title: How does clickthrough data reflect retrieval quality?

abstract: Automatically judging the quality of retrieval functions based on observable user behavior holds promise for making retrieval evaluation faster, cheaper, and more user centered. However, the relationship between observable user behavior and retrieval quality is not yet fully understood. We present a sequence of studies investigating this relationship for an operational search engine on the arXiv.org e-print archive. We find that none of the eight absolute usage metrics we explore (e.g., number of clicks, frequency of query reformulations, abandonment) reliably reflect retrieval quality for the sample sizes we consider. However, we find that paired experiment designs adapted from sensory analysis produce accurate and reliable statements about the relative quality of two retrieval functions. In particular, we investigate two paired comparison tests that analyze clickthrough data from an interleaved presentation of ranking pairs, and we find that both give accurate and consistent results. We conclude that both paired comparison tests give substantially more accurate and sensitive evaluation results than absolute usage metrics in our domain.


title: Redistributing leadership in online creative collaboration

abstract: In this paper, we integrate theories of distributed leadership and distributed cognition to account for the roles of people and technology in online leadership. When leadership is distributed effectively, the result can be success stories like Wikipedia and Linux. However, finding a successful distribution is challenging. In the online community Newgrounds, hundreds of collaborative animation projects called "collabs" are started each year, but less than 20% are completed. We suggest that many collabs fail because leaders are overburdened and lack adequate technological support. We introduce Pipeline, a collaboration tool designed to support and transform leadership, with the goal of easing the burden on leaders of online creative projects. Through a case study of a six-week, 30-artist collaboration called Holiday Flood, we show how Pipeline supported redistributed leadership. We conclude with implications for theory and the design of social computing systems.


Multiplicative Binding, Representation Operators and Analogy


title: Securing SDN Infrastructure of IoT–Fog Networks From MitM Attacks

abstract: While the Internet of Things (IoT) is making our lives much easier, managing the IoT becomes a big issue due to the huge number of connections, and the lack of protections for devices. Recent work shows that software-defined networking (SDN) has a great capability in automatically and dynamically managing network flows. Besides, switches in SDNs are usually powerful machines, which can be used as fog nodes simultaneously. Therefore, SDN seems a good choice for IoT–Fog networks. However, before deploying to IoT–Fog networks, the security of the OpenFlow channel between the controller and its switches need to be addressed. Since all the controller commands are sent through this channel, once compromised, the network will be completely controlled by an attacker. This is a disaster for both the network service providers and their customers. Previous works on SDN security either protect controllers themselves or make a strong assumption that the OpenFlow channel is already secured. Using TLS to encrypt the channel is not a “silver-bullet” solution due to the known TLS vulnerabilities. In this paper, we specifically investigate the potential threats of man-in-the-middle attacks on the OpenFlow control channel. We first introduce a feasible attack model in an IoT–Fog architecture, and then we implement attack demonstrations to show the severe consequences of such attacks. Additionally, we propose a lightweight countermeasure using Bloom filters. We implement a prototype for this method to monitor stealthy packet modifications. The result of our evaluation shows that our Bloom filter monitoring system is efficient and consumes few resources.


title: Playing Tic-Tac-Toe Using Genetic Neural Network with Double Transfer Functions

abstract: Computational intelligence is a powerful tool for game development. In this paper, an algorithm of playing the game Tic-Tac-Toe with computational intelligence is developed. This algorithm is learned by a Neural Network with Double Transfer functions (NNDTF), which is trained by genetic algorithm (GA). In the NNDTF, the neuron has two transfer functions and exhibits a node-to-node relationship in the hidden layer that enhances the learning ability of the net-work. A Tic-Tac-Toe game is used to show that the NNDTF provide a better performance than the traditional neural network does.


title: Introspective Neural Networks for Generative Modeling

abstract: We study unsupervised learning by developing a generative model built from progressively learned deep convolutional neural networks. The resulting generator is additionally a discriminator, capable of "introspection" in a sense — being able to self-evaluate the difference between its generated samples and the given training data. Through repeated discriminative learning, desirable properties of modern discriminative classifiers are directly inherited by the generator. Specifically, our model learns a sequence of CNN classifiers using a synthesis-by-classification algorithm. In the experiments, we observe encouraging results on a number of applications including texture modeling, artistic style transferring, face modeling, and unsupervised feature learning.


title: Time-Dependent Error-Detection Rate Model for Software Reliability and Other Performance Measures

abstract: This paper presents a stochastic model for the software failure phenomenon based on a nonhomogeneous Poisson process (NHPP). The failure process is analyzed to develop a suitable meanvalue function for the NHPP; expressions are given for several performance measures. Actual software failure data are analyzed and compared with a previous analysis.


title: EMI reduction in switched power converters using frequency Modulation techniques

abstract: Frequency-modulation techniques have been used to reduce electromagnetic interference (EMI) produced by the clock of digital systems working in the range of hundreds of megahertz. The working principle consists of modulating the original constant clock frequency in order to spread the energy of each single harmonic into a certain frequency band, thus reducing the peak amplitude of EMI at harmonic frequencies. Nowadays, the switching frequency of power converters has increased up to values that make interesting the application of such techniques to reduce EMI emissions due to switching of power circuits. This paper presents the theoretical principles of frequency modulation using deterministic profiles for the modulating function. It shows the effectiveness of such methods in terms of EMI reduction for different modulation profiles and other parameters. The method is compared with other methods using random modulation. Tests carried out on a buck converter are presented for experimental validation of the method. A short discussion on optimal modulation profiles and parameters is also included.


title: Predicting age and gender in online social networks

abstract: A common characteristic of communication on online social networks is that it happens via short messages, often using non-standard language variations. These characteristics make this type of text a challenging text genre for natural language processing. Moreover, in these digital communities it is easy to provide a false name, age, gender and location in order to hide one's true identity, providing criminals such as pedophiles with new possibilities to groom their victims. It would therefore be useful if user profiles can be checked on the basis of text analysis, and false profiles flagged for monitoring. This paper presents an exploratory study in which we apply a text categorization approach for the prediction of age and gender on a corpus of chat texts, which we collected from the Belgian social networking site Netlog. We examine which types of features are most informative for a reliable prediction of age and gender on this difficult text type and perform experiments with different data set sizes in order to acquire more insight into the minimum data size requirements for this task.


title: The first cleavage of the mouse zygote predicts the blastocyst axis

abstract: One of the unanswered questions in mammalian development is how the embryonic–abembryonic axis of the blastocyst is first established. It is possible that the first cleavage division contributes to this process, because in most mouse embryos the progeny of one two-cell blastomere primarily populate the embryonic part of the blastocyst and the progeny of its sister populate the abembryonic part1,2,3,4. However, it is not known whether the embryonic–abembryonic axis is set up by the first cleavage itself, by polarity in the oocyte that then sets the first cleavage plane with respect to the animal pole, or indeed whether it can be divorced entirely from the first cleavage and established in relation to the animal pole. Here we test the importance of the orientation of the first cleavage by imposing an elongated shape on the zygote so that the division no longer passes close to the animal pole, marked by the second polar body. Non-invasive lineage tracing shows that even when the first cleavage occurs along the short axis imposed by this experimental treatment, the progeny of the resulting two-cell blastomeres tend to populate the respective embryonic and abembryonic parts of the blastocyst. Thus, the first cleavage contributes to breaking the symmetry of the embryo, generating blastomeres with different developmental characteristics.


title: Role of Sleep and Sleep Loss in Hormonal Release and Metabolism

abstract: Compared to a few decades ago, adults, as well as children, sleep less. Sleeping as little as possible is often seen as an admirable behavior in contemporary society. However, sleep plays a major role in neuroendocrine function and glucose metabolism. Evidence that the curtailment of sleep duration may have adverse health effects has emerged in the past 10 years. Accumulating evidence from both epidemiologic studies and well-controlled laboratory studies indicates that chronic partial sleep loss may increase the risk of obesity and weight gain. The present chapter reviews epidemiologic studies in adults and children and laboratory studies in young adults indicating that sleep restriction results in metabolic and endocrine alterations, including decreased glucose tolerance, decreased insulin sensitivity, increased evening concentrations of cortisol, increased levels of ghrelin, decreased levels of leptin and increased hunger and appetite. Altogether, the evidence points to a possible role of decreased sleep duration in the current epidemic of obesity. Bedtime extension in short sleepers should be explored as a novel behavioral intervention that may prevent weight gain or facilitate weight loss. Avoiding sleep deprivation may help to prevent the development of obesity, particularly in children.


title: Tightly coupled UWB/IMU pose estimation

abstract: In this paper we propose a 6DOF tracking system combining Ultra-Wideband measurements with low-cost MEMS inertial measurements. A tightly coupled system is developed which estimates position as well as orientation of the sensor-unit while being reliable in case of multipath effects and NLOS conditions. The experimental results show robust and continuous tracking in a realistic indoor positioning scenario.


title: What You See is What I Need: Mobile Reporting Practices in Emergencies

abstract: Decisions of emergency response organisations (police, fire fighters, infrastructure providers, etc.) rely on accurate and timely information. Some necessary information is integrated into control centre’s IT (weather, availability of electricity, gauge information, etc.), but almost every decision needs to be based on very specific information of the current crisis situation. Due to the unpredictable nature of a crisis, gathering this kind of information requires much improvisation and articulation work which we aim to support. We present a study on how different emergency response organisations communicate with teams on-site to generate necessary information for the coordinating instances, and we described, implemented and evaluated an interaction concept as well as a prototype to support this communication by a semi-structured request-and-report system based on Android devices. We learned that (1) the accuracy of request and reports can be improved by using an appropriate metadata structure in addition to creating multimedia-based information content, (2) requirements of trusted and fast information need to be respected in support concepts although they may even be contradictory, and (3) the coordination strategy of the emergency response organisation also shapes the way this interaction needs to be designed.


