title: An online localization method for a subway train utilizing the barometer on a smartphone

abstract: Knowing the location of a train is necessary for the development of useful services for train passengers. However, popular localization methods such as GPS and Wi-Fi are not accurate, especially on a subway. This paper proposes an online algorithm for localization on a subway using only a barometer. We estimate the motion state from the change of elevation, then estimate the last station stopped at using the similarity of a series of elevations recorded when the train stopped to the actual elevations of the stations. We evaluated the proposed method using data from the subway in Tokyo. We also developed a mobile application to demonstrate the proposed method.


Dimensionality Reduction as a Defense against Evasion Attacks on Machine Learning Classifiers.


title: Aspect Extraction with Automated Prior Knowledge Learning

abstract: Aspect extraction is an important task in sentiment analysis. Topic modeling is a popular method for the task. However, unsupervised topic models often generate incoherent aspects. To address the issue, several knowledge-based models have been proposed to incorporate prior knowledge provided by the user to guide modeling. In this paper, we take a major step forward and show that in the big data era, without any user input, it is possible to learn prior knowledge automatically from a large amount of review data available on the Web. Such knowledge can then be used by a topic model to discover more coherent aspects. There are two key challenges: (1) learning quality knowledge from reviews of diverse domains, and (2) making the model fault-tolerant to handle possibly wrong knowledge. A novel approach is proposed to solve these problems. Experimental results using reviews from 36 domains show that the proposed approach achieves significant improvements over state-of-the-art baselines.


title: EnergyViz: an interactive system for visualization of energy systems

abstract: Energy systems are under pressure to transform to address concerns about climate change. The modeling and visualization of energy systems can play an important role in communicating the costs, benefits and trade-offs of energy systems choices. We introduce EnergyViz, a visualization system that provides an interface for exploring time-varying, multi-attribute and spatial properties of a particular energy system. EnergyViz integrates several visualization techniques to facilitate exploration of a particular energy system. These techniques include flow diagram representation to show energy flow, 3D interaction with flow diagrams for expanding viewable data attributes such as emissions and an interactive map integrated with flow diagrams for simultaneous exploration of spatial and abstract information. We also perform level-of-detail exploration on flow diagrams and use smooth animation across the visualizations to represent time-varying data. Finally, we include evaluation results of EnergyViz collected from expert and inexperienced participants.


title: A new approach for EEG feature extraction in P300-based lie detection

abstract: P300-based Guilty Knowledge Test (GKT) has been suggested as an alternative approach for conventional polygraphy. The purpose of this study was to extend a previously introduced pattern recognition method for the ERP assessment in this application. This extension was done by the further extending the feature set and also the employing a method for the selection of optimal features. For the evaluation of the method, several subjects went through the designed GKT paradigm and their respective brain signals were recorded. Next, a P300 detection approach based on some features and a statistical classifier was implemented. The optimal feature set was selected using a genetic algorithm from a primary feature set including some morphological, frequency and wavelet features and was used for the classification of the data. The rates of correct detection in guilty and innocent subjects were 86%, which was better than other previously used methods.


Force-Directed Drawing Algorithms


title: A Learning-Rate Schedule for Stochastic Gradient Methods to Matrix Factorization

abstract: Stochastic gradient methods are effective to solve matrix factorization problems. However, it is well known that the performance of stochastic gradient method highly depends on the learning rate schedule used; a good schedule can significantly boost the training process. In this paper, motivated from past works on convex optimization which assign a learning rate for each variable, we propose a new schedule for matrix factorization. The experiments demonstrate that the proposed schedule leads to faster convergence than existing ones. Our schedule uses the same parameter on all data sets included in our experiments; that is, the time spent on learning rate selection can be significantly reduced. By applying this schedule to a state-of-the-art matrix factorization package, the resulting implementation outperforms available parallel matrix factorization packages.


title: Orthographic Feature Transform for Monocular 3D Object Detection

abstract: 3D object detection from monocular images has proven to be an enormously challenging task, with the performance of leading systems not yet achieving even 10\% of that of LiDAR-based counterparts. One explanation for this performance gap is that existing systems are entirely at the mercy of the perspective image-based representation, in which the appearance and scale of objects varies drastically with depth and meaningful distances are difficult to infer. In this work we argue that the ability to reason about the world in 3D is an essential element of the 3D object detection task. To this end, we introduce the orthographic feature transform, which enables us to escape the image domain by mapping image-based features into an orthographic 3D space. This allows us to reason holistically about the spatial configuration of the scene in a domain where scale is consistent and distances between objects are meaningful. We apply this transformation as part of an end-to-end deep learning architecture and achieve state-of-the-art performance on the KITTI 3D object benchmark.\footnote{We will release full source code and pretrained models upon acceptance of this manuscript for publication.


State of the Art on Stylized Fabrication


The Social and the Neural Network: How to Make Natural Language Processing about People again


title: Sclera recognition using dense-SIFT

abstract: In this paper we propose a biometric sclera recognition and validation system. Here the sclera segmentation is performed bya time-adaptive active contour-based region growing technique. The sclera vessels are not prominent so image enhancement is required and hence a bank of 2D decomposition. A Haar wavelet multi-resolution filter is used to enhance the vessels pattern for better accuracy. For feature extraction, Dense Scale Invariant Feature Transform (D-SIFT) is used. D-SIFT patch descriptors of each training image are used to form bag of features by using k-means clustering and a spatial pyramid model, which is used to produce the training model. Support Vector Machines (SVMs) are used for classification. The UBIRIS version 1 dataset is used here for experimentation. Anencouraging Equal Error Rate (EER) of 0.66% is attained in the experiments presented.


title: A General MIMO Framework for NOMA Downlink and Uplink Transmission Based on Signal Alignment

abstract: The application of multiple-input multiple-output (MIMO) techniques to non-orthogonal multiple access (NOMA) systems is important to enhance the performance gains of NOMA. In this paper, a novel MIMO-NOMA framework for downlink and uplink transmission is proposed by applying the concept of signal alignment. By using stochastic geometry, closed-form analytical results are developed to facilitate the performance evaluation of the proposed framework for randomly deployed users and interferers. The impact of different power allocation strategies, such as fixed power allocation and cognitive radio inspired power allocation, on the performance of MIMO-NOMA is also investigated. Computer simulation results are provided to demonstrate the performance of the proposed framework and the accuracy of the developed analytical results.


title: Toward meta-cognitive tutoring: A model of help seeking with a Cognitive Tutor

abstract: The research reported in this paper focuses on the hypothesis that an intelligent tutoring system that provides guidance with respect to students' meta-cognitive abilities can help them to become better learners. Our strategy is to extend a Cognitive Tutor (Anderson, Corbett, Koedinger, & Pelletier, 1995) so that it not only helps students acquire domain-specific skills, but also develop better general help-seeking strategies. In developing the Help Tutor, we used the same Cognitive Tutor technology at the meta-cognitive level that has been proven to be very effective at the cognitive level. A key challenge is to develop a model of how students should use a Cognitive Tutor's help facilities. We created a preliminary model, implemented by 57 production rules that capture both effective and ineffective help-seeking behavior. As a first test of the model's efficacy, we used it off-line to evaluate students' help-seeking behavior in an existing data set of student-tutor interactions. We then refined the model based on the results of this analysis. Finally, we conducted a pilot study with the Help Tutor involving four students. During one session, we saw a statistically significant reduction in students' meta-cognitive error rate, as determined by the Help Tutor's model. These preliminary results inspire confidence as we gear up for a larger-scale controlled experiment to evaluate whether tutoring on help seeking has a positive effect on students' learning outcomes.


title: Multi-Criteria-Based Active Learning For Named Entity Recognition

abstract: In this paper, we propose a multi-criteria-based active learning approach and effectively apply it to named entity recognition. Active learning targets to minimize the human annotation efforts by selecting examples for labeling. To maximize the contribution of the selected examples, we consider the multiple criteria: informativeness, representativeness and diversity and propose measures to quantify them. More comprehensively, we incorporate all the criteria using two selection strategies, both of which result in less labeling cost than single-criterion-based method. The results of the named entity recognition in both MUC-6 and GENIA show that the labeling cost can be reduced by at least 80% without degrading the performance.


title: The Modern Day Baby Book: Enacting Good Mothering and Stewarding Privacy on Facebook

abstract: The practice of sharing family photographs is as old as the camera itself. Many mothers now share baby photos online, yet little is known about what kinds of baby photos they share and their motivations for doing so. Drawing on semi-structured interviews with 22 new mothers, we find that they share cute, funny, milestone, and family and friend photos but refrain from sharing crying and naked photos. While some mothers harbor concerns about controlling information, oversharing, and digital footprints, the benefits of receiving validation outweighs their concerns. Sharing baby photos on Facebook helps new mothers enact and receive validation of "good mothering." However, mothers are charged with the responsibility of stewarding their children's privacy and identities online. We introduce the concept of privacy stewardship to describe the responsibility parents take on when deciding what is appropriate to share about their children online and ensuring that family and friends respect and maintain the integrity of those rules. As a result, mothers must exchange benefits of sharing baby photos with risks of creating digital footprints for their child.


