive, discriminative features. On the standard TIMIT corpus, they give phone error rates of 19.6% using monophone HMMs and a bigram language model and 19.4% using monophone HMMs and a trigram language model.


title: Open Device Control (OpenDC): Human Interface Device Framework for Interactive Applications Including Educational Contents in Ubiquitous Environments

abstract: This paper proposes a human interface device framework for interactive applications including educational contents in ubiquitous environments. Recently, mobile and portable devices such as iPad, iPhone and Android tablet PC have become common and popular. We should use these devices for providing learners with more attractive educational contents supporting various intuitive interfaces. Using the proposed framework, it becomes possible to easily develop such educational contents. Furthermore, the combined use of the framework with WebIB, which is a software development system for Web 3D contents, it also becomes possible to develop more attractive educational contents using 3D graphics that run on Web and Digital TV. To clarify the usefulness of the framework, this paper introduces interactive application examples including 3D educational contents available on Web and Digital TV actually developed using the proposed framework and WebIB.


title: Decentralized Power Control for Random Access with Successive Interference Cancellation

abstract: This paper is concerned with the decentralized power allocation problem in random access systems. We propose a scheme that is especially suitable for systems requiring high throughput but with difficulty in establishing centralized control, such as cognitive radio environments. Specifically, we assume successive interference cancellation (SIC) at the receiver for multi-packet reception (MPR). We consider a decentralized random power transmission strategy where each user selects its transmitted power level randomly according to a power distribution conditioned on its own channel state. Our focus is on the design of this distribution such that the system packet throughput is maximized under rate and power constraints. We start from a two-user system. A main finding of this paper is that the supports of the optimal power distributions are of discrete nature. This finding greatly simplifies the distribution optimization problem. We also discuss a sub-optimal solution to systems with more than two users. Numerical results demonstrate that the proposed scheme can achieve noticeable performance improvement compared with conventional single-user detection (SUD) based ones and offer a flexible tradeoff between the system throughput and power consumption.


title: EmotionLines: An Emotion Corpus of Multi-Party Conversations

abstract: Feeling emotion is a critical characteristic to distinguish people from machines. Among all the multi-modal resources for emotion detection, textual datasets are those containing the least additional information in addition to semantics, and hence are adopted widely for testing the developed systems. However, most of the textual emotional datasets consist of emotion labels of only individual words, sentences or documents, which makes it challenging to discuss the contextual flow of emotions. In this paper, we introduce EmotionLines, the first dataset with emotions labeling on all utterances in each dialogue only based on their textual content. Dialogues in EmotionLines are collected from Friends TV scripts and private Facebook messenger dialogues. Then one of seven emotions, six Ekman's basic emotions plus the neutral emotion, is labeled on each utterance by 5 Amazon MTurkers. A total of 29,245 utterances from 2,000 dialogues are labeled in EmotionLines. We also provide several strong baselines for emotion detection models on EmotionLines in this paper.


title: Sensitive and simple sonoluminescence detection of melamine via aggregation of Au nanoparticles

abstract: In this work, we report a novel Au nanoparticle-sonoluminescence (AuNP-SL) design for simple and high throughput detection of melamine with a lab-made SL vial. Aqueous SL at 520 nm can be sensitively quenched and restored by dispersed and aggregated AuNPs, respectively. Based on this, the melamine-induced aggregation state of AuNPs is characterized by measuring the SL variation, as well as the content of melamine. All the ultrasound parameters and experimental variables, including the duration and interval of ultrasound irradiation, the composition of the test solution, and the melamine–AuNP interaction time, were separately investigated and optimized. The established SL method exhibits a good linear SL response to melamine concentrations in the range of 10 to 240 nM with a limit of detection (3σ) of 3 nM. The proposed method has been applied to the quantification of melamine in milk products. Statistical analysis results from F and t tests demonstrated the agreement between the SL method and the official method both in precision and accuracy.


