title: Clothing the Masses: Real-Time Clothed Crowds With Variation

abstract: The animation and rendering of crowds of realistically clothed characters presents a difficult challenge in computer graphics, which is further exacerbated when real-time frame rates a re required. To date, due to real-time constraints, standard skinning methods have been used to animate the cloth es of individuals in real-time crowds, which does not create the appropriate secondary motion for flowing garm ents. However, plausible cloth simulation is vital for the depiction of realistic characters, so we have developed a nov el crowd system in which the individuals are endowed with realism and variety through the addition of physically s imulated clothing and hardware assisted pattern variation.


title: Design of a novel six-dimensional force/torque sensor and its calibration based on NN

abstract: This paper describes the design of a six-axis force/torque sensor, the purpose of which is to provide decoupled and accurate F/T information for the closed-loop control of the manipulator system. Firstly, the manipulator system and the adopted measuring principle are introduced. Then, a novel static component based on dual annulus diaphragms is presented. At last, the calibration and decoupling test based on Neural Network (NN) is carried out. The results of calibration test show superiority of the structure of the elastic component of the developed sensor and the improvement of the calibration method.


title: NE-Rank: A Novel Graph-Based Keyphrase Extraction in Twitter

abstract: The massive growth of the micro-blogging service Twitter has shed the light on the challenging problem of summarizing a collection of large number of tweets. This paper attempts to extract topical key phrases that would represent topics in tweets. Due to the informality, noise, and short length of tweets, such research is nontrivial. We tackle such challenges with extensive preprocessing approach. Followed by, introduction of new features that improve topical key phrase extraction in Twitter. We start by proposing a novel unsupervised graph-based keyword ranking method, called NE-Rank, that considers word weights in addition to edge weights when calculating the ranking. Then we introduce a new approach of leveraging hash tags when extracting key phrases. We have conducted a set of experiments showing the potential of both approaches with 16% to 39% improvement for NE-Rank and 20% improvement for hash tag enhanced extraction.


title: Lemmatisation as a Tagging Task

abstract: We present a novel approach to the task of word lemmatisation. We formalise lemmatisation as a category tagging task, by describing how a word-to-lemma transformation rule can be encoded in a single label and how a set of such labels can be inferred for a specific language. In this way, a lemmatisation system can be trained and tested using any supervised tagging model. In contrast to previous approaches, the proposed technique allows us to easily integrate relevant contextual information. We test our approach on eight languages reaching a new state-of-the-art level for the lemmatisation task.


title: Structured Indoor Modeling

abstract: This paper presents a novel 3D modeling framework that reconstructs an indoor scene as a structured model from panorama RGBD images. A scene geometry is represented as a graph, where nodes correspond to structural elements such as rooms, walls, and objects. The approach devises a structure grammar that defines how a scene graph can be manipulated. The grammar then drives a principled new reconstruction algorithm, where the grammar rules are sequentially applied to recover a structured model. The paper also proposes a new room segmentation algorithm and an offset-map reconstruction algorithm that are used in the framework and can enforce architectural shape priors far beyond existing state-of-the-art. The structured scene representation enables a variety of novel applications, ranging from indoor scene visualization, automated floorplan generation, Inverse-CAD, and more. We have tested our framework and algorithms on six synthetic and five real datasets with qualitative and quantitative evaluations. The source code and the data are available at the project website [15].


title: Scene Text Localization and Recognition with Oriented Stroke Detection

abstract: An unconstrained end-to-end text localization and recognition method is presented. The method introduces a novel approach for character detection and recognition which combines the advantages of sliding-window and connected component methods. Characters are detected and recognized as image regions which contain strokes of specific orientations in a specific relative position, where the strokes are efficiently detected by convolving the image gradient field with a set of oriented bar filters. Additionally, a novel character representation efficiently calculated from the values obtained in the stroke detection phase is introduced. The representation is robust to shift at the stroke level, which makes it less sensitive to intra-class variations and the noise induced by normalizing character size and positioning. The effectiveness of the representation is demonstrated by the results achieved in the classification of real-world characters using an euclidian nearest-neighbor classifier trained on synthetic data in a plain form. The method was evaluated on a standard dataset, where it achieves state-of-the-art results in both text localization and recognition.


title: A novel way of computing similarities between nodes of a graph, with application to collaborative recommendation

abstract: This work presents a new perspective on characterizing the similarity between elements of a database or, more generally, nodes of a weighted, undirected, graph. It is based on a Markov-chain model of random walk through the database. The suggested quantities, representing dissimilarities (or similarities) between any two elements, have the nice property of decreasing (increasing) when the number of paths connecting those elements increases and when the "length" of any path decreases. The model is evaluated on a collaborative recommendation task where suggestions are made about which movies people should watch based upon what they watched in the past. The model, which nicely fits into the so-called "statistical relational learning" framework as well as the "link analysis" paradigm, could also be used to compute document or word similarities, and, more generally, could be applied to other database or web mining tasks.


title: Visual servo control. II. Advanced approaches [Tutorial]

abstract: This article is the second of a two-part tutorial on visual servo control. In this tutorial, we have only considered velocity controllers. It is convenient for most of classical robot arms. However, the dynamics of the robot must of course be taken into account for high speed task, or when we deal with mobile nonholonomic or underactuated robots. As for the sensor, geometrical features coming from a classical perspective camera is considered. Features related to the image motion or coming from other vision sensors necessitate to revisit the modeling issues to select adequate visual features. Finally, fusing visual features with data coming from other sensors at the level of the control scheme will allow to address new research topics


title: NixOS: a purely functional Linux distribution

abstract: Existing package and system configuration management tools suffer from an imperative model, where system administration actions such as upgrading packages or changes to system configuration files are stateful: they destructively update the state of the system. This leads to many problems, such as the inability to roll back changes easily, to run multiple versions of a package side-by-side, to reproduce a configuration deterministically on another machine, or to reliably upgrade a system. In this paper we show that we can overcome these problems by moving to a purely functional system configuration model. This means that all static parts of a system (such as software packages, configuration files and system startup scripts) are built by pure functions and are immutable, stored in a way analogously to a heap in a purely function language. We have implemented this model in NixOS, a non-trivial Linux distribution that uses the Nix package manager to build the entire system configuration from a purely functional specification.


title: Detection and Analysis of Irregular Streaks in Dermoscopic Images of Skin Lesions

abstract: Irregular streaks are important clues for Melanoma (a potentially fatal form of skin cancer) diagnosis using dermoscopy images. This paper extends our previous algorithm to identify the absence or presence of streaks in a skin lesions, by further analyzing the appearance of detected streak lines, and performing a three-way classification for streaks, Absent, Regular, and Irregular, in a pigmented skin lesion. In addition, the directional pattern of detected lines is analyzed to extract their orientation features in order to detect the underlying pattern. The method uses a graphical representation to model the geometric pattern of valid streaks and the distribution and coverage of the structure. Using these proposed features of the valid streaks along with the color and texture features of the entire lesion, an accuracy of 76.1% and weighted average area under ROC curve (AUC) of 85% is achieved for classifying dermoscopy images into streaks Absent, Regular, or Irregular on 945 images compiled from atlases and the internet without any exclusion criteria. This challenging dataset is the largest validation dataset for streaks detection and classification published to date. The data set has also been applied to the two-class sub-problems of Absent/Present classification (accuracy of 78.3% with AUC of 83.2%) and to Regular/Irregular classification (accuracy 83.6% with AUC of 88.9%). When the method was tested on a cleaned subset of 300 images randomly selected from the 945 images, the AUC increased to 91.8%, 93.2% and 90.9% for the Absent/Regular/Irregular, Absent/Present, and Regular/Irregular problems, respectively.


title: Fault tolerant data structures

abstract: The authors consider the tolerance of data structures to memory faults. They observe that many pointer-based data structures (e.g. linked lists, trees, etc.) are highly nonresilient to faults. A single fault in a linked list or tree may result in the loss of the entire set of data. They present a formal framework for studying the fault tolerance properties of pointer-based data structures, and provide fault tolerant versions of the stack, the linked list, and the dictionary tree.


title: Multi-Agent Influence Diagrams for Representing and Solving Games

abstract: The traditional representations of games using the extensive form or the strategic (normal) form obscure much of the structure that is present in real-world games. In this paper, we propose a new representation language for general multiplayer games--multi-agent influence diagrams (MAIDs). This representation extends graphical models for probability distributions to a multi-agent decision-making context. MAIDs explicitly encode structure involving the dependence relationships among variables. As a consequence, we can define a notion of strategic relevance of one decision variable to another: D′ is strategically relevant to D if, to optimize the decision rule at D, the decision maker needs to take into consideration the decision rule at D′. We provide a sound and complete graphical criterion for determining strategic relevance. We then show how strategic relevance can be used to detect structure in games, allowing a large game to be broken up into a set of interacting smaller games, which can be solved in sequence. We show that this decomposition can lead to substantial savings in the computational cost of finding Nash equilibria in these games.


title: Simulations of water at the interface with hydrophilic self-assembled monolayers.

