title: An Efficient Fraud Identification Method Combining Manifold Learning and Outliers Detection in Mobile Healthcare Services

abstract: With the development of mobile Internet, various mobile applications have become increasingly popular. Many people are being benefited from the mobile healthcare services. Compared with the traditional healthcare services, patients’ medical behavior trajectories can be recorded by mobile healthcare services meticulously. They monitor the entire healthcare services process and help to improve the quality and standardization of healthcare services. By tracking and analyzing the patients’ medical records, they provide real-time protection for the patients’ healthcare activities. Therefore, medical fraud can be avoided and the loss of public health funds can be reduced. Although mobile healthcare services can provide a large amount of timely data, an effective real-time online algorithm is needed due to the timeliness of detecting the medical insurance fraud claims. However, because of the complex granularity of medical data, existing fraud detection approaches tend to be less effective in terms of monitoring the healthcare services process. In this paper, we propose an approach to deal with these problems. By means of the proposed SSIsomap activity clustering method, SimLOF outlier detection method, and the Dempster–Shafer theory-based evidence aggregation method, our approach is able to detect unusual categories and frequencies of behaviors simultaneously. Our approach is applied to a real-world data set containing more than 40 million medical insurance claim activities from over 40 000 users. Compared with two state-of-the-art approaches, the extensive experimental results show that our approach is significantly more effective and efficient. Our approach agent which provides decision support for the approval sender during the medical insurance claim approval process is undergoing trial in mobile healthcare services.


title: A quantitative theory of immediate visual recognition

abstract: Human and non-human primates excel at visual recognition tasks. The primate visual system exhibits a strong degree of selectivity while at the same time being robust to changes in the input image. We have developed a quantitative theory to account for the computations performed by the feedforward path in the ventral stream of the primate visual cortex. Here we review recent predictions by a model instantiating the theory about physiological observations in higher visual areas. We also show that the model can perform recognition tasks on datasets of complex natural images at a level comparable to psychophysical measurements on human observers during rapid categorization tasks. In sum, the evidence suggests that the theory may provide a framework to explain the first 100-150 ms of visual object recognition. The model also constitutes a vivid example of how computational models can interact with experimental observations in order to advance our understanding of a complex phenomenon. We conclude by suggesting a number of open questions, predictions, and specific experiments for visual physiology and psychophysics.


title: Interactive Volumetric Textures

abstract: This paper presents a method for interactively rendering complex repetitive scenes such as landscapes, fur, organic tissues, etc. It is an adaptation to Z-buffer of volumetric textures, a ray-traced method, in order to use the power of existing graphics hardware. Our approach consists in slicing a piece of 3D geometry (one repetitive detail of the complex data) into a series of thin layers. A layer is a rectangle containing the shaded geometry that falls in that slice. These layers are used as transparent textures, that are mapped onto the underlying surface (e.g. a hill or an animal skin) with an extrusion offset. We show some results obtained with our first implementation, such as a scene of 13 millions of virtual polygons animated at 2.5 frames per second on a SGI O2.


title: Simulation of Wireless Digital Communication Systems

abstract: Due to the explosive demands for high speed wireless services, such as wireless Internet, email and cellular video conferencing, digital wireless communications has become one of the most exciting ...


title: Connecting Corresponding Identities across Communities

abstract: One of the most interesting challenges in the area of social computing and social media analysis is the so-called community analysis. A well known barrier in cross-community (multiple website) analysis is the disconnectedness of these websites. In this paper, our aim is to provide evidence on the existence of a mapping among identities across multiple communities, providing a method for connecting these websites. Our studies have shown that simple, yet effective approaches, which leverage social media’s collective patterns can be utilized to find such a mapping. The employed methods successfully reveal this mapping with 66% accuracy.


title: Combined EEG-Gyroscope-tDCS Brain Machine Interface System for Early Management of Driver Drowsiness

abstract: In this paper, we present the design and implementation of a wireless, wearable brain machine interface (BMI) system dedicated to signal sensing and processing for driver drowsiness detection (DDD). Owing to the importance of driver drowsiness and the possibility for brainwaves-based DDD, many electroencephalogram (EEG)-based approaches have been proposed. However, few studies focus on the early detection of driver drowsiness and on the early management of driver drowsiness using a closed-loop algorithm. The reported wireless and wearable BMI system is used for 1) simultaneous EEG and gyroscope-based head movement measurement for the early detection of driver drowsiness and 2) simultaneous EEG and transcranial direct current stimulation (tDCS) for the early management of driver drowsiness. To achieve the purposes of easy-to-use and distraction-free driving, a Bluetooth low-energy module is embedded in this BMI system and used to communicate with a fully wearable consumer device, a smartwatch, which coordinates the work of drowsiness monitoring and brain stimulation with its embedded closed-loop algorithm. The proposed system offers a 128 Hz sampling rate per channel, 12-bit and 16-bit resolution for a single-channel EEG and a three-channel gyroscope, and a maximum 2 mA current for the tDCS. The current consumption of the whole headset system is 56 mA. The battery life of the smartwatch is 9 h. The DDD experimental results show that the proposed system obtained a 93.67% five-level overall accuracy, a 96.15% two-level (alert versus slightly drowsy) accuracy, and maximum 16- to 23-min wakefulness maintenance.


title: Deep Convolutional Framelets: A General Deep Learning for Inverse Problems

abstract: Recently, deep learning approaches have achieved significant performance improvement in various imaging problems. However, it is still unclear why these deep learning architectures work. Moreover, the link between the deep learning and the classical signal processing approaches such as wavelet, non-local processing, compressed sensing, etc, is still not well understood, which often makes signal processors in deep troubles. To address these issues, here we show that the long-searched-for missing link is the convolutional framelets for representing a signal by convolving local and non-local bases. The convolutional framelets was originally developed to generalize the recent theory of low-rank Hankel matrix approaches, and this paper significantly extends the idea to derive a deep neural network using multi-layer convolutional framelets with perfect reconstruction (PR) under rectified linear unit (ReLU). Our analysis also shows that the popular deep network components such as residual block, redundant filter channels, and concatenated ReLU (CReLU) indeed help to achieve the PR, while the pooling and unpooling layers should be augmented with multi-resolution convolutional framelets to achieve PR condition. This discovery reveals the limitations of many existing deep learning architectures for inverse problems, and leads us to propose a novel deep convolutional framelets neural network. Using numerical experiments with sparse view x-ray computed tomography (CT), we demonstrated that our deep convolution framelets network shows consistent improvement. This discovery suggests that the success of deep learning is not from a magical power of a black-box, but rather comes from the power of a novel signal representation using non-local basis combined with data-driven local basis, which is indeed a natural extension of classical signal processing theory.


Robot Competitions Ideal Benchmarks for Robotics Research


title: HydraDB: a resilient RDMA-driven key-value middleware for in-memory cluster computing

abstract: In this paper, we describe our experiences and lessons learned from building a general-purpose in-memory key-value middleware, called HydraDB. HydraDB synthesizes a collection of state-of-the-art techniques, including continuous fault-tolerance, Remote Direct Memory Access (RDMA), as well as awareness for multicore systems, etc, to deliver a high-throughput, low-latency access service in a reliable manner for cluster computing applications. The uniqueness of HydraDB mainly lies in its design commitment to fully exploit the RDMA protocol to comprehensively optimize various aspects of a general-purpose key-value store, including latency-critical operations, read enhancement, and data replications for high-availability service, etc. At the same time, HydraDB strives to efficiently utilize multicore systems to prevent data manipulation on the servers from curbing the potential of RDMA. Many teams in our organization have adopted HydraDB to improve the execution of their cluster computing frameworks, including Hadoop, Spark, Sensemaking analytics, and Call Record Processing. In addition, our performance evaluation with a variety of YCSB workloads also shows that HydraDB can substantially outperform several existing in-memory key-value stores by an order of magnitude. Our detailed performance evaluation further corroborates our design choices.


The Rise of Consumer Health Wearables: Promises and Barriers


title: Switchable ON-/OFF-Body Antenna for 2.45 GHz WBAN Applications

abstract: A switchable ON-/OFF-body antenna operating at the same frequency of 2.45 GHz industrial, scientific, and medical (ISM) band for wireless body area network (WBAN) applications is presented. P-i-n diodes are chosen to act as the switches between ON-body and OFF-body modes with the forward or reverse direct current (dc) voltage. To achieve the same operating frequency at ON-body and OFF-body modes, suitable slots are cut on the radiator to have size reduction at OFF-body mode, and it will have less effect at ON-body mode. Bias circuit is integrated in the antenna. Total size of the antenna is $\pi \times 24^{2}\times 3.2$ mm3. Specific absorption rate (SAR) value is also analyzed in this communication. Then, the antenna is fabricated to validate the design concept. Measured radiation pattern of ON-body mode is omni-direction with vertical polarization and OFF-body mode is with broadside, respectively. Communication links of ON-body mode, which assessed by $\vert S_{21}\vert $ and the performance at OFF-body mode, are also studied in this communication.


title: Realistic virtual grasping

abstract: We present a physically-based approach to grasping and manipulation of virtual objects that produces visually realistic results, addresses the problem of visual interpenetration of hand and object models, and performs force rendering for force-feedback gloves in a single framework. Our approach couples tracked hand configuration to a simulation-controlled articulated hand model using a system of linear and torsional spring-dampers. We discuss an implementation of our approach that uses a widely-available simulation tool for collision detection and response. We illustrate the resulting behavior of the virtual hand model and of grasped objects, and we show that the simulation rate is sufficient for control of current force-feedback glove designs. We also present a prototype of a system we are developing to support natural whole-hand interactions in a desktop-sized workspace.