title: A Review Paper on Face Recognition Techniques

abstract: Face recognition presents a challenging problem in the process of image analysis and computer vision. Face recognition techniques can be broadly classified into three categories based on the face data acquisition methodology: methods that operates on intensity images; those that deal with video sequences; and those that requires other sensory data such as 3D information or infra-red imagery. High data dimensionality makes it challenging to distinguish the required discriminative information. In this paper, an overview of some well-known dimension reduction techniques is presented and some of the benefits and drawbacks of the schemes mentioned therein are examined. This paper also mentions the classification of dimension reduction techniques being developed and tries to give an idea of the state of the art of face recognition technology.


title: On generalized fat trees

abstract: We introduce and analyze a new family of multiprocesser interconnection networks, called generalized fat trees, which include as special cases the fat trees used for the connection machine architecture CM-5, pruned butterflies, and various other fat trees proposed in the literature. The generalized fat trees provide a formal unifying concept to design and analyse a fat tree based architecture. The extended generalized fat tree network XGFT(h; m/sub 1/, ..., m/sub h/; w/sub 1/, ..., w/sub h/) of height h has /spl Pi//sub i=1//sup h/ m/sub i/ leaf processors and the inner nodes serve only as switches or routers. Each non-leaf node in level i has m/sub i/ children and each non-root has w/sub i+1/ parent nodes. The generalized fat trees provide regularity, symmetry, recursive scalability, maximal fault-tolerance, logarithmic diameter bisection scalability, and permit simple algorithms for fault tolerant self-routing and broadcasting. These networks are also versatile, since they can efficiently embed rings, meshes and tori, trees, pyramids and hypercubes. >


title: Multi-criteria routing metric for Wireless Mesh Networks

abstract: This paper proposes a multi-criteria routing metric LB-WCETT for wireless mesh networks by comparing several existing routing metrics,in which the link qualities,channel diversity and load of the channel in the networks are considered.Simulation results demonstrate that LB-WCETT can improve the performance of wireless mesh networks greatly.


title: Characterizing the differences between pre- and post- release versions of software

abstract: Many software producers utilize beta programs to predict post-release quality and to ensure that their products meet quality expectations of users. Prior work indicates that software producers need to adjust predictions to account for usage environments and usage scenarios differences between beta populations and post-release populations. However, little is known about how usage characteristics relate to field quality and how usage characteristics differ between beta and post-release. In this study, we examine application crash, application hang, system crash, and usage information from millions of Windows® users to 1) examine the effects of usage characteristics differences on field quality (e.g. which usage characteristics impact quality), 2) examine usage characteristics differences between beta and post-release (e.g. do impactful usage characteristics differ), and 3) report experiences adjusting field quality predictions for Windows. Among the 18 usage characteristics that we examined, the five most important were: the number of application executed, whether the machines was pre-installed by the original equipment manufacturer, two sub-populations (two language/geographic locales), and whether Windows was 64-bit (not 32-bit). We found each of these usage characteristics to differ between beta and post-release, and by adjusting for the differences, accuracy of field quality predictions for Windows improved by ~59%.


title: Detecting Linkedin Spammers and its Spam Nets

abstract: Spam is one of the main problems of the WWW. ::: Many studies exist about characterising and detecting several ::: types of Spam (mainly Web Spam, Email Spam, Forum/Blob ::: Spam and Social Networking Spam). Nevertheless, to the best of ::: our knowledge, there are no studies about the detection of Spam ::: in Linkedin. In this article, we propose a method for detecting ::: Spammers and Spam nets in the Linkedin social network. As ::: there are no public or private Linkedin datasets in the state of ::: the art, we have manually built a dataset of real Linkedin users, ::: classifying them as Spammers or legitimate users. ::: The proposed method for detecting Linkedin Spammers ::: consists of a set of new heuristics and their combinations using ::: a kNN classifier. Moreover, we proposed a method for detecting ::: Spam nets (fake companies) in Linkedin, based on the idea that ::: the profiles of these companies share content similarities. We have ::: found that the proposed methods were very effective. We achieved ::: an F-Measure of 0.971 and an AUC close to 1 in the detection ::: of Spammer profiles, and in the detection of Spam nets, we have ::: obtained an F-Measure of 1.