abstract: Simulations of water at hydrophilic self-assembled monolayer (SAM) surfaces are especially relevant for biological interfaces. Well-defined, atomically smooth surfaces that can be continuously varied are possible with SAMs. These characteristics enable more accurate measurements than many other surfaces with the added advantage of tailoring the surface to treat specific chemical groups. A fundamental question is how solid surfaces affect the structure and dynamics of water. Measurements of the structure and dynamics of water at solid surfaces have improved significantly, but there remain differences among the experiments. In this article, the authors review simulations of water at the interface with hydrophilic SAMs. These simulations find that while the interfacial water molecules are slower than the bulk water molecules, the interfacial dynamics remains that of a liquid. A major biological application of SAMs is for making coatings resistant to protein adsorption. SAMs terminated with ethylene glycol monomers have proven to be excellent at resisting protein adsorption. Understanding the mechanisms behind this resistance remains an unresolved issue. Recent simulations suggest a new perspective of the role of interfacial water and the inseparable interplay between the SAM and the water.


title: Secure Integration of Web Content and Applications on Commodity Mobile Operating Systems

abstract: A majority of today's mobile apps integrate web content of various kinds. Unfortunately, the interactions between app code and web content expose new attack vectors: a malicious app can subvert its embedded web content to steal user secrets; on the other hand, malicious web content can use the privileges of its embedding app to exfiltrate sensitive information such as the user's location and contacts. In this paper, we discuss security weaknesses of the interface between app code and web content through attacks, then introduce defenses that can be deployed without modifying the OS. Our defenses feature WIREframe, a service that securely embeds and renders external web content in Android apps, and in turn, prevents attacks between em- bedded web and host apps. WIREframe fully mediates the interface between app code and embedded web content. Un- like the existing web-embedding mechanisms, WIREframe allows both apps and embedded web content to define simple access policies to protect their own resources. These policies recognize fine-grained security principals, such as origins, and control all interactions between apps and the web. We also introduce WIRE (Web Isolation Rewriting Engine), an offline app rewriting tool that allows app users to inject WIREframe protections into existing apps. Our evaluation, based on 7166 popular apps and 20 specially selected apps, shows these techniques work on complex apps and incur acceptable end-to-end performance overhead.


title: Perceived Integrity of Transformational Leaders in Organisational Settings

abstract: The ethical nature of transformational leadership has been hotly debated. This debate is demonstrated in the range of descriptors that have been used to label transformational leaders including narcissistic, manipulative, and self-centred, but also ethical, just and effective. Therefore, the purpose of the present research was to address this issue directly by assessing the statistical relationship between perceived leader integrity and transformational leadership using the Perceived Leader Integrity Scale (PLIS) and the Multi-Factor Leadership Questionnaire (MLQ). In a national sample of 1354 managers a moderate to strong positive relationship was found between perceived integrity and the demonstration of transformational leadership behaviours. A similar relationship was found between perceived integrity and developmental exchange leadership. A systematic leniency bias was identified when respondents rated subordinates vis-a-vis peer ratings. In support of previous findings, perceived integrity was also found to correlate positively with leader and organisational effectiveness measures.


title: Context-Aware Driver Behavior Detection System in Intelligent Transportation Systems

abstract: Vehicular ad hoc networks (VANETs) have emerged as an application of mobile ad hoc networks (MANETs), which use dedicated short-range communication (DSRC) to allow vehicles in close proximity to communicate with each other or to communicate with roadside equipment. Applying wireless access technology in vehicular environments has led to the improvement of road safety and a reduction in the number of fatalities caused by road accidents through development of road safety applications and facilitation of information sharing between moving vehicles regarding the road. This paper focuses on developing a novel and nonintrusive driver behavior detection system using a context-aware system in VANETs to detect abnormal behaviors exhibited by drivers and to warn other vehicles on the road to prevent accidents from happening. A five-layer context-aware architecture is proposed, which is able to collect contextual information about the driving environment, to perform reasoning about certain and uncertain contextual information, and to react upon that information. A probabilistic model based on dynamic Bayesian networks (DBNs) in real time, inferring four types of driving behavior (normal, drunk, reckless, and fatigue) by combining contextual information about the driver, the vehicle, and the environment, is presented. The dynamic behavior model can capture the static and the temporal aspects related to the behavior of the driver, thus leading to robust and accurate behavior detection. The evaluation of behavior detection using synthetic data proves the validity of our model and the importance of including contextual information about the driver, the vehicle, and the environment.


title: Modulation and Multiple Access for 5G Networks

abstract: Fifth generation (5G) wireless networks face various challenges in order to support large-scale heterogeneous traffic and users, therefore new modulation and multiple access (MA) schemes are being developed to meet the changing demands. As this research space is ever increasing, it becomes more important to analyze the various approaches, therefore in this article we present a comprehensive overview of the most promising modulation and MA schemes for 5G networks. We first introduce the different types of modulation that indicate their potential for orthogonal multiple access (OMA) schemes and compare their performance in terms of spectral efficiency, out-of-band leakage, and bit-error rate. We then pay close attention to various types of non-orthogonal multiple access (NOMA) candidates, including power-domain NOMA, code-domain NOMA, and NOMA multiplexing in multiple domains. From this exploration we can identify the opportunities and challenges that will have significant impact on the design of modulation and MA for 5G networks.


title: Hybrid semantic clustering of hashtags

abstract: Abstract Clustering hashtags based on their semantics is an important problem with many applications. The uncontrolled usage of hashtags in social media, however, makes the quality of semantics and the frequency of usage vary a lot, and this poses a challenge to the current approaches which capitalize on either the lexical semantics of a hashtag (by using metadata) or the contextual semantics of a hashtag (by using the texts associated with a hashtag). This paper presents a hybrid semantic clustering algorithm that uses the complementary strengths of lexical and contextual semantics of a hashtag to produce accurate clusters on a wider range of input data. The hybrid algorithm uses a consensus clustering approach, which finds the consensus between metadata-based sense-level semantic clusters and text-based semantic clusters. A gold standard test shows that the hybrid algorithm outperforms both the text-based algorithm and the metadata-based algorithm for a majority of ground truths tested and that it never underperforms both base algorithms. In addition, a larger-scale performance study, conducted with a focus on disagreements in cluster assignments between algorithms, show that the hybrid algorithm makes the correct cluster assignment in a majority of disagreement cases.


title: Leveraging Joint Interactions for Credibility Analysis in News Communities

abstract: Media seems to have become more partisan, often providing a biased coverage of news catering to the interest of specific groups. It is therefore essential to identify credible information content that provides an objective narrative of an event. News communities such as digg, reddit, or newstrust offer recommendations, reviews, quality ratings, and further insights on journalistic works. However, there is a complex interaction between different factors in such online communities: fairness and style of reporting, language clarity and objectivity, topical perspectives (like political viewpoint), expertise and bias of community members, and more. This paper presents a model to systematically analyze the different interactions in a news community between users, news, and sources. We develop a probabilistic graphical model that leverages this joint interaction to identify 1) highly credible news articles, 2) trustworthy news sources, and 3) expert users who perform the role of "citizen journalists" in the community. Our method extends CRF models to incorporate real-valued ratings, as some communities have very fine-grained scales that cannot be easily discretized without losing information. To the best of our knowledge, this paper is the first full-fledged analysis of credibility, trust, and expertise in news communities.


title: Incremental Medians via Online Bidding

abstract: In the k-median problem we are given sets of facilities and customers, and distances between them. For a given set F of facilities, the cost of serving a customer u is the minimum distance between u and a facility in F. The goal is to find a set F of k facilities that minimizes the sum, over all customers, of their service costs. ::: ::: Following the work of Mettu and Plaxton, we study the incremental medians problem, where k is not known in advance. An incremental algorithm produces a nested sequence of facility sets F1⊆F2⊆⋅⋅⋅⊆Fn, where |Fk|=k for each k. Such an algorithm is called c-cost-competitive if the cost of each Fk is at most c times the optimum k-median cost. We give improved incremental algorithms for the metric version of this problem: an 8-cost-competitive deterministic algorithm, a 2e≈5.44-cost-competitive randomized algorithm, a (24+e)-cost-competitive, polynomial-time deterministic algorithm, and a 6e+e≈16.31-cost-competitive, polynomial-time randomized algorithm. ::: ::: We also consider the competitive ratio with respect to size. An algorithm is s-size-competitive if the cost of each Fk is at most the minimum cost of any set of k facilities, while the size of Fk is at most sk. We show that the optimal size-competitive ratios for this problem, in the deterministic and randomized cases, are 4 and e. For polynomial-time algorithms, we present the first polynomial-time O(log m)-size-approximation algorithm for the offline problem, as well as a polynomial-time O(log m)-size-competitive algorithm for the incremental problem. ::: ::: Our upper bound proofs reduce the incremental medians problem to the following online bidding problem: faced with some unknown threshold T∈ℝ+, an algorithm must submit “bids” b∈ℝ+ until it submits a bid b≥T, paying the sum of all its bids. We present folklore algorithms for online bidding and prove that they are optimally competitive. ::: ::: We extend some of the above results for incremental medians to approximately metric distance functions and to incremental fractional medians. Finally, we consider a restricted version of the incremental medians problem where k is restricted to one of two given values, for which we give a deterministic algorithm with a nearly optimal cost-competitive ratio.