title: Offset quad ridged ortho-mode transducer with a 3.4:1 bandwidth

abstract: We describe the design and measured parameters of an offset probe, quad ridged, ortho-mode transducer (OMT). The OMT operates over a 3.4:1 bandwidth from 3.6-12.4GHz. The OMT exhibits a return loss of greater than 15dB and an isolation of greater than 50dB across the full band. Simulations predict spurious mode production at the circular waveguide port to be greater than 15dB below the TE11 mode.


title: Design considerations for direct-conversion receivers

abstract: This paper describes the issues and tradeoffs in the design and monolithic implementation of direct-conversion receivers and proposes circuit techniques that can alleviate the drawbacks of this architecture. Following a brief study of heterodyne and image-reject topologies, the direct-conversion architecture is introduced and effects such as dc offset, I/Q mismatch, even-order distortion, flicker noise, and oscillator leakage are analyzed. Related design techniques for amplification and mixing, quadrature phase calibration, and baseband processing are also described.


title: Food Image Segmentation for Dietary Assessment

abstract: The prevalence of diet-related chronic diseases strongly impacts global health and health services. Currently, it takes training and strong personal involvement to manage or treat these diseases. One way to assist with dietary assessment is through computer vision systems that can recognize foods and their portion sizes from images and output the corresponding nutritional information. When multiple food items may exist, a food segmentation stage should also be applied before recognition. In this study, we propose a method to detect and segment the food of already detected dishes in an image. The method combines region growing/merging techniques with a deep CNN-based food border detection. A semi-automatic version of the method is also presented that improves the result with minimal user input. The proposed methods are trained and tested on non-overlapping subsets of a food image database including 821 images, taken under challenging conditions and annotated manually. The automatic and semi-automatic dish segmentation methods reached average accuracies of 88% and 92%, respectively, in roughly 0.5 seconds per image.


title: An Approach to Source-Code Plagiarism Detection and Investigation Using Latent Semantic Analysis

abstract: Plagiarism is a growing problem in academia. Academics often use plagiarism detection tools to detect similar source-code files. Once similar files are detected, the academic proceeds with the investigation process which involves identifying the similar source-code fragments within them that could be used as evidence for proving plagiarism. This paper describes PlaGate, a novel tool that can be integrated with existing plagiarism detection tools to improve plagiarism detection performance. The tool also implements a new approach for investigating the similarity between source-code files with a view to gathering evidence for proving plagiarism. Graphical evidence is presented that allows for the investigation of source-code fragments with regards to their contribution toward evidence for proving plagiarism. The graphical evidence indicates the relative importance of the given source-code fragments across files in a corpus. This is done by using the Latent Semantic Analysis information retrieval technique to detect how important they are within the specific files under investigation in relation to other files in the corpus.


Agriculture sector implications of a green economy transition in the Western Cape Province of South Africa : a system dynamics modelling approach to food crop production


title: Spectra for solvable Lie algebras of bundle endomorphisms

abstract: The aim of the paper is to investigate spectral properties of the Lie algebras correspond- ing to the symmetry groups of certain flags of vector bundles over a compact space. Under natural hypotheses, such Lie algebras are solvable, being in general infinite dimensional. The spectral theory of finite-dimensional solvable Lie algebras of operators is extended to this natural class of infinite-dimensional solvable Lie algebras. The discussion uses the language of continuous fields of C ∗ -algebras. The flag manifolds in C ∗ -algebraic framework are naturally involved here, they providing the basic method for obtaining flags of vector bundles. Mathematics Subject Classification (2000): 47A13, 22E65, 58B25, 46L10


Semi-Supervised Neural Machine Translation with Language Models.


title: Deep Dynamical Modeling and Control of Unsteady Fluid Flows

abstract: The design of flow control systems remains a challenge due to the nonlinear nature of the equations that govern fluid flow. However, recent advances in computational fluid dynamics (CFD) have enabled the simulation of complex fluid flows with high accuracy, opening the possibility of using learning-based approaches to facilitate controller design. We present a method for learning the forced and unforced dynamics of airflow over a cylinder directly from CFD data. The proposed approach, grounded in Koopman theory, is shown to produce stable dynamical models that can predict the time evolution of the cylinder system over extended time horizons. Finally, by performing model predictive control with the learned dynamical models, we are able to find a straightforward, interpretable control law for suppressing vortex shedding in the wake of the cylinder.


title: On-Demand Deep Model Compression for Mobile Devices: A Usage-Driven Model Selection Framework

abstract: Recent research has demonstrated the potential of deploying deep neural networks (DNNs) on resource-constrained mobile platforms by trimming down the network complexity using different compression techniques. The current practice only investigate stand-alone compression schemes even though each compression technique may be well suited only for certain types of DNN layers. Also, these compression techniques are optimized merely for the inference accuracy of DNNs, without explicitly considering other application-driven system performance (e.g. latency and energy cost) and the varying resource availabilities across platforms (e.g. storage and processing capability). In this paper, we explore the desirable tradeoff between performance and resource constraints by user-specified needs, from a holistic system-level viewpoint. Specifically, we develop a usage-driven selection framework, referred to as AdaDeep, to automatically select a combination of compression techniques for a given DNN, that will lead to an optimal balance between user-specified performance goals and resource constraints. With an extensive evaluation on five public datasets and across twelve mobile devices, experimental results show that AdaDeep enables up to 9.8x latency reduction, 4.3x energy efficiency improvement, and 38x storage reduction in DNNs while incurring negligible accuracy loss. AdaDeep also uncovers multiple effective combinations of compression techniques unexplored in existing literature.


title: Disc-aware Ensemble Network for Glaucoma Screening from Fundus Image

abstract: Glaucoma is a chronic eye disease that leads to irreversible vision loss. Most of the existing automatic screening methods firstly segment the main structure, and subsequently calculate the clinical measurement for detection and screening of glaucoma. However, these measurement-based methods rely heavily on the segmentation accuracy, and ignore various visual features. In this paper, we introduce a deep learning technique to gain additional image-relevant information, and screen glaucoma from the fundus image directly. Specifically, a novel Disc-aware Ensemble Network (DENet) for automatic glaucoma screening is proposed, which integrates the deep hierarchical context of the global fundus image and the local optic disc region. Four deep streams on different levels and modules are respectively considered as global image stream, segmentation-guided network, local disc region stream, and disc polar transformation stream. Finally, the output probabilities of different streams are fused as the final screening result. The experiments on two glaucoma datasets (SCES and new SINDI datasets) show our method outperforms other state-of-the-art algorithms.


title: Early Detection of Students at Risk – Predicting Student Dropouts Using Administrative Student Data and Machine Learning Methods

abstract: To successfully reduce student attrition, it is imperative to understand what the underlying determinants of attrition are and which students are at risk of dropping out. We develop an early detection system (EDS) using administrative student data from a state and a private university to predict student success as a basis for a targeted intervention. The EDS uses regression analysis, neural networks, decision trees, and the AdaBoost algorithm to identify student characteristics which distinguish potential dropouts from graduates. Prediction accuracy at the end of the first semester is 79% for the state university and 85% for the private university of applied sciences. After the fourth semester, the accuracy improves to 90% for the state university and 95% for the private university of applied sciences.


Human-in-the-Loop Parsing


title: Autonomous Flight at Low Altitude Using Light Sensors and Little Computational Power

abstract: The ability to fly at low altitude while actively avoiding collisions with the terrain and objects such as trees and buildings is a great challenge for small unmanned aircraft. This paper builds on top of a control strategy called optiPilot whereby a series of optic-flow detectors pointed at divergent viewing directions around the aircraft main axis are linearly combined into roll and pitch commands using two sets of weights. This control strategy already proved successful at controlling flight and avoiding collisions in reactive navigation experiments. This paper describes how optiPilot can efficiently steer a flying platform during the critical phases of hand-launched take off and landing. It then shows how optiPilot can be coupled with a GPS in order to provide goal-directed, nap-of-the-earth flight control in presence of obstacles. Two fully autonomous flights of 25 minutes each are described where a 400-gram unmanned aircraft flies at approx. 10 m above ground in a circular path including two copses of trees requiring efficient collision avoidance actions.


title: Modular Multitask Reinforcement Learning with Policy Sketches

abstract: We describe a framework for multitask deep reinforcement learning guided by ::: policy sketches. Sketches annotate each task with a sequence of named subtasks, ::: providing high-level structural relationships among tasks, but not providing the ::: detailed guidance required by previous work on learning policy abstractions for ::: RL (e.g. intermediate rewards, subtask completion signals, or intrinsic motivations). ::: Our approach associates every subtask with its own modular subpolicy, ::: and jointly optimizes over full task-specific policies by tying parameters across ::: shared subpolicies. This optimization is accomplished via a simple decoupled ::: actor–critic training objective that facilitates learning common behaviors from ::: dissimilar reward functions. We evaluate the effectiveness of our approach on a ::: maze navigation game and a 2-D Minecraft-inspired crafting game. Both games ::: feature extremely sparse rewards that can be obtained only after completing a ::: number of high-level subgoals (e.g. escaping from a sequence of locked rooms or ::: collecting and combining various ingredients in the proper order). Experiments ::: illustrate two main advantages of our approach. First, we outperform standard ::: baselines that learn task-specific or shared monolithic policies. Second, our ::: method naturally induces a library of primitive behaviors that can be recombined ::: to rapidly acquire policies for new tasks.