title: Active Task Selection for Lifelong Machine Learning

abstract: In a lifelong learning framework, an agent acquires knowledge incrementally over consecutive learning tasks, continually building upon its experience. Recent lifelong learning algorithms have achieved nearly identical performance to batch multi-task learning methods while reducing learning time by three orders of magnitude. In this paper, we further improve the scalability of lifelong learning by developing curriculum selection methods that enable an agent to actively select the next task to learn in order to maximize performance on future learning tasks. We demonstrate that active task selection is highly reliable and effective, allowing an agent to learn high performance models using up to 50% fewer tasks than when the agent has no control over the task order. We also explore a variant of transfer learning in the lifelong learning setting in which the agent can focus knowledge acquisition toward a particular target task.


title: Probabilistic Interpretations for MYCIN's Certainty Factors

abstract: We propose a redefinition of the quantities called certainty factors used by MYCIN to manage uncertainty. This definition is based on the desiderata of certainty factors given in the original work. It is emphasized that this definition reflects the not ion that certainty factors represent changes in belief about hypotheses given evidence as opposed to absolute beliefs. It is shown that this redefinition accommodates an infinite number of probabilistic interpretations. In constructing the interpretations, insight is provided into the assumptions made when propagating certainty factors through an inference net. For example, it is shown that all evidence which bears directly on a hypothesis must be conditionally independent on the hypothesis and its negation. Also, this analysis demonstrates that certainty factors must be elicited from experts in a precise fashion and a straightforward method for doing so is presented. Improvements in the methods currently used to propagate certainty factors through an inference net will also be suggested.


Algorithms and Architectures for use with Nanoelectronic Computers: 1


title: The "baby lung" became an adult

abstract: The baby lung was originally defined as the fraction of lung parenchyma that, in acute respiratory distress syndrome (ARDS), still maintains normal inflation. Its size obviously depends on ARDS severity and relates to the compliance of the respiratory system. CO2 clearance and blood oxygenation primarily occur within the baby lung. While the specific compliance suggests the intrinsic mechanical characteristics to be nearly normal, evidence from positron emission tomography suggests that at least a part of the well-aerated baby lung is inflamed. The baby lung is more a functional concept than an anatomical one; in fact, in the prone position, the baby lung "shifts" from the ventral lung regions toward the dorsal lung regions while usually increasing its size. This change is associated with better gas exchange, more homogeneously distributed trans-pulmonary forces, and a survival advantage. Positive end expiratory pressure also increases the baby lung size, both allowing better inflation of already open units and adding new pulmonary units. Viewed as surrogates of stress and strain, tidal volume and plateau pressures are better tailored to baby lung size than to ideal body weight. Although less information is available for the baby lung during spontaneous breathing efforts, the general principles regulating the safety of ventilation are also applicable under these conditions.


title: CIELuv color space for identification and segmentation of disease affected plant leaves using fuzzy based approach

abstract: Economy of developing countries mainly depends on the agriculture productivity. The identification and control of plant disease plays a vital role in improving the quality and quantity of agriculture productivity. It is very difficult to monitor or detect the crop disease using the traditional manual methods. Computer based automated techniques for the identification of plant disease is advantageous as it detects very early stage of symptoms of plant diseases and also it lessens the burden of workers in monitoring the large fields. Image processing techniques can be applied to solve the problem of identification and extraction of the affected parts of plants in efficient manner. In this work, fuzzy based method for the early identification and segmentation of disease affected plant leaves is proposed.


title: Building symbolic information for 3D human body modeling from range data

abstract: The work is concerned with the signal-to-symbol problem of building skinned, segmented, landmarked and labeled 3D models of the whole human body from range data. A fully automated model based process is presented that takes raw range data, cleans and skins it, and then locates "interesting" features, to enrich the surface with symbolic information for specific applications. The method is validated via volumetrics in medicine and surface anthropometry.