title: Deep Action- and Context-Aware Sequence Learning for Activity Recognition and Anticipation

abstract: Action recognition and anticipation are key to the success of many computer vision applications. Existing methods can roughly be grouped into those that extract global, context-aware representations of the entire image or sequence, and those that aim at focusing on the regions where the action occurs. While the former may suffer from the fact that context is not always reliable, the latter completely ignore this source of information, which can nonetheless be helpful in many situations. In this paper, we aim at making the best of both worlds by developing an approach that leverages both context-aware and action-aware features. At the core of our method lies a novel multi-stage recurrent architecture that allows us to effectively combine these two sources of information throughout a video. This architecture first exploits the global, context-aware features, and merges the resulting representation with the localized, action-aware ones. Our experiments on standard datasets evidence the benefits of our approach over methods that use each information type separately. We outperform the state-of-the-art methods that, as us, rely only on RGB frames as input for both action recognition and anticipation.


title: Winding design for pole-phase modulation of induction machines

abstract: Induction machines with pole-phase modulation (PPM) can extend speed / torque capabilities for applications in integrated starter/generator and hybrid electric vehicles. In this paper, a general winding design rule for the PPM of induction machines is proposed. A prototype is used to verify the proposed method and the feasibility of the designed pole-changing winding. Besides, the characteristics of three different structure machines — conventional winding machine, toroidal winding machine and dual-rotor torodial winding machine operated in the same mode are compared by using the finite element software JMAG-studio. The results show that both conventional winding machine and dual-rotor torodial winding machine present good performances. Moreover, conventional winding machine has many advantages such as a simple structure, so its experimental prototype is designed and manufactured for next control work.


title: Dirichlet Variational Autoencoder for Text Modeling

abstract: We introduce an improved variational autoencoder (VAE) for text modeling with topic information explicitly modeled as a Dirichlet latent variable. By providing the proposed model topic awareness, it is more superior at reconstructing input texts. Furthermore, due to the inherent interactions between the newly introduced Dirichlet variable and the conventional multivariate Gaussian variable, the model is less prone to KL divergence vanishing. We derive the variational lower bound for the new model and conduct experiments on four different data sets. The results show that the proposed model is superior at text reconstruction across the latent space and classifications on learned representations have higher test accuracies.


title: Joint Optical Flow and Temporally Consistent Semantic Segmentation

abstract: The importance and demands of visual scene understanding have been steadily increasing along with the active development of autonomous systems. Consequently, there has been a large amount of research dedicated to semantic segmentation and dense motion estimation. In this paper, we propose a method for jointly estimating optical flow and temporally consistent semantic segmentation, which closely connects these two problem domains and leverages each other. Semantic segmentation provides information on plausible physical motion to its associated pixels, and accurate pixel-level temporal correspondences enhance the accuracy of semantic segmentation in the temporal domain. We demonstrate the benefits of our approach on the KITTI benchmark, where we observe performance gains for flow and segmentation. We achieve state-of-the-art optical flow results, and outperform all published algorithms by a large margin on challenging, but crucial dynamic objects.


title: The use of virtual reality in acrophobia research and treatment.

abstract: Acrophobia, or fear of heights, is a widespread and debilitating anxiety disorder affecting perhaps 1 in 20 adults. Virtual reality (VR) technology has been used in the psychological treatment of acrophobia since 1995, and has come to dominate the treatment of numerous anxiety disorders. It is now known that virtual reality exposure therapy (VRET) regimens are highly effective for acrophobia treatment. This paper reviews current theoretical understanding of acrophobia as well as the evolution of its common treatments from the traditional exposure therapies to the most recent virtually guided ones. In particular, the review focuses on recent innovations in the use of VR technology and discusses the benefits it may offer for examining the underlying causes of the disorder, allowing for the systematic assessment of interrelated factors such as the visual, vestibular and postural control systems.


title: Sentiment-enhanced multidimensional analysis of online social networks: Perception of the mediterranean refugees crisis

abstract: We propose an analytical framework able to investigate discussions about polarized topics in online social networks from many different angles. The framework supports the analysis of social networks along several dimensions: time, space and sentiment. We show that the proposed analytical framework and the methodology can be used to mine knowledge about the perception of complex social phenomena. We selected the refugee crisis discussions over Twitter as the case study. This difficult and controversial topic is an increasingly important issue for the EU. The raw stream of tweets is enriched with space information (user and mentioned locations), and sentiment (positive vs. negative) w.r.t. refugees. Our study shows differences in positive and negative sentiment in EU countries, in particular in UK, and by matching events, locations and perception it underlines opinion dynamics and common prejudices regarding the refugees.


title: What's in a p-value in NLP?

abstract: In NLP, we need to document that our proposed methods perform significantly better with respect to standard metrics than previous approaches, typically by reporting p-values obtained by rank- or randomization-based tests. We show that significance results following current research standards are unreliable and, in addition, very sensitive to sample size, covariates such as sentence length, as well as to the existence of multiple metrics. We estimate that under the assumption of perfect metrics and unbiased data, we need a significance cut-off at ⇠0.0025 to reduce the risk of false positive results to <5%. Since in practice we often have considerable selection bias and poor metrics, this, however, will not do alone.


title: A Corpus and Evaluation Framework for Deeper Understanding of Commonsense Stories

abstract: Representation and learning of commonsense knowledge is one of the foundational problems in the quest to enable deep language understanding. This issue is particularly challenging for understanding casual and correlational relationships between events. While this topic has received a lot of interest in the NLP community, research has been hindered by the lack of a proper evaluation framework. This paper attempts to address this problem with a new framework for evaluating story understanding and script learning: the 'Story Cloze Test'. This test requires a system to choose the correct ending to a four-sentence story. We created a new corpus of ~50k five-sentence commonsense stories, ROCStories, to enable this evaluation. This corpus is unique in two ways: (1) it captures a rich set of causal and temporal commonsense relations between daily events, and (2) it is a high quality collection of everyday life stories that can also be used for story generation. Experimental evaluation shows that a host of baselines and state-of-the-art models based on shallow language understanding struggle to achieve a high score on the Story Cloze Test. We discuss these implications for script and story learning, and offer suggestions for deeper language understanding.


title: An ensemble-based system for automatic screening of diabetic retinopathy

abstract: In this paper, an ensemble-based method for the screening of diabetic retinopathy (DR) is proposed. This approach is based on features extracted from the output of several retinal image processing algorithms, such as image-level (quality assessment, pre-screening, AM/FM), lesion-specific (microaneurysms, exudates) and anatomical (macula, optic disk) components. The actual decision about the presence of the disease is then made by an ensemble of machine learning classifiers. We have tested our approach on the publicly available Messidor database, where 90% sensitivity, 91% specificity and 90% accuracy and 0.989 AUC are achieved in a disease/no-disease setting. These results are highly competitive in this field and suggest that retinal image processing is a valid approach for automatic DR screening.


title: Learning Graph Matching

abstract: As a fundamental problem in pattern recognition, graph matching has found a variety of applications in the field of computer vision. In graph matching, patterns are modeled as graphs and pattern recognition amounts to finding a correspondence between the nodes of different graphs. There are many ways in which the problem has been formulated, but most can be cast in general as a quadratic assignment problem, where a linear term in the objective function encodes node compatibility functions and a quadratic term encodes edge compatibility functions. The main research focus in this theme is about designing efficient algorithms for solving approximately the quadratic assignment problem, since it is NP-hard. In this paper, we turn our attention to the complementary problem: how to estimate compatibility functions such that the solution of the resulting graph matching problem best matches the expected solution that a human would manually provide. We present a method for learning graph matching: the training examples are pairs of graphs and the "labels" are matchings between pairs of graphs. We present experimental results with real image data which give evidence that learning can improve the performance of standard graph matching algorithms. In particular, it turns out that linear assignment with such a learning scheme may improve over state-of-the-art quadratic assignment relaxations. This finding suggests that for a range of problems where quadratic assignment was thought to be essential for securing good results, linear assignment, which is far more efficient, could be just sufficient if learning is performed.


title: SemEval-2017 Task 8: RumourEval: Determining rumour veracity and support for rumours

abstract: Media is full of false claims. Even Oxford Dictionaries named "post-truth" as the word of 2016. This makes it more important than ever to build systems that can identify the veracity of a story, and the kind of discourse there is around it. RumourEval is a SemEval shared task that aims to identify and handle rumours and reactions to them, in text. We present an annotation scheme, a large dataset covering multiple topics - each having their own families of claims and replies - and use these to pose two concrete challenges as well as the results achieved by participants on these challenges.


title: A feedforward architecture accounts for rapid categorization

abstract: Primates are remarkably good at recognizing objects. The level of performance of their visual system and its robustness to image degradations still surpasses the best computer vision systems despite decades of engineering effort. In particular, the high accuracy of primates in ultra rapid object categorization and rapid serial visual presentation tasks is remarkable. Given the number of processing stages involved and typical neural latencies, such rapid visual processing is likely to be mostly feedforward. Here we show that a specific implementation of a class of feedforward theories of object recognition (that extend the Hubel and Wiesel simple-to-complex cell hierarchy and account for many anatomical and physiological constraints) can predict the level and the pattern of performance achieved by humans on a rapid masked animal vs. non-animal categorization task.