title: Cerebral cartography and connectomics

abstract: Cerebral cartography and connectomics pursue similar goals in attempting to create maps that can inform our understanding of the structural and functional organization of the cortex. Connectome maps explicitly aim at representing the brain as a complex network, a collection of nodes and their interconnecting edges. This article reflects on some of the challenges that currently arise in the intersection of cerebral cartography and connectomics. Principal challenges concern the temporal dynamics of functional brain connectivity, the definition of areal parcellations and their hierarchical organization into large-scale networks, the extension of whole-brain connectivity to cellular-scale networks, and the mapping of structure/function relations in empirical recordings and computational models. Successfully addressing these challenges will require extensions of methods and tools from network science to the mapping and analysis of human brain connectivity data. The emerging view that the brain is more than a collection of areas, but is fundamentally operating as a complex networked system, will continue to drive the creation of ever more detailed and multi-modal network maps as tools for on-going exploration and discovery in human connectomics.


title: Low-power static and dynamic high-voltage CMOS level-shifter circuits

abstract: Pseudo-NMOS level-shifters consume large static current making them unsuitable for portable devices implemented with HV CMOS. Dynamic level-shifters help reduce power consumption. To reduce on-current to a minimum (sub-nanoamp), modifications are proposed to existing pseudo-NMOS and dynamic level-shifter circuits. A low power three transistor static level-shifter design with a resistive load is also presented.


title: Saturated Super-Twisting Algorithm: Lyapunov based approach

abstract: A saturated control Super-Twisting Algorithm is presented. Lyapunov level curves are used for the design of the saturation of Super-Twisting Algorithm.


title: Sinogram concept approach in image reconstruction algorithm of a Computed Tomography System using MATLAB

abstract: Computed Tomography (CT) is among the most current technique applied in medical imaging. Base on this reason CT courses has been introduced in most of the Biomedical Engineering especially in electronic based program in many universities in the world including Malaysia. CT involves several steps in producing real time slices images of the scanned body parts which comes from the application of the several mathematical theoretical concepts behind it. Sinogram which is based on line integral and Radon transform concept is among the important part in the image reconstruction algorithm processes which is sometime not well explained and abandon by the researchers. This concept is quite important for an academician and also the researchers since sinogram capable in explaining the beautiful part of radon transform and line integral application in producing the image of an object. The quality of the produced image also can analyzed based on the generated sinogram. In this paper, sinogram is generated through the use of MATLAB software with Shepp-Logan head phantom as the object.


title: School toilets: facilitating hand hygiene? A review of primary school hygiene facilities in a developed country.

abstract: BACKGROUND ::: Clean hands play an important role in preventing infectious disease transmission. The physical quality of any toilet and handwashing facilities is an important determinant of whether and how it is used, especially for school children. ::: ::: ::: METHODS ::: This study assessed the physical quality of toilet and handwashing facilities used by 9 year olds at 68 primary schools in three cities in the South Island of New Zealand. The facilities were assessed for availability, functionality and provision of hand basins, hygiene products and drying facilities. ::: ::: ::: RESULTS ::: Nineteen schools (28%) followed the New Zealand Ministry of Education Code of Practice for toilet and bathroom facilities in schools, by providing warm water, liquid soap at every basin and functioning hand drying facilities. A further 25 schools (37%) would have met the standards except they provided only cold water (21 schools) or the cloth roller towels were unusable (4 schools). The other 24 schools' toilet facilities were deficient in some way, including one with no soap and six that provided no drying facilities. School socioeconomic position and toilet facility quality were not related. ::: ::: ::: CONCLUSIONS ::: These results suggest that a significant number of New Zealand children do not currently have access to high quality hygiene facilities at school.


title: Multi-dimensional visualization of large-scale marine hydrological environmental data

abstract: This paper develops large scale marine hydrological environmental data-oriented visualization software and realizes oceanographic planar graph, contour line rendering, isosurface rendering, factor field volume rendering and dynamic simulation of current field.This study establishes nodes in the scene for the management of geometric objects to realize high-performance dynamic rendering of massive marine hydrological environmental data.The system employs CUDA parallel computing to improve the computation rate of volume rendering of marine water environmental factors based on NetCDF (Network Common Data Form) format.The visualization software of marine hydrological environment developed can simulate and show properties and change process of marine water environmental factors efficiently and intuitively. With the constant deepening of research on marine environment simulation and information expression, there are higher and higher requirements for the sense of reality of ocean data visualization results and the real-time interaction in the visualization process. This paper tackle the challenge of key technology of three-dimensional interaction and volume rendering technology based on GPU technology, develops large scale marine hydrological environmental data-oriented visualization software and realizes oceanographic planar graph, contour line rendering, isosurface rendering, factor field volume rendering and dynamic simulation of current field. To express the spatial characteristics and real-time update of massive marine hydrological environmental data better, this study establishes nodes in the scene for the management of geometric objects to realize high-performance dynamic rendering. The system employs CUDA (Computing Unified Device Architecture) parallel computing for the improvement of computation rate, uses NetCDF (Network Common Data Form) file format for data access and applies GPU programming technology to realize fast volume rendering of marine water environmental factors. The visualization software of marine hydrological environment developed can simulate and show properties and change process of marine water environmental factors efficiently and intuitively.


title: A 31 ns Random Cycle VCAT-Based 4F $^{2}$ DRAM With Manufacturability and Enhanced Cell Efficiency

abstract: A functional 4F 2 DRAM was implemented based on the technology combination of stack capacitor and surrounding-gate vertical channel access transistor (VCAT). A high performance VCAT has been developed showing excellent Ion-Ioff characteristics with more than twice turn-on current compared with the conventional recessed channel access transistor (RCAT). A new design methodology has been applied to accommodate 4F 2 cell array, achieving both high performance and manufacturability. Especially, core block restructuring, word line (WL) strapping and hybrid bit line (BL) sense-amplifier (SA) scheme play an important role for enhancing AC performance and cell efficiency. A 50 Mb test chip was fabricated by 80 nm design rule and the measured random cycle time (tRC) and read latency (tRCD) are 31 ns and 8 ns, respectively. The median retention time for 88 Kb sample array is about 30 s at 90 °C under dynamic operations. The core array size is reduced by 29% compared with conventional 6F 2 DRAM.


Analysis of Microplastics using FTIR Imaging


title: RF Localization for Wireless Video Capsule Endoscopy

abstract: RF localization science and technology started with the global positioning systems for outdoor areas, and it then transformed into wireless indoor geolocation. The next step in the evolution of this science is the transfor- mation into RF localization inside the human body. The first major application for this technology is the localiza- tion of the wireless video capsule endoscope (VCE) that has been in the clinical arena for 12 years. While physi- cians can receive clear images of abnormalities in the gastrointestinal tract with VCE devices, they have little idea of their exact location inside the GI tract. To localize intestinal abnormalities, physicians routinely use radio- logical, endoscopic or surgical operations. If we could use the RF signal radiated from the capsule to also locate these devices, not only can physicians discover medical prob- lems, but they can also learn where the problems are located. However, finding a realistic RF localization solu- tion for the endoscopy capsule is a very challenging task, because the inside of the human body is a difficult envi- ronment for experimentation and visualization. In addition, we have no-idea how the capsule moves and rotates in its 3D journey in this non-homogeneous medium for radio propagation. In this paper, we describe how we can design a cyber physical system (CPS) for experimental testing and visualization of interior of the human body that can be used for solving the RF localization problem for the endoscopy capsule. We also address the scientific challenges that face and the appropriate technical approaches for solving this problem.


title: Temporal Interaction and Causal Influence in Community-Based Question Answering

abstract: During the last decade, community-based question answering (CQA) sites have accumulated a vast amount of questions and their crowdsourced answers over time. How to efficiently identify the quality of answers that are relevant to a given question has become an active line of research in CQA. The major challenge of CQA is the accurate selection of high-quality answers w.r.t given questions. Previous approaches tend to model the semantic matching between individual pair of one question and its corresponding answer (how fitting an answer is to a posted question). However, these works ignore the temporal interactions between answers (how previous answers influence the late posted answers). For example, a rational user likely adapts others’ opinions, revises his inclinations, and posts a more appropriate answer after understanding the given question and previously posted answers. As a result, this paper devises an architecture named Temporal Interaction and Causal Influence LSTM (TC-LSTM) to effectively leverage not only the causal influence between question-answer (how appropriate an answer is for a given question) but also the temporal interactions between answers-answer (how a high-quality answer gradually forms). In particular, long short-term memory (LSTM) is used to capture the explicit question-answer influence and the implicit answers-answer interactions. Experiments are conducted on SemEval 2015 CQA dataset for answer classification task and Baidu Zhidao Dataset for answer ranking task. The experimental results show the advantage of our model comparing with other state-of-the-art methods.


title: Image-based camera localization: an overview

abstract: Virtual reality, augmented reality, robotics, and autonomous driving, have recently attracted much attention from both academic and industrial communities, in which image-based camera localization is a key task. However, there has not been a complete review on image-based camera localization. It is urgent to map this topic to enable individuals enter the field quickly. In this paper, an overview of image-based camera localization is presented. A new and complete classification of image-based camera localization approaches is provided and the related techniques are introduced. Trends for future development are also discussed. This will be useful not only to researchers, but also to engineers and other individuals interested in this field.