A Broad-Coverage Normalization System for Social Media Language


title: A Wireless Magnetoresistive Sensing System for an Intraoral Tongue-Computer Interface

abstract: Tongue drive system (TDS) is a tongue-operated, minimally invasive, unobtrusive, and wireless assistive technology (AT) that infers users' intentions by detecting their voluntary tongue motion and translating them into user-defined commands. Here we present the new intraoral version of the TDS (iTDS), which has been implemented in the form of a dental retainer. The iTDS system-on-a-chip (SoC) features a configurable analog front-end (AFE) that reads the magnetic field variations inside the mouth from four 3-axial magnetoresistive sensors located at four corners of the iTDS printed circuit board (PCB). A dual-band transmitter (Tx) on the same chip operates at 27 and 432 MHz in the Industrial/Scientific/Medical (ISM) band to allow users to switch in the presence of external interference. The Tx streams the digitized samples to a custom-designed TDS universal interface, built from commercial off-the-shelf (COTS) components, which delivers the iTDS data to other devices such as smartphones, personal computers (PC), and powered wheelchairs (PWC). Another key block on the iTDS SoC is the power management integrated circuit (PMIC), which provides individually regulated and duty-cycled 1.8 V supplies for sensors, AFE, Tx, and digital control blocks. The PMIC also charges a 50 mAh Li-ion battery with constant current up to 4.2 V, and recovers data and clock to update its configuration register through a 13.56 MHz inductive link. The iTDS SoC has been implemented in a 0.5-μm standard CMOS process and consumes 3.7 mW on average.


Real-Time Eye Blink Detection using Facial Landmarks


All Together Now : Collective Knowledge , Collective Narratives , and Architectures of Participation


title: Real-time three-dimensional smooth path planning for unmanned aerial vehicles in completely unknown cluttered environments

abstract: This paper presents a real-time three-dimensional path planning algorithm for improving autonomous navigation of Unmanned Aerial Vehicles (UAVs) operating in completely unknown cluttered environments. The algorithm generates smooth paths consisting of continuous piecewise Bezier curves in real time. Specifically, a RRT-based waypoint generation algorithm is firstly proposed for the exploration of collision-free waypoints successively during flight. Besides, a novel real-time path smoothing technique is developed to generate continuous collision-free paths that satisfy the motion constraints of UAVs. This is achieved by fitting Bezier curves between consecutive waypoints based on the particle swarm optimization (PSO) algorithm. Lastly, a path selection strategy is also introduced to seek for an optimum path when multiple solutions are available. The simulation results demonstrate the superiority of the proposed real-time three-dimensional smooth path planning algorithm.


title: A core ontology for requirements

abstract: In their seminal paper ACM T. Softw. Eng. Methodol., 61 1997, 1--30, Zave and Jackson established a core ontology for Requirements Engineering RE and used it to formulate the “requirements problem”, thereby defining what it means to successfully complete RE. Starting from the premise that the stakeholders of the system-to-be communicate to the software engineer the information needed to perform RE, Zave and Jackson's ontology is shown to be incomplete, in that it does not cover all classes of basic concerns --namely, the beliefs, desires, intentions, and evaluations --that the stakeholders communicate. In response, we provide a new core ontology for requirements that covers these classes of basic stakeholder concerns. The proposed new core ontology leads to a new formulation of the requirements problem. We thereby establish a new framework for the information that needs to be elicited over the course of RE and new criteria for determining whether an RE problem has been successfully addressed.


title: Representation-Aggregation Networks for Segmentation of Multi-Gigapixel Histology Images

abstract: Convolutional Neural Network (CNN) models have become the state-of-the-art for most computer vision tasks with natural images. However, these are not best suited for multi-gigapixel resolution Whole Slide Images (WSIs) of histology slides due to large size of these images. Current approaches construct smaller patches from WSIs which results in the loss of contextual information. We propose to capture the spatial context using novel Representation-Aggregation Network (RAN) for segmentation purposes, wherein the first network learns patch-level representation and the second network aggregates context from a grid of neighbouring patches. We can use any CNN for representation learning, and can utilize CNN or 2D-Long Short Term Memory (2D-LSTM) for context-aggregation. Our method significantly outperformed conventional patch-based CNN approaches on segmentation of tumour in WSIs of breast cancer tissue sections.


title: Length Adaptive Recurrent Model for Text Classification

abstract: In recent years, recurrent neural networks have been widely used for various text classification tasks. However, most of the recurrent architectures will not assign a class label to a text until they read the last word, while human beings are able to determine the text class before reading the whole text. In this paper, we propose a Length Adaptive Recurrent Model (LARM) which can automatically determine the minimum text length that is necessary to perform the classification. With three parts includingReader, Predictor andAgent, our model is designed to read a text word by word, and terminate the process when the adequate information has been caught for the text classification task. The experimental results show that our model has comparable or even better performance compared to the vanilla LSTM when both are fed with partial text input. Besides, we can speed up text classification by truncating the text when sufficient evidence is found for classification. Furthermore, we also visualize our model and show that our model works like human beings, who can gradually come up with the general idea of a text while reading texts sequentially.


title: A Survey of Non-Orthogonal Multiple Access for 5G

abstract: In the fifth generation (5G) of wireless communication systems, hitherto unprecedented requirements are expected to be satisfied. As one of the promising techniques of addressing these challenges, non-orthogonal multiple access (NOMA) has been actively investigated in recent years. In contrast to the family of conventional orthogonal multiple access (OMA) schemes, the key distinguishing feature of NOMA is to support a higher number of users than the number of orthogonal resource slots with the aid of non-orthogonal resource allocation. This may be realized by the sophisticated inter-user interference cancellation at the cost of an increased receiver complexity. In this paper, we provide a comprehensive survey of the original birth, the most recent development, and the future research directions of NOMA. Specifically, the basic principle of NOMA will be introduced at first, with the comparison between NOMA and OMA especially from the perspective of information theory. Then, the prominent NOMA schemes are discussed by dividing them into two categories, namely, power-domain and code-domain NOMA. Their design principles and key features will be discussed in detail, and a systematic comparison of these NOMA schemes will be summarized in terms of their spectral efficiency, system performance, receiver complexity, etc. Finally, we will highlight a range of challenging open problems that should be solved for NOMA, along with corresponding opportunities and future research trends to address these challenges.


title: Design of multiple RESURF LDMOS with P-top rings and STI regions in 65nm CMOS technology

abstract: In this work, a novel multiple RESURF P-top rings LDMOS with shallow trench isolation (STI) structure based on the 65 nm baseline low-voltage CMOS technology by three-dimensional Sentaurus process and device simulations. A optimized uniform electric filed distribution in N-drift region can be obtained by employing the multiple P-top rings process instead of the past proposed gate field plates method in the extended drain regions. By this way, not only both of high breakdown voltage exceeded over 40V and low on-resistance below 20 mΩ-mm2 can be achieved, but also the effect of W N-drift /W STI ratio on device can be reduce to obtain the larger optimal window of device characteristics, as compared with the conventional DIELER and graded gate field plate devices.


title: Sorting of β-Actin mRNA and Protein to Neurites and Growth Cones in Culture

abstract: The transport of mRNAs into developing dendrites and axons may be a basic mechanism to localize cytoskeletal proteins to growth cones and influence microfilament organization. Using isoform-specific antibodies and probes for in situ hybridization, we observed distinct localization patterns for β- and γ-actin within cultured cerebrocortical neurons. β-Actin protein was highly enriched within growth cones and filopodia, in contrast to γ-actin protein, which was distributed uniformly throughout the cell. β-Actin protein also was shown to be peripherally localized after transfection of β-actin cDNA bearing an epitope tag. β-Actin mRNAs were localized more frequently to neuronal processes and growth cones, unlike γ-actin mRNAs, which were restricted to the cell body. The rapid localization of β-actin mRNA, but not γ-actin mRNA, into processes and growth cones could be induced by dibutyryl cAMP treatment. Using high-resolution in situ hybridization and image-processing methods, we showed that the distribution of β-actin mRNA within growth cones was statistically nonrandom and demonstrated an association with microtubules. β-Actin mRNAs were detected within minor neurites, axonal processes, and growth cones in the form of spatially distinct granules that colocalized with translational components. Ultrastructural analysis revealed polyribosomes within growth cones that colocalized with cytoskeletal filaments. The transport of β-actin mRNA into developing neurites may be a sequence-specific mechanism to synthesize cytoskeletal proteins directly within processes and growth cones and would provide an additional means to deliver cytoskeletal proteins over long distances.


title: On fault resilience of OpenStack

abstract: Cloud-management stacks have become an increasingly important element in cloud computing, serving as the resource manager of cloud platforms. While the functionality of this emerging layer has been constantly expanding, its fault resilience remains under-studied. This paper presents a systematic study of the fault resilience of OpenStack---a popular open source cloud-management stack. We have built a prototype fault-injection framework targeting service communications during the processing of external requests, both among OpenStack services and between OpenStack and external services, and have thus far uncovered 23 bugs in two versions of OpenStack. Our findings shed light on defects in the design and implementation of state-of-the-art cloud-management stacks from a fault-resilience perspective.


title: Female Stereotypes in Print Advertising: A Retrospective Analysis

abstract: Abstract The depiction of females in advertising has received considerable academic attention, fuelled by the feminist movement and the evolution of women's roles in the society. Early studies revealed the prevalence of traditional portrayals such as housewives, women dependent on a man's protection and depictions of sexual objectification. Even though a decrease in female stereotyping would be reasonable considering the contemporary structure of society that prescribes multiple gender roles, this does not seem to be the case: The literature is replete with studies documenting that female stereotyping is alive and well, even if it manifests itself with different types and patterns than it did some decades ago. This study strives to adopt a holistic outlook to the phenomenon of female stereotypes in print advertisements, tracing its origins, analyzing the interplay of stereotypes and advertising, undertaking an exhaustive perusal of the particular stream of literature, addressing methodological issues and proposing directions for further research.


title: A Benchmark Dataset and Evaluation for Non-Lambertian and Uncalibrated Photometric Stereo

abstract: Classic photometric stereo is often extended to deal with real-world materials and work with unknown lighting conditions for practicability. To quantitatively evaluate non-Lambertian and uncalibrated photometric stereo, a photometric stereo image dataset containing objects of various shapes with complex reflectance properties and high-quality ground truth normals is still missing. In this paper, we introduce the ‘ DiLiGenT ’ dataset with calibrated Di rectional Li ghtings, objects of Gen eral reflectance with different shininess, and ‘ground T ruth’ normals from high-precision laser scanning. We use our dataset to quantitatively evaluate state-of-the-art photometric stereo methods for general materials and unknown lighting conditions, selected from a newly proposed photometric stereo taxonomy emphasizing non-Lambertian and uncalibrated methods. The dataset and evaluation results are made publicly available, and we hope it can serve as a benchmark platform that inspires future research.