title: New substrate-integrated to coplanar waveguide transition

abstract: A novel design for a transition from substrate-integrated waveguide (SIW) to regular coplanar waveguide (CPW) is presented. In comparison with previously published transitions, which employ conductor-backed CPWs and via holes, the new design achieves better return-loss performance. Dispersion curves highlight the related modes and the available bandwidths. The new design is optimized in an HFSS environment and is presented as a single transition and as a back-to-back version with more than 30 dB and 26 dB return loss, respectively, over a bandwidth of 19.5 GHz to 27 GHz. All design parameters are specified. A SIW-to-conductor-backed CPW and via holes is presented for comparison, showing return-loss values of only 21 dB and 14.5 dB. All presented performances include dielectric and metallic losses and are verified by comparisons between HFSS and CST Microwave Studio.


Improving Clinical Diagnosis Inference through Integration of Structured and Unstructured Knowledge


title: Binary decision rules for multistage adaptive mixed-integer optimization

abstract: Decision rules provide a flexible toolbox for solving computationally demanding, multistage adaptive optimization problems. There is a plethora of real-valued decision rules that are highly scalable and achieve good quality solutions. On the other hand, existing binary decision rule structures tend to produce good quality solutions at the expense of limited scalability and are typically confined to worst-case optimization problems. To address these issues, we first propose a linearly parameterised binary decision rule structure and derive the exact reformulation of the decision rule problem. In the cases where the resulting optimization problem grows exponentially with respect to the problem data, we provide a systematic methodology that trades-off scalability and optimality, resulting to practical binary decision rules. We also apply the proposed binary decision rules to the class of problems with random-recourse and show that they share similar complexity as the fixed-recourse problems. Our numerical results demonstrate the effectiveness of the proposed binary decision rules and show that they are (i) highly scalable and (ii) provide high quality solutions.


title: Time-Selective Signaling and Reception for Communication over Multipath Fading Channels

abstract: The mobile wireless channel affords inherent diversity to combat the effects of fading. Existing code-division multiple-access systems, by virtue of spread-spectrum signaling and RAKE reception, exploit only part of the channel diversity via multipath combination. Moreover, their performance degrades under fast fading commonly encountered in mobile scenarios. In this paper, we develop new signaling and reception techniques that maximally exploit channel diversity via joint multipath-Doppler processing. Our approach is based on a canonical representation of the wireless channel, which leads to a time-frequency generalization of the RAKE receiver for diversity processing. Our signaling scheme facilitates joint multipath-Doppler diversity by spreading the symbol waveform beyond the intersymbol duration to make the channel time-selective. A variety of detection schemes are developed to account for the intersymbol interference (ISI) due to overlapping symbols. However, our results indicate that the effects of ISI are virtually negligible due to the excellent correlation properties of the pseudorandom codes. Performance analysis also shows that relatively small Doppler spreads can yield significant diversity gains. The inherently higher level of diversity achieved by time-selective signaling brings the fading channel closer to an additive white Gaussian noise channel, thereby facilitating the use of powerful existing coding techniques for Gaussian channels.


title: A hybrid method to face class overlap and class imbalance on neural networks and multi-class scenarios

abstract: Class imbalance and class overlap are two of the major problems in data mining and machine learning. Several studies have shown that these data complexities may affect the performance or behavior of artificial neural networks. Strategies proposed to face with both challenges have been separately applied. In this paper, we introduce a hybrid method for handling both class imbalance and class overlap simultaneously in multi-class learning problems. Experimental results on five remote sensing data show that the combined approach is a promising method.


title: Vertical dry adhesive climbing with a 100× bodyweight payload

abstract: The ability to carry large payloads could greatly increase the applications of small, low cost climbing robots. We present a linear inchworm gait that uses a single powerful actuator to climb. To make this gait possible, we leveraged two new methods of achieving controllable, anisotropic adhesion (one method produces over 200 times stronger adhesion in the preferred direction). With controllable, anisotropic adhesion, the gait is robust to missed steps. In addition, the gait provides a stance in which the robot can rest without requiring power. An autonomous 9 gram robot is able to climb a smooth vertical surface at 3 mm/s, while hoisting more than a kilogram. We also present a scaled down version of the robot, which is considerably smaller than any previous dry adhesive climbing mechanism. It is actuated by externally powered Shape Memory Alloy, weighs 20 mg, and is capable of hoisting 500 mg. These climbers show that a large hoisting ability while climbing can be achieved using dry adhesives, and the presented concepts could aid in the development of autonomous, highly functional, small robots.