title: Operating Room Personnel Viewpoints About Certified Registered Nurse Anesthetists

abstract: The purpose of this project was to explore what attitudes physicians, nurses, and operating room technicians had about working with Certified Registered Nurse Anesthetists (CRNAs) to better understand practice barriers and facilitators. This Q methodology study used a purposive sample of operating room personnel from four institutions in the Midwestern United States. Participants completed a −4 to +4 rank-ordering of their level of agreement with 34 attitude statements representing a wide range of beliefs about nurse anesthetists. Centroid factor analysis with varimax rotation was used to analyze 24 returned Q sorts. Three distinct viewpoints emerged that explained 66% of the variance: favoring unrestricted practice, favoring anesthesiologist supervision, and favoring anesthesiologist practice. Research is needed on how to develop workplace attitudes that support autonomous nurse anesthetist practice and to understand preferences for restricted practice in team members other than physicians.


title: Estimating conversion rate in display advertising from past erformance data

abstract: In targeted display advertising, the goal is to identify the best opportunities to display a banner ad to an online user who is most likely to take a desired action such as purchasing a product or signing up for a newsletter. Finding the best ad impression, i.e., the opportunity to show an ad to a user, requires the ability to estimate the probability that the user who sees the ad on his or her browser will take an action, i.e., the user will convert. However, conversion probability estimation is a challenging task since there is extreme data sparsity across different data dimensions and the conversion event occurs rarely. In this paper, we present our approach to conversion rate estimation which relies on utilizing past performance observations along user, publisher and advertiser data hierarchies. More specifically, we model the conversion event at different select hierarchical levels with separate binomial distributions and estimate the distribution parameters individually. Then we demonstrate how we can combine these individual estimators using logistic regression to accurately identify conversion events. In our presentation, we also discuss main practical considerations such as data imbalance, missing data, and output probability calibration, which render this estimation problem more difficult but yet need solving for a real-world implementation of the approach. We provide results from real advertising campaigns to demonstrate the effectiveness of our proposed approach.


title: Fast and reliable anomaly detection in categorical data

abstract: Spotting anomalies in large multi-dimensional databases is a crucial task with many applications in finance, health care, security, etc. We introduce COMPREX, a new approach for identifying anomalies using pattern-based compression. Informally, our method finds a collection of dictionaries that describe the norm of a database succinctly, and subsequently flags those points dissimilar to the norm---with high compression cost---as anomalies. Our approach exhibits four key features: 1) it is parameter-free; it builds dictionaries directly from data, and requires no user-specified parameters such as distance functions or density and similarity thresholds, 2) it is general; we show it works for a broad range of complex databases, including graph, image and relational databases that may contain both categorical and numerical features, 3) it is scalable; its running time grows linearly with respect to both database size as well as number of dimensions, and 4) it is effective; experiments on a broad range of datasets show large improvements in both compression, as well as precision in anomaly detection, outperforming its state-of-the-art competitors.


title: A Lyapunov approach for robust nonlinear state feedback synthesis

abstract: The authors apply the input/output linearization approach for nonlinear state feedback synthesis. The model uncertainty under consideration is a class of state model perturbations that satisfy appropriate matching conditions. The controller design uses a Lyapunov-based approach to guarantee uniform ultimate boundedness of the states and the output. >


title: New Digital Metal-Oxide (MOx) Sensor Platform

abstract: The application of metal oxide gas sensors in Internet of Things (IoT) devices and mobile platforms like wearables and mobile phones offers new opportunities for sensing applications. Metal-oxide (MOx) sensors are promising candidates for such applications, thanks to the scientific progresses achieved in recent years. For the widespread application of MOx sensors, viable commercial offerings are required. In this publication, the authors show that with the new Sensirion Gas Platform (SGP) a milestone in the commercial application of MOx technology has been reached. The architecture of the new platform and its performance in selected applications are presented.


title: Latent Fingerprint Image Segmentation Using Fractal Dimension Features and Weighted Extreme Learning Machine Ensemble

abstract: Latent fingerprints are fingerprints unintentionally left at a crime scene. Due to the poor quality and often complex image background and overlapping patterns characteristic of latent fingerprint images, separating the fingerprint region-of-interest from complex image background and overlapping patterns is a very challenging problem. In this paper, we propose a latent fingerprint segmentation algorithm based on fractal dimension features and weighted extreme learning machine. We build feature vectors from the local fractal dimension features and use them as input to a weighted extreme learning machine ensemble classifier. The patches are classified into fingerprint and nonfingerprint classes. We evaluated the proposed segmentation algorithm by comparing the results with the published results from the state of the art latent fingerprint segmentation algorithms. The experimental results of our proposed approach show significant improvement in both the false detection rate (FDR) and overall segmentation accuracy compared to the existing approaches.


title: DAISY Filter Flow: A Generalized Discrete Approach to Dense Correspondences

abstract: Establishing dense correspondences reliably between a pair of images is an important vision task with many applications. Though significant advance has been made towards estimating dense stereo and optical flow fields for two images adjacent in viewpoint or in time, building reliable dense correspondence fields for two general images still remains largely unsolved. For instance, two given images sharing some content exhibit dramatic photometric and geometric variations, or they depict different 3D scenes of similar scene characteristics. Fundamental challenges to such an image or scene alignment task are often multifold, which render many existing techniques fall short of producing dense correspondences robustly and efficiently. This paper presents a novel approach called DAISY filter flow (DFF) to address this challenging task. Inspired by the recent PatchMatch Filter technique, we leverage and extend a few established methods: 1) DAISY descriptors, 2) filter-based efficient flow inference, and 3) the PatchMatch fast search. Coupling and optimizing these modules seamlessly with image segments as the bridge, the proposed DFF approach enables efficiently performing dense descriptor-based correspondence field estimation in a generalized high-dimensional label space, which is augmented by scales and rotations. Experiments on a variety of challenging scenes show that our DFF approach estimates spatially coherent yet discontinuity-preserving image alignment results both robustly and efficiently.


title: Novel zero-current-switching (ZCS) PWM switch cell minimizing additional conduction loss

abstract: This paper proposes a new zero-current-switching (ZCS) pulsewidth modulation (PWM) switch cell that has no additional conduction loss of the main switch. In this cell, the main switch and the auxiliary switch turn on and turn off under zero-current condition. The diodes commutate softly and the reverse-recovery problems are alleviated. The conduction loss and the current stress of the main switch are minimized, since the resonating current for the soft switching does not flow through the main switch. Based on the proposed ZCS PWM switch cell, a new family of DC-to-DC PWM converters is derived. The new family of ZCS PWM converters is suitable for the high-power applications employing insulated gate bipolar transistors. Among the new family of DC-to-DC PWM converters, a boost converter was taken as an example and has been analyzed. Design guidelines with a design example are described and verified by experimental results from the 2.5 kW prototype boost converter operating at 40 kHz.


title: Design of geometric-based inverse kinematics for a low cost robotic arm

abstract: In order to effectively control end effector of a robotic manipulator, one needs to solve inverse kinematics of the robot. This paper reports an investigation to solve inverse kinematics of a low cost 6 degrees of freedom (6 DOF) robotic arm. Since this robot provide no feedback signal, we propose to use a simple geometric-based inverse kinematics. Experimental results show the effectiveness of the proposed method, while the accuracy is still acceptable to some extent.


title: An open framework for multi-source, cross-domain personalisation with semantic interest graphs

abstract: Cross-domain recommendations are currently available in closed, proprietary social networking ecosystems such as Facebook, Twitter and Google+. I propose an open framework as an alternative, which enables cross-domain recommendations with domain-agnostic user profiles modeled as semantic interest graphs. This novel framework covers all parts of a recommender system. It includes an architecture for privacy-enabled profile exchange, a distributed and domain-agnostic user model and a cross-domain recommendation algorithm. This enables users to receive recommendations for a target domain (e.g. food) based on any kind of previous interests.


title: Time Series Topic Modeling and Bursty Topic Detection of Correlated News and Twitter

abstract: News and twitter are sometimes closely correlated, while sometimes each of them has quite independent flow of information, due to the difference of the concerns of their information sources. In order to effectively capture the nature of those two text streams, it is very important to model both their correlation and their difference. This paper first models their correlation by applying a time series topic model to the document stream of the mixture of time series news and twitter. Next, we divide news streams and twitter into distinct two series of document streams, and then we apply our model of bursty topic detection based on the Kleinberg’s burst detection model. This approach successfully models the difference of the two time series topic models of news and twitter as each having independent information source and its own concern.


title: Pathways to Value from Business Analytics

abstract: Through what pathways does business analytics (BA) contribute to business value? To answer this question we argued that BA tools and capabilities only produce value if they are used, so we set out to explore different types of BA use. This led to the identification of two types of BA users—analytics professionals and analytics end-users (from executives to the shop floor)—which in turn led to identification of the three “pathways to value from business analytics”, namely provision of advisory services, creation and enhancement of BA tools and the BI-platform, and use of BA tools by end users. As a preliminary empirical assessment of the validity of these three pathways, we conducted eleven one-hour interviews with thirteen senior managers with a wide range of interests in BA. Results from those interviews are consistent with our claim that the three pathways exist and are important sources of business value from BA.