title: Dark-spot detection from SAR intensity imagery with spatial density thresholding for oil-spill monitoring

abstract: Abstract Dark-spot detection is a critical and fundamental step in marine oil-spill detection and monitoring. In this paper, a novel approach for automated dark-spot detection using synthetic aperture radar (SAR) intensity imagery is presented. The key to the approach is making use of a spatial density feature to differentiate between dark spots and the background. A detection window is passed through the entire SAR image. First, intensity threshold segmentation is applied to each window. Pixels with intensities below the threshold are regarded as potential dark-spot pixels while the others are potential background pixels. Second, the density of potential background pixels is estimated using kernel density estimation within each window. Pixels with densities below a certain threshold are the real dark-spot pixels. Third, an area threshold and a contrast threshold are used to eliminate any remaining false targets. In the last step, the individual detection results are mosaicked to produce the final result. The proposed approach was tested on 60 RADARSAT-1 ScanSAR intensity images which contain verified oil-spill anomalies. The same parameters were used in all tests. For the overall dataset, the average of commission error, omission error, and average difference were 7.0%, 6.1%, and 0.4 pixels, respectively. The average number of false alarms was 0.5 per unit image and the average computational time for a detection window was 1.2 s using a PC-based MATLAB platform. Our experimental results demonstrate that the proposed approach is fast, robust and effective.


title: Using Semantic Unification to Generate Regular Expressions from Natural Language

abstract: We consider the problem of translating natural language text queries into regular expressions which represent their meaning. The mismatch in the level of abstraction between the natural language representation and the regular expression representation make this a novel and challenging problem. However, a given regular expression can be written in many semantically equivalent forms, and we exploit this flexibility to facilitate translation by finding a form which more directly corresponds to the natural language. We evaluate our technique on a set of natural language queries and their associated regular expressions which we gathered from Amazon Mechanical Turk. Our model substantially outperforms a stateof-the-art semantic parsing baseline, yielding a 29% absolute improvement in accuracy. 1


title: Competitive Sharing of Spectrum: Reservation Obfuscation and Verification Strategies

abstract: Sharing of radio spectrum between different types of wireless systems (e.g., different service providers) is the foundation for making more efficient usage of spectrum. Cognitive radio technologies have spurred the design of spectrum servers that coordinate the sharing of spectrum between different wireless systems. These servers receive information regarding the needs of each system, and then provide instructions back to each system regarding the spectrum bands they may use. This sharing of information is complicated by the fact that these systems are often in competition with each other: each system desires to use as much of the spectrum as possible to support its users, and each system could learn and harm the bands of the other system. Three problems arise in such a spectrum-sharing problem: (1) how to maintain reliable performance for each system-shared resource (licensed spectrum); (2) whether to believe the resource requests announced by each agent; and (3) if they do not believe, how much effort should be devoted to inspecting spectrum so as to prevent possible malicious activity. Since this problem can arise for a variety of wireless systems, we present an abstract formulation in which the agents or spectrum server introduces obfuscation in the resource assignment to maintain reliability. We derive a closed form expression for the expected damage that can arise from possible malicious activity, and using this formula we find a tradeoff between the amount of extra decoys that must be used in order to support higher communication fidelity against potential interference, and the cost of maintaining this reliability. Then, we examine a scenario where a smart adversary may also use obfuscation itself, and formulate the scenario as a signaling game, which can be solved by applying a classical iterative forward-induction algorithm. For an important particular case, the game is solved in a closed form, which gives conditions for deciding whether an agent can be trusted, or whether its request should be inspected and how intensely it should be inspected.


title: Investor Overconfidence And Trading Volume: The Case Of An Emergent Market

abstract: It has been a challenge for financial economists to explain some stylized facts observed in securities markets, among them, high levels of trading volume. The most prominent explanation of excess volume is overconfidence. High market returns make investors overconfident and as a consequence, these investors trade more subsequently. The aim of our paper is to study the impact of the phenomenon of overconfidence on the trading volume and its role in the formation of the excess volume on the Tunisian stock market. Based on the work of Statman, Thorley and Vorkink (2006) and by using VAR models and impulse response functions, we find little evidence of the overconfidence hypothesis when we use volume (shares traded) as proxy of trading volume.


title: Improving biometric identification through quality-based face and fingerprint biometric fusion

abstract: Multi-modal biometric fusion is more accurate and reliable compared to recognition using a single biometric modality. However, most existing fusion approaches neglect the influence of the qualities of the biometric samples in information fusion. Our goal is to advance the state-of-the-art in biometric fusion technology by providing a more universal and more accurate solution for personal identification and verification with predictive quality metrics. In this work, we developed score-level multi-modal fusion algorithms based on predictive quality metrics and employed them for the task of face and fingerprint biometric fusion. The causal relationships in the context of the fusion scenario are modeled by Bayesian Networks. The recognition/verification decision is then made through probabilistic inference. Our experiments demonstrated that the proposed score-level fusion algorithms significantly improve the verification performance over the methods based on the raw match score of a single modality (face or fingerprint). Furthermore, the fusion framework with both face and fingerprint image qualities achieves the best verification performance and outperforms all other baseline fusion algorithms tested including other straightforward quality-based fusion methods.


title: Prevention of Cardioembolic Stroke

abstract: Cardiac causes of ischemic stroke lead to severe neurological deficits from large intracranial artery occlusion compared to small vessel ischemic stroke. The most common cause of cardioembolic stroke is atrial fibrillation (AF), which has an increasing incidence with age. AF stroke trials demonstrate that anti-coagulation is superior to anti-platelet therapy in terms of ischemic stroke prevention. Recently, warfarin was compared with dabigatran, an oral, direct thrombin inhibitor, and was found to be at least equally effective in reducing ischemic stroke with less intracranial bleeding risk. Future research is investigating other direct thrombin inhibitors as potential alternatives to warfarin, which has a narrow therapeutic index, requires frequent blood monitoring, has multiple drug interactions, and a higher rate of intracranial bleeding. Other causes of cardioembolic stroke include myocardial infarction, left ventricular thrombus, reduced ejection fraction, valvular abnormalities, and endocarditis. Patent foramen ovale is a common finding on echocardiograms in patients with and without stroke (up to 20% of the population), and it is a controversial source of cryptogenic stroke. The best way to prevent cardioembolic stroke remains early detection and treatment of AF, and treating the underlying stroke mechanism. Cardiac magnetic resonance imaging is an emerging technology and reveals some sources of cardiac embolism missed by echocardiography, and might provide an additional diagnostic tool in investigating cardioembolic stroke.


title: Whole-genome sequencing and comprehensive molecular profiling identify new driver mutations in gastric cancer

abstract: Gastric cancer is a heterogeneous disease with diverse molecular and histological subtypes. We performed whole-genome sequencing in 100 tumor-normal pairs, along with DNA copy number, gene expression and methylation profiling, for integrative genomic analysis. We found subtype-specific genetic and epigenetic perturbations and unique mutational signatures. We identified previously known (TP53, ARID1A and CDH1) and new (MUC6, CTNNA2, GLI3, RNF43 and others) significantly mutated driver genes. Specifically, we found RHOA mutations in 14.3% of diffuse-type tumors but not in intestinal-type tumors (P < 0.001). The mutations clustered in recurrent hotspots affecting functional domains and caused defective RHOA signaling, promoting escape from anoikis in organoid cultures. The top perturbed pathways in gastric cancer included adherens junction and focal adhesion, in which RHOA and other mutated genes we identified participate as key players. These findings illustrate a multidimensional and comprehensive genomic landscape that highlights the molecular complexity of gastric cancer and provides a road map to facilitate genome-guided personalized therapy.


title: Lampung - a new handwritten character benchmark: database, labeling and recognition

abstract: This research paper deals with our effort of creation and recognition of isolated Lampung characters, a script originated from Indonesia. The aim is to describe this new script with all its peculiarities, propose a labeling scheme to manage a large isolated character dataset and finally a recognition scheme based on water reservoir concept. The Lampung script originally descending from Brahmi script is used in Lampung Province and it is close to extinction if no such initiative as ours will direct the focus to this cultural heritage. The collected dataset contains isolated characters coming from fairy tales transcriptions and were annotated with a semi-automatic labeling method using a limited human effort. Our attention is focused not only on the database collection but on recognition as well. For this purpose a water reservoir based feature set is proposed exploiting the different cavities and the subsequent measures of the character shapes. The experimental results (94.27%) prove the efficiency of the method considering a brand new script and feature set.


title: Parameterization of Faceted Surfaces for Meshing using Angle-Based Flattening

abstract: We propose a new method to compute planar triangulations of faceted surfaces for surface parameterization. In contrast to previous approaches that define the flattening problem as a mapping of the three-dimensional node locations to the plane, our method defines the flattening problem as a constrained optimization problem in terms of angles (only). After applying a scaling that derives from the ‘curvature’ at a node, we minimize the relative deformation of the angles in the plane with respect to their counterparts in the three-dimensional surface. This approach makes the method more stable and robust than previous approaches, which used node locations in their formulations. The new method can handle any manifold surface for which a connected, valid, two-dimensional parameterization exists, including surfaces with large curvature gradients. It does not require the boundary of the flat two-dimensional domain to be prede-fined or convex. We use only the necessary and sufficient constraints for a valid two-dimensional triangulation. As a result, the existence of a theoretical solution to the minimization procedure is guaranteed.


title: From D-H to inverse kinematics: A fast numerical solution for general robotic manipulators using parallel processing

abstract: We propose a robust and fast solution for the inverse kinematic problem of general serial manipulators - i.e. any number and any combination of revolute and prismatic joints. The algorithm only requires the Denavit-Hartenberg (D-H) representation of the robot as input and no training or robot-dependent optimization function is needed. In order to handle singularities and to overcome the possibility of multiple paths in redundant robots, our approach relies on the computation of multiple (parallel) numerical estimations of the inverse Jacobian while it selects the current best path to the desire configuration of the end-effector. But unlike other iterative methods, our method achieves sub-millimeter accuracy in 20.48ms in average. The algorithm was implemented in C/C++ using 16 POSIX threads, and it can be easily expanded to use more threads and/or many-core GPUs. We demonstrate the high accuracy and the real-time performance of our method by testing it with five different robots, at both non-singular and singular configurations, including a 7-DoF redundant robot.