Chapter 1 Feature Engineering for Twitter-based Applications


title: Positive Feedback Keeps Duration of Mitosis Temporally Insulated from Upstream Cell-Cycle Events

abstract: Cell division is characterized by a sequence of events by which a cell gives rise to two daughter cells. Quantitative measurements of cell-cycle dynamics in single cells showed that despite variability in G1-, S-, and G2 phases, duration of mitosis is short and remarkably constant. Surprisingly, there is no correlation between cell-cycle length and mitotic duration, suggesting that mitosis is temporally insulated from variability in earlier cell-cycle phases. By combining live cell imaging and computational modeling, we showed that positive feedback is the molecular mechanism underlying the temporal insulation of mitosis. Perturbing positive feedback gave rise to a sluggish, variable entry and progression through mitosis and uncoupled duration of mitosis from variability in cell cycle length. We show that positive feedback is important to keep mitosis short, constant, and temporally insulated and anticipate it might be a commonly used regulatory strategy to create modularity in other biological systems.


title: Bias and Reciprocity in Online Reviews: Evidence From Field Experiments on Airbnb

abstract: Reviews and other evaluations are used by consumers to decide what goods to buy and by firms to choose whom to trade with, hire, or promote. However, because potential reviewers are not compensated for submitting reviews and may have reasons to omit relevant information in their reviews, reviews may be biased. We use the setting of Airbnb to study the determinants of reviewing behavior, the extent to which reviews are biased, and whether changes in the design of reputation systems can reduce that bias. We find that reviews on Airbnb are generally informative and 97% of guests privately report having positive experiences. Using two field experiments intended to reduce bias, we show that non-reviewers tend to have worse experiences than reviewers and that strategic reviewing behavior occurred on the site, although the aggregate effect of the strategic behavior was relatively small. We use a quantitative exercise to show that the mechanisms for bias that we document decrease the rate of reviews with negative text and a non-recommendation by just .86 percentage points. Lastly, we discuss how online marketplaces can design more informative review systems.


title: Security testing for chatbots

abstract: Services like chatbots that provide information to customers in real-time are of increasing importance for the online market. Chatbots offer an intuitive interface to answer user requests in an interactive manner. The inquiries are of wide-range and include information about specific goods and services but also financial issues and personal advices. The notable advantages of these programs are the simplicity of use and speed of the search process. In some cases, chatbots have even surpassed classical web, mobile applications, and social networks. Chatbots might have access to huge amount of data or personal information. Therefore, they might be a valuable target for hackers, and known web application vulnerabilities might be a security issue for chatbots as well. In this paper, we discuss the challenges of security testing for chatbots. We provide an overview about an automated testing approach adapted to chatbots, and first experimental results.


title: Quantified Markov Logic Networks

abstract: Markov Logic Networks (MLNs) are well-suited for expressing statistics such as “with high probability a smoker knows another smoker” but not for expressing statements such as “there is a smoker who knows most other smokers”, which is necessary for modeling, e.g. influencers in social networks. To overcome this shortcoming, we study quantified MLNs which generalize MLNs by introducing statistical universal quantifiers, allowing to express also the latter type of statistics in a principled way. Our main technical contribution is to show that the standard reasoning tasks in quantified MLNs, maximum a posteriori and marginal inference, can be reduced to their respective MLN counterparts in polynomial time.


title: Coevolution of Role-Based Cooperation in Multiagent Systems

abstract: In tasks such as pursuit and evasion, multiple agents need to coordinate their behavior to achieve a common goal. An interesting question is, how can such behavior be best evolved? A powerful approach is to control the agents with neural networks, coevolve them in separate subpopulations, and test them together in the common task. In this paper, such a method, called multiagent enforced subpopulations (multiagent ESP), is proposed and demonstrated in a prey-capture task. First, the approach is shown to be more efficient than evolving a single central controller for all agents. Second, cooperation is found to be most efficient through stigmergy, i.e., through role-based responses to the environment, rather than communication between the agents. Together these results suggest that role-based cooperation is an effective strategy in certain multiagent tasks.


title: GFRAL is the receptor for GDF15 and is required for the anti-obesity effects of the ligand

abstract: Growth differentiation factor 15 (GDF15; also known as MIC-1) is a divergent member of the TGF-β superfamily and is associated with body-weight regulation in humans and rodents. However, the cognate receptor of GDF15 is unknown. Here we show that GDF15 binds specifically to GDNF family receptor α-like (GFRAL) with high affinity, and that GFRAL requires association with the coreceptor RET to elicit intracellular signaling in response to GDF15 stimulation. We also found that GDF15-mediated reductions in food intake and body weight of mice with obesity were abolished in GFRAL-knockout mice. We further found that GFRAL expression was limited to hindbrain neurons and not present in peripheral tissues, which suggests that GDF15-GFRAL-mediated regulation of food intake is by a central mechanism. Lastly, given that GDF15 did not increase energy expenditure in treated mice with obesity, the anti-obesity actions of the cytokine are likely driven primarily by a reduction in food intake.


Advances in the mapping of flow networks from digital elevation data


Churn Prediction: Does Technology Matter?


title: Salient object detection via fast R-CNN and low-level cues

abstract: Recent advances in salient object detection have exploited the deep Convolutional Neural Network (CNN) to represent high-level semantic, however, due to the presence of convolutional and pooling layers, it is difficult for CNN to generate saliency map with sharp boundaries. In this paper, we propose multi-scale mask-based Fast R-CNN framework which generate saliency score of each region. Since the regions are segmented using edge-preserved methods, the results are naturally with sharp boundaries. To consider context information, we also propose low-level contrast and backgroundness prior which are complementary with high-level semantic. Finally, an edge-based propagation method which takes advantages of edge information is proposed to refine the saliency map. Experiments on three benchmark datasets demonstrate that the proposed method outperforms previous methods and achieves state-of-the-art performance.


title: Mastering 3D Printing

abstract: Mastering 3D Printing shows you how to get the most out of your printer, including how to design models, choose materials, work with different printers, and integrate 3D printing with traditional prototyping to make techniques like sand casting more efficient. You've printed key chains. You've printed simple toys. Now you're ready to innovate with your 3D printer to start a business or teach and inspire others. Joan Horvath has been an educator, engineer, author, and startup 3D printing company team member. She shows you all of the technical details you need to know to go beyond simple model printing to make your 3D printer work for you as a prototyping device, a teaching tool, or a business machine. What youll learn The history of 3D printers and the differences between them Why open source is important in 3D printing How to create models and use G-code with your printer How to make a challenging print work well How to choose and print with different materials How to integrate 3D printing into traditional prototyping processes Who this book is for This book is for new 3D printer owners, makers of all kinds, entrepreneurs, technology educators, and anyone curious about what you can do with a 3D printer.


title: Reliable detection of LSB steganography in color and grayscale images

abstract: A large number of commercial steganographic programs use the Least Significant Bit embedding (LSB) as the method of choice for message hiding in 24-bit, 8-bit color images, and grayscale images. It is commonly believed that changes to the LSBs of colors cannot be detected due to noise that is always present in digital images. In this paper, we describe a new very accurate and reliable method that can detect LSB embedding in randomly scattered pixels in both 24-bit color images and 8-bit grayscale or color images. It is based on our previous work on lossless data embedding [1]. By inspecting the differences in the number of regular and singular groups for the LSB and the "shifted LSB plane", we can reliably detect messages as short as 0.03bpp.


