title: Compact broadband amplifiers with up to 105 GHz bandwidth in SiGe BiCMOS

abstract: In this paper the design and measurements of a single-ended and a fully differential broadband amplifier are presented. The amplifiers are based on a lumped circuit design topology and are highly compact with an active chip area of around 0.02mm2. The amplifiers are fabricated in a 0.13-µm SiGe BiCMOS HBT technology. Wide bandwidth is achieved with a common-base input stage and an output cascode stage buffered with emitter followers. A 3-dB bandwidth of around 91 GHz and 105 GHz, and a small-signal gain of 13 dB and 12 dB are achieved for the single-ended and the differential-ended amplifiers, respectively. The differential-ended amplifier shows a superior performance with an input return loss greater than 10 dB from DC-110 GHz, ±1 dB in-band gain ripple and a low group delay variation of ±6 psec. Large signal measurements show a single-ended saturated power of around 2.8 dBm.


title: Attitude estimation and control of a quadrocopter

abstract: The research interest in unmanned aerial vehicles (UAV) has grown rapidly over the past decade. UAV applications range from purely scientific over civil to military. Technical advances in sensor and signal processing technologies enable the design of light weight and economic airborne platforms. This paper presents a complete mechatronic design process of a quadrotor UAV, including mechanical design, modeling of quadrotor and actuator dynamics and attitude stabilization control. Robust attitude estimation is achieved by fusion of low-cost MEMS accelerometer and gyroscope signals with a Kalman filter. Experiments with a gimbal mounted quadrotor testbed allow a quantitative analysis and comparision of the PID and Integral-Backstepping (IB) controller design for attitude stabilization with respect to reference signal tracking, disturbance rejection and robustness.


RNN-based labeled data generation for spoken language understanding.


title: Social Internet of Things: The Potential of the Internet of Things for Defining Human Behaviours

abstract: The potential of the artificial intelligence, data mining and the so-called Big Data to build solutions based on the understanding of human behaviours is being extended with the capabilities of the Future Internet and the Internet of Things (IoT). The described potential of data and networks have been analyzed by the complex network theory, offering a specialized branch, the defined as Human Dynamics. The goal with the Internet of Things in the social area is to describe in real-time the human behaviours and activities. These goals are starting to be feasible through the quantity of data provided by the personal devices such as smart phone, and the smart environments such as Smart cities that makes more intelligent the environment and offer a smart space that sense our movements, actions, and the evolution of the ecosystem. This work analyses the ecosystem defined through the triangle formed by Big Data, Smart Cities and Personal/Wearable Computing to determinate human behaviours and human dynamics. For this purpose, the life-cycle of human dynamics determinations in Smart Cities have been analysed in order to determinate the current status of the technology, challenges, and opportunities.


title: Loss of E-cadherin promotes migration and invasion of cholangiocarcinoma cells and serves as a potential marker of metastasis

abstract: Tumor progression is characterized by loss of cell adhesion and increase of invasion and metastasis. E-cadherin, a cell adhesion molecule, is frequently downregulated and has been proposed as an important mediator in epithelial-mesenchymal transition (EMT) in tumors. In this study, we investigated the expression of E-cadherin and its association with cancer invasion and prognosis in cholangiocarcinoma (CCA). Immunohistochemistry results demonstrated a statistically significant association between the positive metastasis status with low E-cadherin protein expression in human CCA tissues (P = 0.04). Statistical trends were identified for low E-cadherin level and shorter survival time (P = 0.08). Targeting the E-cadherin expression in CCA cells with siRNA caused upregulation of vimentin, a mesenchymal marker, and disappearance of the E-cadherin/β-catenin adhesion complex from cell membranes. Moreover, migration and invasion abilities of the cells were increased under this condition. These findings suggest that reduction of E-cadherin contributes to CCA progression by attenuating the strength of cellular adhesion, which affects motility as well as regulating the expression of EMT-related genes during CCA invasion and metastasis. Thus, E-cadherin can act as a central modulator of tumor cell phenotype and is a potential metastasis marker in CCA.