title: Optimizing colormaps with consideration for color vision deficiency to enable accurate interpretation of scientific data

abstract: Color vision deficiency (CVD) affects more than 4% of the population and leads to a different visual perception of colors. Though this has been known for decades, colormaps with many colors across the visual spectra are often used to represent data, leading to the potential for misinterpretation or difficulty with interpretation by someone with this deficiency. Until the creation of the module presented here, there were no colormaps mathematically optimized for CVD using modern color appearance models. While there have been some attempts to make aesthetically pleasing or subjectively tolerable colormaps for those with CVD, our goal was to make optimized colormaps for the most accurate perception of scientific data by as many viewers as possible. We developed a Python module, cmaputil, to create CVD-optimized colormaps, which imports colormaps and modifies them to be perceptually uniform in CVD-safe colorspace while linearizing and maximizing the brightness range. The module is made available to the science community to enable others to easily create their own CVD-optimized colormaps. Here, we present an example CVD-optimized colormap created with this module that is optimized for viewing by those without a CVD as well as those with red-green colorblindness. This colormap, cividis, enables nearly-identical visual-data interpretation to both groups, is perceptually uniform in hue and brightness, and increases in brightness linearly.


title: Test Case Prioritization Using Test Similarities

abstract: A classical heuristic in software testing is to reward diversity, which implies that a higher priority must be assigned to test cases that differ the most from those already prioritized. This approach is commonly known as similarity-based test prioritization (SBTP) and can be realized using a variety of techniques. The objective of our study is to investigate whether SBTP is more effective at finding defects than random permutation, as well as determine which SBTP implementations lead to better results. To achieve our objective, we implemented five different techniques from the literature and conducted an experiment using the defects4j dataset, which contains 395 real faults from six real-world open-source Java programs. Findings indicate that running the most dissimilar test cases early in the process is largely more effective than random permutation (Vargha–Delaney A [VDA]: 0.76–0.99 observed using normalized compression distance). No technique was found to be superior with respect to the effectiveness. Locality-sensitive hashing was, to a small extent, less effective than other SBTP techniques (VDA: 0.38 observed in comparison to normalized compression distance), but its speed largely outperformed the other techniques (i.e., it was approximately 5–111 times faster). Our results bring to mind the well-known adage, “don’t put all your eggs in one basket”. To effectively consume a limited testing budget, one should spread it evenly across different parts of the system by running the most dissimilar test cases early in the testing process.


title: MesoNet: a Compact Facial Video Forgery Detection Network

abstract: This paper presents a method to automatically and efficiently detect face tampering in videos, and particularly focuses on two recent techniques used to generate hyper-realistic forged videos: Deepfake and Face2Face. Traditional image forensics techniques are usually not well suited to videos due to the compression that strongly degrades the data. Thus, this paper follows a deep learning approach and presents two networks, both with a low number of layers to focus on the mesoscopic properties of images. We evaluate those fast networks on both an existing dataset and a dataset we have constituted from online videos. The tests demonstrate a very successful detection rate with more than 98% for Deepfake and 95% for Face2Face.


title: Visual Transfer between Atari Games using Competitive Reinforcement Learning

abstract: This paper explores the use of deep reinforcement learning agents to transfer knowledge from one environment to another. More specifically, the method takes advantage of asynchronous advantage actor critic (A3C) architecture to generalize a target game using an agent trained on a source game in Atari. Instead of fine-tuning a pre-trained model for the target game, we propose a learning approach to update the model using multiple agents trained in parallel with different representations of the target game. Visual mapping between video sequences of transfer pairs is used to derive new representations of the target game; training on these visual representations of the target game improves model updates in terms of performance, data efficiency and stability. In order to demonstrate the functionality of the architecture, Atari games Pong-v0 and Breakout-v0 are being used from the OpenAI gym environment; as the source and target environment.


title: Thickness and Permittivity Measurement in Multi-Layered Dielectric Structures Using Complementary Split-Ring Resonators

abstract: This paper presents a non-invasive microwave method based on a square-shaped complementary split-ring resonator (CSRR) to measure the thickness and permittivity of multilayer dielectric structures. The CSRR sensor is etched on the ground plane of a microstrip line. The change of resonance frequency depends on the thickness and permittivity of the multilayer dielectric sample below the ground plane. For resolution analysis, the resonance frequency shifts caused by a variation of permittivity (Δe = 0.01) and thickness (Δd=0.01 mm) in the detection layer were compared across various design dimensions. Sensor size optimization improved the resolution in permittivity and thickness measurement by 66% and 37%, respectively. Subsequently, the permittivity and thickness resolution was improved by 28% and 16%, respectively, by optimizing the separation of the etched CSRRs. The analysis results show that a CSRR sensor can be designed with excellent resolution in core layer permittivity changes and thickness resolution in multilayered dielectric structures.


title: Human–Robot Interaction Control of Rehabilitation Robots With Series Elastic Actuators

abstract: Rehabilitation robots, by necessity, have direct physical interaction with humans. Physical interaction affects the controlled variables and may even cause system instability. Thus, human-robot interaction control design is critical in rehabilitation robotics research. This paper presents an interaction control strategy for a gait rehabilitation robot. The robot is driven by a novel compact series elastic actuator, which provides intrinsic compliance and backdrivablility for safe human-robot interaction. The control design is based on the actuator model with consideration of interaction dynamics. It consists mainly of human interaction compensation, friction compensation, and is enhanced with a disturbance observer. Such a control scheme enables the robot to achieve low output impedance when operating in human-in-charge mode and achieve accurate force tracking when operating in force control mode. Due to the direct physical interaction with humans, the controller design must also meet the stability requirement. A theoretical proof is provided to show the guaranteed stability of the closed-loop system under the proposed controller. The proposed design is verified with an ankle robot in walking experiments. The results can be readily extended to other rehabilitation and assistive robots driven with compliant actuators without much difficulty.


title: An attentive neural architecture for joint segmentation and parsing and its application to real estate ads

abstract: In this paper we develop a relatively simple and effective neural joint model that performs both segmentation and dependency parsing. While the model arose in the context of a particular application, we believe the general idea could be translated to other settings where both (1) entities have to be identified from text, and (2) dependency relationships between them. The application we focus on here is the recently introduced problem of extracting the structured description, which we name property tree, of a real estate property based on the textual advertisement: convert an ad to a tree-structure indicating the buildings, floors, rooms, etc. and how one is part of another. Previous work on the problem focused on a hand-crafted feature-based pipeline approach comprising two different modules, solving the two subtasks of the structured prediction problem: (1) identify important entities of a property (e.g., rooms) from classifieds and (2) structure them into a tree format. In this work, we propose a new joint model that is able to tackle the two tasks simultaneously and construct the property tree by (i) avoiding the error propagation and (ii) exploiting the interactions between the subtasks. For this purpose, we perform an extensive comparative study of the pipeline methods and the new proposed joint model, reporting an improvement of over three percentage points in the overall edge $F_1$ score of the property tree. Also, we have considered several attention methods, to encourage our model to focus on salient tokens during the construction of the property tree. Thus we experimentally demonstrate the usefulness of attentive neural architectures for the proposed joint model, showcasing a further improvement of two percentage points in edge $F_1$ score for our application.


title: A Multi-Object Rectified Attention Network for Scene Text Recognition

abstract: Irregular text is widely used. However, it is considerably difficult to recognize because of its various shapes and distorted patterns. In this paper, we thus propose a multi-object rectified attention network (MORAN) for general scene text recognition. The MORAN consists of a multi-object rectification network and an attention-based sequence recognition network. The multi-object rectification network is designed for rectifying images that contain irregular text. It decreases the difficulty of recognition and enables the attention-based sequence recognition network to more easily read irregular text. It is trained in a weak supervision way, thus requiring only images and corresponding text labels. The attention-based sequence recognition network focuses on target characters and sequentially outputs the predictions. Moreover, to improve the sensitivity of the attention-based sequence recognition network, a fractional pickup method is proposed for an attention-based decoder in the training phase. With the rectification mechanism, the MORAN can read both regular and irregular scene text. Extensive experiments on various benchmarks are conducted, which show that the MORAN achieves state-of-the-art performance. The source code is available.


title: Wafer Level Multi-chip Gang Bonding Using TCNCF

abstract: High throughput interconnection technology has been achieved using a multi-chip gang bonding process with an advanced chip on wafer (CoW) test vehicle (TV). The TV had 30 µm of fine-pitch copper pillar (CuP) and the bonding test was performed using non-conductive film (NCF). Therefore, all the steps including NCF lamination, wafer back-grinding and sawing, and thermo-compression bonding (TCB) were accomplished at the wafer level. Firstly, a layer of non-conductive film (NCF) was laminated on top of the wafer prior to back-grind and saw processes. Secondly, eight multi chips of 6x8 mm2 and 4x8 mm2 having 200 µm thickness were attached and aligned serially on the interposer wafer. For micro-bump interconnection, parallel bonding for the eight chips was applied for a few seconds after the serial chip attach process. Visual inspection and measurement confirmed the lateral fillet coverage was less than 110 µm without resin bleed-out and creeping. Destructive and non-destructive analyses were performed to examine solder joint formation & void inspection. Finally, reliability test was performed to confirm stability of the bonding process in a moisture resistance test (MRT) of L2Aa (60'C/60%RH, 120hrs) and L3 (30'C/60RH%, 192hrs) and temperature cycle (T/C) B 1000x. Electrical resistance was measured by probing for micro-bumps that are daisy-chained together. The resistance showed negligible change in upper and lower limit and no electrical failures or delamination occurred after reliability conditioning.