title: Photogeometric structured light: A self-calibrating and multi-viewpoint framework for accurate 3D modeling

abstract: Structured-light methods actively generate geometric correspondence data between projectors and cameras in order to facilitate robust 3D reconstruction. In this paper, we present photogeometric structured light whereby a standard structured light method is extended to include photometric methods. Photometric processing serves the double purpose of increasing the amount of recovered surface detail and of enabling the structured-light setup to be robustly self-calibrated. Further, our framework uses a photogeometric optimization that supports the simultaneous use of multiple cameras and projectors and yields a single and accurate multi-view 3D model which best complies with photometric and geometric data.


title: Design Study of LineSets, a Novel Set Visualization Technique

abstract: Computing and visualizing sets of elements and their relationships is one of the most common tasks one performs when analyzing and organizing large amounts of data. Common representations of sets such as convex or concave geometries can become cluttered and difficult to parse when these sets overlap in multiple or complex ways, e.g., when multiple elements belong to multiple sets. In this paper, we present a design study of a novel set visual representation, LineSets, consisting of a curve connecting all of the set's elements. Our approach to design the visualization differs from traditional methodology used by the InfoVis community. We first explored the potential of the visualization concept by running a controlled experiment comparing our design sketches to results from the state-of-the-art technique. Our results demonstrated that LineSets are advantageous for certain tasks when compared to concave shapes. We discuss an implementation of LineSets based on simple heuristics and present a study demonstrating that our generated curves do as well as human-drawn ones. Finally, we present two applications of our technique in the context of search tasks on a map and community analysis tasks in social networks.


title: Impact of radiographic findings on prognosis for skin carcinoma with clinical perineural invasion.

abstract: BACKGROUND ::: The objective of the current study was to correlate pretreatment computed tomography and magnetic resonance imaging studies with outcomes for patients with squamous or basal cell carcinoma of the skin and clinical perineural invasion. ::: ::: ::: METHODS ::: Between 1986 and 2002, 45 patients were treated with radiotherapy alone (21 patients) or combined with surgery (24 patients), and 4 patients received concomitant chemotherapy. Follow-up ranged from 0.85 years to 17.4 years (median, 3.8 years). Patients were stratified as follows: imaging negative, 10 patients; minimal or moderate peripheral disease, 14 patients; and central and/or macroscopic disease, 21 patients. ::: ::: ::: RESULTS ::: The 5-year local control rates were as follows: imaging negative, 76%; minimal or moderate peripheral disease, 57%; and central and/or macroscopic disease, 25%. The 5-year absolute and cause-specific survival rates were as follows: imaging negative, 90% and 100%, respectively; minimal or moderate peripheral disease, 50% and 56%, respectively; and central and/or macroscopic disease, 58% and 61%, respectively. ::: ::: ::: CONCLUSIONS ::: Patients who had symptomatic but imaging-negative perineural invasion had a relatively good prognosis after receiving definitive radiotherapy alone or combined with surgery. Patients who had imaging-positive minimal or moderate peripheral disease had a better local control rate but a similar survival rate compared with patients who had central and/or macroscopic disease.


title: Analysis on the Charateristics of Variable Reluctance Resolver Considering Uneven Magnetic Fields

abstract: Variable reluctance (VR) resolver is widely used in traction motor for battery-powered electric vehicles (EVs), as well as for hybrid EVs as rotor position sensor. VR resolver generates absolute position signal using resolver-to-digital converter (RDC) to deliver the exact position of permanent magnets in the rotor of a traction motor to the motor controller. This paper deals with analysis methodology on a VR resolver using cosimulation analysis with RDC tracking algorithm for position-angle detection considering uneven magnetic fields. The eccentricity of the VR resolver, short-circuit conditions of the excitation and output-signal coils, and shape of rotor saliency are considered as sources of uneven magnetic fields in the VR resolver. Two-dimensional FEM is used for the SIN and COS output-signal waveforms, and these waveforms are converted into absolute position angle using RDC algorithm. To verify the analysis results of the proposed resolver, experiments on different uneven magnetic field conditions were conducted and compared with the simulation results.


title: Ankle-Knee prosthesis with powered ankle and energy transfer for CYBERLEGs α-prototype

abstract: Restoring natural walking for amputees has been increasingly investigated because of demographic evolution, leading to increased number of amputations, and increasing demand for independence. The energetic disadvantages of passive pros-theses are clear, and active prostheses are limited in autonomy. This paper presents the simulation, design and development of an actuated knee-ankle prosthesis based on a variable stiffness actuator with energy transfer from the knee to the ankle. This approach allows a good approximation of the joint torques and the kinematics of the human gait cycle while maintaining compliant joints and reducing energy consumption during level walking. This first prototype consists of a passive knee and an active ankle, which are energetically coupled to reduce the power consumption.


title: Improved Named Entity Translation and Bilingual Named Entity Extraction

abstract: Named entities (NE), the noun or noun phrases referring to persons, locations and organizations, are among the most information-bearing linguistic structures. Extracting and translating named entities benefits many natural language processing problems such as cross-lingual information retrieval, cross-lingual question answering and machine translation. ::: In this theisis we propose an efficient and effective framework to extract and translate NEs from text and speech. We adopt the hidden Markov model (HMM) as a baseline NE extraction system, and investigate its performance in multiple language pairs with varying amounts of training data. We expand the baseline text NE tagger with a context-based NE extraction model, which aims to detect and correct NE recognition errors from automatic speech recognition hypotheses. We also adapt the broadcast stews trained NE tagger for meeting transcripts. ::: We develop several language-independent features to capture phonetic and semantic similarity measures between source and target NE pairs. We incorporate these features to solve various NE translation problems presented in different language pairs (Chinese to English, Arabic to English and Hindi to English), with varying resources (parallel and non-parallel corpora as well as the World Wide Web) and different input data streams (text and speech). ::: We also propose a cluster-specific name transliteration framework. By grouping names from similar origins into one cluster and training cluster-specific transliteration and language models, we manage to dramatically reduce the name transliteration error rates.


title: A slope detection method based on 3D LiDAR suitable for quadruped robots

abstract: This paper introduces a slope detection method based on point cloud data from 3D LiDAR for quadruped robots in unknown environments. For quadruped robots, they need to adjust their gaits according to different slope angles to avoid some potential dangers. 3D LiDAR is used to gather point cloud data, which is superior to 2D LiDAR in speed and accuracy. In this paper, a slope detection method using bilateral filtering and RANSAC algorithms is discussed. The experiments of slope detection are fulfilled with the consideration of different angles of slopes and different orientations to the slopes, and the results demonstrate that errors of angle estimation is small.


title: Dual photography

abstract: We present a novel photographic technique called dual photography, which exploits Helmholtz reciprocity to interchange the lights and cameras in a scene. With a video projector providing structured illumination, reciprocity permits us to generate pictures from the viewpoint of the projector, even though no camera was present at that location. The technique is completely image-based, requiring no knowledge of scene geometry or surface properties, and by its nature automatically includes all transport paths, including shadows, inter-reflections and caustics. In its simplest form, the technique can be used to take photographs without a camera; we demonstrate this by capturing a photograph using a projector and a photo-resistor. If the photo-resistor is replaced by a camera, we can produce a 4D dataset that allows for relighting with 2D incident illumination. Using an array of cameras we can produce a 6D slice of the 8D reflectance field that allows for relighting with arbitrary light fields. Since an array of cameras can operate in parallel without interference, whereas an array of light sources cannot, dual photography is fundamentally a more efficient way to capture such a 6D dataset than a system based on multiple projectors and one camera. As an example, we show how dual photography can be used to capture and relight scenes.


title: A Survey on Human-Centric Communications in Non-Cooperative Wireless Relay Networks

abstract: The performance of data delivery in wireless relay networks (WRNs), such as delay-tolerant networks and device-to-device communications heavily relies on the cooperation of mobile nodes (i.e., users and their carried devices). However, selfish nodes may refuse to relay data to others or share their resources with them due to various reasons, such as resource limitations or social preferences. Meanwhile, misbehaving nodes can launch different types of internal attacks (e.g., blackhole and trust-related attacks) to disrupt the normal operation of the network. Numerous mechanisms have been recently proposed to establish secure and efficient communications in WRNs in the presence of selfish and malicious nodes (referred as non-cooperative WRNs). In this paper, we present an in-depth survey on human-centric communication challenges and solutions in the non-cooperative WRNs that focuses on: 1) an overview of the non-cooperative WRNs and introduction to various types of node selfish and malicious behaviors; 2) the impact analysis of node selfish and malicious behaviors on the performance of data forwarding and distribution; 3) selfish and malicious node detection and defense systems; and 4) incentive mechanisms. Finally, we discuss several open problems and future research challenges.