title: Using coarse GPS data to quantify city-scale transportation system resilience to extreme events

abstract: Abstract This article proposes a method to quantitatively measure the resilience of transportation systems using GPS data from probe vehicles such as taxis. The granularity of the GPS data necessary for the method is relatively coarse; it only requires coordinates for the beginning and end of trips, the metered distance, and the total travel time. The method works by computing the historical distribution of pace (normalized travel times) between various regions of a city and measuring the pace deviations during an unusual event. Periods of time containing extreme deviations are identified as events. The method is applied to a dataset of nearly 700 million taxi trips in New York City, which is used to analyze the city transportation infrastructure resilience to Hurricane Sandy. The analysis indicates that Hurricane Sandy impacted traffic conditions for more than five days, and caused a peak delay of two minutes per mile. Practically, it identifies that the evacuation announcements coincided with only minor disruptions, but significant delays were encountered during the post-disaster response period. Since the implementation of this method is very efficient, it could potentially be used as an online monitoring tool, representing a first step toward quantifying city scale resilience with coarse GPS data.


title: Industrial Big Data Analytics for Prediction of Remaining Useful Life Based on Deep Learning

abstract: Due to the recent development of cyber-physical systems, big data, cloud computing, and industrial wireless networks, a new era of industrial big data is introduced. Deep learning, which brought a revolutionary change in computer vision, natural language processing, and a variety of other applications, has significant potential for solutions providing in sophisticated industrial applications. In this paper, a concept of device electrocardiogram (DECG) is presented, and an algorithm based on deep denoising autoencoder (DDA) and regression operation is proposed for the prediction of the remaining useful life of industrial equipment. First, the concept of electrocardiogram is explained. Then, a problem statement based on manufacturing scenario is presented. Subsequently, the architecture of the proposed algorithm called integrated DDA and the algorithm workflow are provided. Moreover, DECG is compared with traditional factory information system, and the feasibility and effectiveness of the proposed algorithm are validated experimentally. The proposed concept and algorithm combine typical industrial scenario and advance artificial intelligence, which has great potential to accelerate the implementation of industry 4.0.


title: Learning Decision Trees for Unbalanced Data

abstract: Learning from unbalanced datasets presents a convoluted problem in which traditional learning algorithms may perform poorly. The objective functions used for learning the classifiers typically tend to favor the larger, less important classes in such problems. This paper compares the performance of several popular decision tree splitting criteria --- information gain, Gini measure, and DKM --- and identifies a new skew insensitive measure in Hellinger distance. We outline the strengths of Hellinger distance in class imbalance, proposes its application in forming decision trees, and performs a comprehensive comparative analysis between each decision tree construction method. In addition, we consider the performance of each tree within a powerful sampling wrapper framework to capture the interaction of the splitting metric and sampling. We evaluate over this wide range of datasets and determine which operate best under class imbalance.


title: Detection of glaucoma using retinal fundus images

abstract: This paper proposes an image processing technique for the detection of glaucoma which mainly affects the optic disc by increasing the cup size. During early stages it was difficult to detect Glaucoma, which is in fact second leading cause of blindness. In this paper glaucoma is categorized through extraction of features from retinal fundus images. The features include (i) Cup to Disc Ratio (CDR), which is one of the primary physiological parameter for the diagnosis of glaucoma and (ii) Ratio of Neuroretinal Rim in inferior, superior, temporal and nasal quadrants i.e. (ISNT quadrants) for verification of the ISNT rule. The novel technique is implemented on 80 retinal images and an accuracy of 97.5% is achieved taking an average computational time of 0.8141 seconds.


title: Implementation of a zero voltage switching Sepic-Cuk converter

abstract: This paper presents the system analysis and implementation of a soft switching Sepic-Cuk converter to achieve zero voltage switching (ZVS). In the proposed converter, the Sepic and Cuk topologies are combined together in the output side. The features of the proposed converter are to reduce the circuit components (share the power components in the transformer primary side) and to share the load current. Active snubber is connected in parallel with the primary side of transformer to release the energy stored in the leakage inductor of transformer and to limit the peak voltage stress of switching devices when the main switch is turned off. The active snubber can achieve ZVS turn-on for power switches. Experimental results, taken from a laboratory prototype rated at 300W, are presented to verify the effectiveness of the proposed converter. I. Introduction Modern


title: A mobile application framework for the geospatial web

abstract: In this paper we present an application framework that leverages geospatial content on the World Wide Web by enabling innovative modes of interaction and novel types of user interfaces on advanced mobile phones and PDAs. We discuss the current development steps involved in building mobile geospatial Web applications and derive three technological pre-requisites for our framework: spatial query operations based on visibility and field of view, a 2.5D environment model, and a presentationindependent data exchange format for geospatial query results. We propose the Local Visibility Model as a suitable XML-based candidate and present a prototype implementation.