title: Towards secure mobile cloud computing: A survey

abstract: Mobile cloud computing is gaining popularity among mobile users. The ABI Research predicts that the number of mobile cloud computing subscribers is expected to grow from 42.8 million (1.1% of total mobile users) in 2008 to 998 million (19% of total mobile users) in 2014. Despite the hype achieved by mobile cloud computing, the growth of mobile cloud computing subscribers is still below expectations. According to the recent survey conducted by the International Data Corporation, most IT Executives and CEOs are not interested in adopting such services due to the risks associated with security and privacy. The security threats have become a hurdle in the rapid adaptability of the mobile cloud computing paradigm. Significant efforts have been devoted in research organizations and academia to build secure mobile cloud computing environments and infrastructures. In spite of the efforts, there are a number of loopholes and challenges that still exist in the security policies of mobile cloud computing. This literature review: (a) highlights the current state of the art work proposed to secure mobile cloud computing infrastructures, (b) identifies the potential problems, and (c) provides a taxonomy of the state of the art.


title: Automatic Repair of Buggy If Conditions and Missing Preconditions with SMT

abstract: We present Nopol, an approach for automatically repairing buggy if conditions and missing preconditions. As input, it takes a program and a test suite which contains passing test cases modeling the expected behavior of the program and at least one failing test case embodying the bug to be repaired. It consists of collecting data from multiple instrumented test suite executions, transforming this data into a Satisfiability Modulo Theory (SMT) problem, and translating the SMT result -- if there exists one -- into a source code patch. Nopol repairs object oriented code and allows the patches to contain nullness checks as well as specific method calls.


BiometricJammer: Method to Prevent Acquisition of Biometric Information by Surreptitious Photography on Fingerprints


title: Workload and Delay Analysis in Manufacturing Process Using Process Mining

abstract: Process analysis is one of the important topics in manufacturing industry. Recently process mining has been applied to analyze manufacturing processes. In this paper we investigate the characteristics of event logs in make-to-order production and propose a method to analyze manufacturing processes in make-to-order production such as construction, shipbuilding and aviation by utilizing and extending existing process mining techniques. Among three major analysis perspectives in process mining such as process discovery, performance analysis, and conformance checking, this paper focuses on the performance analysis including workload analysis and delay analysis. To validate the proposed method, a case study with real data is conducted.


title: Investigation and management of tall stature

abstract: Referral for an assessment of tall stature is much less common than for short stature. Although the commonest cause is an underlying familial tendency to tallness, there are important disorders that should be considered at the initial assessment. Distinguishing these conditions from normal variations of growth is the key objective when managing the child and family. In some children, further targeted investigations will be needed and in rare instances intervention to limit final height may be appropriate. This article discusses a structured approach to the assessment and management of a child with tall stature.


title: PATTERNS OF INFORMATION SOURCES USED BY GRADUATE STUDENTS: A CITATION ANALYSIS OF DOCTORAL DISSERTATIONS IN THE FIELD OF ISLAMIC STUDIES

abstract: This article depicts the results of a study carried out to ascertain the information pattern based on the sources ::: used by graduate students from the Islamic Studies Academy submitted at the University of Malaya, Kuala Lumpur. A total of 14377 citations consisting of 54 doctoral dissertations from the Year 2005 to 2009 were examined using the citation analysis. The highest citations per dissertation was 684, while the lowest being 105 citations. The result shows that the materials used by graduate students in this field vary and are multidisciplinary by nature. Books were cited more than other forms of sources contributing 65%, where journal articles contributed 20%.Conference proceedings contributed 11%, dissertations and thesis 3% and other categories consisted of web sites, interviews and legal documents contributing 9%. These findings corroborate with previous citations done in the Humanities discipline. Among the most popular cited journals are in-house journals namely Jurnal Syariah and Jurnal Usuluddin. In addition, graduate students used a substantial amount of Malaysian language sources at the rate of 60%, Arabic language scholarships contributed to 40% of the total citations. Approximately 30% of all sources cited are over 10 years of age. Hence, this study provides valuable insights to guide librarians in understanding the sources used and serves as an analytic tool for the development of source collection in the library services.


title: Layered Neural Networks with Gaussian Hidden Units as Universal Approximations

abstract: A neural network with a single layer of hidden units of gaussian type is proved to be a universal approximator for real-valued maps defined on convex, compact sets of Rn.


title: Mixed-Mode PWM for High-Performance Stepping Motors

abstract: This paper describes the realization of a high-performance drive based on stepping motors, with a specific reference to the optimal choice of pulsewidth modulation. An original modulation technique is proposed that allows, in conjunction with current feedback, to achieve high performances together with a reduced current ripple. A prototype digital signal processor-based drive was realized for a small-size stepping motor.


title: Mind outside Brain: A radically non-dualist foundation for distributed cognition

abstract: We approach the problem of the extended mind from a radically non-dualist perspective. The separation between mind and matter is an artefact of the outdated mechanistic worldview, which leaves no room for mental phenomena such as agency, intentionality, or feeling. We propose to replace it by an action ontology, which conceives mind and matter as aspects of the same network of processes. By adopting the intentional stance, we interpret the catalysts of elementary reactions as agents exhibiting desires, intentions, and sensations. Autopoietic networks of reactions constitute more complex superagents, which moreover exhibit memory, deliberation and sense-making. In the specific case of social networks, individual agents coordinate their actions via the propagation of challenges. The distributed cognition that emerges from this interaction cannot be situated in any individual brain. This non-dualist, holistic view extends and operationalizes process metaphysics and Eastern philosophies. It is supported by both mindfulness experiences and mathematical models of action, self-organization, and cognition.


title: Performance analysis of efficient pipeline architectures for underwater big data analytics

abstract: Underwater sensor networks (UWSNs) have emerged as an essential technology for various undersea applications. However, the use of acoustic links in UWSNs poses critical challenges in terms of overall delay, energy consumption and low bandwidth, which make the transmission of raw data in large sizes impractical. In our previous work, we employed various nodes (processing nodes, gateway nodes, and sensing nodes) that are used to construct different candidate architectures (i.e. single, pipeline, and hybrid of parallel/pipeline). We exploit the idea of in-network data processing in order to reduce the volume of data and extract only valuable information. In this paper, we analytically calculate and compare the performance of the various types of proposed architectures. The tradeoff between the cost of processing and the expected speedup is also analyzed. The results confirm our previous research hypothesis that processing the collected data locally and applying the idea of pipeline/parallel processing leads to significant improvement in the performance of UWSNs.


title: The 'Blackboard Pattern' for Metaheuristics

abstract: We describe the 'Blackboard' design pattern for metaheuristics which allows multiple agents to combine their expertise opportunistically to contribute towards a solution. Features of the Blackboard pattern may include heterogeneity of solution representations (e.g. both graph and permutation for the TSP) and asynchronous processing, the latter rendering the traditionally hard distinction between 'online' and 'offline' activity less significant.


title: Locality-Preserving Dimensionality Reduction and Classification for Hyperspectral Image Analysis

abstract: Hyperspectral imagery typically provides a wealth of information captured in a wide range of the electromagnetic spectrum for each pixel in the image; however, when used in statistical pattern-classification tasks, the resulting high-dimensional feature spaces often tend to result in ill-conditioned formulations. Popular dimensionality-reduction techniques such as principal component analysis, linear discriminant analysis, and their variants typically assume a Gaussian distribution. The quadratic maximum-likelihood classifier commonly employed for hyperspectral analysis also assumes single-Gaussian class-conditional distributions. Departing from this single-Gaussian assumption, a classification paradigm designed to exploit the rich statistical structure of the data is proposed. The proposed framework employs local Fisher's discriminant analysis to reduce the dimensionality of the data while preserving its multimodal structure, while a subsequent Gaussian mixture model or support vector machine provides effective classification of the reduced-dimension multimodal data. Experimental results on several different multiple-class hyperspectral-classification tasks demonstrate that the proposed approach significantly outperforms several traditional alternatives.


title: Microwave filter and antenna for wireless communication systems

abstract: This paper presents the investigation and characterization of integrated microwave filter and antenna to reduce the overall physical volume for RF/ microwave front-end subsystems. This study focuses on the integrating of rectangular Substrate Integrated Waveguide (SIW) filter with rectangular microstrip patch antenna to produce filtering and radiating element in a single device. To proof the concept, the integrated microwave filter and antenna at center frequency of 2 GHz is demonstrated and validated through simulation and analysis. This class of microwave filters and antenna integration would be useful in microwave systems where the reduction of size and weight as well as cost is very important such as in base stations and multiplexer in any communication systems.