title: The effects of head-mounted display mechanical properties and field of view on distance judgments in virtual environments

abstract: Research has shown that people are able to judge distances accurately in full-cue, real-world environments using visually directed actions. However, in virtual environments viewed with head-mounted display (HMD) systems, there is evidence that people act as though the virtual space is smaller than intended. This is a surprising result given how well people act in real environments. The behavior in the virtual setting may be linked to distortions in the available visual cues or to a person's ability to locomote without vision. Either could result from issues related to added mass, moments of inertia, and restricted field of view in HMDs. This article describes an experiment in which distance judgments based on normal real-world and HMD viewing are compared with judgments based on real-world viewing while wearing two specialized devices. One is a mock HMD, which replicated the mass, moments of inertia, and field of view of the HMD and the other an inertial headband designed to replicate the mass and moments of inertia of the HMD, but constructed to not restrict the field of view of the observer or otherwise feel like wearing a helmet. Distance judgments using the mock HMD showed a statistically significant underestimation relative to the no restriction condition but not of a magnitude sufficient to account for all the distance compression seen in the HMD. Indicated distances with the inertial headband were not significantly smaller than those made with no restrictions.


title: Probabilistic Graphical Models: Principles and Techniques

abstract: Most tasks require a person or an automated system to reason -- to reach conclusions based on available information. The framework of probabilistic graphical models, presented in this book, provides a general approach for this task. The approach is model-based, allowing interpretable models to be constructed and then manipulated by reasoning algorithms. These models can also be learned automatically from data, allowing the approach to be used in cases where manually constructing a model is difficult or even impossible. Because uncertainty is an inescapable aspect of most real-world applications, the book focuses on probabilistic models, which make the uncertainty explicit and provide models that are more faithful to reality. Probabilistic Graphical Models discusses a variety of models, spanning Bayesian networks, undirected Markov networks, discrete and continuous models, and extensions to deal with dynamical systems and relational data. For each class of models, the text describes the three fundamental cornerstones: representation, inference, and learning, presenting both basic concepts and advanced techniques. Finally, the book considers the use of the proposed framework for causal reasoning and decision making under uncertainty. The main text in each chapter provides the detailed technical development of the key ideas. Most chapters also include boxes with additional material: skill boxes, which describe techniques; case study boxes, which discuss empirical cases related to the approach described in the text, including applications in computer vision, robotics, natural language understanding, and computational biology; and concept boxes, which present significant concepts drawn from the material in the chapter. Instructors (and readers) can group chapters in various combinations, from core topics to more technically advanced material, to suit their particular needs.


title: Analysis of skin color models for face detection

abstract: This paper describes face detection method using skin color information. Processing color information is faster than processing other facial features. There are different color models present for skin color segmentation like RGB, CMY, YUV, YIQ, YCbCr, YCgCr, HSI, etc. But to choose best option among these models for efficient skin color segmentation is difficult task. In this paper we have explored and analyse three most popular color models-YCbCr, YCgCr and HSI for face detection.


title: RESEARCH ON CATEGORIES IN THE SOCIOLOGY OF ORGANIZATIONS

abstract: The concept of a “category” and the social process of “categorization” occupy a crucial place in current theories of organizations. In this introductory chapter to Research in the Sociology of Organization's volume on Categories in Markets: Origins and Evolution, we review published work in various streams of research and find that studies of organizational forms and identities, institutional logics, collective action frames, and product conceptual systems have key commonalities and predictable differences.