title: Advanced auditory menus: design and evaluation of auditory scroll bars

abstract: Auditory menus have the potential to make devices that use visual menus accessible to a wide range of users. Visually impaired users could especially benefit from the auditory feedback received during menu navigation. However, auditory menus are a relatively new concept, and there are very few guidelines that describe how to design them. This paper details how visual menu concepts may be applied to auditory menus in order to help develop design guidelines. Specifically, this set of studies examined possible ways of designing an auditory scrollbar for an auditory menu. The following different auditory scrollbar designs were evaluated: single-tone, double-tone, alphabetical grouping, and proportional grouping. Three different evaluations were conducted to determine the best design. The first two evaluations were conducted with sighted users, and the last evaluation was conducted with visually impaired users. The results suggest that pitch polarity does not matter, and proportional grouping is the best of the auditory scrollbar designs evaluated here.


title: A Survey on Trust in Autonomous Systems

abstract: As a result of the exponential growth in technology and computing in recent years, autonomous systems are becoming more relevant in our daily lives. As these systems evolve and become more complex, the notion of trust in human-autonomy interaction becomes a prominent issue that affects the performance of human-autonomy teaming. Prior studies indicate that humans have low levels of trust in semi and fully autonomous systems. In this survey, we review a wide range of technical papers and articles and go over the related experimental techniques in the literature of trust. We also explain limitations that are present in existing research works, and discuss open problems in this domain. It’s apparent that trust management is critical for the development of future artificial intelligence technologies.


title: Reservoir computing for emotion valence discrimination from EEG signals

abstract: In this paper we propose a new approach for feature dimensionality reduction based on Reservoir Computing (Echo State Networks). The method is validated with EEG data to identify the common neural signatures based on which the positive and negative valence of human emotions across multiple subjects can be reliably discriminated. The key step in the proposed approach is the Intrinsic Plasticity (IP) adaptation of the reservoir states. Learning Echo State Networks (ESN) with IP maximizes the entropy of the distribution of reservoir vectors given static data as a fixed input, which is supposed to follow Gaussian distribution. The equilibrium reservoir vector is extracted for each static input vector by iterating updates of the reservoir vector until it converges. Standard classification and clustering models provided with selected combinations of reservoir neurons are ranked based on their discriminate performance. The IP tuned ESNs is more powerful technique to map the high dimensional input feature vector into a low dimensional representation and improve the emotion valence discrimination compared to classical ESNs and Deep Neural Encoders.


title: An experimental comparison of naive Bayesian and keyword-based anti-spam filtering with personal e-mail messages

abstract: The growing problem of unsolicited bulk e-mail, also known as “spam”, has generated a need for reliable anti-spam e-mail filters. Filters of this type have so far been based mostly on manually constructed keyword patterns. An alternative approach has recently been proposed, whereby a Naive Bayesian classifier is trained automatically to detect spam messages. We test this approach on a large collection of personal e-mail messages, which we make publicly available in “encrypted” form contributing towards standard benchmarks. We introduce appropriate cost-sensitive measures, investigating at the same time the effect of attribute-set size, training-corpus size, lemmatization, and stop lists, issues that have not been explored in previous experiments. Finally, the Naive Bayesian filter is compared, in terms of performance, to a filter that uses keyword patterns, and which is part of a widely used e-mail reader.


Analyzing conferences in Twitter with Social Aviary


title: A Neurobiological Model of Visual Attention and Invariant Pattern Recognition Based on Dynamic Routing of Information

abstract: We present a biologically plausible model of an attentional mechanism for forming position- and scale-invariant representations of objects in the visual world. The model relies on a set of control neurons to dynamically modify the synaptic strengths of intracortical connections so that information from a windowed region of primary visual cortex (V1) is selectively routed to higher cortical areas. Local spatial relationships (i.e., topography) within the attentional window are preserved as information is routed through the cortex. This enables attended objects to be represented in higher cortical areas within an object-centered reference frame that is position and scale invariant. We hypothesize that the pulvinar may provide the control signals for routing information through the cortex. The dynamics of the control neurons are governed by simple differential equations that could be realized by neurobiologically plausible circuits. In preattentive mode, the control neurons receive their input from a low-level “saliency map” representing potentially interesting regions of a scene. During the pattern recognition phase, control neurons are driven by the interaction between top-down (memory) and bottom-up (retinal input) sources. The model respects key neurophysiological, neuroanatomical, and psychophysical data relating to attention, and it makes a variety of experimentally testable predictions.


title: A 5.3µA quiescent current fully-integrated low-dropout (LDO) regulator with Transient Recovery Time Enhancement

abstract: A new technique to decrease the transient recovery time in a very low-quiescent current low-dropout (LDO) voltage regulator is introduced. The new Transient Recovery Time Enhancement (TRTE) block comprises a voltage-to-current converter, current comparator and an NMOS output transistor. The proposed LDO using the TRTE block was fabricated in a 0.5-μm 2P3M CMOS process. The circuit operates at a total quiescent current of 5.3 μA with a maximum load current of 50 mA while supplying a regulated output voltage of 1.5 V to a 100 pF load. Experimental results indicate a drop-out voltage of 123 mV with an average recovery time of 5.22 μs under maximum load current changes.


title: A deep bidirectional LSTM approach for video-realistic talking head

abstract: This paper proposes a deep bidirectional long short-term memory approach in modeling the long contextual, nonlinear mapping between audio and visual streams for video-realistic talking head. In training stage, an audio-visual stereo database is firstly recorded as a subject talking to a camera. The audio streams are converted into acoustic feature, i.e. Mel-Frequency Cepstrum Coefficients (MFCCs), and their textual labels are also extracted. The visual streams, in particular, the lower face region, are compactly represented by active appearance model (AAM) parameters by which the shape and texture variations can be jointly modeled. Given pairs of the audio and visual parameter sequence, a DBLSTM model is trained to learn the sequence mapping from audio to visual space. For any unseen speech audio, whether it is original recorded or synthesized by text-to-speech (TTS), the trained DBLSTM model can predict a convincing AAM parameter trajectory for the lower face animation. To further improve the realism of the proposed talking head, the trajectory tiling method is adopted to use the DBLSTM predicted AAM trajectory as a guide to select a smooth real sample image sequence from the recorded database. We then stitch the selected lower face image sequence back to a background face video of the same subject, resulting in a video-realistic talking head. Experimental results show that the proposed DBLSTM approach outperforms the existing HMM-based approach in both objective and subjective evaluations.


title: Context-sensitive learning methods for text categorization

abstract: Two recently implemented machine-learning algorithms, RIPPER and sleeping-experts for phrases, are evaluated on a number of large text categorization problems. These algorithms both construct classifiers that allow the “context” of a word w to affect how (or even whether) the presence or absence of w will contribute to a classification. However, RIPPER and sleeping-experts differ radically in many other respects: differences include different notions as to what constitutes a context, different ways of combining contexts to construct a classifier, different methods to search for a combination of contexts, and different criteria as to what contexts should be included in such a combination. In spite of these differences, both RIPPER and sleeping-experts perform extremely well across a wide variety of categorization problems, generally outperforming previously applied learning methods. We view this result as a confirmation of the usefulness of classifiers that represent contextual information.


A Comparative Analysis of Crowdsourced Natural Language Corpora for Spoken Dialog Systems.


title: A study on password input method using authentication pattern and puzzle

abstract: The emergence of modern ubiquitous IT (such as cloud computing, grid computing etc.) increases the usage of web services. All these web service providers use user's personal information; (such as name, password) to authenticate the user. Furthermore, these web service providers use different methods to protect user's personal data. However, existing security is not secure enough; malicious attackers may steal user entered personal information such as users entered authentication information (ID, Password, certificate number, etc.) through keyboard hooking. In this regard, this paper proposes a keyboard hooking, SSA, replay attack protected password input method using authentication pattern and puzzle. The proposed password input method is based on entering the password using mouse. The puzzle image and puzzle pixels mapping is randomly generated.


title: A hybrid 2D/3D user interface for immersive object modeling

abstract: Virtual environments and other applications of computer-generated scenes are emerging. This increases the demand for tools for efficient creation of the virtual worlds and interaction with the components in the virtual world. Introduction of immersiveness in the modeling process seems promising, but a number of issues related to immersive modeling still have to be investigated. The authors present a framework for an immersive modeling system with a hybrid 2D/3D user interface. The designer is immersed in the design space, which improves his awareness of the design objects under consideration. 2D and 3D human-computer interaction techniques are combined in the user interface to improve the efficiency of the modeler. While 3D interaction is natural for a number of design tasks, 2D interaction is particularly helpful for interaction with menus and dialog boxes, for precise manipulations and for editing operations that are subject to constraints.


title: Intelligent shopping assistant system

abstract: In this paper we provide an intelligent and convenient shopping cart, Intelligent Shopping Assistant System (ISAS). With ISAS, customers can concentrate on what he/she has to buy, and do not be bothered where he/she has to buy or it is want to buy. In contrast to conventional shopping cart, two modes of autonomous functions are added to ISAS in order to reduce the labor of customers for pushing goods in the cart. First, ISAS can follow customers by steadily recognition and tracking, then customers could get more freedom which they didn't need push traditional shopping cart by themselves. Second, ISAS can go to products that customers want to buy by localization and navigation. In addition, Q-learning obstacles avoidance will execute when ISAS around is full of obstacles or people. For convenience of customers, they could construct shopping list by ISAS app. with just a few action. Also with two technic combination of data mining and deep learning, ISAS will provide products information and let customer decide it is want to buy.


title: Responses of the internal carotid artery to different endogenous vasoconstrictor substances.