title: Geographic Data Mining and Knowledge Discovery

abstract: Introduction Harvey J. Miller and Jiawei Han Spatiotemporal Data Mining Paradigms and Methodologies John F. Roddick and Brian G. Lees Fundamentals of Spatial Data Warehousing for Geographic Knowledge Discovery Yvan Bedard and Jiawei Han Analysis of Spatial Data with Map Cubes: Highway Traffic Data Chang-Tien Lu, Arnold P. Boedihardjo, and Shashi Shekhar NEW! Data Quality Issues and Geographic Knowledge Discovery Marc Gervais, Yvan Bedard, Marie-Andree Levesque, Eveline Bernier, and Rodolphe Devillers Spatial Classification and Prediction Models for Geospatial Data Mining Shashi Shekhar, Ranga Raju Vatsavai, and Sanjay Chawla An Overview of Clustering Methods in Geographic Data Analysis Jiawei Han, Jae-Gil Lee, and Micheline Kamber NEW! Computing Medoids in Large Spatial Datasets Kyriakos Mouratidis, Dimitris Papadias, Spiros Papadimitriou NEW! Looking for a Relationship? Try GWR A. Stewart Fotheringham, Martin Charlton, and Urska Demsar Leveraging the Power of Spatial Data Mining to Enhance the Applicability of GIS Technology Donato Malerba, Antonietta Lanza, and Annalisa Appice Visual Exploration and Explanation in Geography: Analysis with Light Mark Gahegan NEW! Multivariate Spatial Clustering and Geovisualization Diansheng Guo NEW! Toward Knowledge Discovery about Geographic Dynamics in Spatiotemporal Databases} May Yuan NEW! The Role of a Multitier Ontological Framework in Reasoning to Discover Meaningful Patterns of Sustainable Mobility Monica Wachowicz, Jose Macedo, Chiara Renso, and Arend Ligtenberg NEW! Periodic Pattern Discovery from Trajectories of Moving Objects Huiping Cao, Nikos Mamoulis, and David W. Cheung NEW! Decentralized Spatial Data Mining for Geosensor Networks Patrick Laube and Matt Duckham NEW! Beyond Exploratory Visualization of Space-Time Paths Menno-Jan Kraak and Otto Huisman


title: Car security using Internet of Things

abstract: The upcoming technology where machine to machine communication is possible by deploying the intelligent sensors on machine for smart interaction. Internet of things (IoT) gives a wide scope in many application domains where number of smart gadgets per person is increasing exponentially with time. The automobile sector is also one of the application domain where vehicle can be made intelligent by using IoT. This paper presents the interface of Arduino Uno R3 development board, sensor shield and smartphone. The proposed schematic is for car security which gives the access of car through internet and GPS, to the owner of car, in case car is stolen or damaged.


Phaseolus vulgaris and alpha-Amylase Inhibition


title: Steganographic generative adversarial networks

abstract: Steganography is collection of methods to hide secret information ("payload") within non-secret information ("container"). Its counterpart, Steganalysis, is the practice of determining if a message contains a hidden payload, and recovering it if possible. Presence of hidden payloads is typically detected by a binary classifier. In the present study, we propose a new model for generating image-like containers based on Deep Convolutional Generative Adversarial Networks (DCGAN). This approach allows to generate more setganalysis-secure message embedding using standard steganography algorithms. Experiment results demonstrate that the new model successfully deceives the steganography analyzer, and for this reason, can be used in steganographic applications.


title: A fast MPEG-7 dominant color extraction with new similarity measure for image retrieval

abstract: Dominant color descriptor (DCD) is one of the color descriptors proposed by MPEG-7 that has been extensively used for image retrieval. Among the color descriptors, DCD describes the salient color distributions in an image or a region of interest. DCD provides an effective, compact, and intuitive representation of colors presented in an image. In this paper, we will develop an efficient scheme for dominant color extraction. This approach significantly improves the efficiency of computation for dominant color extraction. In addition, we propose a modification for the MPEG-7 dissimilarity measure, which effectively improves the accuracy of perceptive similarity. Experimental results show that the proposed method achieves performance improvement not only in saving features extraction cost but also perceptually similar image retrieval.


title: Embedded Wafer Level Packaging for 77-GHz Automotive Radar Front-End With Through Silicon Via and its 3-D Integration

abstract: In this paper, a 77-GHz automotive radar sensor transceiver front-end module is packaged with a novel embedded wafer level packaging (EMWLP) technology. The bare transceiver die and the pre-fabricated through silicon via (TSV) chip are reconfigured to form a molded wafer through a compression molding process. The TSVs built on a high resistivity wafer serve as vertical interconnects, carrying radio-frequency (RF) signals up to 77 GHz. The RF path transitions are carefully designed to minimize the insertion loss in the frequency band of concern. The proposed EMWLP module also provides a platform to design integrated passive components. A substrate-integrated waveguide resonator is implemented with TSVs as the via fences, and it is later used to design a second-order 77-GHz high performance bandpass filter. Both the resonator and the bandpass filter are fabricated and measured, and the measurement results match with the simulation results very well.


title: 56-Gb/s direct modulation in InGaAlAs BH-DFB lasers at 55°C

abstract: Direct modulation at 56 Gb/s of 1.3-µm InGaAlAs-MQW DFB laser, incorporating a ridge-shaped buried heterostructure (RS-BH), operating at 55°C, is experimentally demonstrated for the first time.


title: Data-driven Soft Sensors in the process industry

abstract: In the last two decades Soft Sensors established themselves as a valuable alternative to the traditional means for the acquisition of critical process variables, process monitoring and other tasks which are related to process control. This paper discusses characteristics of the process industry data which are critical for the development of data-driven Soft Sensors. These characteristics are common to a large number of process industry fields, like the chemical industry, bioprocess industry, steel industry, etc. The focus of this work is put on the data-driven Soft Sensors because of their growing popularity, already demonstrated usefulness and huge, though yet not completely realised, potential. A comprehensive selection of case studies covering the three most important Soft Sensor application fields, a general introduction to the most popular Soft Sensor modelling techniques as well as a discussion of some open issues in the Soft Sensor development and maintenance and their possible solutions are the main contributions of this work.


title: A PWM LLC Type Resonant Converter Adapted to Wide Output Range in PEV Charging Applications

abstract: In conventional LLC-based plug-in electric vehicle (PEV) onboard chargers, the battery pack voltage varies in a wide range with the change of state of charge. This makes it difficult to optimally design the pulse frequency modulated LLC resonant converter. Besides, the voltage regulation of the LLC converter is highly dependent on the load conditions. In this paper, a modified pulse width modulated (PWM) LLC type resonant topology (PWM-LLC) is proposed and investigated in PEV charging applications. The switching frequency of the primary LLC network is constant and equal to the resonant frequency. The voltage regulation is achieved by modulating the duty cycle of the secondary side auxiliary mosfet . Compared with the conventional LLC topology, the proposed topology shrinks the magnetic component size and achieves a wide and fixed voltage gain range independent of load conditions. Meanwhile, zero-voltage-switching and zero-current-switching are realized among all MOSFETs and diodes, respectively. A 100-kHz, 1-kW converter prototype, generating 250–420 V output from the 390-V dc link, is designed and tested to verify the proof of concept. The prototype demonstrates 96.7% peak efficiency and robust performance over wide voltage and load ranges.


title: Adult attachment security and symptoms of depression: The mediating roles of dysfunctional attitudes and low self-esteem.

abstract: Three studies investigated the relation between adult attachment security and symptoms of depression. Study 1 examined the overall magnitude of the association between adult attachment and depression, and Studies 2 and 3 tested whether this relation was mediated by dysfunctional attitudes and low self-esteem. Results from the three studies were consistent with a mediation model. This model suggests that insecure adult attachment styles are associated with dysfunctional attitudes, which in turn predispose to lower levels of self-esteem. Such depletions in self-esteem are directly associated with increases in depressive symptoms over time. Insecure attachment appears to lead to depressive symptoms in adulthood through its impact on self-worth contingencies and self-esteem.


title: Exploratory Innovation, Exploitative Innovation, and Performance: Effects of Organizational Antecedents and Environmental Moderators

abstract: Research on exploration and exploitation is burgeoning, yet our understanding of the antecedents and consequences of both activities remains rather unclear. We advance the growing body of literature by focusing on the apparent differences of exploration and exploitation and examining implications for using formal (i.e. centralization and formalization) and informal (i.e. connectedness) coordination mechanisms. This study further examines how environmental aspects (i.e. dynamism and competitiveness) moderate the effectiveness of exploratory and exploitative innovation. Results indicate that centralization negatively affects exploratory innovation while formalization positively influences exploitative innovation. Interestingly, connectedness within units appears to be an important antecedent of both exploratory and exploitative innovation. Furthermore, our findings reveal that pursuing exploratory innovation is more effective in dynamic environments whereas pursuing exploitative innovation is more beneficial to a unit’s financial performance in more competitive environments. Through this richer explanation and empirical assessment, we contribute to a greater clarity and better understanding of how ambidextrous organizations coordinate the development of exploratory and exploitative innovation in organizational units and successfully respond to multiple environmental conditions.


title: Melody extraction and detection through LSTM-RNN with harmonic sum loss

abstract: This paper proposes a long short-term memory recurrent neural network (LSTM-RNN) for extracting melody and simultaneously detecting regions of melody from polyphonic audio using the proposed harmonic sum loss. The previous state-of-the-art algorithms have not been based on machine learning techniques and certainly not on deep architectures. The harmonics structure in melody is incorporated in the loss function to attain robustness against both octave mismatch and interference from background music. Experimental results show that the performance of the proposed method is better than or comparable to other state-of-the-art algorithms.


title: Ambulatory respiratory rate detection using ECG and a triaxial accelerometer

abstract: Continuous monitoring of respiratory rate in ambulatory conditions has widespread applications for screening of respiratory diseases and remote patient monitoring. Unfortunately, minimally obtrusive techniques often suffer from low accuracy. In this paper, we describe an algorithm with low computational complexity for combining multiple respiratory measurements to estimate breathing rate from an unobtrusive chest patch sensor. Respiratory rates derived from the respiratory sinus arrhythmia (RSA) and modulation of the QRS amplitude of electrocardiography (ECG) are combined with a respiratory rate derived from tri-axial accelerometer data. The three respiration rates are combined by a weighted average using weights based on quality metrics for each signal. The algorithm was evaluated on 15 elderly subjects who performed spontaneous and metronome breathing as well as a variety of activities of daily living (ADLs). When compared to a reference device, the mean absolute error was 1.02 breaths per minute (BrPM) during metronome breathing, 1.67 BrPM during spontaneous breathing, and 2.03 BrPM during ADLs.


title: Review HVEM/LIGHT/BTLA/CD160 cosignaling pathways

abstract: Immunosuppression is currently the treatment of choice to attenuate the chronic deterioration of tissue function as a result of the effector mechanisms of the immunological response in transplant rejection and autoimmune diseases. However, global immunosuppression greatly increases the risk of acquiring life-threatening infections and is associated with organ toxicity when used long-term. Thus, alternative approaches that inhibit only the unwanted immune responses and preserve general immunity are highly desirable. The receptor/ligand pairs involved in the cross-talk between DC and T cells have been the focus of intense and exciting research during the last decade. The HVEM/LIGHT/BTLA/CD160 costimulatory/coinhibitory pathway has emerged as a potential target for the development of immune therapeutic interventions. Herein, we will summarize and discuss how blockade of the costimulatory HVEM/LIGHT interaction or agonist signaling through the inhibitory BTLA and CD160 receptors could contribute to the control of deleterious immune responses.