title: Management Options for Reducing the Release of Antibiotics and Antibiotic Resistance Genes to the Environment

abstract: Background: There is growing concern worldwide about the role of polluted soil and water environments in the development and dissemination of antibiotic resistance. ::: ::: Objective: Our aim in this study was to identify management options for reducing the spread of antibiotics and antibiotic-resistance determinants via environmental pathways, with the ultimate goal of extending the useful life span of antibiotics. We also examined incentives and disincentives for action. ::: ::: Methods: We focused on management options with respect to limiting agricultural sources; treatment of domestic, hospital, and industrial wastewater; and aquaculture. ::: ::: Discussion: We identified several options, such as nutrient management, runoff control, and infrastructure upgrades. Where appropriate, a cross-section of examples from various regions of the world is provided. The importance of monitoring and validating effectiveness of management strategies is also highlighted. Finally, we describe a case study in Sweden that illustrates the critical role of communication to engage stakeholders and promote action. ::: ::: Conclusions: Environmental releases of antibiotics and antibiotic-resistant bacteria can in many cases be reduced at little or no cost. Some management options are synergistic with existing policies and goals. The anticipated benefit is an extended useful life span for current and future antibiotics. Although risk reductions are often difficult to quantify, the severity of accelerating worldwide morbidity and mortality rates associated with antibiotic resistance strongly indicate the need for action.


Why It Is Hard to Label Our Concepts


title: SpaRTA - Tracking across occlusions via global partitioning of 3D clouds of points

abstract: Any 3D tracking algorithm has to deal with occlusions: multiple targets get so close to each other that the loss of their identities becomes likely, hence potentially affecting the very quality of the data with interrupted trajectories and identity switches. Here, we present a novel tracking method that addresses the problem of occlusions within large groups of featureless objects by means of three steps: i) it represents each target as a cloud of points in 3D; ii) once a 3D cluster corresponding to an occlusion occurs, it defines a partitioning problem by introducing a cost function that uses both attractive and repulsive spatio-temporal proximity links; iii) it minimizes the cost function through a semi-definite optimization technique specifically designed to cope with the presence of multi-minima landscapes. The algorithm is designed to work on 3D data regardless of the experimental method used: multi--camera systems, lidars, radars and RGB-D systems. By performing tests on public data-sets, we show that the new algorithm produces a significant improvement over the state-of-the-art tracking methods, both by reducing the number of identity switches and by increasing the accuracy of the estimated positions of the targets in real space.


title: Detecting Anomalous Faces with 'No Peeking' Autoencoders

abstract: Detecting anomalous faces has important applications. For example, a system might tell when a train driver is incapacitated by a medical event, and assist in adopting a safe recovery strategy. These applications are demanding, because they require accurate detection of rare anomalies that may be seen only at runtime. Such a setting causes supervised methods to perform poorly. We describe a method for detecting an anomalous face image that meets these requirements. We construct a feature vector that reliably has large entries for anomalous images, then use various simple unsupervised methods to score the image based on the feature. Obvious constructions (autoencoder codes; autoencoder residuals) are defeated by a 'peeking' behavior in autoencoders. Our feature construction removes rectangular patches from the image, predicts the likely content of the patch conditioned on the rest of the image using a specially trained autoencoder, then compares the result to the image. High scores suggest that the patch was difficult for an autoencoder to predict, and so is likely anomalous. We demonstrate that our method can identify real anomalous face images in pools of typical images, taken from celeb-A, that is much larger than usual in state-of-the-art experiments. A control experiment based on our method with another set of normal celebrity images - a 'typical set', but nonceleb-A are not identified as anomalous; confirms this is not due to special properties of celeb-A.


title: Smart Contracts Make Bitcoin Mining Pools Vulnerable

abstract: Despite their incentive structure flaws, mining pools account for more than 95% of Bitcoin’s computation power. This paper introduces an attack against mining pools in which a malicious party pays pool members to withhold their solutions from their pool operator. We show that an adversary with a tiny amount of computing power and capital can execute this attack. Smart contracts enforce the malicious party’s payments, and therefore miners need neither trust the attacker’s intentions nor his ability to pay. Assuming pool members are rational, an adversary with a single mining ASIC can, in theory, destroy all big mining pools without losing any money (and even make some profit).