abstract: In order to study the action of serotonin (5-HT), noradrenaline (NA), hypertensin (HT), prostaglandins A1, B1 and E2 (PGA1, PGB1 and PGE2) and vasopressin (VP), internal carotid arteries were isolated in situ from both cerebral and general circulation and perfused continuously with oxygenated Ringers' bicarbonate solution. The order of potencies of the vasoactive substances when administered intra-arterially was: 5-HT greater than HT greater than PGE2- greater than PGB1 greater than NA. The relative duration of the constrictor effects was: 5-HT less than PGA1-less than HT and PGE2 less than PGB1 and NA less than VP. The relaxation index of these substances on the vascular wall was: 5-HT less than PGE2 less than HT less than PGB1-less than NA less than PGA1 less than VP. Some of these substances, specifically PGB1, PGE2 and VP, frequently caused a residual constriction of the smooth muscle following their dilator effect. The role of these vasoactive substances in the development of vasospasm is discussed.


title: Motion planning with worker's trajectory prediction for assembly task partner robot

abstract: We have developed a novel assembly task partner robot to support workers in their task. This system, PaDY (in-time Parts/tools Delivery to You robot), delivers parts and tools to a worker by recognizing the worker's behavior in the car production line; thus, improving the efficiency of the work by reducing the worker's physical workload for picking parts and tools. For this purpose, it is necessary to plan the trajectory of the robot before the worker moves to the next location for another assembling task. First a prediction method for the worker's trajectory using a Markov model for a discretized work space into cells is proposed, then motion planning method is proposed using the predicted worker's trajectory and a mixture Gaussian distribution for each area corresponding to each procedure of the work process in the automobile coordinate system. Experimental results illustrate the validity of the proposed motion planning method.


BENCHMARKING NEURAL NETWORK ROBUSTNESS


title: Lung Segmentation in Chest Radiographs Using Anatomical Atlases With Nonrigid Registration

abstract: The National Library of Medicine (NLM) is developing a digital chest X-ray (CXR) screening system for deployment in resource constrained communities and developing countries worldwide with a focus on early detection of tuberculosis. A critical component in the computer-aided diagnosis of digital CXRs is the automatic detection of the lung regions. In this paper, we present a nonrigid registration-driven robust lung segmentation method using image retrieval-based patient specific adaptive lung models that detects lung boundaries, surpassing state-of-the-art performance. The method consists of three main stages: 1) a content-based image retrieval approach for identifying training images (with masks) most similar to the patient CXR using a partial Radon transform and Bhattacharyya shape similarity measure, 2) creating the initial patient-specific anatomical model of lung shape using SIFT-flow for deformable registration of training masks to the patient CXR, and 3) extracting refined lung boundaries using a graph cuts optimization approach with a customized energy function. Our average accuracy of 95.4% on the public JSRT database is the highest among published results. A similar degree of accuracy of 94.1% and 91.7% on two new CXR datasets from Montgomery County, MD, USA, and India, respectively, demonstrates the robustness of our lung segmentation approach.


title: Sentiment Analysis using Word-Graphs

abstract: The Word-Graph Sentiment Analysis Method is proposed to identify the sentiment that expressed in a microblog document using the sequence of the words that contains. The sequence of the words can be represented using graphs in which graph similarity metrics and classification algorithms can be applied to produce sentiment predictions. Experiments that were carried out with this method in a Twitter dataset validate the proposed model and allow us to further understand the metrics and the criteria that can be applied in words-graphs to predict the sentiment disposition of short, microblog documents.


title: PCB Defect Detection Using Image Subtraction Algorithm

abstract: A Printed Circuit board (PCB) is used to connect different electronic components mounted on it using pathways or tracks which is etched from copper sheets. An automatic PCB defect detection is an approach that can be used to counter difficulties occurred in manual inspection that can eliminate subjective aspects and then provides fast quantitative and dimensional assessments. In this study different approaches have been implemented on reference and test PCB images to detect defects on bare PCBs before etching process, since etching usually contributes most destructive defect found on PCBs. We first compare a PCB standard image with the PCB test image using a simple subtraction algorithm that can highlight the main problem-regions. The defects that can be detected are over etchings (opens), under etchings (shorts), holes etc.


title: Mirror neurons and the simulation theory of mind-reading

abstract: A new class of visuomotor neuron has been recently discovered in the monkey’s premotor cortex: mirror neurons. These neurons respond both when a particular action is performed by the recorded monkey and when the same action, performed by another individual, is observed. Mirror neurons appear to form a cortical system matching observation and execution of goal-related motor actions. Experimental evidence suggests that a similar matching system also exists in humans. What might be the functional role of this matching system? One possible function is to enable an organism to detect certain mental states of observed conspecifics. This function might be part of, or a precursor to, a more general mind-reading ability. Two different accounts of mindreading have been suggested. According to ‘theory theory’, mental states are represented as inferred posits of a naive theory. According to ‘simulation theory’, other people’s mental states are represented by adopting their perspective: by tracking or matching their states with resonant states of one’s own. The activity of mirror neurons, and the fact that observers undergo motor facilitation in the same muscular groups as those utilized by target agents, are findings that accord well with simulation theory but would not be predicted by theory theory.


Lessons learned from the past on mental health care of refugee children in Serbia


title: Distributed Top-k Keyword Search over Very Large Databases with MapReduce

abstract: In the last decade, keyword search over relational databases has been extensively studied because it promises to allow users lacking knowledge of structured query languages or unaware of the database schema to query the database in an intuitive way. The existing works about keyword search on databases proposed many approaches and have gain remarkable results. However, most of these approaches are designed for the centralized setting where keyword search is processed by only a single server. In reality, the scale of databases increases sharply and centralized methods hardly can handle keyword queries over these large databases. Moreover, processing keyword search over relational databases is a very time-consuming task, and the efficiency of the existing centralized approaches will degrade notably because the single server cannot provide enough computation power for the keyword search over very large databases. To address these challenges, we propose a distributed keyword search (DKS) approach with MapReduce and this approach can be well deployed on a cluster of servers to deal with keyword search over large databases in a parallel way.


title: Comparative analysis of LDR and OPT 101 detectors in reflectance type PPG sensor

abstract: Detection of blood volume change in the skin by using Photoplethysmogram (PPG) sensor is based on the principle that hemoglobin in the blood absorbs infrared light than the other tissue. Favorable optical window is around 990 nm range. The reflectance type photoplethysmographic sensor is designed using two different detectors. Objective is to compare the response of PPG sensor by using Light Dependent resistors (LDR) and OPT101 as a detector. Signal is recorded by placing the sensor on a finger tip for wavelength ranging from visible to near IR (400 to 1000 nm) range. It is observed that the PPG signal captured using LDR is around 660 nm wavelength but for the OPT101 response is 500 nm to 1000 nm. That is OPT 101 can be used to capture PPG in visible and infrared region. Brain mapping using optical sensor OPT101 is preferable for the measurement of blood volume and blood flow. For source of 660 nm, LDR or OPT 101 can be used to detect the peripheral pulse. LDR is not suitable for the measurement in infrared range.


title: Realization of a Sliding-Correlator-Based Continuous-Wave Pseudorandom Binary Phase-Coded Radar Operating in $W$ -Band

abstract: In this paper, we present a continuous-wave (CW) mm-wave radar, which is based on binary phase-coded pseudorandom signals. The receiving section is operated as a sliding correlator, which allows to greatly reduce the bandwidth required in the baseband blocks of the system. A certain class of pseudorandom sequences, with so-called almost perfect autocorrelation properties, is used as probing signal. This leads to a good dynamic range, even for short sequence lengths. The presented hardware is based on a SiGe front-end chip, which includes a frequency multiplier to generate a 79-GHz carrier from a fixed-frequency source operating at 4.39 GHz. A field-programmable gate array generates the binary sequences with a bit rate of 1 Gb/s. This sequence is modulated onto the $W$ -band carrier by binary phase-shift keying modulators realized within the SiGe chip. In this way, a flexible prototype was realized that was used to perform different radar experiments ranging from static single-target scenarios to range-Doppler measurements with multiple targets. It could be shown that the realized pseudorandom radar achieves a range resolution of 15 cm and ranging standard deviations in the millimeter range. This is comparable to frequency-modulated CW measurements with a bandwidth of 2 GHz, which were carried out using the same hardware for comparison purposes.


title: EDAL: an energy-efficient, delay-aware, and lifetime-balancing data collection protocol for heterogeneous wireless sensor networks

abstract: Our work in this paper stems from our insight that recent research efforts on open vehicle routing (OVR) problems, an active area in operations research, are based on similar assumptions and constraints compared to sensor networks. Therefore, it may be feasible that we could adapt these techniques in such a way that they will provide valuable solutions to certain tricky problems in the wireless sensor network (WSN) domain. To demonstrate that this approach is feasible, we develop one data collection protocol called EDAL, which stands for Energy-efficient Delay-aware Lifetime-balancing data collection. The algorithm design of EDAL leverages one result from OVR to prove that the problem formulation is inherently NP-hard. Therefore, we proposed both a centralized heuristic to reduce its computational overhead and a distributed heuristic to make the algorithm scalable for large-scale network operations. We also develop EDAL to be closely integrated with compressive sensing, an emerging technique that promises considerable reduction in total traffic cost for collecting sensor readings under loose delay bounds. Finally, we systematically evaluate EDAL to compare its performance to related protocols in both simulations and a hardware testbed.