Sentiment strength detection in short informal text


title: Touchscreen usability and input performance in vehicles under different road conditions: an evaluative study

abstract: With the proliferation of the touchscreen technology, interactive displays are becoming an integrated part of the modern vehicle environment. However, due to road and driving conditions, the user input on such displays can be perturbed resulting in erroneous selections. This paper describes an evaluative study of the usability and input performance of in-vehicle touchscreens. The analysis is based on data collected in instrumented cars driven under various road/driving conditions. We assess the frequency of failed selection attempts, distances by which users miss the intended on-screen target and the durations of undertaken free hand pointing gestures to accomplish the selection tasks. It is shown that the road/driving conditions can notably undermine the usability of an interactive display when the user input is perturbed, e.g. due to the experienced vibrations and lateral accelerations in the vehicle. The distance between the location of an erroneous on-screen selection and the intended endpoint on the display, is closely related to the level of present in-vehicle noise. The conducted study can advise graphical user interfaces design for the vehicle environment where the user free hand pointing gestures can be subject to varying levels of perturbations.


title: Computed Axial Lithography (CAL): Toward Single Step 3D Printing of Arbitrary Geometries

abstract: Most additive manufacturing processes today operate by printing voxels (3D pixels) serially point-by-point to build up a 3D part. In some more recently-developed techniques, for example optical printing methods such as projection stereolithography [Zheng et al. 2012], [Tumbleston et al. 2015], parts are printed layer-by-layer by curing full 2d (very thin in one dimension) layers of the 3d part in each print step. There does not yet exist a technique which is able to print arbitrarily-defined 3D geometries in a single print step. If such a technique existed, it could be used to expand the range of printable geometries in additive manufacturing and relax constraints on factors such as overhangs in topology optimization. It could also vastly increase print speed for 3D parts. In this work, we develop the principles for an approach for single exposure 3D printing of arbitrarily defined geometries. The approach, termed Computed Axial Lithgography (CAL), is based on tomographic reconstruction, with mathematical optimization to generate a set of projections to optically define an arbitrary dose distribution within a target volume. We demonstrate the potential ability of the technique to print 3D parts using a prototype CAL system based on sequential illumination from many angles. We also propose new hardware designs which will help us to realize true single-shot arbitrary-geometry 3D CAL.


title: Cooperative intelligent transport systems standards in europe

abstract: Information exchange among vehicles, and between vehicles and the roadside infrastructure is commonly regarded as a base technology to sustainably reduce road accidents and improve traffic efficiency. After more than a decade of research and development efforts, a technological basis has been established that applies WiFibased, wireless communication in the 5.9 GHz frequency band, ad hoc communication and dedicated message sets, as well as management and security procedures. In Europe, Release 1 of standards for cooperative systems has been completed, indicating deployment of a basic system starting in 2015. This article provides a comprehensive overview of standards and complementary industry specifications for cooperative systems in Europe, covering relevant aspects of access technologies, network and transport protocols, facilities, applications, security, and management.


Rapid or slow bladder decompression?


67 Vowel Epenthesis


title: Human vs. robotic tactile sensing: Detecting lumps in soft tissue

abstract: Humans can localize lumps in soft tissue using the distributed tactile feedback and processing afforded by the fingers and brain. This task becomes extremely difficult when the fingers are not in direct contact with the tissue, such as in laparoscopic or robot-assisted procedures. Tactile sensors have been proposed to characterize and detect lumps in robot-assisted palpation. In this work, we compare the performance of a capacitive tactile sensor with that of the human finger. We evaluate the response of the sensor as it pertains to robot-assisted palpation and compare the sensor performance to that of human subjects performing an equivalent task on the same set of artificial tissue models. Furthermore, we investigate the effects of various tissue parameters (lump size, lump depth, and surrounding tissue stiffness) on the performance of both the human finger and the tactile sensor. Using signal detection theory for determining tactile sensor lump detection thresholds, the tactile sensor outperforms the human finger in a palpation task.


SenticNet 4: A Semantic Resource for Sentiment Analysis Based on Conceptual Primitives


title: Wireless power transmission system for charging the inspection robot on power transmission lines

abstract: In order to prolong continuous working time of the mobile robot to inspect on transmission lines, a robotic power management system is introduced in this paper based on the wireless power transmission technology. Magnetic resonance coupled WPT (wireless power transmission) unit is applied and its performance is analyzed in form of electrical circuit mode method. By modeling the output power of WPT unit, the relationship between coupled coefficient, angular frequency and amount of output power is given, and furthermore the energy consumption of each unit in power management system is quantified. Experiment results verified the validity of the circuit model of WPT and showed that the system could work conveniently in wireless way to provide power for the inspection robot. Keywords-wireless power transfer, coupled coefficient, Q-factor technologies recently due to its capability to transfers energy across large air gap with high efficiency. Although the coupled resonance based WPT need much more attention to design and test the drive circuit as its working frequency is usually up to several mega Hz, it's effective working distance which is up to several meters (decides by the material and the size of the resonance coil in application) is still really attractive for medium distance wireless power transmission applications. In this paper, the fundamental characteristics of a special magnetic resonance system for transmission line inspection robot have been examined. Equivalent circuit is given in order to give numerical analysis of the resonance system. In order to test the output power of the WPT unit, current in load coil is calculated, and the relationship between coupled coefficient, angular frequency and output power is given. Power transmission ratio is also given in chart which shows that amount of power loss of WPT accounts for few part of the total loss of the power management system and the ratio is acceptable. Equivalent circuit are also verified by experiment which shows that the proposed method is suitable for charging the inspection robot and the coupled resonance wireless power transmission system could also be applied in many other fields to avoid the trouble or obstacle of using electric cord. A wireless power transmission technique based robotic power management system is introduced: Section2 shows the general function charts of the power management system, and especially the wireless power transmission unit of the system. Section 3 details the circuit model of WPT for calculating the output power of this unit; Section 4 introduces the experiments results and proves that the WPT based power management system can efficiently transmit power wirelessly and the design in this paper has the function of prolonging continuous working period of the inspection robot.


title: In-execution dynamic malware analysis and detection by mining information in process control blocks of Linux OS

abstract: Run-time behavior of processes - running on an end-host - is being actively used to dynamically detect malware. Most of these detection schemes build model of run-time behavior of a process on the basis of its data flow and/or sequence of system calls. These novel techniques have shown promising results but an efficient and effective technique must meet the following performance metrics: (1) high detection accuracy, (2) low false alarm rate, (3) small detection time, and (4) the technique should be resilient to run-time evasion attempts. To meet these challenges, a novel concept of genetic footprint is proposed, by mining the information in the kernel process control blocks (PCB) of a process, that can be used to detect malicious processes at run time. The genetic footprint consists of selected parameters - maintained inside the PCB of a kernel for each running process - that define the semantics and behavior of an executing process. A systematic forensic study of the execution traces of benign and malware processes is performed to identify discriminatory parameters of a PCB (task_struct is PCB in case of Linux OS). As a result, 16 out of 118 task structure parameters are short listed using the time series analysis. A statistical analysis is done to corroborate the features of the genetic footprint and to select suitable machine learning classifiers to detect malware. The scheme has been evaluated on a dataset that consists of 105 benign processes and 114 recently collected malware processes for Linux. The results of experiments show that the presented scheme achieves a detection accuracy of 96% with 0% false alarm rate in less than 100ms of the start of a malicious activity. Last but not least, the presented technique utilizes partial knowledge that is available at a given time while the process is still executing; as a result, the kernel of OS can devise mitigation strategies. It is also shown that the presented technique is robust to well known run-time evasion attempts.


title: Exploring the Conceptual Model of Digital Ecosystem

abstract: Digital ecosystem, as a neoteric terminology, has emerged along with the appearance of Business Ecosystem which is a form of naturally existing business network of small and medium enterprises. However, few researches have been found in the field of defining digital ecosystem. In this paper, by means of ontology technology as our research methodology, we propose to develop a conceptual model for digital ecosystem. By introducing an innovative ontological notation system, we create the hierarchical framework of digital ecosystem form up to down, based on the related theories form Digital ecosystem and business intelligence institute.


title: Efficient decoherence-free entanglement distribution over lossy quantum channels

abstract: We propose and demonstrate a scheme for boosting the efficiency of entanglement distribution based on a decoherence-free subspace over lossy quantum channels. By using backward propagation of a coherent light, our scheme achieves an entanglement-sharing rate that is proportional to the transmittance T of the quantum channel in spite of encoding qubits in multipartite systems for the decoherence-free subspace. We experimentally show that highly entangled states, which can violate the Clauser-Horne-Shimony-Holt inequality, are distributed at a rate proportional to T.