title: Continuous Integration and Visual GUI Testing: Benefits and Drawbacks in Industrial Practice

abstract: Continuous integration (CI) is growing in industrial popularity, spurred on by market trends towards faster delivery and higher quality software. A key facilitator of CI is automated testing that should be executed, automatically, on several levels of system abstraction. However, many systems lack the interfaces required for automated testing. Others lack test automation coverage of the system under test's (SUT) graphical user interface (GUI) as it is shown to the user. One technique that shows promise to solve these challenges is Visual GUI Testing (VGT), which uses image recognition to stimulate and assert the SUT's behavior. Research has presented the technique's applicability and feasibility in industry but only limited support, from an academic setting, that the technique is applicable in a CI environment. In this paper we presents a study from an industrial design research study with the objective to help bridge the gap in knowledge regarding VGT's applicability in a CI environment in industry. Results, acquired from interviews, observations and quantitative analysis of 17.567 test executions, collected over 16 weeks, show that VGT provides similar benefits to other automated test techniques for CI. However, several significant drawbacks, such as high costs, are also identified. The study concludes that, although VGT is applicable in an industrial CI environment, its severe challenges require more research and development before the technique becomes efficient in practice.


title: Efficient organometal trihalide perovskite planar-heterojunction solar cells on flexible polymer substrates.

abstract: Organometal trihalide perovskite solar cells offer the promise of a low-cost easily manufacturable solar technology, compatible with large-scale low-temperature solution processing. Within 1 year of development, solar-to-electric power-conversion efficiencies have risen to over 15%, and further imminent improvements are expected. Here we show that this technology can be successfully made compatible with electron acceptor and donor materials generally used in organic photovoltaics. We demonstrate that a single thin film of the low-temperature solution-processed organometal trihalide perovskite absorber CH3NH3PbI3-xClx, sandwiched between organic contacts can exhibit devices with power-conversion efficiency of up to 10% on glass substrates and over 6% on flexible polymer substrates. This work represents an important step forward, as it removes most barriers to adoption of the perovskite technology by the organic photovoltaic community, and can thus utilize the extensive existing knowledge of hybrid interfaces for further device improvements and flexible processing platforms.


title: Mineralization in newborn calves contributes to health, improve the antioxidant system and reduces bacterial infections

abstract: Abstract The first phase of life of dairy calves has elevated mortality indices linked with low immunity and sanitary challenges, mainly bacterial infections are involved in the pathogenesis of diarrhea, the leading cause of death. Also, other important problem is the nutritional deficiencies, such as the mineral deficiency. Thus, the aim of this study was to evaluate whether an intramuscular mineral supplementation based on selenium, copper, potassium, magnesium and phosphorus possess beneficial effects on health of dairy calves. For this, ten calves were divided in two groups: the group A was supplemented with injectable mineral, while the group B was used as control group (without mineral supplementation). The mineral complex was administrated via intramuscularly at dose of 3 mL/animal on days 2 and 14 post-birth. The total blood was collected on days 2, 10, 20 and 30 of life of animals in order to analyze the antioxidant enzymes (catalase (CAT), superoxide dismutase (SOD) and glutathione peroxidase (GPx)), blood count and seric biochemistry linked with proteic, lipid and carbohydrate metabolism. Feces samples were also collected on days 10, 20 and 30 of life of animals to perform the total bacterial count, parasitological exam and fecal consistency score. Moreover, the weight and corporal temperature were also evaluated. The mineral supplementation presented beneficial properties to calves from birth to the 30th of life through the increase on activity of antioxidant enzymes, improvement of immunity, and avoiding problems linked with diarrhea and anemia, can be considered an interesting approach to prevent these alterations linked with high mortality in the period of life.


title: Dynamic Egocentric Models for Citation Networks

abstract: The analysis of the formation and evolution of networks over time is of fundamental importance to social science, biology, and many other fields. While longitudinal network data sets are increasingly being recorded at the granularity of individual time-stamped events, most studies only focus on collapsed cross-sectional snapshots of the network. In this paper, we introduce a dynamic egocentric framework that models continuous-time network data using multivariate counting processes. For inference, an efficient partial likelihood approach is used, allowing our methods to scale to large networks. We apply our techniques to various citation networks and demonstrate the predictive power and interpretability of the learned statistical models.


title: Human detection in fish-eye images using HOG-based detectors over rotated windows

abstract: Fish-eye cameras are efficient means to provide an omni-view video recording over a large area using a single camera. Although effective algorithms for human detection in images captured by conventional cameras have been developed, human detection in fish-eye images remains an open challenge. Recognizing that humans typically appear on radial lines emitted from the center in fish-eye images, we propose to apply the popular human detection algorithm based on the Histogram of Oriented Gradient (HOG) features after rotating each search window on a radial line to the vertical reference line. We extract positive and negative examples by such rotations to train the SVM classifier using HOG features. To detect humans in a given image, we rotate the image successively and detect windows containing humans along the reference line after each rotation using the trained classifier. We use multiple window sizes to detect people with different appearance sizes. We further develop an algorithm to discover multiple overlapping windows covering the same person and identify the window that encloses the person the best. The proposed method has yielded highly accurate human detection in low-resolution, low-contrast images containing multiple people with varying poses and sizes.


title: Electromagnetic Energy-Harvesting Shock Absorbers: Design, Modeling, and Road Tests

abstract: This paper presents the design, modeling, bench experiments, and road tests for a retrofit regenerative shock absorber based on a permanent magnetic generator and a rack-pinion mechanism for the purposes of energy harvesting and vibration damping. Results show that variable damping coefficients and the asymmetric feature in jounce and rebound motions are achieved by controlling the electrical load of the shock absorber. Improved efficiency and reliability are achieved by utilizing a roller to guide the rack and preload on the gear transmission to reduce backlash and friction. A peak power of 68 W and average power of 19 W are attained from one prototype shock absorber when the vehicle is driven at 48 km/h (30 mi/h) on a fairly smooth campus road.


title: The Persistent Problem of Colorism: Skin Tone, Status, and Inequality

abstract: Colorism is a persistent problem for people of color in the USA. Colorism, or skin color stratification, is a process that privileges light-skinned people of color over dark in areas such as income, education, housing, and the marriage market. This essay describes the experiences of African Americans, Latinos, and Asian Americans with regard to skin color. Research demonstrates that light-skinned people have clear advantages in these areas, even when controlling for other background variables. However, dark-skinned people of color are typically regarded as more ethnically authentic or legitimate than light-skinned people. Colorism is directly related to the larger system of racism in the USA and around the world. The color complex is also exported around the globe, in part through US media images, and helps to sustain the multibillion-dollar skin bleaching and cosmetic surgery industries. Racial discrimination is a pervasive problem in the USA. African Americans, Latinos, Asian Americans, and other people of color are routinely denied access to resources and fair competition for jobs and schooling. Despite this pattern of exclusion, people of color have made great progress in combating persistent discrimination in housing, the labor market, and education. However, hidden within the process of racial discrimination is the often overlooked issue of colorism. Colorism is the process of discrimination that privileges light-skinned people of color over their dark-skinned counterparts (Hunter 2005). Colorism is concerned with actual skin tone, as opposed to racial or ethnic identity. This is an important distinction because race is a social concept, not significantly tied to biology (Hirschman 2004). Lighter-skinned people of color enjoy substantial privileges that are still unattainable to their darker-skinned brothers and sisters. In fact, light-skinned people earn more money, complete more years of schooling, live in better neighborhoods, and marry higher-status people than darker-skinned people of the same race or ethnicity (Arce et al. 1987; Espino and Franz 2002; Hill 2000; Hughes and Hertel 1990; Hunter 1998, 2005; Keith and Herring 1991; Murguia and Telles 1996; Rondilla and Spickard 2007).


title: PLUG: flexible lookup modules for rapid deployment of new protocols in high-speed routers

abstract: New protocols for the data link and network layer are being proposed to address limitations of current protocols in terms of scalability, security, and manageability. High-speed routers and switches that implement these protocols traditionally perform packet processing using ASICs which offer high speed, low chip area, and low power. But with inflexible custom hardware, the deployment of new protocols could happen only through equipment upgrades. While newer routers use more flexible network processors for data plane processing, due to power and area constraints lookups in forwarding tables are done with custom lookup modules. Thus most of the proposed protocols can only be deployed with equipment upgrades. To speed up the deployment of new protocols, we propose a flexible lookup module, PLUG (Pipelined Lookup Grid). We can achieve generality without loosing efficiency because various custom lookup modules have the same fundamental features we retain: area dominated by memories, simple processing, and strict access patterns defined by the data structure. We implemented IPv4, Ethernet, Ethane, and SEATTLE in our dataflow-based programming model for the PLUG and mapped them to the PLUG hardware which consists of a grid of tiles. Throughput, area, power, and latency of PLUGs are close to those of specialized lookup modules.


title: Factors Affecting Faculty Web Portal Usability

abstract: The study investigated the factors that might significantly affect web portal usability. Results of the study were intended to serve as inputs for faculty web portal development of the University of the East-Manila. Descriptive statistics utilized questionnaire data from 82 faculty members. The data showed that most of the respondents were relatively young, were Master’s degree holders, were skilled in using the computer and the internet, had internet access at home, and were committed to using the web portal. Respondents perceived that the different web portal design-related factors were moderately evident in the existing faculty web portal. Multiple regression analysis showed that information content as a web portal design-related factor was the only significant predictor of web portal usability. Thus, the null hypothesis stating that faculty-related and web portal design-related factors do not significantly affect faculty web portal usability is accepted except Information Content. The study also discussed the guidelines for web portal developers, as well as the limitations and implications of the study for future research.