title: Detecting Collusive Spamming Activities in Community Question Answering

abstract: Community Question Answering (CQA) portals provide rich sources of information on a variety of topics. However, the authenticity and quality of questions and answers (Q&As) has proven hard to control. In a troubling direction, the widespread growth of crowdsourcing websites has created a large-scale, potentially difficult-to-detect workforce to manipulate malicious contents in CQA. The crowd workers who join the same crowdsourcing task about promotion campaigns in CQA collusively manipulate deceptive Q&As for promoting a target (product or service). The collusive spamming group can fully control the sentiment of the target. How to utilize the structure and the attributes for detecting manipulated Q&As? How to detect the collusive group and leverage the group information for the detection task? ::: ::: To shed light on these research questions, we propose a unified framework to tackle the challenge of detecting collusive spamming activities of CQA. First, we interpret the questions and answers in CQA as two independent networks. Second, we detect collusive question groups and answer groups from these two networks respectively by measuring the similarity of the contents posted within a short duration. Third, using attributes (individual-level and group-level) and correlations (user-based and content-based), we proposed a combined factor graph model to detect deceptive Q&As simultaneously by combining two independent factor graphs. With a large-scale practical data set, we find that the proposed framework can detect deceptive contents at early stage, and outperforms a number of competitive baselines.


title: Principles of Constraint Programming

abstract: Scheduling, vehicle routing and timetabling are all examples of constraint problems, and methods to solve them rely on the idea of constraint propagation and search. This book meets the need for a modern, multidisciplinary introduction to the field that covers foundations and applications. Written by Krzysztof Apt, an authority on the subject, it will be welcomed by graduate students and professionals. With the insertion of constraint techniques into programming environments, new developments have accelerated the solution process. Constraint programming combines ideas from artificial intelligence, programming languages, databases, and operational research.


title: Social Resilience in Online Communities: The Autopsy of Friendster

abstract: We empirically analyze five online communities: Friendster, Livejournal, Facebook, Orkut, and Myspace, to study how social networks decline. We define social resilience as the ability of a community to withstand changes. We do not argue about the cause of such changes, but concentrate on their impact. Changes may cause users to leave, which may trigger further leaves of others who lost connection to their friends. This may lead to cascades of users leaving. A social network is said to be resilient if the size of such cascades can be limited. To quantify resilience, we use the k-core analysis, to identify subsets of the network in which all users have at least k friends. These connections generate benefits (b) for each user, which have to outweigh the costs (c) of being a member of the network. If this difference is not positive, users leave. After all cascades, the remaining network is the k-core of the original network determined by the cost-to-benefit (c/b) ratio. By analysing the cumulative distribution of k-cores we are able to calculate the number of users remaining in each community. This allows us to infer the impact of the c/b ratio on the resilience of these online communities. We find that the different online communities have different k-core distributions. Consequently, similar changes in the c/b ratio have a different impact on the amount of active users. Further, our resilience analysis shows that the topology of a social network alone cannot explain its success of failure. As a case study, we focus on the evolution of Friendster. We identify time periods when new users entering the network observed an insufficient c/b ratio. This measure can be seen as a precursor of the later collapse of the community. Our analysis can be applied to estimate the impact of changes in the user interface, which may temporarily increase the c/b ratio, thus posing a threat for the community to shrink, or even to collapse.


title: Incremental Solid Modeling from Sparse and Omnidirectional Structure-from-Motion Data

abstract: This paper introduces a sparse and incremental 2-manifold surface reconstruction method. It uses a sparse 3D point cloud generated by a Structure-from-Motion algorithm (SfM) as its main input as opposed to the more common dense algorithms. Furthermore, our method is incremental: the surface is updated for every new camera pose computed by SfM, and the update occurs in a small neighborhood of the new camera pose. Compared to the other surface reconstruction methods, our method has the advantage to have all these properties at the same time. The quality and execution time of the proposed algorithm is evaluated on a large scale (2.5 km.) real sequence taken in an urban environment, and the method is quantitatively evaluated on a synthetic urban scene.


title: OUR-CVFH – Oriented, Unique and Repeatable Clustered Viewpoint Feature Histogram for Object Recognition and 6DOF Pose Estimation

abstract: We propose a novel method to estimate a unique and repeatable reference frame in the context of 3D object recognition from a single viewpoint based on global descriptors. We show that the ability of defining a robust reference frame on both model and scene views allows creating descriptive global representations of the object view, with the beneficial effect of enhancing the spatial descriptiveness of the feature and its ability to recognize objects by means of a simple nearest neighbor classifier computed on the descriptor space. Moreover, the definition of repeatable directions can be deployed to efficiently retrieve the 6DOF pose of the objects in a scene. We experimentally demonstrate the effectiveness of the proposed method on a dataset including 23 scenes acquired with the Microsoft Kinect sensor and 25 full-3D models by comparing the proposed approach with state-of-the-art global descriptors. A substantial improvement is presented regarding accuracy in recognition and 6DOF pose estimation, as well as in terms of computational performance.


title: Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations

abstract: The key idea behind the unsupervised learning of disentangled representations is that real-world data is generated by a few explanatory factors of variation which can be recovered by unsupervised learning algorithms. In this paper, we provide a sober look at recent progress in the field and challenge some common assumptions. We first theoretically show that the unsupervised learning of disentangled representations is fundamentally impossible without inductive biases on both the models and the data. Then, we train more than 12000 models covering most prominent methods and evaluation metrics in a reproducible large-scale experimental study on seven different data sets. We observe that while the different methods successfully enforce properties ``encouraged'' by the corresponding losses, well-disentangled models seemingly cannot be identified without supervision. Furthermore, increased disentanglement does not seem to lead to a decreased sample complexity of learning for downstream tasks. Our results suggest that future work on disentanglement learning should be explicit about the role of inductive biases and (implicit) supervision, investigate concrete benefits of enforcing disentanglement of the learned representations, and consider a reproducible experimental setup covering several data sets.


title: Hands in action: real-time 3D reconstruction of hands in interaction with objects

abstract: This paper presents a method for vision based estimation of the pose of human hands in interaction with objects. Despite the fact that most robotics applications of human hand tracking involve grasping and manipulation of objects, the majority of methods in the literature assume a free hand, isolated from the surrounding environment. Our hand tracking method is non-parametric, performing a nearest neighbor search in a large database (100000 entries) of hand poses with and without grasped objects. The system operates in real time, it is robust to self occlusions, object occlusions and segmentation errors, and provides full hand pose reconstruction from markerless video. Temporal consistency in hand pose is taken into account, without explicitly tracking the hand in the high dimensional pose space.


title: A comparison of extended fingerprint hashing and locality sensitive hashing for binary audio fingerprints

abstract: Hash tables have been proposed for the indexing of high-dimensional binary vectors, specifically for the identification of media by fingerprints. In this paper we develop a new model to predict the performance of a hash-based method (Fingerprint Hashing) under varying levels of noise. We show that by the adjustment of two parameters, robustness to a higher level of noise is achieved. We extend Fingerprint Hashing to a multi-table range search (Extended Fingerprint Hashing) and show this approach also increases robustness to noise. We then show the relationship between Extended Fingerprint Hashing and Locality Sensitive Hashing and investigate design choices for dealing with higher noise levels. If index size must be held constant, the Extended Fingerprint Hash is a superior method. We also show that to achieve similar performance at a given level of noise a Locality Sensitive Hash requires nearly a six-fold increase in index size which is likely to be impractical for many applications.


title: A Quantitative Framework for Task Allocation in Distributed Agile Software Development

abstract: Distributed agile software development is a promising paradigm, addressing the necessities of emergent software application markets that are described by huge user base and small time to market characteristics. A key decision involved during the development process, is task allocation to team members. An appropriate task-member assignment facilitates project management, lessens the complexities and influences chances of project success. Task allocation becomes a more challenging activity in a distributed agile software development due to insufficient understanding of different factors and dependencies involved. We propose a task allocation framework comprising of two phases: one, identifying factors and dependencies that strongly influence the task allocation decision; two, proposing a quantitative method that allocates tasks to team members who best match the task requirements. Task requirements are expressed as capabilities, catering for different aspects, such as technical, personal, and environment. Our method stays transparent to the targeted objectives, in this case the best match. Other objectives, such as quality and cost may be introduced conveniently; even multiple objectives can be addressed. Such a method also allows quality evaluation of task-member assignment during and after the project completion, toward minimizing related risks.


title: FlowFox: a web browser with flexible and precise information flow control

abstract: We present FlowFox, the first fully functional web browser that implements a precise and general information flow control mechanism for web scripts based on the technique of secure multi-execution. We demonstrate how FlowFox subsumes many ad-hoc script containment countermeasures developed over the last years. We also show that FlowFox is compatible with the current web, by investigating its behavior on the Alexa top-500 web sites, many of which make intricate use of JavaScript. The performance and memory cost of FlowFox is substantial (a performance cost of around 20% on macro benchmarks for a simple two level policy), but not prohibitive. Our prototype implementation shows that information flow enforcement based on secure multi-execution can be implemented in full-scale browsers. It can support powerful, yet precise policies refining the same-origin-policy in a way that is compatible with existing websites.