title: An evidence-based review

abstract: BACKGROUND: Cell phone use and texting are prevalent within society and have thus pervaded the driving population. This technology is a growing concern within the confines of distracted driving, as all diversions from attention to the road have been shown to increase the risk of crashes. Adolescent, inexperienced drivers, who have the greatest prevalence of texting while driving, are at a particularly higher risk of crashes because of distraction. METHODS: Members of the Injury Control Violence Prevention Committee of the Eastern Association for the Surgery of Trauma performed a PubMed search of articles related to distracted driving and cell phone use as a distractor of driving between 2000 and 2013. RESULTS: A total of 19 articles were found to merit inclusion as evidence in the evidence-based review. These articles provided evidence regarding the relationship between distracted driving and crashes, cell phone use contributing to automobile accidents, and/or the relationship between driver experience and automobile accidents. (Adjust methods/results sections to the number of articles that correctly corresponds to the number of references, as well as the methodology for reference inclusion.) CONCLUSION: Based on the evidence reviewed, we can recommend the following. All drivers should minimize all in-vehicle distractions while on the road. All drivers should not text or use any touch messaging system (including the use of social media sites such as Facebook and Twitter) while driving. Younger, inexperienced drivers should especially not use cell phones, texting, or any touch messaging system while driving because they pose an increased risk for death and injury caused by distractions while driving. Keywords: Driver distraction; Language: en


title: The Addition of Manual Lymph Drainage to Compression Therapy For Breast Cancer Related Lymphedema: a Randomized Controlled Trial

abstract: PURPOSE ::: The purpose of this investigation was to compare the reduction in arm lymphedema volume achieved from manual lymph drainage massage (MLD) in combination with multi-layered compression bandaging (CB) to that achieved by CB alone. ::: ::: ::: METHODS AND MATERIALS ::: Fifty women with lymphedema (mean age of 59 years +/- 13 years) were randomly assigned to 4 weeks of combined MLD/CB or CB alone. The primary study endpoint was the reduction in arm lymphedema volume, which was determined by water displacement volumetry and measurement of circumference. Independent assessors, blinded to subject treatment assignment, performed the outcome measurements. ::: ::: ::: RESULTS ::: Arm lymphedema volume decreased significantly after 4 weeks irrespective of treatment assignment (p < 0.001). Individuals with mild lymphedema receiving combined MLD/CB had a significantly larger percentage reduction in volume compared to individuals with mild lymphedema receiving CB alone, and compared to individuals with moderate or severe lymphedema receiving either treatment. ::: ::: ::: CONCLUSION ::: These findings indicate that CB, with or without MLD, is an effective intervention in reducing arm lymphedema volume. The findings suggest that CB on its own should be considered as a primary treatment option in reducing arm lymphedema volume. There may be an additional benefit from the application of MLD for women with mild lymphedema; however, this finding will need to be further examined in the research setting.


Internet of Things: Converging Technologies for Smart Environments and Integrated Ecosystems


title: Informetrics at the beginning of the 21st century - A review

abstract: This paper reviews developments in informetrics between 2000 and 2006. At the beginning of the 21st century we witness considerable growth in webometrics, mapping and visualization and open access. A new topic is comparison between citation databases, as a result of the introduction of two new citation databases Scopus and Google Scholar. There is renewed interest in indicators as a result of the introduction of the h-index. Traditional topics like citation analysis and informetric theory also continue to develop. The impact factor debate, especially outside the informetric literature continues to thrive. Ranked lists (of journal, highly cited papers or of educational institutions) are of great public interest.


title: PII S0272-7358(97)00105-0 THE FUNCTIONS OF SELF-MUTILATION

abstract: While pathological self-mutilating behavior has been clinically examined for over 65 years, and much of the literature hypothesizes some function for the behavior, there has been little attempt to integrate or differentiate between different functional ideas. This review uses six functional models extracted from the literature to organize a discussion of the multiple functions of self-mutilation, acknowledging the overdetermined nature of the behavior and attempting to understand how self-mutilation can serve multiple functions simultaneously. Contextual information about the definition, prevalence, phenomenology, patient characteristics, associated diagnoses, and associated symptoms of self-mutilation is first presented. Six functional models are then presented: the environmental model, the antisuicide model, the sexual model, the affect regulation model, the dissociation model, and the boundaries model. Support for these models in the empirical and theoretical literature is presented and treatment implications are explored.


title: Linking and Extending an Open Multilingual Wordnet

abstract: We create an open multilingual wordnet with large wordnets for over 26 languages and smaller ones for 57 languages. It is made by combining wordnets with open licences, data from Wiktionary and the Unicode Common Locale Data Repository. Overall there are over 2 million senses for over 100 thousand concepts, linking over 1.4 million words in hundreds of languages.


title: Comparison of conventional color fundus photography and multicolor imaging in choroidal or retinal lesions

abstract: PURPOSE ::: Our purpose was to compare the characteristics of the retinal and choroidal lesions including choroidal nevus, choroidal melanoma and congenital hypertrophy of the retina pigment epithelium using conventional color fundus photography (CFP) and multicolor imaging (MCI). ::: ::: ::: METHODS ::: The paired images of patients with retinal or choroidal lesions were assessed for the visibility of lesion's border, halo and drusen using a grading scale (0-2). The area of the lesion was measured on both imaging modalities. The same grading was also done on the individual color channels of MCI for a further evaluation. ::: ::: ::: RESULTS ::: Thirty-three eyes of 33 patients were included. There were no significant differences in the mean border, drusen and halo visibility scores between the two imaging modalities (p = 0.12, p = 0.70, p = 0.35). However, the mean area of the lesion was significantly smaller on MCI than that on CFP (14.9±3.3 versus 18.7±3.4 mm2, p = 0.01). ::: ::: ::: CONCLUSION ::: The appearance of choroidal and/ or retinal lesions on MCI may be different than that on CFP. Though MCI can provide similar information with CFP for the features of retinal and/ or choroidal lesions including border, halo and drusen; the infrared light reflection on MCI underestimates the extent of the choroidal lesion by 33%.


title: Catalytic Reactions in Surfactant Systems:Product Isolation and Catalyst Recycling

abstract: The potential of surfactant based reaction media was studied with different homogeneous catalytic reactions. Micellar systems with the surfactants (p-tert-octylphenoxy) polyethoxyethanol (Triton X-100) and dodecyl sulfate sodium salt were used as reaction media for the enantioselective catalytic hydrogenation of dimethyl itaconate (DMI) with the Rh catalyst complexed with the chiral ligand (2S,4S)-1-tert-butoxycarbonyl-4-diphenylphosphino-2-(diphenylphosphinometyl)-pyrrolidine (BPPM) at 30 °C and 1.1 bar, obtaining an enantiomeric excess (ee) of up to 69%. After complete hydrogenation was achieved, micellar enhanced ultrafiltration (MEUF) was used to recycle the catalyst achieving up to 95% retention. A microemulsion system stabilized with the surfactant Triton X-100 was used as alternative reaction media for the hydrogenation of DMI with a Rh catalyst complexed with the water-soluble tris(3-sulfophenyl)phosphine trisodium salt (TPPTS) at 50 °C and 1.1 bar. With the Triton X-100 system, phase separation b...


title: Real-Time Top-View People Counting Based on a Kinect and NVIDIA Jetson TK1 Integrated Platform

abstract: In this paper, we describe how to establish an embedded framework for real-time top-view people counting. The development of our system consists of two parts, i.e. establishing an embedded signal processing platform and designing a people counting algorithm for the embedded system. For the hardware platform construction, we use Kinect as the camera and exploit NVIDIA Jetson TK1 board as the embedded processing platform. We describe how to build a channel to make Kinect for windows version 2.0 communicate with Jetson TK1. Based on the embedded system, we adapt a water filling based scheme for top-view people counting, which integrates head detection based on water drop, people tracking and counting. Gaussian Mixture Model is used to construct and update the background model. The moving people in each video frame are extracted using background subtraction method. Additionally, the water filling algorithm is used to segment head area as Region Of Interest(ROI). Tracking and counting people are performed by calculating the distance of ROI center point before and after the frame. The whole framework is flexible and practical for real-time application.


title: An Ultra-Wideband Horizontally Polarized Omnidirectional Circular Connected Vivaldi Antenna Array

abstract: An ultra-wideband horizontally polarized omnidirectional antenna is proposed. The connected Vivaldi antenna array mechanism is exploited to achieve the ultra-wide bandwidths of both the impedance and the radiation patterns simultaneously. Sixteen Vivaldi antenna elements are connected with each other and distributed along a circular substrate to form the proposed antenna. The designed antenna exhibits a relative impedance bandwidth of 159.97% (1.28–11.51 GHz) for $\vert S_{11}\vert dB. Meanwhile, uniform omnidirectional radiation patterns with a gain variation of less than 1.5 dB have been obtained within the frequency band of 1.25–7.6 GHz (142.73%).


title: Segmentation Based Sparse Reconstruction of Optical Coherence Tomography Images

abstract: We demonstrate the usefulness of utilizing a segmentation step for improving the performance of sparsity based image reconstruction algorithms. In specific, we will focus on retinal optical coherence tomography (OCT) reconstruction and propose a novel segmentation based reconstruction framework with sparse representation, termed segmentation based sparse reconstruction (SSR). The SSR method uses automatically segmented retinal layer information to construct layer-specific structural dictionaries. In addition, the SSR method efficiently exploits patch similarities within each segmented layer to enhance the reconstruction performance. Our experimental results on clinical-grade retinal OCT images demonstrate the effectiveness and efficiency of the proposed SSR method for both denoising and interpolation of OCT images.