A Comparison of Cultural Connotations between Chinese and English Animal Words


title: CONSUMER SWITCHING BEHAVIOR FROM ONLINE BANKING TO MOBILE BANKING

abstract: Through investigating factors that influence consumers to make a transition from online to mobile banking, this empirical study shows that relative attitude and relative subjective norm positively motivated respondents to switch from Internet to mobile banking while relative perceived behavior control deterred respondents from transitioning. Empirical results also demonstrated that Internet banking is superior to mobile banking in terms of consumer relative compatibility, self-efficacy, resource facilitating conditions, and technology facilitating conditions. Meanwhile, mobile banking emerged as superior to Internet banking for other constructs. By adding a comparative concept into an extended decomposed theory of planned behavior (DTPB) model, this study may expand the applicable domain of current social psychology theories from the adoption of single products or services to the choice between competing products or services that achieve similar purposes and functions. To cite this document: Chian-Son Yu, "Consumer switching behavior from online banking to mobile banking", International Journal of Cyber Society and Education, Vol. 7, No. 1, pp. 1-28, 2014. Permanent link to this document: http://dx.doi.org/10.7903/ijcse.1108


title: Finding Top-k Min-Cost Connected Trees in Databases

abstract: It is widely realized that the integration of database and information retrieval techniques will provide users with a wide range of high quality services. In this paper, we study processing an l-keyword query, p1, p1, ..., pl, against a relational database which can be modeled as a weighted graph, G(V, E). Here V is a set of nodes (tuples) and E is a set of edges representing foreign key references between tuples. Let Vi ⊆ V be a set of nodes that contain the keyword pi. We study finding top-k minimum cost connected trees that contain at least one node in every subset Vi, and denote our problem as GST-k When k = 1, it is known as a minimum cost group Steiner tree problem which is NP-complete. We observe that the number of keywords, l, is small, and propose a novel parameterized solution, with l as a parameter, to find the optimal GST-1, in time complexity O(3ln + 2l ((l + logn)n + m)), where n and m are the numbers of nodes and edges in graph G. Our solution can handle graphs with a large number of nodes. Our GST-1 solution can be easily extended to support GST-k, which outperforms the existing GST-k solutions over both weighted undirected/directed graphs. We conducted extensive experimental studies, and report our finding.


title: Sculpting by numbers

abstract: We propose a method that allows an unskilled user to create an accurate physical replica of a digital 3D model. We use a projector/camera pair to scan a work in progress, and project multiple forms of guidance onto the object itself that indicate which areas need more material, which need less, and where any ridges, valleys or depth discontinuities are. The user adjusts the model using the guidance and iterates, making the shape of the physical object approach that of the target 3D model over time. We show how this approach can be used to create a duplicate of an existing object, by scanning the object and using that scan as the target shape. The user is free to make the reproduction at a different scale and out of different materials: we turn a toy car into cake. We extend the technique to support replicating a sequence of models to create stop-motion video. We demonstrate an end-to-end system in which real-world performance capture data is retargeted to claymation. Our approach allows users to easily and accurately create complex shapes, and naturally supports a large range of materials and model sizes.


title: dr0wned - Cyber-Physical Attack with Additive Manufacturing

abstract: Additive manufacturing (AM), or 3D printing, is an emerging manufacturing technology that is expected to have far-reaching socioeconomic, environmental, and geopolitical implications. As use of this technology increases, it will become more common to produce functional parts, including components for safety-critical systems. AM's dependence on computerization raises the concern that the manufactured part's quality can be compromised by sabotage. This paper demonstrates the validity of this concern, as we present the very first full chain of attack involving AM, beginning with a cyber attack aimed at compromising a benign AM component, continuing with malicious modification of a manufactured object's blueprint, leading to the sabotage of the manufactured functional part, and resulting in the physical destruction of a cyber-physical system that employs this part. The contributions of this paper are as follows. We propose a systematic approach to identify opportunities for an attack involving AM that enables an adversary to achieve his/her goals. Then we propose a methodology to assess the level of difficulty of an attack, thus enabling differentiation between possible attack chains. Finally, to demonstrate the experimental proof for the entire attack chain, we sabotage the 3D printed propeller of a quadcopter UAV, causing the quadcopter to literally fall from the sky.


title: An improved equivalent circuit model for analysis of frequency selective surface based on MDECM

abstract: In order to accurately predict the frequency selective surface (FSS) characteristics in wide band, an improved equivalent circuit model for slot FSS is proposed. Based on modal decomposition equivalent circuit method (MDECM), higher-order waveguide modes have been taken into account for approximating the FSS element aperture field. Taking rectangular slot FSS as example, the ratio of the transformers is derivate. Numerical results are given to demonstrate good performance of the proposed method in wide band. The mutual coupling between higher-order waveguide modes for rectangular slot FSS has been investigated.


title: Nanocellulose-Based Materials for Water Purification

abstract: Nanocellulose is a renewable material that combines a high surface area with high strength, chemical inertness, and versatile surface chemistry. In this review, we will briefly describe how nanocellulose is produced, and present—in particular, how nanocellulose and its surface modified versions affects the adsorption behavior of important water pollutants, e.g., heavy metal species, dyes, microbes, and organic molecules. The processing of nanocellulose-based membranes and filters for water purification will be described in detail, and the uptake capacity, selectivity, and removal efficiency will also be discussed. The processing and performance of nanocellulose-based membranes, which combine a high removal efficiency with anti-fouling properties, will be highlighted.


title: Eliminating the hypervisor attack surface for a more secure cloud

abstract: Cloud computing is quickly becoming the platform of choice for many web services. Virtualization is the key underlying technology enabling cloud providers to host services for a large number of customers. Unfortunately, virtualization software is large, complex, and has a considerable attack surface. As such, it is prone to bugs and vulnerabilities that a malicious virtual machine (VM) can exploit to attack or obstruct other VMs -- a major concern for organizations wishing to move to the cloud. In contrast to previous work on hardening or minimizing the virtualization software, we eliminate the hypervisor attack surface by enabling the guest VMs to run natively on the underlying hardware while maintaining the ability to run multiple VMs concurrently. Our NoHype system embodies four key ideas: (i) pre-allocation of processor cores and memory resources, (ii) use of virtualized I/O devices, (iii) minor modifications to the guest OS to perform all system discovery during bootup, and (iv) avoiding indirection by bringing the guest virtual machine in more direct contact with the underlying hardware. Hence, no hypervisor is needed to allocate resources dynamically, emulate I/O devices, support system discovery after bootup, or map interrupts and other identifiers. NoHype capitalizes on the unique use model in cloud computing, where customers specify resource requirements ahead of time and providers offer a suite of guest OS kernels. Our system supports multiple tenants and capabilities commonly found in hosted cloud infrastructures. Our prototype utilizes Xen 4.0 to prepare the environment for guest VMs, and a slightly modified version of Linux 2.6 for the guest OS. Our evaluation with both SPEC and Apache benchmarks shows a roughly 1% performance gain when running applications on NoHype compared to running them on top of Xen 4.0. Our security analysis shows that, while there are some minor limitations with cur- rent commodity hardware, NoHype is a significant advance in the security of cloud computing.


title: Evaluation of Interactive Machine Learning Systems

abstract: The evaluation of interactive machine learning systems remains a difficult task. These systems learn from and adapt to the human, but at the same time, the human receives feedback and adapts to the system. Getting a clear understanding of these subtle mechanisms of co-operation and co-adaptation is challenging. In this chapter, we report on our experience in designing and evaluating various interactive machine learning applications from different domains. We argue for coupling two types of validation: algorithm-centred analysis, to study the computational behaviour of the system; and human-centred evaluation, to observe the utility and effectiveness of the application for end-users. We use a visual analytics application for guided search, built using an interactive evolutionary approach, as an exemplar of our work. Our observation is that human-centred design and evaluation complement algorithmic analysis, and can play an important role in addressing the “black-box” effect of machine learning. Finally, we discuss research opportunities that require human-computer interaction methodologies, in order to support both the visible and hidden roles that humans play in interactive machine learning.


title: Journal of Intelligent & Robotic Systems manuscript No. (will be inserted by the editor) An Unmanned Aircraft System for Automatic Forest Fire Monitoring and Measurement

abstract: The paper presents an Unmanned Aircraft System (UAS), consisting of several aerial vehicles and a central station, for forest fire monitoring. Fire monitoring is defined as the computation in real-time of the evolution of the fire front shape and potentially other parameters related to the fire propagation, and is very important for forest fire fighting. The paper shows how an UAS can automatically obtain this information by means of on-board infrared or visual cameras. Moreover, it is shown how multiple aerial vehicles can collaborate in this application, allowing to cover bigger areas or to obtain complementary views of a fire. The paper presents results obtained in experiments considering actual controlled forest fires in quasi-operational conditions, involving a fleet of three vehicles, two autonomous helicopters and one blimp.


title: Sequential unscented Kalman filter for radar target tracking with range rate measurements

abstract: To solve the radar target tracking problem with range rate measurements, in which the errors between range and range rate measurements are correlated, a sequential unscented Kalman filter (SUKF) is proposed in this paper. A pseudo measurement is constructed by block-partitioned Cholesky factorization first, this can keep the range, bearing and elevation (or two direction cosine) measurements unchanged, while the errors between the original range and range rate measurement are decorrelated; then based on the UKF, the bearing, elevation (or two direction cosine) and the pseudo measurement are sequentially processed to enhance the filtering precision and the computational efficiency simultaneously. Validity and consistency of the new proposed algorithm is verified by Monte-Carlo simulation.


title: TweetLID : a benchmark for tweet language identification

abstract: Language identification, as the task of determining the language a given text is written in, has progressed substantially in recent decades. However, three main issues remain still unresolved: (1) distinction of similar languages, (2) detection of multilingualism in a single document, and (3) identifying the language of short texts. In this paper, we describe our work on the development of a benchmark to encourage further research in these three directions, set forth an evaluation framework suitable for the task, and make a dataset of annotated tweets publicly available for research purposes. We also describe the shared task we organized to validate and assess the evaluation framework and dataset with systems submitted by seven different participants, and analyze the performance of these systems. The evaluation of the results submitted by the participants of the shared task helped us shed some light on the shortcomings of state-of-the-art language identification systems, and gives insight into the extent to which the brevity, multilingualism, and language similarity found in texts exacerbate the performance of language identifiers. Our dataset with nearly 35,000 tweets and the evaluation framework provide researchers and practitioners with suitable resources to further study the aforementioned issues on language identification within a common setting that enables to compare results with one another.