title: DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs

abstract: In this work we address the task of semantic image segmentation with Deep Learning and make three main contributions that are experimentally shown to have substantial practical merit. First , we highlight convolution with upsampled filters, or ‘atrous convolution’, as a powerful tool in dense prediction tasks. Atrous convolution allows us to explicitly control the resolution at which feature responses are computed within Deep Convolutional Neural Networks. It also allows us to effectively enlarge the field of view of filters to incorporate larger context without increasing the number of parameters or the amount of computation. Second , we propose atrous spatial pyramid pooling (ASPP) to robustly segment objects at multiple scales. ASPP probes an incoming convolutional feature layer with filters at multiple sampling rates and effective fields-of-views, thus capturing objects as well as image context at multiple scales. Third , we improve the localization of object boundaries by combining methods from DCNNs and probabilistic graphical models. The commonly deployed combination of max-pooling and downsampling in DCNNs achieves invariance but has a toll on localization accuracy. We overcome this by combining the responses at the final DCNN layer with a fully connected Conditional Random Field (CRF), which is shown both qualitatively and quantitatively to improve localization performance. Our proposed “DeepLab” system sets the new state-of-art at the PASCAL VOC-2012 semantic image segmentation task, reaching 79.7 percent mIOU in the test set, and advances the results on three other datasets: PASCAL-Context, PASCAL-Person-Part, and Cityscapes. All of our code is made publicly available online.


title: Rumors at the Speed of Light? Modeling the Rate of Rumor Transmission During Crisis

abstract: Social media have become an established feature of the dynamic information space that emerges during crisis events. Both emergency responders and the public use these platforms to search for, disseminate, challenge, and make sense of information during crises. In these situations rumors also proliferate, but just how fast such information can spread is an open question. We address this gap, modeling the speed of information transmission to compare retransmission times across content and context features. We specifically contrast rumor-affirming messages with rumor-correcting messages on Twitter during a notable hostage crisis to reveal differences in transmission speed. Our work has important implications for the growing field of crisis informatics.


title: Letter-based classification of Arabic scripts style in ancient Arabic manuscripts: Preliminary results

abstract: Classifying ancient Arabic manuscripts based on handwriting styles is one of the important roles in the field of paleography. Recognizing the style of handwriting in Arabic manuscripts helps in identifying the origin and date of ancient documents. In this paper we proposed using segmented letters from Arabic manuscripts to recognize handwriting style. Both Gabor Filters (GF) and Local Binary Pattern (LBP) are used to extract features from letters. The fused features are sent to Support Vector Machine (SVM) classifier. Experimental results have been implemented using manuscripts images from the Qatar National Library (QNL) and other online datasets. Better results are achieved when both GF and LBP descriptors are combined. The recognized Handwritten Arabic styles are Diwani, Kufic, Naskh, Farsi, Ruq'ah and Thuluth.


title: A survey on decentralized random access MAC protocols for cognitive radio networks

abstract: The scarcity of spectrum radio and the immense growth of mobile applications that demand real-time data communications have raised several challenges in the level of medium access control (MAC) design which impose the necessity for a real review and enhancement of MAC protocols. The MAC layer has gained new capabilities with cognitive radio (CR) and opportunistic spectrum access. It is one of the challenging problems in cognitive radio systems. In this paper, we present a survey of some decentralized MAC protocols for CR networks with analysis and comparative assessment of CR-MAC protocols using some Key Performance Indicators.


title: A third-order continuous-time sigma-delta modulator for Bluetooth

abstract: This paper presents the design of a third-order continuous-time (CT) single-bit active-RC sigma-delta (ΣΔ) modulator for Bluetooth application. Through the use of the architecture, cascade of resonators with distributed feedback (CRFB), the signal bandwidth can be improved without increasing the order of the modulator. All integrators are implemented by active-RC type to have better linearity. Furthermore, in order to reduce the effect of the clock jitter, the feedback digital-to-analog converter (DAC) shape is realized by non-return-to-zero (NRZ). The modulator is designed in a standard digital 0.18µm CMOS process with a chip area of 1.32×1.23 mm2. The measurement results show that the modulator achieves 56.8dB SNDR and 60dB dynamic range over 1MHz signal bandwidth, consuming 22.2mW at 1.8V supply.


title: Optimal Stopping of Markov Processes: Hilbert Space Theory, Approximation Algorithms, and an Application to Pricing High-Dimensional Financial Derivatives

abstract: The authors develop a theory characterizing optimal stopping times for discrete-time ergodic Markov processes with discounted rewards. The theory differs from prior work by its view of per-stage and terminal reward functions as elements of a certain Hilbert space. In addition to a streamlined analysis establishing existence and uniqueness of a solution to Bellman's equation, this approach provides an elegant framework for the study of approximate solutions. In particular, the authors propose a stochastic approximation algorithm that tunes weights of a linear combination of basis functions in order to approximate a value function. They prove that this algorithm converges (almost surely) and that the limit of convergence has some desirable properties. The utility of the approximation method is illustrated via a computational case study involving the pricing of a path dependent financial derivative security that gives rise to an optimal stopping problem with a 100-dimensional state space.


title: Credibility Theory in a Fuzzy Environment

abstract: In insurance, credibility theory (CT) is used to develop a weighted average of the claims experience of an individual contract and the experience for the whole portfolio, where the weight factor is the credibility attached to the individual experience. Recently, Liu and Liu (2002) and Liu (2007), in the study of the behavior of fuzzy phenomena, formulated an alternate version of credibility theory, which involves a weighted average based on the concepts of possibility measure and necessity measure. This latter version of credibility theory will hereafter be referred to as credibility theory in a fuzzy environment (CT-F).


title: Joint Representation Learning of Text and Knowledge for Knowledge Graph Completion

abstract: Joint representation learning of text and knowledge within a unified semantic space enables us to perform knowledge graph completion more accurately. In this work, we propose a novel framework to embed words, entities and relations into the same continuous vector space. In this model, both entity and relation embeddings are learned by taking knowledge graph and plain text into consideration. In experiments, we evaluate the joint learning model on three tasks including entity prediction, relation prediction and relation classification from text. The experiment results show that our model can significantly and consistently improve the performance on the three tasks as compared with other baselines.


title: Semi-direct EKF-based monocular visual-inertial odometry

abstract: We propose a novel monocular visual inertial odometry algorithm that combines the advantages of EKF-based approaches with those of direct photometric error minimization methods. The method is based on sparse, very small patches and incorporates the minimization of photometric error directly into the EKF measurement model so that inertial data and vision-based surface measurements are used simultaneously during camera pose estimation. We fuse vision-based and inertial measurements almost at the raw-sensor level, allowing the estimated system state to constrain and guide image-space measurements. Our formulation allows for an efficient implementation that runs in real-time on a standard CPU and has several appealing and unique characteristics such as being robust to fast camera motion, in particular rotation, and not depending on the presence of corner-like features in the scene. We experimentally demonstrate robust and accurate performance compared to ground truth and show that our method works on scenes containing only non-intersecting lines.


title: Skin cancer reorganization and classification with deep neural network

abstract: As one kind of skin cancer, melanoma is very dangerous. Dermoscopy based early detection and recarbonization strategy is critical for melanoma therapy. However, well-trained dermatologists dominant the diagnostic accuracy. In order to solve this problem, many effort focus on developing automatic image analysis systems. Here we report a novel strategy based on deep learning technique, and achieve very high skin lesion segmentation and melanoma diagnosis accuracy: 1) we build a segmentation neural network (skin_segnn), which achieved very high lesion boundary detection accuracy; 2) We build another very deep neural network based on Google inception v3 network (skin_recnn) and its well-trained weight. The novel designed transfer learning based deep neural network skin_inceptions_v3_nn helps to achieve a high prediction accuracy.


title: Streaming High-Quality Mobile Video with Multipath TCP in Heterogeneous Wireless Networks

abstract: The proliferating wireless infrastructures with complementary characteristics prompt the bandwidth aggregation for concurrent video transmission in heterogeneous access networks. Multipath TCP (MPTCP) is an important transport-layer protocol recommended by IETF to integrate different access medium (e.g., Cellular and Wi-Fi). This paper investigates the problem of mobile video delivery using MPTCP in heterogeneous wireless networks with multihomed terminals. To achieve the optimal quality of real-time video streaming, we have to seriously consider the path asymmetry in different access networks and the disadvantages of the data retransmission mechanism in MPTCP. Motivated by addressing these critical issues, this study presents a novel quAlity-Driven MultIpath TCP (ADMIT) scheme that integrates the utility maximization based Forward Error Correction (FEC) coding and rate allocation. We develop an analytical framework to model the MPTCP-based video delivery quality over multiple communication paths. ADMIT is able to effectively integrate the most reliable access networks with FEC coding to minimize the end-to-end video distortion. The performance of ADMIT is evaluated through extensive semi-physical emulations in Exata involving H.264 video streaming. Experimental results show that ADMIT outperforms the reference transport protocols in terms of video PSNR (Peak Signal-to-Noise Ratio), end-to-end delay, and goodput. Thus, we recommend ADMIT for streaming high-quality mobile video in heterogeneous wireless networks with multihomed terminals.


BTCSpark : Scalable Analysis of the Bitcoin Blockchain using Spark


title: Subjectivity and Sentiment Analysis of Modern Standard Arabic and Arabic Microblogs

abstract: Though much research has been conducted on Subjectivity and Sentiment Analysis (SSA) during the last decade, little work has focused on Arabic. In this work, we focus on SSA for both Modern Standard Arabic (MSA) news articles and dialectal Arabic microblogs from Twitter. We showcase some of the challenges associated with SSA on microblogs. We adopted a random graph walk approach to extend the Arabic SSA lexicon using ArabicEnglish phrase tables, leading to improvements for SSA on Arabic microblogs. We used different features for both subjectivity and sentiment classification including stemming, part-of-speech tagging, as well as tweet specific features. Our classification features yield results that surpass Arabic SSA results in the literature.