Deep Packet Inspection using Snort


title: Benchmarking Database Systems A Systematic Approach

abstract: This paper describes a customized database and a comprehensive set of queries that can be used for systematic benchmarking of relational database systems. Designing this database and a set of carefully tuned benchmarks represents a first attempt in developing a scientific methodology for performance evaluation of database management systems. We have used this database to perform a comparative evaluation of the database machine DIRECT, the "university" and "commercial" versions of the INGRES database system, the relational database system ORACLE, and the IDM 500 database machine. We present a subset of our measurements (for the single user case only), that constitute a preliminary performance evaluation of these systems.


title: A new Schmitt trigger circuit in a 0.13-/spl mu/m 1/2.5-V CMOS process to receive 3.3-V input signals

abstract: A new Schmitt trigger circuit, which is implemented by low-voltage devices to receive the high-voltage input signals without gate-oxide reliability problem, is proposed. The new proposed circuit, which can be operated in a 3.3-V signal environment without suffering high-voltage gate-oxide overstress, has been fabricated in a 0.13-/spl mu/m 1/2.5-V 1P8M CMOS process. The experimental results have confirmed that the measured transition threshold voltages of the new proposed Schmitt trigger circuit are about 1 and 2.5 V, respectively. The new proposed Schmitt trigger circuit is suitable for mixed-voltage input-output interfaces to receive input signals and reject input noise.


title: Can exergames impart health messages? Game play, framing, and drivers of physical activity among children.

abstract: This study examines the effectiveness of incorporating exergaming into physical education lessons as a platform for imparting health education messages and influencing children's beliefs about and attitudes toward physical activity. The authors launched a 6-week intervention program using Nintendo Wii games coupled with protection motivation theory–based health messaging among 5th-grade school children in Singapore. Results indicated that when children who were exposed to threat-framed messages played Wii exergames during physical education lessons, they reported more positive physical activity attitude, self-efficacy, and perceived behavioral control than did those who underwent regular physical education lessons and were exposed to the same message. In addition, among children playing Wii, the threat and coping frames had similar effects on the degree of message influence on physical activity attitudes and beliefs. The implications for schools, parents, and health policy are discussed.


title: Impact of the gut microbiota on inflammation, obesity, and metabolic disease

abstract: The human gut harbors more than 100 trillion microbial cells, which have an essential role in human metabolic regulation via their symbiotic interactions with the host. Altered gut microbial ecosystems have been associated with increased metabolic and immune disorders in animals and humans. Molecular interactions linking the gut microbiota with host energy metabolism, lipid accumulation, and immunity have also been identified. However, the exact mechanisms that link specific variations in the composition of the gut microbiota with the development of obesity and metabolic diseases in humans remain obscure owing to the complex etiology of these pathologies. In this review, we discuss current knowledge about the mechanistic interactions between the gut microbiota, host energy metabolism, and the host immune system in the context of obesity and metabolic disease, with a focus on the importance of the axis that links gut microbes and host metabolic inflammation. Finally, we discuss therapeutic approaches aimed at reshaping the gut microbial ecosystem to regulate obesity and related pathologies, as well as the challenges that remain in this area.


title: D.; Free space estimation for autonomous navigation

abstract: One of the issues in autonomous navigation is the free space estimation. This paper presents an original framework and a method for the extraction of such an area by using a stereovision system. The v- disparity algorithm (11) is extended to provide a reliable and precise road proflle on all types of roads. The free space is estimated by classifying the pixels of the disparity map. This classiflcation is performed by using the road proflle and the u-disparity image. Each stage of the algorithm is presented and experimental results are shown.


title: Modeling and Simulation of 6-DOF Parallel Manipulator Based on PID Control with Gravity Compensation in Simulink/ADAMS

abstract: A novel model-based controller for six degree of freedom (dof) parallel manipulator-PID controller with gravity compensation is developed, in order to improve the control precision of six-dof hydraulic driven parallel manipulator with long stroke hydraulic actuator and heavy load. In this paper, six-dof parallel manipulator is described as a thirteen rigid-body systems. The mathematical model of gravity item including upper platform and hydraulic cylinders is built using Kane method in six dimensions generalized coordinates system, based on close-form solutions and Newton-Raphson method, forward kinematics and inverse kinematics of the six-dof parallel manipulator are established. The PID controller with gravity compensation is presented with the feedback of cylinder length of platform, desired trajectories and dynamics gravity as the input. The physical systems of 6dof parallel manipulator are modeled by ADAMS, and the PID controller with gravity compensation is built with Simulink. The whole control system of 6-dof parallel manipulator is obtained by connecting ADAMS plant with PID control system with gravity compensation in Simulink. The simulation of the whole systems is executed in Simulink, the simulation results indicate the PID controller with gravity compensation can eliminate steady state error of 6-dof hydraulic driven parallel manipulator and improve the control precision.


title: Daily and Compulsive Internet Use and Well-Being in Adolescence: A Diathesis-Stress Model Based on Big Five Personality Traits

abstract: This study examined the associations between adolescents’ daily Internet use and low well-being (i.e., loneliness, low self-esteem, and depressive moods). We hypothesized that (a) linkages between high levels of daily Internet use and low well-being would be mediated by compulsive Internet use (CIU), and (b) that adolescents with low levels of agreeableness and emotional stability, and high levels of introversion would be more likely to develop CIU and lower well-being. Data were used from a sample of 7888 Dutch adolescents (11–21 years). Results from structural equation modeling analyses showed that daily Internet use was indirectly related to low well-being through CIU. In addition, daily Internet use was found to be more strongly related to CIU in introverted, low-agreeable, and emotionally less-stable adolescents. In turn, again, CIU was more strongly linked to loneliness in introverted, emotionally less-stable, and less agreeable adolescents.


title: Development of Autonomous Robotic Wheelchair Controller Using Embedded Systems

abstract: Autonomous robotic wheelchairs play important roles to disabled persons or elders. In general, an autonomous robotic wheelchair includes the techniques of obstacle sensing and avoidance, local path navigation, and friendly interactions with users when compared to conventional powered wheelchairs. Most of researches on autonomous robotic wheelchairs used the personal computer as the supervisory controller since its powerful computing capacity and familiar coding environment. Nevertheless, the personal computer is not feasible to the wheelchair users due to inefficient size, lower reliability and larger power consumptions. To promote feasible robotic wheelchair control architecture, the embedded system is surveyed in this paper. By using the embedded computing architecture, the proposed robotic wheelchair controller performs characteristics of compact size, better reliability and lower power consumptions while the benefits of powerful computing capacity and familiar coding environment are maintained. On the other hand, this paper also implements the fuzzy logic based autonomous navigation functions such as goal-seeking, wall-following and obstacle avoidance. Theses fuzzy logic based navigation functions are fused together to solve practical situations of the robotic wheelchairs. Finally, a real robotic wheelchair with three-omni- wheel configuration is constructed based on the proposed control architecture, and several practical experiments are also discussed in this paper.


title: An improved TIQ comparator based 3-bit flash ADC

abstract: This paper presents an improved TIQ comparator based 3-bit Flash ADC. A modification is suggested for improvement in switching power consumption by eliminating halt stage from 2PASC based TIQ comparator used for ADC. It has been found that switching power consumption is reduced in comparison with other types of FLASH ADC. Switching power dissipation of 27.35pW at 1MHz input signal frequency for 3-bit flash ADC is obtained. The simulation has been carried out at TSMC 180nm Technology in LTspice.


title: HORNET: A Cycle-Level Multicore Simulator

abstract: We present hornet, a parallel, highly configurable, cycle-level multicore simulator based on an ingress-queued wormhole router network-on-chip (NoC) architecture. The parallel simulation engine offers cycle-accurate as well as periodic synchronization; while preserving functional accuracy, this permits tradeoffs between perfect timing accuracy and high speed with very good accuracy. When run on six separate physical cores on a single die, speedups can exceed a factor of over 5, and when run on a two-die 12-core system with 2-way hyperthreading, speedups exceed 12×. Most hardware parameters are configurable, including memory hierarchy, interconnect geometry, bandwidth, crossbar dimensions, parameters driving power, and thermal effects. A highly parametrized table-based NoC design allows a variety of routing and virtual channel allocation algorithms out of the box, ranging from simple dimension-ordered routing to complex Valiant, ROMM, O1Turn or PROM schemes, BSOR, and adaptive routing. Hornet can run in network-only mode using synthetic traffic or traces, or directly emulate a MIPS-based multicore. Hornet is freely available under the open-source MIT license at http://csg.csail.mit.edu/hornet/.


title: Extraction and Recognition of Text From Digital English Comic Image Using Median Filter

abstract: Text extraction from image is one of the complicated areas in digital image processing. Text characters entrenched in image represents a rich source of information for text retrieval application. It is a complex process to detect and recognize the text from comic image due to their various size, gray scale values, complex backgrounds and different styles of font. Text extraction process from comic image helps to preserve the text and formatting during conversion process and provide high quality of text from the printed document. Automatic text extraction from comic images receives a growing attention because of prospective application in image retrieval. In existing work, Japanese text is extracted vertically from Manga Comic Image using Blob extraction functions. At the same time, text is extracted from multiple constraints using optical character recognition (OCR) and make translation of Japanese language of Manga into some other languages in conventional way to share the enjoyment of reading Manga through the Internet. This paper talks about English text extraction from blob comic image using various methods.


title: Network Anomaly Detection Using Time Series Analysis

abstract: This paper presents a method of detecting network anomalies by analyzing the abrupt change of time series data obtained from management information base (MIB) variables. The method applies the auto-regressive (AR) process to model the abrupt change of time series data, and performs sequential hypothesis test to detect the anomalies. With time correlation and location correlation, the method determines not only the presence of anomalous activity, but also its occurring time and location. The experimental results show that the proposed method performs well in detecting the traffic-related anomalies


Condition monitoring with ordinary wind turbine SCADA data – A neuro-fuzzy approach