Predicting the winning side of DotA 2


title: Palindrome Recognition In The Streaming Model

abstract: In the Palindrome Problem one tries to find all palindromes (palindromic substrings) in a given string. A palindrome is defined as a string which reads forwards the same as backwards, e.g., the string "racecar". A related problem is the Longest Palindromic Substring Problem in which finding an arbitrary one of the longest palindromes in the given string suffices. We regard the streaming version of both problems. In the streaming model the input arrives over time and at every point in time we are only allowed to use sublinear space. The main algorithms in this paper are the following: The first one is a one-pass randomized algorithm that solves the Palindrome Problem. It has an additive error and uses $O(\sqrt n$) space. The second algorithm is a two-pass algorithm which determines the exact locations of all longest palindromes. It uses the first algorithm as the first pass. The third algorithm is again a one-pass randomized algorithm, which solves the Longest Palindromic Substring Problem. It has a multiplicative error using only $O(\log(n))$ space. We also give two variants of the first algorithm which solve other related practical problems.


title: Scale-space derived from B-splines

abstract: This paper proposes a scale-space theory based on B-spline kernels. Our aim is twofold: 1) present a general framework, and show how B-splines provide a flexible tool to design various scale-space representations. In particular, we focus on the design of continuous scale-space and dyadic scale-space frame representations. A general algorithm is presented for fast implementation of continuous scale-space at rational scales. In the dyadic case, efficient frame algorithms are derived using B-spline techniques to analyze the geometry of an image. The relationship between several scale-space approaches is explored. The behavior of edge models, the properties of completeness, causality, and other properties in such a scale-space representation are examined in the framework of B-splines. It is shown that, besides the good properties inherited from the Gaussian kernel, the B-spline derived scale-space exhibits many advantages for modeling visual mechanism including the efficiency, compactness, orientation feature and parallel structure.


title: A Multiple Instance Learning Approach for Content Based Image Retrieval Using One-Class Support Vector Machine

abstract: Multiple Instance Learning (MIL) is a special kind of supervised learning problem that has been studied actively in recent years. In this paper, we propose an approach based on One-Class Support Vector Machine (SVM) to solve MIL problem in the region-based Content Based Image Retrieval (CBIR). Relevance Feedback technique is incorporated to provide progressive guidance to the learning process. Performance is evaluated and the effectiveness of our retrieval algorithm has been shown through comparative studies.


title: Frames of reference in architectural design: analysing the hyperacclamation (a-h-a!)’, Design Studies 17

abstract: Abstract The discovery of a creative solution occasionally corresponds to the sudden attainment of a mental insight. Our purpose is to formally describe this phenomenon and the cognitive mechanisms that lead to it. The approach is based on the replicability of just such an insight which underlies the solution to a well known puzzle: the nine-dot puzzle. The insight coincides with the realization that the problem can only be solved when a spurious constraint is removed. Two experimental results are reported: one on the nine-dot puzzle and the other on an architectural sketch design problem. The sketch design problem was structured with several restricting frames of reference to create a situation analogous to the nine-dot puzzle. Subjects' design behaviour was analysed to identify the mechanisms used in achieving the mental insights which allow designers to go beyond the implicit restrictions of these frames. A general model, called SMI-GI, of the mental insight based design and discovery process is described. This model foresees a computer system that can be used to simulate the mental insight mechanism and consequently lead to the systematic examination of this aspect of design creativity.


title: Common sponges of the Cayman Islands

abstract: Sponges are one of the most colourful and ubiquitous components of the coral reef community. Their morphological diversity and wide range of ecological tolerance make them an important part of the reef fauna, and nowhere are they more flourishing and abundant than in the Cayman Islands.


Event Studies in Economics and


title: Flow Theory and Student Engagement

abstract: Most teachers across America would agree that the student who is engaged in school is more likely to be academically successful and successful as an adult later in life. In fact, research regarding student engagement has shown that increases in student engagement are correlated to increases in positive student learning outcomes (Appelton, Christenson, Kim, & Reschly, 2006; Nystrand & Gamoran, 1989) such as higher GPA, more credits earned in high school, higher attendance rates, and higher classroom participation (Appelton et al., 2006). While the relationship between student engagement and positive learning outcomes is not new to educators, understanding how to keep students engaged in the classroom is still an on-going concern. Regardless of the debate, educators generally agree that they must create learning environments that are challenging for all students, and in which students want to learn and feel good about learning. Flow Theory, though largely undiscovered by educators, was developed by Csikszentmihalyi (1990) as a theoretical perspective of student learning that integrated cognition, motivation, and emotion. While not a theory of student engagement, application of flow theory in classrooms will help teachers create learning environments in which there is an increase in student engagement.


title: Voice activity detection using subband noncircularity

abstract: Many voice activity detection (VAD) systems use the magnitude of complex-valued spectral representations. However, using only the magnitude often does not fully characterize the statistical behavior of the complex values. We present two novel methods for performing VAD on single- and dual-channel audio that do completely account for the second-order statistical behavior of complex data. Our methods exploit the second-order noncircularity (also known as impropriety) of complex subbands of speech and noise. Since speech tends to be more improper than noise, higher impropriety suggests speech activity. Our single-channel method is blind in the sense that it is unsupervised and, unlike many VAD systems, does not rely on non-speech periods for noise parameter estimation. Our methods achieve improved performance over other state-of-the-art magnitude-based VADs on the QUT-NOISE-TIMIT corpus, which indicates that impropriety is a compelling new feature for voice activity detection.


title: Compact Quad-Mode Bandpass Filter Using Modified Coaxial Cavity Resonator With Improved $Q$-Factor

abstract: This paper presents a novel quadruple-mode coaxial cavity resonator and its application to bandpass filters with compact size and improved quality factor ( $Q$ -factor). Unlike the conventional coaxial cavity resonator, the proposed resonator is made up of four inner conductive posts within a single cavity, which provides new quadruple resonant modes to realize bandpass filter. No metallic walls inside the cavity are required, and thus the utilization efficiency of the cavity space is improved. As a result, the unloaded $Q$ can be approximately 15% higher in comparison to the conventional coaxial resonator, or 30%–35% volume saving can be achieved while maintaining the similar $Q$ -factor value with the conventional designs. In addition, due to the multiple cross-coupling occurring within the cavity, including the source-to-load direct coupling, four flexible transmission zeros can be created to realize different filtering functions. The complete design methodology of filters based on the proposed resonator is discussed, starting with the resonator characteristics and the coupling scheme. Simulations as well as experimental results of four- and eight-pole filters are presented to validate this attractive design concept. Good agreement between measured and computed results is obtained.


title: Recognizing Implicit Discourse Relations in the Penn Discourse Treebank

abstract: We present an implicit discourse relation classifier in the Penn Discourse Treebank (PDTB). Our classifier considers the context of the two arguments, word pair information, as well as the arguments' internal constituent and dependency parses. Our results on the PDTB yields a significant 14.1% improvement over the baseline. In our error analysis, we discuss four challenges in recognizing implicit relations in the PDTB.


title: ReVirt: enabling intrusion analysis through virtual-machine logging and replay

abstract: Current system loggers have two problems: they depend on the integrity of the operating system being logged, and they do not save sufficient information to replay and analyze attacks that include any non-deterministic events. ReVirt removes the dependency on the target operating system by moving it into a virtual machine and logging below the virtual machine. This allows ReVirt to replay the system's execution before, during, and after an intruder compromises the system, even if the intruder replaces the target operating system. ReVirt logs enough information to replay a long-term execution of the virtual machine instruction-by-instruction. This enables it to provide arbitrarily detailed observations about what transpired on the system, even in the presence of non-deterministic attacks and executions. ReVirt adds reasonable time and space overhead. Overheads due to virtualization are imperceptible for interactive use and CPU-bound workloads, and 13--58% for kernel-intensive workloads. Logging adds 0--8% overhead, and logging traffic for our workloads can be stored on a single disk for several months.


title: Design and optimization of magnetic wheel for wall and ceiling climbing robot

abstract: Magnetic wall and ceiling climbing robots have been proposed in many industrial applications where robots must move over ferromagnetic material surfaces. The magnetic circuit design with magnetic attractive force calculation of permanent magnetic wheel plays an important role which significantly affects the system reliability, payload ability and power consumption of the robot. In this paper, a flexible wall and ceiling climbing robot with six permanent magnetic wheels is proposed to climb along the vertical wall and overhead ceiling of steel cargo containers as part of an illegal contraband inspection system. The permanent magnetic wheels are designed to apply to the wall and ceiling climbing robot, whilst finite element method is employed to estimate the permanent magnetic wheels with various wheel rims. The distributions of magnetic flux lines and magnetic attractive forces are compared on both plane and corner scenarios so that the robot can adaptively travel through the convex and concave surfaces of the cargo container. Optimisation of wheel rims is presented to achieve the equivalent magnetic adhesive forces along with the estimation of magnetic ring dimensions in the axial and radial directions. Finally, the practical issues correlated with the applications of the techniques are discussed and the conclusions are drawn with further improvement and prototyping.