title: Prediction of externalizing behavior problems from early to middle childhood: The role of parental socialization and emotion expression

abstract: Parental emotions and behaviors that contribute to continuity and change in preschool ::: children's externalizing problems were examined. Mothers and fathers were observed ::: interacting with their children, and child-rearing styles were reported. Teachers, mothers, and ::: children reported children's antisocial, oppositional behavior. Externalizing problems ::: showed strong continuity 2 and 4 years later. Proactive parenting (i.e., supportive presence, clear ::: instruction, and limit setting) predicted fewer behavior problems over time, after controlling for ::: initial problems; the converse was true for parental anger. In contrast, the hypothesized ::: ameliorative contribution of parents' positive emotion was not found. Parental ::: contributions were most influential for children whose initial problems were in the clinical range. ::: In particular, parental anger predicted continuation of problems over time. Paternal, as well as ::: maternal, influences were identified. Examination of parental emotions and inclusion of fathers is ::: important to research and intervention with young antisocial children.


title: Physical condition of red deer in a high density population

abstract: Physical condition in a living at high density (120 individuals/100 ha of forest area) red deer Cervus elaphus Linnaeus, 1758 population was studied by determining: dressed body weight, kidney fat index (KFI) and amount of visceral fat in 628 animals. Antler weights were also considered as condition index of stags.


title: Active Rectifier With Integrated System Control for Microwind Power Systems

abstract: This paper presents simple and effective control strategies for the active rectifier stage (ac/dc stage) of a grid-connected low-power system for microwind applications employing permanent magnet synchronous generator (PMSG). In particular, a novel algorithm for the estimation of the rotor angle of the PMSG, based on flux estimators, was implemented using an adaptive low-pass filter coupled with a feed-forward compensator. This enabled a very smooth start-up operation of the PMSG, obtained by preloading the values of the flux estimator and using a single-voltage transformer (VT) transducer. The solution for the power flow control between the active rectifier and the other(s) power converters connected to the common dc link was implemented without any digital communication between them, in order to obtain a solution suitable for modular architectures (e.g., to be used in conjunction with a grid-connected converter and/or an energy storage system). Simulation and experimental results confirmed the effectiveness of the proposed solutions. The experimental validation was conducted using a grid-connected converter as load for the proposed active rectifier.


title: Symmetric nonnegative matrix factorization for graph clustering

abstract: Nonnegative matrix factorization (NMF) provides a lower rank approximation of a nonnegative matrix, and has been successfully used as a clustering method. In this paper, we offer some conceptual understanding for the capabilities and shortcomings of NMF as a clustering method. Then, we propose Symmetric NMF (SymNMF) as a general framework for graph clustering, which inherits the advantages of NMF by enforcing nonnegativity on the clustering assignment matrix. Unlike NMF, however, SymNMF is based on a similarity measure between data points, and factorizes a symmetric matrix containing pairwise similarity values (not necessarily nonnegative). We compare SymNMF with the widely-used spectral clustering methods, and give an intuitive explanation of why SymNMF captures the cluster structure embedded in the graph representation more naturally. In addition, we develop a Newton-like algorithm that exploits second-order information efficiently, so as to show the feasibility of SymNMF as a practical framework for graph clustering. Our experiments on artificial graph data, text data, and image data demonstrate the substantially enhanced clustering quality of SymNMF over spectral clustering and NMF. Therefore, SymNMF is able to achieve better clustering results on both linear and nonlinear manifolds, and serves as a potential basis for many extensions


title: Additive cubic spline regression with Dirichlet process mixture errors

abstract: The goal of this article is to develop a flexible Bayesian analysis of regression models for continuous and categorical outcomes. In the models we study, covariate (or regression) effects are modeled additively by cubic splines, and the error distribution (that of the latent outcomes in the case of categorical data) is modeled as a Dirichlet process mixture. We employ a relatively unexplored but attractive basis in which the spline coefficients are the unknown function ordinates at the knots. We exploit this feature to develop a proper prior distribution on the coefficients that involves the first and second differences of the ordinates, quantities about which one may have prior knowledge. We also discuss the problem of comparing models with different numbers of knots or different error distributions through marginal likelihoods and Bayes factors which are computed within the framework ofÂ Chib (1995) as extended to DPM models by Basu and Chib (2003). The techniques are illustrated with simulated and real data.


title: Towards State-of-the-art English-Vietnamese Neural Machine Translation

abstract: Machine translation is one of the most challenging topics in natural language processing. The common approaches to machine translation base on either statistical or rule-based methods. Rule-based translation analyzes sentence structures, requires extensive lexicons with morphological, syntactic, semantic information, and large sets of manually created rules. Statistics-based translation faces the challenge of collecting bilingual text corpora, which is particularly difficult for low resource language pairs as English-Vietnamese. This research aims at building state-of-the-art English-Vietnamese machine translation. Our contribution includes: (1) an enormous effort in collecting training dataset, (2) adaptation of current neural machine for English-Vietnamese translation, (3) an experimental result suggested the unnecessary of Vietnamese word segmentation as a common pre-processing step. Our model achieves a highest BLEU score in comparison with other researches.


title: Application heartbeats: a generic interface for specifying program performance and goals in autonomous computing environments

abstract: The rise of multicore computing has greatly increased system complexity and created an additional burden for software developers. This burden is especially troublesome when it comes to optimizing software on modern computing systems. Autonomic or adaptive computing has been proposed as one method to help application programmers handle this complexity. In an autonomic computing environment, system services monitor applications and automatically adapt their behavior to increase the performance of the applications they support. Unfortunately, applications often run as performance black-boxes and adaptive services must infer application performance from low-level information or rely on system-specific ad hoc methods. This paper proposes a standard framework, Application Heartbeats, which applications can use to communicate both their current and target performance and which autonomic services can use to query these values. The Application Heartbeats framework is designed around the well-known idea of a heartbeat. At important points in the program, the application registers a heartbeat. In addition, the interface allows applications to express their performance in terms of a desired heart rate and/or a desired latency between specially tagged heartbeats. Thus, the interface provides a standard method for an application to directly communicate its performance and goals while allowing autonomic services access to this information. Thus, Heartbeat-enabled applications are no longer performance black-boxes. This paper presents the Applications Heartbeats interface, characterizes two reference implementations (one suitable for clusters and one for multicore), and illustrates the use of Heartbeats with several examples of systems adapting behavior based on feedback from heartbeats.


title: Designs on dignity: perceptions of technology among the homeless

abstract: Technology, it is argued, has the potential to improve everyone's life: from the workplace, to entertainment, to easing chores around the home. But what of people who have neither job nor home? We undertook a qualitative study of the homeless population in a metropolitan U.S. city to better understand what it means to be homeless and how technology--from cell phones to bus passes--affects their daily lives. The themes we identify provide an array of opportunities for technological interventions that can empower the homeless population. Our investigation also reveals the need to reexamine some of the assumptions made in HCI about the relationship people have with technology. We suggest a broader awareness of the social context of technology use as a critical component when considering design innovation for the homeless.


title: Spatial disease clusters: Detection and inference

abstract: We present a new method of detection and inference for spatial clusters of a disease. To avoid ad hoc procedures to test for clustering, we have a clearly defined alternative hypothesis and our test statistic is based on the likelihood ratio. The proposed test can detect clusters of any size, located anywhere in the study region. It is not restricted to clusters that conform to predefined administrative or political borders. The test can be used for spatially aggregated data as well as when exact geographic co-ordinates are known for each individual. We illustrate the method on a data set describing the occurrence of leukaemia in Upstate New York.


title: Virtual machine time travel using continuous data protection and checkpointing

abstract: Virtual machine (VM) time travel enables reverting a virtual machine's state, both transient and persistent, to past points in time. This capability can be used to improve virtual machine availability, to enable forensics on past VM states, and to recover from operator errors. We present an approach to virtual machine time travel which combines Continuous Data Protection (CDP) storage support with live-migration-based virtual machine checkpointing. In particular, we present a novel approach for CDP which enables efficient reverts of the storage state to past points in time and makes it possible to undo a revert, and this is achieved using a simple branched-temporal data structure. We also present a design and implementation of a simple live-migration-based checkpointing mechanism in Xen.


title: FPGA based system for real-time structure from motion computation

abstract: In this paper a system for real-time structure from motion computation is presented. The module is dedicated for a video based Advanced Driver Assistance System, where information about the surrounding environment and scene depth is often required to facilitate advanced applications. A heterogeneous System on a Chip solution, based on a FPGA device and an ARM processor is presented. The described hardware-software co-design is able to perform all steps required for structure from motion computation: feature extraction for every video frame, feature matching between two consecutive frames, finding the fundamental matrix, recovering the projection matrix and points triangulation. The design space exploration for the hardware part of the system is presented, which clearly points out the tradeoffs between resource usage, maximum clock and results accuracy. The system can process in real-time the images of 720 × 576 pixel resolution @ 50 fps. While the triangulation module allows for even high definition images processing and is able to compute results for up to 388 million points per second.