title: Raga Identification of Carnatic music for Music Information Retrieval

abstract: In this work we propose a method to identify the raga of a Carnatic music signal. Raga is analogous to melody in Western music but is much richer and stronger in its representation. The main motive behind Raga identification is that it can be used as a good basis for music information retrieval of Carnatic music songs or Film songs based on Carnatic music. The input polyphonic music signal is analyzed and made to pass through a signal separation algorithm to separate the instrument and the vocal signal. After extracting the vocal signal we segment the vocal signal using our proposed segmentation algorithm. Using our proposed singer identification algorithm we determine the singer to know the fundamental frequency of the singer. The frequency components of the signal are then determined and we map these frequency components into the swara sequence and thereby determine the Raga of the particular song, which could be used to index the songs and further for retrieval based on the Raga. Index Terms—Carnatic music, Raga, Music information retrieval


title: Matrix Profile I: All Pairs Similarity Joins for Time Series: A Unifying View That Includes Motifs, Discords and Shapelets

abstract: The all-pairs-similarity-search (or similarity join) problem has been extensively studied for text and a handful of other datatypes. However, surprisingly little progress has been made on similarity joins for time series subsequences. The lack of progress probably stems from the daunting nature of the problem. For even modest sized datasets the obvious nested-loop algorithm can take months, and the typical speed-up techniques in this domain (i.e., indexing, lower-bounding, triangular-inequality pruning and early abandoning) at best produce one or two orders of magnitude speedup. In this work we introduce a novel scalable algorithm for time series subsequence all-pairs-similarity-search. For exceptionally large datasets, the algorithm can be trivially cast as an anytime algorithm and produce high-quality approximate solutions in reasonable time. The exact similarity join algorithm computes the answer to the time series motif and time series discord problem as a side-effect, and our algorithm incidentally provides the fastest known algorithm for both these extensively-studied problems. We demonstrate the utility of our ideas for two time series data mining problems, including motif discovery and novelty discovery.


title: A Strong Baseline for Learning Cross-Lingual Word Embeddings from Sentence Alignments

abstract: While cross-lingual word embeddings have been studied extensively in recent years, the qualitative differences between the different algorithms remain vague. We observe that whether or not an algorithm uses a particular feature set (sentence IDs) accounts for a significant performance gap among these algorithms. This feature set is also used by traditional alignment algorithms, such as IBM Model-1, which demonstrate similar performance to state-of-the-art embedding algorithms on a variety of benchmarks. Overall, we observe that different algorithmic approaches for utilizing the sentence ID feature space result in similar performance. This paper draws both empirical and theoretical parallels between the embedding and alignment literature, and suggests that adding additional sources of information, which go beyond the traditional signal of bilingual sentence-aligned corpora, may substantially improve cross-lingual word embeddings, and that future baselines should at least take such features into account.


USING FUNCTIONAL DISTANCE MEASURES WHEN CALIBRATING JOURNEY-TO-CRIME DISTANCE DECAY ALGORITHMS


title: Mobile big data analysis with machine learning

abstract: This paper investigates to identify the requirement and the development of machine learning-based mobile big data analysis through discussing the insights of challenges in the mobile big data (MBD). Furthermore, it reviews the state-of-the-art applications of data analysis in the area of MBD. Firstly, we introduce the development of MBD. Secondly, the frequently adopted methods of data analysis are reviewed. Three typical applications of MBD analysis, namely wireless channel modeling, human online and offline behavior analysis, and speech recognition in the internet of vehicles, are introduced respectively. Finally, we summarize the main challenges and future development directions of mobile big data analysis.


title: An Interactive Node-Link Visualization of Convolutional Neural Networks

abstract: Convolutional neural networks are at the core of state-of-the-art approaches to a variety of computer vision tasks. Visualizations of neural networks typically take the form of static diagrams, or interactive toy-sized networks, which fail to illustrate the networks’ scale and complexity, and furthermore do not enable meaningful experimentation. Motivated by this observation, this paper presents a new interactive visualization of neural networks trained on handwritten digit recognition, with the intent of showing the actual behavior of the network given user-provided input. The user can interact with the network through a drawing pad, and watch the activation patterns of the network respond in real-time. The visualization is available at http://scs.ryerson.ca/~aharley/vis/.


title: Integrated pathology and radiology learning for a musculoskeletal system module: an example of interdisciplinary integrated form

abstract: Introduction ::: Many curricula integrate radiology with anatomy courses but none of these curricula adopt integration of pathology with radiology as interdisciplinary form at the undergraduate level. The aim of the current study was to identify the outcome of interdisciplinary integrated course of pathology and radiology in musculoskeletal system module (MSK). ::: ::: ::: Methods ::: A comparative interventional study was conducted on 60 students representing a whole class of the third year of level V. MSK and gastrointestinal module (GIT) were selected as study and control module, respectively, as being adopted for the same level/allocated hours, enriched with many subject areas for both fields, and availability of learning resources for both. A planned interdisciplinary integrated course for MSK pathology and radiology was implemented in the pathology lab. The subject area was selected and taught for both fields in consecutive ways by pathology and radiology experts. After teaching, gross/histopathologic specimens and radiology imaging/reports were distributed over benches and the students investigated the same. Conversely, in GIT control module, both fields were delivered separately, and no interdisciplinary form of integration occurred. Students' scores for both fields were filtered from the objective structured practical exam, quiz, and final exam. Students' marks and satisfaction were subjected to multiple comparisons using independent student's t-test. SPSS version 17 was used. ::: ::: ::: Results ::: Significances were obtained between total marks of students for both modules and between radiology courses for both with P=0.0152 and 0.0199, respectively. Number of students who achieved >80% in MSK was 20 and 26 compared to 15 and 17 in GIT for pathology and radiology, respectively. Student satisfaction was high for interdisciplinary integration in MSK with significant difference obtained between MSK and GIT. ::: ::: ::: Conclusion ::: The integration of both fields augments student performance for both. This experience must encourage curriculum committee to globalize it over all other modules.


title: Feature normalization and likelihood-based similarity measures for image retrieval

abstract: Distance measures like the Euclidean distance are used to measure similarity between images in content-based image retrieval. Such geometric measures implicitly assign more weighting to features with large ranges than those with small ranges. This paper discusses the effects of five feature normalization methods on retrieval performance. We also describe two likelihood ratio-based similarity measures that perform significantly better than the commonly used geometric approaches like the Lp metrics.


title: A Framework of Translator From English Speech To Sanskrit Text

abstract: Human computer interaction is defined as Users (Humans) interact with the computers. Speech recognition is an area of computer science that deals with the designing of systems that recognize spoken words. Speech recognition system allows ordinary people to speak to the system. Recognizing and understanding a spoken sentence is obviously a knowledge-intensive process, which must take into account all variable information about the speech communication process, from acoustics to semantics and pragmatics. This paper is the survey of how speech is converted in text and that text in translated into another language. In this paper, we outline a speech recognition system, learning based approach and target language generation mechanism with the help of language English- Sanskrit language pair using rule based machine translation technique (1). Rule Based Machine Translation provides high quality translation and requires in depth knowledge of the language apart from real world knowledge and the differences in cultural background and conceptual divisions. Here the English speech is first converted into text and that will translated into Sanskrit language.


title: Model-Agnostic Interpretability of Machine Learning

abstract: Understanding why machine learning models behave the way they do empowers both system designers and end-users in many ways: in model selection, feature engineering, in order to trust and act upon the predictions, and in more intuitive user interfaces. Thus, interpretability has become a vital concern in machine learning, and work in the area of interpretable models has found renewed interest. In some applications, such models are as accurate as non-interpretable ones, and thus are preferred for their transparency. Even when they are not accurate, they may still be preferred when interpretability is of paramount importance. However, restricting machine learning to interpretable models is often a severe limitation. In this paper we argue for explaining machine learning predictions using model-agnostic approaches. By treating the machine learning models as black-box functions, these approaches provide crucial flexibility in the choice of models, explanations, and representations, improving debugging, comparison, and interfaces for a variety of users and models. We also outline the main challenges for such methods, and review a recently-introduced model-agnostic explanation approach (LIME) that addresses these challenges.


title: Styling Evolution for Tight-Fitting Garments

abstract: We present an evolution method for designing the styling curves of garments. The procedure of evolution is driven by aesthetics-inspired scores to evaluate the quality of styling designs, where the aesthetic considerations are represented in the form of streamlines on human bodies. A dual representation is introduced in our platform to process the styling curves of designs, based on which robust methods for realizing the operations of evolution are developed. Starting from a given set of styling designs on human bodies, we demonstrate the effectiveness of set evolution inspired by aesthetic factors. The evolution is adaptive to the change of aesthetic inspirations. By this adaptation, our platform can automatically generate new designs fulfilling the demands of variations in different human bodies and poses.


title: Motion-Based Calibration of Multimodal Sensor Extrinsics and Timing Offset Estimation

abstract: This paper presents a system for calibrating the extrinsic parameters and timing offsets of an array of cameras, 3-D lidars, and global positioning system/inertial navigation system sensors, without the requirement of any markers or other calibration aids. The aim of the approach is to achieve calibration accuracies comparable with state-of-the-art methods, while requiring less initial information about the system being calibrated and thus being more suitable for use by end users. The method operates by utilizing the motion of the system being calibrated. By estimating the motion each individual sensor observes, an estimate of the extrinsic calibration of the sensors is obtained. Our approach extends standard techniques for motion-based calibration by incorporating estimates of the accuracy of each sensor's readings. This yields a probabilistic approach that calibrates all sensors simultaneously and facilitates the estimation of the uncertainty in the final calibration. In addition, we combine this motion-based approach with appearance information. This gives an approach that requires no initial calibration estimate and takes advantage of all available alignment information to provide an accurate and robust calibration for the system. The new framework is validated with datasets collected with different platforms and different sensors’ configurations, and compared with state-of-the-art approaches.


title: Sex affects immunity.

abstract: Sex based differences in immune responses, affecting both the innate and adaptive immune responses, contribute to differences in the pathogenesis of infectious diseases in males and females, the response to viral vaccines and the prevalence of autoimmune diseases [1]; [2]; [3]; [4] ; [5]. Indeed, females have a lower burden of bacterial, viral and parasitic infections, most evident during their reproductive years [2] ; [6]. Conversely, females have a higher prevalence of a number of autoimmune diseases, including Sjogren’s syndrome, systemic lupus erythematosus (SLE), scleroderma, rheumatoid arthritis (RA) and multiple sclerosis (MS). These observations suggest that gonadal hormones may have a role in this sex differential. The fundamental differences in the immune systems of males and females are attributed not only to differences in sex hormones, but are related to X chromosome gene contributions and the effects of environmental factors [7] ; [8]. A comprehensive understanding of the role that sex plays in the immune response is required for therapeutic intervention strategies against infections and the development of appropriate and effective therapies for autoimmune diseases for both males and females. This review will focus on the differences between male and female immune responses in terms of innate and adaptive immunity, and the effects of sex hormones in SLE, MS and RA.


title: Acute otitis media in the era of effective pneumococcal conjugate vaccine: will new pathogens emerge?

abstract: The immunogenicity of pneumococcal conjugate vaccine (PCV) in young infants and its serotype-specific efficacy in otitis media (OM) results in a modest reduction in total episodes of OM and a more substantial reduction in disease due to the most frequent pneumococcal serotypes. Since PCV will only prevent disease due to the most common serotypes, concerns about potential changes in the microbiology of OM have emerged. Insight into potential changes can be obtained from reviewing middle ear and nasopharyngeal isolates from studies of antimicrobial prophylaxis and bacterial polysaccharide immune globulin for prevention of OM and PCV for prevention of invasive pneumococcal disease, respectively. In children receiving PCV, a shift in serotypes of SP colonizing the nasopharynx has been observed. Since non-vaccine serotypes are already present in the community as the etiology of acute purulent OM, it is predictable that these non-vaccine serotypes will become more common especially in children less than two years of age.


title: Generic summarization and keyphrase extraction using mutual reinforcement principle and sentence clustering

abstract: A novel method for simultaneous keyphrase extraction and generic text summarization is proposed by modeling text documents as weighted undirected and weighted bipartite graphs. Spectral graph clustering algorithms are useed for partitioning sentences of the documents into topical groups with sentence link priors being exploited to enhance clustering quality. Within each topical group, saliency scores for keyphrases and sentences are generated based on a mutual reinforcement principle. The keyphrases and sentences are then ranked according to their saliency scores and selected for inclusion in the top keyphrase list and summaries of the document. The idea of building a hierarchy of summaries for documents capturing different levels of granularity is also briefly discussed. Our method is illustrated using several examples from news articles, news broadcast transcripts and web documents.


title: A review of opinion mining and sentiment classification framework in social networks

abstract: The Web has dramatically changed the way we express opinions on certain products that we have purchased and used, or for services that we have received in the various industries. Opinions and reviews can be easily posted on the Web, such as in merchant sites, review portals, blogs, Internet forums, and much more. These data are commonly referred to as user-generated content or user-generated media. Both the product manufacturers, as well as potential customers are very interested in this online ‘word-of-mouth’, as it provides product manufacturers information on their customers likes and dislikes, as well as the positive and negative comments on their products whenever available, giving them better knowledge of their products limitations and advantages over competitors; and also providing potential customers with useful and ‘first-hand’ information on the products and/or services to aid in their purchase decision making process. This paper discusses the existing works on opinion mining and sentiment classification of customer feedback and reviews online, and evaluates the different techniques used for the process. It focuses on the areas covered by the evaluated papers, points out the areas that are well covered by many researchers and areas that are neglected in opinion mining and sentiment classification which are open for future research opportunity.


title: Data envelopment analysis for weight derivation and aggregation in the analytic hierarchy process

abstract: Data envelopment analysis (DEA) is proposed in this paper to generate local weights of alternatives from pair-wise comparison judgment matrices used in the analytic hierarchy process (AHP). The underlying assumption behind the approach is explained, and some salient features are explored. It is proved that DEA correctly estimates the true weights when applied to a consistent matrix formed using a known set of weights. DEA is further proposed to aggregate the local weights of alternatives in terms of different criteria to compute final weights. It is proved further that the proposed approach, called DEAHP in this paper, does not suffer from rank reversal when an irrelevant alternative(s) is added or removed.


title: Emotional Intelligence and Transformational and Transactional Leadership: A Meta-Analysis

abstract: The purpose of this study is to evaluate claims that emotional intelligence is significantly related to transformational and other leadership be haviors. Results (based on 62 independent samples) indicated a validity estimate of .59 when ratings of both emotional intelligence and leadership behaviors were provided by the same source (self, subordinates, peers, or superiors). However, when ratings of the constructs were derived from different sources, the validity estimate was .12. Lower validity estimates were found for transactional and laissez-faire leadership behaviors. Separate analyses were performed for each measure of emotional intelligence. Trait measures of emotional intelligence tended to show higher validities than ability-based measures of emotional intelligence. Agreement across ratings sources for the same construct was low for both transformational leadership (.14) and emotional intelligence (.16).


title: Political Ideology Detection Using Recursive Neural Networks

abstract: An individual’s words often reveal their political ideology. Existing automated techniques to identify ideology from text focus on bags of words or wordlists, ignoring syntax. Taking inspiration from recent work in sentiment analysis that successfully models the compositional aspect of language, we apply a recursive neural network (RNN) framework to the task of identifying the political position evinced by a sentence. To show the importance of modeling subsentential elements, we crowdsource political annotations at a phrase and sentence level. Our model outperforms existing models on our newly annotated dataset and an existing dataset.


title: In Vitro Characterization of the Anti-PD-1 Antibody Nivolumab, BMS-936558, and In Vivo Toxicology in Non-Human Primates

abstract: The programmed death-1 (PD-1) receptor serves as an immunologic checkpoint, limiting bystander tissue damage and preventing the development of autoimmunity during inflammatory responses. PD-1 is expressed by activated T cells and down-modulates T-cell effector functions upon binding to its ligands, PD-L1 and PD-L2, on antigen-presenting cells. In patients with cancer, the expression of PD-1 on tumor-infiltrating lymphocytes and its interaction with the ligands on tumor and immune cells in the tumor microenvironment undermines antitumor immunity and supports the rationale for PD-1 blockade in cancer immunotherapy. This report details the development and characterization of nivolumab, a fully human IgG4 (S228P) anti-PD-1 receptor blocking monoclonal antibody. Nivolumab binds to PD-1 with high affinity and specificity, and effectively inhibits the interaction between PD-1 and its ligands. In vitro assays demonstrated the ability of nivolumab to potently enhance T-cell responses and cytokine production in the mixed lymphocyte reaction and superantigen or cytomegalovirus stimulation assays. No in vitro antibody-dependent cell-mediated or complement-dependent cytotoxicity was observed using nivolumab and activated T cells as targets. Nivolumab treatment did not induce adverse events when given to cynomolgus macaques at high concentrations, independent of circulating anti-nivolumab antibodies where observed. These data provide a comprehensive preclinical characterization of nivolumab whose antitumor activity and safety profile have been demonstrated in human clinical trials in various solid tumors.


title: Promoting Self-Regulation in Science Education: Metacognition as Part of a Broader Perspective on Learning

abstract: The purpose of this article is to review recent research on self-regulated learning and discuss the implications of this research for science education. We draw on examples of self-regulated learning from the science education literature to summarise and illustrate effective instructional methods and the development of metacognitive understanding (Gunstone; 1999a; Rickey & Stacy, 2000; White & Mitchell, 1994). We also focus on the crucial role that metacognition plays in self-regulation (Baird & White, 1996; Nichols, Tippins, & Wieseman, 1997; White, 1998). We divide our discussion into two main parts. The first focuses on three components of selfregulated learning, including cognition, metacognition, and motivation. We relate these aspects of self-regulation to current practices in science education. The second section focuses on six general instructional strategies for improving self-regulation in the science classroom. We focus on the use of inquiry based learning, the role of collaborative support, strategy and problem solving instruction, the construction of mental models, the use of technology to support learning, and the role of personal beliefs such as self-efficacy and epistemological world views. These instructional strategies are selected because they reflect extensive research agendas over the last decade within the science education literature and are essential to metacognition and self-regulation (Butler & Winne, 1995; Gunstone, 1999b).


title: Authenticated Key Exchange over Bitcoin

abstract: Bitcoin is designed to protect user anonymity or pseudo nymity in a financial transaction, and has been increasingly adopted by major e-commerce websites such as Dell, PayPal and Expedia. While the anonymity of Bitcoin transactions has been extensively studied, little attention has been paid to the security of post-transaction correspondence. In a commercial application, the merchant and the user often need to engage in follow-up correspondence after a Bitcoin transaction is completed, e.g., to acknowledge the receipt of payment, to confirm the billing address, to arrange the product delivery, to discuss refund and so on. Currently, such follow-up correspondence is typically done in plaintext via email with no guarantee on confidentiality. Obviously, leakage of sensitive data from the correspondence e.g., billing address can trivially compromise the anonymity of Bitcoin users. In this paper, we initiate the first study on how to realise end-to-end secure communication between Bitcoin users in a post-transaction scenario without requiring any trusted third party or additional authentication credentials. This is an important new area that has not been covered by any IEEE or ISO/IEC security standard, as none of the existing PKI-based or password-based AKE schemes are suitable for the purpose. Instead, our idea is to leverage the Bitcoin's append-only ledger as an additional layer of authentication between previously confirmed transactions. This naturally leads to a new category of AKE protocols that bootstrap trust entirely from the block chain. We call this new category "Bitcoin-based AKE" and present two concrete protocols: one is non-interactive with no forward secrecy, while the other is interactive with additional guarantee of forward secrecy. Finally, we present proof-of-concept prototypes for both protocols with experimental results to demonstrate their practical feasibility.