title: Human genetics of plasma dopamine β-hydroxylase activity: applications to research in psychiatry and neurology

abstract: Rationale ::: Norepinephrine (NE) is a key neurotransmitter in the central and peripheral nervous systems. Dopamine β-hydroxylase (DβH) catalyzes the synthesis of NE from dopamine (DA) and occurs in the plasma as a stable heritable trait. Studies of this trait have been useful in psychiatric and neurological research.


title: Lip Tracking Towards an Automatic Lip Reading Approach

abstract: Current era is to make the interaction between humans and their artificial partners (Computers) and make communication easier and more reliable. One of the actual tasks is the use of vocal interaction. Speech recognition may be improved by visual information of human face. In literature, the lip shape and its movement are referred to as lip reading. Lip reading computing plays a vital role in automatic speech recognition and is an important step towards accurate and robust speech recognition. In this paper we proposed method to detect the lips, shape and movement and use towards automatic lip tracking in detail by using Active Appearance Model (AAM). Purposed method shows the how accurate lip detection and tracking which is useful for speech recognition. Appearance-based methods consider the raw image for lip extraction. AAM uses for detection of speaker’s lips features and feature points from faces automatically by appearance parameters of a speaker’s lips. In this paper, we will describe the Lips shape, movement and appearance descriptions for lip detection and detailed information about the automatic lip tracking system. Individual characteristics and efficiency of both approaches towards the Lip reading is also discuss. The efficiency of purposed method will be evaluated individually by characteristics and their performances are then compared. The recognition process of common visual features (i.e. selection of lip, shape and extraction of movement) for an improved lip reading.


title: Semi-supervised Learning with Weakly-Related Unlabeled Data: Towards Better Text Categorization

abstract: The cluster assumption is exploited by most semi-supervised learning (SSL) methods. However, if the unlabeled data is merely weakly related to the target classes, it becomes questionable whether driving the decision boundary to the low density regions of the unlabeled data will help the classification. In such case, the cluster assumption may not be valid; and consequently how to leverage this type of unlabeled data to enhance the classification accuracy becomes a challenge. We introduce "Semi-supervised Learning with Weakly-Related Unlabeled Data" (SSLW), an inductive method that builds upon the maximum-margin approach, towards a better usage of weakly-related unlabeled information. Although the SSLW could improve a wide range of classification tasks, in this paper, we focus on text categorization with a small training pool. The key assumption behind this work is that, even with different topics, the word usage patterns across different corpora tends to be consistent. To this end, SSLW estimates the optimal word-correlation matrix that is consistent with both the co-occurrence information derived from the weakly-related unlabeled documents and the labeled documents. For empirical evaluation, we present a direct comparison with a number of state-of-the-art methods for inductive semi-supervised learning and text categorization. We show that SSLW results in a significant improvement in categorization accuracy, equipped with a small training set and an unlabeled resource that is weakly related to the test domain.


abstract: This paper describes our proposal for Quality of Service (QoS) for Financial Modeling and Prediction as a Service (FMPaaS), since a majority of papers does not focus on SaaS level. We focus on two factors for delivering successful QoS, which are performance and accuracy for FMPaaS. The design process, theories and models behind the FMPaaS service have been explained. To support our FMPaaS service, two APIs have been developed to improve on performance and accuracy. Two major experiments have been illustrated and results show that each API processing can be completed in 2.12 seconds and 100,000 simulations can be completed in an acceptable period of time. Accuracy tests have been performed while using Facebook as an example. Three points of comparisons between actual and predicted prices have been undertaken. Results support accuracy since results are between 93.72 % and 99.63 % for Facebook. Three case studies have been used and results can support the accuracy and validity of the high level of accuracy offered by FMPaaS.


title: A visual recipe book for persons with language impairments

abstract: Cooking is a daily activity for many people. However, traditional text recipes are often prohibitively difficult to follow for people with language disorders, such as aphasia. We have developed a multi-modal application that leverages the retained ability of aphasic individuals to recognize image-based representations of objects, providing a presentation format that can be more easily followed than a traditional text recipe. Through a systematic approach to developing a visual language for cooking, and the subsequent case study evaluation of a prototype developed according to this language, we show that a combination of visual instructions and navigational structure can help individuals with relatively large language deficits to cook more independently.


title: An overview of gradient descent optimization algorithms

abstract: Gradient descent optimization algorithms, while increasingly popular, are often used as black-box optimizers, as practical explanations of their strengths and weaknesses are hard to come by. This article aims to provide the reader with intuitions with regard to the behaviour of different algorithms that will allow her to put them to use. In the course of this overview, we look at different variants of gradient descent, summarize challenges, introduce the most common optimization algorithms, review architectures in a parallel and distributed setting, and investigate additional strategies for optimizing gradient descent.


title: The wavelet transform-domain LMS adaptive filter with partial subband-coefficient updating

abstract: In this paper, we propose and analyze the wavelet transform-domain LMS (WTDLMS) algorithm where only a subset of the adaptive filter coefficients are updated at each iteration. The use of wavelets or subband filterbanks can lead to several novel schemes whose combinations can lead to other sub-schemes as well. In the proposed scheme, the coefficients can be selected altogether as a block and not just individually as done earlier in the literature. This algorithm is then tested in the context of system identification and equalization


title: Blockout: Dynamic Model Selection for Hierarchical Deep Networks

abstract: Most deep architectures for image classification–even those that are trained to classify a large number of diverse categories–learn shared image representations with a single model. Intuitively, however, categories that are more similar should share more information than those that are very different. While hierarchical deep networks address this problem by learning separate features for subsets of related categories, current implementations require simplified models using fixed architectures specified via heuristic clustering methods. Instead, we propose Blockout, a method for regularization and model selection that simultaneously learns both the model architecture and parameters. A generalization of Dropout, our approach gives a novel parametrization of hierarchical architectures that allows for structure learning via back-propagation. To demonstrate its utility, we evaluate Blockout on the CIFAR and Image Net datasets, demonstrating improved classification accuracy, better regularization performance, faster training, and the clear emergence of hierarchical network structures.


title: A scoping study of electric and magnetic field energy harvesting for wireless sensor networks in power system applications

abstract: This paper explores the existing energy harvesting technologies, their stage of maturity and their feasibility for powering sensor nodes. It contains a study of the energy requirements of the sensor nodes that are a part of the commercial domain. Further, it investigates methods and concepts for harvesting the energy from electric and magnetic fields present near utility assets through laboratory experimentation. The flux concentrator based approach that scavenges the magnetic field was considered to be the most promising solution providing nearly 250mW of power sufficient to power a sensor node.


title: Imaging of Neurovascular Compression Syndromes: Trigeminal Neuralgia, Hemifacial Spasm, Vestibular Paroxysmia, and Glossopharyngeal Neuralgia

abstract: Neurovascular compression syndromes are usually caused by arteries that directly contact the cisternal portion of a cranial nerve. Not all cases of neurovascular contact are clinically symptomatic. ...


title: PRF-ambiguity resolving by wavelength diversity

abstract: For high-precision synthetic aperture radar (SAR) processing, the determination of the Doppler centroid is indispensable. The Doppler frequency estimated from azimuth spectra, however, suffers from the fact that the data are sampled with the pulse repetition frequency (PRF) and an ambiguity about the correct PRF band remains. A novel algorithm to resolve this ambiguity is proposed. It uses the fact that the Doppler centroid depends linearly on the transmitted radar frequency for a given antenna squint angle. This dependence is not subject to PRF ambiguities. It can be measured by Fourier-transforming the SAR data in the range direction and estimating the Doppler centroid at each range frequency. The achievable accuracy is derived theoretically and verified with Seasat data of different scene content. The algorithm works best with low contrast scenes, where the conventional look correlation technique fails. It needs no iterative processing of the SAR data and causes only low computational load. >


title: 3-D Object Recognition Using 2-D Views

abstract: We consider the problem of recognizing 3-D objects from 2-D images using geometric models and assuming different viewing angles and positions. Our goal is to recognize and localize instances of specific objects (i.e., model-based) in a scene. This is in contrast to category-based object recognition methods where the goal is to search for instances of objects that belong to a certain visual category (e.g., faces or cars). The key contribution of our work is improving 3-D object recognition by integrating algebraic functions of views (AFoVs), a powerful framework for predicting the geometric appearance of an object due to viewpoint changes, with indexing and learning. During training, we compute the space of views that groups of object features can produce under the assumption of 3-D linear transformations, by combining a small number of reference views that contain the object features using AFoVs. Unrealistic views (e.g., due to the assumption of 3-D linear transformations) are eliminated by imposing a pair of rigidity constraints based on knowledge of the transformation between the reference views of the object. To represent the space of views that an object can produce compactly while allowing efficient hypothesis generation during recognition, we propose combining indexing with learning in two stages. In the first stage, we sample the space of views of an object sparsely and represent information about the samples using indexing. In the second stage, we build probabilistic models of shape appearance by sampling the space of views of the object densely and learning the manifold formed by the samples. Learning employs the expectation-maximization (EM) algorithm and takes place in a ldquouniversal,rdquo lower-dimensional, space computed through random projection (RP). During recognition, we extract groups of point features from the scene and we use indexing to retrieve the most feasible model groups that might have produced them (i.e., hypothesis generation). The likelihood of each hypothesis is then computed using the probabilistic models of shape appearance. Only hypotheses ranked high enough are considered for further verification with the most likely hypotheses verified first. The proposed approach has been evaluated using both artificial and real data, illustrating promising performance. We also present preliminary results illustrating extentions of the AFoVs framework to predict the intensity appearance of an object. In this context, we have built a hybrid recognition framework that exploits geometric knowledge to hypothesize the location of an object in the scene and both geometrical and intensity information to verify the hypotheses.