title: Knowledge-based question answering

abstract: This paper describes the Webclopedia Question Answering system, in which methods to automatically learn patterns and parameterizations are combined with hand-crafted rules and concept ontologies. The source for answers is a collection of 1 million newspaper texts, distributed by NIST. In general, two kinds of knowledge are used by Webclopedia to answer questions: knowledge about language and knowledge about the world. The former is embodied both in the Information Retrieval engine that identifies likely answer sentences and the CONTEX parser that analyses the input question and candidate answers. The latter is embodied in a QA typology of some 140 nodes, a concept ontology of some 10,000 concepts, and the IR engine’s ranking algorithm that takes typical document structure into account.


title: Generalized blockmodeling of two-mode network data

abstract: Abstract We extend the direct approach for blockmodeling one-mode data to two-mode data. The key idea in this development is that the rows and columns are partitioned simultaneously but in different ways. Many (but not all) of the generalized block types can be mobilized in blockmodeling two-mode network data. These methods were applied to some ‘voting’ data from the 2000–2001 term of the Supreme Court and to the classic Deep South data on women attending events. The obtained partitions are easy to interpret and compelling. The insight that rows and columns can be partitioned in different ways can be applied also to one-mode data. This is illustrated by a partition of a journal-to-journal citation network where journals are viewed simultaneously as both producers and consumers of scientific knowledge.


title: Adaptation of the Nomophobia Questionnaire (NMP-Q) to Spanish in a sample of adolescents

abstract: INTRODUCTION ::: Nomophobia is the fear of being out of mobile phone contact. People suffering from this anxiety disorder have feelings of stress and nervousness when access to their mobiles or computers is not possible. This work is an adaptation and validation study of the Spanish version of the Nomophobia Questionnaire (NMP-Q). ::: ::: ::: METHODOLOGY ::: The study included 306 students (46.1% males and 53.9% females) with ages ranging 13 to 19 years (Md=15.41±1.22). ::: ::: ::: RESULTS ::: Exploratory factor analysis revealed four dimensions that accounted for 64.4% of total variance. The ordinal α-value was 0.95, ranging from 0.75 to 0.92 across factors. Measure of stability was calculated by the testretest method (r=0.823). Indicators of convergence with the Spanish versions of the “Mobile Phone Problem Use Scale” (r=0.654) and the “Generalized Problematic Internet Use Scale” (r=0.531) were identified. Problematic mobile phone use patterns were examined taking the 15P, 80P and 95P percentiles as cut-off points. Scores of 39, 87 and 116 on NMP-Q corresponded to occasional, at-risk and problematic users, respectively. ::: ::: ::: CONCLUSIONS ::: Psychometric analysis shows that the Spanish version of the NMP-Q is a valid and reliable tool for the study of nomophobia.


title: Real time vision by FPGA implemented CNNs

abstract: In order to get real time image processing for mobile robot vision, we propose to use a discrete time cellular neural network implementation by a convolutional structure on Altora FPGA using VHDL language. We obtain at least 9 times faster processing than other emulations for the same problem.


title: Detection and Recognition of Road Markings in Panoramic Images

abstract: The detection of road lane markings has many practical applications, such as advanced driver assistance systems and road maintenance. In this paper we propose an algorithm to detect and recognize road lane markings from panoramic images. Our algorithm consists of four steps. First, an inverse perspective mapping is applied to the image, and the potential road markings are segmented based on their intensity difference compared to the surrounding pixels. Second, we extract the distance between the center and the boundary at regular angular steps of each considered potential road marking segment into a feature vector. Third, each segment is classified using a Support Vector Machine (SVM). Finally, by modeling the lane markings, previous false positive detected segments can be rejected based on their orientation and position relative to the lane markings. Our experiments show that the system is capable of recognizing \(93\,\%\), \(95\,\%\) and \(91\,\%\) of striped line segments, blocks and arrows respectively, as well as \(94\,\%\) of the lane markings.


title: High gain RF amplifier for very low frequency receiver application

abstract: Due to the wide usage of very low frequency (VLF) wave for research on natural phenomena such as lightning, earthquake, and weather, a receiver for VLF application particularly with high-gain is absolutely required. This paper discusses about designing, realizing and characterizing a high gain radio frequency (RF) amplifier for VLF receiver application. The proposed amplifier which is designed using a junction field-effect transistor (JFET) of 2SK170 and an operational amplifier (OpAmp) of OP27 is intended to work at frequency range below 50kHz. After achieving the optimum design, the hardware realization is then carried out by deploying the designed amplifier on a printed circuit board. From the experimental characterization, it shows that the measured gain of realized amplifier at frequency of 19.8kHz satisfies with the design criteria and is 46.003dB with 1-dB compression point of −24dBm.


title: Identifying the semantic and textual differences between two versions of a program

abstract: Text-based file comparators ( e.g. , the Unix utility diff ), are very general tools that can be applied to arbitrary files. However, using such tools to compare programs can be unsatisfactory because their only notion of change is based on program text rather than program behavior . This paper describes a technique for comparing two versions of a program, determining which program components represents changes, and classifying each changed component as representing either a semantic or a textual change.


title: Digital control of DC-DC boost converters with inductor current estimation

abstract: This paper investigates a digital control of DC-DC boost converter operating in continuous-conduction mode (CCM) sensing only the output voltage and possibly the input voltage. In order to improve the dynamic performance compared to voltage mode control, the inductor current is estimated and used for the implementation of an internal sensorless current loop. Different solutions, either based on the prediction of inductor current variations or on state observers, are discussed, showing that the sensorless current mode control gives some advantages only if input voltage feedforward is used. The digital control has been implemented in a field programmable gate array (FPGA) using a hardware description language (VHDL), providing flexibility and technology independence. Experimental results on a 100 W DC-DC boost converter confirm the properties and limitations of the proposed approach.


title: Applicability of data mining algorithms for recommendation system in e-learning

abstract: In this paper we consider the applicability of data mining algorithms such as clustering & association rule algorithm for recommending the courses to the student in E-Learning System e.g. the student who liked to study the course "Operating System" is quite like to study the course "Distributed System". We develop the algorithm in java which is the combination of Simple K-means clustering & Apriori association rule algorithm. The result we obtained using these combinations are compared with the result we get using open source data mining tool, Weka & present the same. Results using this developed algorithm contain more number of association rules as compare to the result we obtained using the Weka.


title: A clean slate 4D approach to network control and management

abstract: Today's data networks are surprisingly fragile and difficult to manage. We argue that the root of these problems lies in the complexity of the control and management planes--the software and protocols coordinating network elements--and particularly the way the decision logic and the distributed-systems issues are inexorably intertwined. We advocate a complete refactoring of the functionality and propose three key principles--network-level objectives, network-wide views, and direct control--that we believe should underlie a new architecture. Following these principles, we identify an extreme design point that we call "4D," after the architecture's four planes: decision, dissemination, discovery, and data. The 4D architecture completely separates an AS's decision logic from pro-tocols that govern the interaction among network elements. The AS-level objectives are specified in the decision plane, and en-forced through direct configuration of the state that drives how the data plane forwards packets. In the 4D architecture, the routers and switches simply forward packets at the behest of the decision plane, and collect measurement data to aid the decision plane in controlling the network. Although 4D would involve substantial changes to today's control and management planes, the format of data packets does not need to change; this eases the deployment path for the 4D architecture, while still enabling substantial innovation in network control and management. We hope that exploring an extreme design point will help focus the attention of the research and industrial communities on this crucially important and intellectually challenging area.


title: Issues in evaluation of stream learning algorithms

abstract: Learning from data streams is a research area of increasing importance. Nowadays, several stream learning algorithms have been developed. Most of them learn decision models that continuously evolve over time, run in resource-aware environments, detect and react to changes in the environment generating data. One important issue, not yet conveniently addressed, is the design of experimental work to evaluate and compare decision models that evolve over time. There are no golden standards for assessing performance in non-stationary environments. This paper proposes a general framework for assessing predictive stream learning algorithms. We defend the use of Predictive Sequential methods for error estimate - the prequential error. The prequential error allows us to monitor the evolution of the performance of models that evolve over time. Nevertheless, it is known to be a pessimistic estimator in comparison to holdout estimates. To obtain more reliable estimators we need some forgetting mechanism. Two viable alternatives are: sliding windows and fading factors. We observe that the prequential error converges to an holdout estimator when estimated over a sliding window or using fading factors. We present illustrative examples of the use of prequential error estimators, using fading factors, for the tasks of: i) assessing performance of a learning algorithm; ii) comparing learning algorithms; iii) hypothesis testing using McNemar test; and iv) change detection using Page-Hinkley test. In these tasks, the prequential error estimated using fading factors provide reliable estimators. In comparison to sliding windows, fading factors are faster and memory-less, a requirement for streaming applications. This paper is a contribution to a discussion in the good-practices on performance assessment when learning dynamic models that evolve over time.