title: Real-Time Three-Dimensional Cell Segmentation in Large-Scale Microscopy Data of Developing Embryos.

abstract: We present the Real-time Accurate Cell-shape Extractor (RACE), a high-throughput image analysis framework for automated three-dimensional cell segmentation in large-scale images. RACE is 55-330 times faster and 2-5 times more accurate than state-of-the-art methods. We demonstrate the generality of RACE by extracting cell-shape information from entire Drosophila, zebrafish, and mouse embryos imaged with confocal and light-sheet microscopes. Using RACE, we automatically reconstructed cellular-resolution tissue anisotropy maps across developing Drosophila embryos and quantified differences in cell-shape dynamics in wild-type and mutant embryos. We furthermore integrated RACE with our framework for automated cell lineaging and performed joint segmentation and cell tracking in entire Drosophila embryos. RACE processed these terabyte-sized datasets on a single computer within 1.4 days. RACE is easy to use, as it requires adjustment of only three parameters, takes full advantage of state-of-the-art multi-core processors and graphics cards, and is available as open-source software for Windows, Linux, and Mac OS.


title: Cloud-based DDoS Attacks and Defenses

abstract: Safety and reliability are important in the cloud computing environment. This is especially true today as distributed denial-of-service (DDoS) attacks constitute one of the largest threats faced by Internet users and cloud computing services. DDoS attacks target the resources of these services, lowering their ability to provide optimum usage of the network infrastructure. Due to the nature of cloud computing, the methodologies for preventing or stopping DDoS attacks are quite different compared to those used in traditional networks. In this paper, we investigate the effect of DDoS attacks on cloud resources and recommend practical defense mechanisms against different types of DDoS attacks in the cloud environment.


title: CMDragons: Dynamic passing and strategy on a champion robot soccer team

abstract: After several years of developing multiple RoboCup small-size robot soccer teams, our CMDragons robot team achieved a highly successful level of performance, winning both the 2006 and 2007 competitions without losing a single game. Our small-size team consists of five executing wheeled robots with centralized, off-board perception and decision making. The decision making framework consists of a set of layered components, consisting of perception, evaluation and strategy, robot tactics and skills, and real-time navigation. In this paper, we present the strategy, action selection, and execution aspects of our architecture, with a focus on passing as an example of effective coordinated teamwork. The design enabled our robot team to score using multiple methods, from direct shooting up to 3D passes deflected in midair, resulting in a rich set of actions that were difficult for adversaries to counter. We provide several performance quantified claims supported by testing in our laboratory and in competition settings.


title: Analyzing NIH Funding Patterns over Time with Statistical Text Analysis

abstract: In the past few years various government funding organizations such as the U.S. National Institutes of Health and the U.S.\ National Science Foundation have provided access to large publicly-available online databases documenting the grants that they have funded over the past few decades. These databases provide an excellent opportunity for the application of statistical text analysis techniques to infer useful quantitative information about how funding patterns have changed over time. In this paper we analyze data from the National Cancer Institute (part of National Institutes of Health) and show how text classification techniques provide a useful starting point for analyzing how funding for cancer research has evolved over the past 20 years in the United States.


title: Change impact analysis for Natural Language requirements: An NLP approach

abstract: Requirements are subject to frequent changes as a way to ensure that they reflect the current best understanding of a system, and to respond to factors such as new and evolving needs. Changing one requirement in a requirements specification may warrant further changes to the specification, so that the overall correctness and consistency of the specification can be maintained. A manual analysis of how a change to one requirement impacts other requirements is time-consuming and presents a challenge for large requirements specifications. We propose an approach based on Natural Language Processing (NLP) for analyzing the impact of change in Natural Language (NL) requirements. Our focus on NL requirements is motivated by the prevalent use of these requirements, particularly in industry. Our approach automatically detects and takes into account the phrasal structure of requirements statements. We argue about the importance of capturing the conditions under which change should propagate to enable more accurate change impact analysis. We propose a quantitative measure for calculating how likely a requirements statement is to be impacted by a change under given conditions. We conduct an evaluation of our approach by applying it to 14 change scenarios from two industrial case studies.


title: Register allocation and spilling via graph coloring

abstract: In a previous paper we reported the successful use of graph coloring techniques for doing global register allocation in an experimental PL/I optimizing compiler. When the compiler cannot color the register conflict graph with a number of colors equal to the number of available machine registers, it must add code to spill and reload registers to and from storage. Previously the compiler produced spill code whose quality sometimes left much to be desired, and the ad hoc techniques used took considerable amounts of compile time. We have now discovered how to extend the graph coloring approach so that it naturally solves the spilling problem. Spill decisions are now made on the basis of the register conflict graph and cost estimates of the value of keeping the result of a computation in a register rather than in storage. This new approach produces better object code and takes much less compile time.


title: i-Stylist: Finding the Right Dress Through Your Social Networks

abstract: Searching the Web has become an everyday task for most people. However, the presence of too much information can cause information overload. For example, when shopping online, a user can easily be overwhelmed by too many choices. To this end, we propose a personalized clothing recommendation system, namely i-Stylist, through the analysis of personal images in social networks. To access the available personal images of a user, the i-Stylist system extracts a number of characteristics from each clothing item such as CNN feature vectors and metadata such as color, material and pattern of the fabric. Then, these clothing items are organized as a fully connected graph to later infer the personalized probability distribution of how the user will like each clothing item in a shopping website. The user is able to modify the graph structure, e.g. adding and deleting vertices by giving feedback about the retrieved clothing items. The i-Stylist system is compared against two other baselines and demonstrated to have better performance.


Prayas at EmoInt 2017: An Ensemble of Deep Neural Architectures for Emotion Intensity Prediction in Tweets


title: A survey on wearable biosensor systems for health monitoring

abstract: Wearable biosensor systems for health monitoring are an emerging trend and are expected to enable proactive personal health management and better treatment of various medical conditions. These systems, comprising various types of small physiological sensors, transmission modules and processing capabilities, promise to change the future of health care, by providing low-cost wearable unobtrusive solutions for continuous all-day and any-place health, mental and activity status monitoring. This paper presents a comprehensive survey on the research and development done so far on wearable biosensor systems for health-monitoring, by comparing a variety of current system implementations and approaches and identifying their technological shortcomings. A set of significant features, that best describe the functionality and the characteristics of wearable biosensor systems, has been selected to derive a thorough study. The aim of this survey is not to criticize, but to serve as a reference for current achievements and their maturity level and to provide direction for future research improvements.


title: Visual Tracking by Reinforced Decision Making

abstract: One of the major challenges of model-free visual tracking problem has been the difficulty originating from the unpredictable and drastic changes in the appearance of objects we target to track. Existing methods tackle this problem by updating the appearance model on-line in order to adapt to the changes in the appearance. Despite the success of these methods however, inaccurate and erroneous updates of the appearance model result in a tracker drift. In this paper, we introduce a novel visual tracking algorithm based on a template selection strategy constructed by deep reinforcement learning methods. The tracking algorithm utilizes this strategy to choose the best template for tracking a given frame. The template selection strategy is self-learned by utilizing a simple policy gradient method on numerous training episodes randomly generated from a tracking benchmark dataset. Our proposed reinforcement learning framework is generally applicable to other confidence map based tracking algorithms. The experiment shows that our tracking algorithm effectively decides the best template for visual tracking.


title: Domokos G.: Static equilibria of rigid bodies: dice, pebbles and the Poincaré-Hopf Theorem

abstract: By appealing to the Poincare-Hopf Theorem on topological invariants, we introduce a global classification scheme for homogeneous, convex bodies based on the number and type of their equilibria. We show that beyond trivially empty classes all other classes are non-empty in the case of three-dimensional bodies; in particular we prove the existence of a body with just one stable and one unstable equilibrium. In the case of two-dimensional bodies the situation is radically different: the class with one stable and one unstable equilibrium is empty ( Domokos, Papadopoulos, Ruina, J. Elasticity 36 [1994], 59-66). We also show that the latter result is equivalent to the classical Four-Vertex Theorem in differential geometry. We illustrate the introduced equivalence classes by various types of dice and statistical experimental results concerning pebbles on the seacoast.


title: Interaction Effects between Openness and Fluid Intelligence Predicting Scholastic Performance

abstract: Figural reasoning as an indicator of fluid intelligence and the domains of the Five Factor Model were explored as predictors of scholastic performance. A total of 836 Chinese secondary school students (406 girls) from grades 7 to 11 participated. Figural reasoning, as measured by Raven’s Standard Progressive Matrices, predicted performance in Math, Chinese, and English, and also for a composite score. Among the personality domains, Openness had a positive effect on performance for all subjects after controlling for all the other variables. For Conscientiousness, the effects were smaller and only significant for Math. Neuroticism had a negative effect on Math grades. The effects of Extraversion on all grades were very small and not significant. Most importantly, hierarchical latent regression analyses indicated that all interaction effects between Openness and figural reasoning were significant, revealing a compensatory interaction. Our results further suggest that scholastic performance basically relies on the same traits through the secondary school years. However, importance is given to interaction effects between ability and personality. Implications along with limitations and suggestions for future research are discussed.