title: Magnetic Resonant Coupling As a Potential Means for Wireless Power Transfer to Multiple Small Receivers

abstract: Wireless power transfer via magnetic resonant coupling is experimentally demonstrated in a system with a large source coil and either one or two small receivers. Resonance between source and load coils is achieved with lumped capacitors terminating the coils. A circuit model is developed to describe the system with a single receiver, and extended to describe the system with two receivers. With parameter values chosen to obtain good fits, the circuit models yield transfer frequency responses that are in good agreement with experimental measurements over a range of frequencies that span the resonance. Resonant frequency splitting is observed experimentally and described theoretically for the multiple receiver system. In the single receiver system at resonance, more than 50% of the power that is supplied by the actual source is delivered to the load. In a multiple receiver system, a means for tracking frequency shifts and continuously retuning the lumped capacitances that terminate each receiver coil so as to maximize efficiency is a key issue for future work.


title: Web-based visualization of large 3D urban building models

abstract: Adaptive rendering of large urban building models has become an important research issue in three-dimensional (3D) geographic information system (GIS) applications. This study explores a way for rendering web-based 3D urban building models. A client–server hybrid rendering approach is presented for large 3D city models, stored on a remote server through a network. The approach combines an efficient multi-hierarchical building representation with a novel image-based method, 3D image impostor generated on demand by a remote server. This approach allows transferring complex scenes progressively while keeping high visualization quality. We also evaluated the rendering and data transferring performance of the proposed approach.


title: Urban Magnetism Through The Lens of Geo-tagged Photography

abstract: There is an increasing trend of people leaving digital traces through social media. This reality opens new horizons for urban studies. With this kind of data, researchers and urban planners can detect many aspects of how people live in cities and can also suggest how to transform cities into more efficient and smarter places to live in. In particular, their digital trails can be used to investigate tastes of individuals, and what attracts them to live in a particular city or to spend their vacation there. In this paper we propose an unconventional way to study how people experience the city, using information from geotagged photographs that people take at different locations. We compare the spatial behavior of residents and tourists in 10 most photographed cities all around the world. The study was conducted on both a global and local level. On the global scale we analyze the 10 most photographed cities and measure how attractive each city is for people visiting it from other cities within the same country or from abroad. For the purpose of our analysis we construct the users’ mobility network and measure the strength of the links between each pair of cities as a level of attraction of people living in one city (i.e., origin) to the other city (i.e., destination). On the local level we study the spatial distribution of user activity and identify the photographed hotspots inside each city. The proposed methodology and the results of our study are a low cost mean to characterize touristic activity within a certain location and can help cities strengthening their touristic potential.


title: Anytime Stereo Image Depth Estimation on Mobile Devices

abstract: Many applications of stereo depth estimation in robotics require the generation of accurate disparity maps in real time under significant computational constraints. Current state-of-the-art algorithms force a choice between either generating accurate mappings at a slow pace, or quickly generating inaccurate ones, and additionally these methods typically require far too many parameters to be usable on power- or memory-constrained devices. Motivated by these shortcomings, we propose a novel approach for disparity prediction in the anytime setting. In contrast to prior work, our end-to-end learned approach can trade off computation and accuracy at inference time. Depth estimation is performed in stages, during which the model can be queried at any time to output its current best estimate. Our final model can process $1242 \times 375$ resolution images within a range of 10-35 FPS on an NVIDIA Jetson TX2 module with only marginal increases in error – using two orders of magnitude fewer parameters than the most competitive baseline. The source code is available at https://github.com/mileyan/AnyNet.


title: Differentiable Fine-grained Quantization for Deep Neural Network Compression

abstract: Neural networks have shown great performance in cognitive tasks. When deploying network models on mobile devices with limited resources, weight quantization has been widely adopted. Binary quantization obtains the highest compression but usually results in big accuracy drop. In practice, 8-bit or 16-bit quantization is often used aiming at maintaining the same accuracy as the original 32-bit precision. We observe different layers have different accuracy sensitivity of quantization. Thus judiciously selecting different precision for different layers/structures can potentially produce more efficient models compared to traditional quantization methods by striking a better balance between accuracy and compression rate. In this work, we propose a fine-grained quantization approach for deep neural network compression by relaxing the search space of quantization bitwidth from discrete to a continuous domain. The proposed approach applies gradient descend based optimization to generate a mixed-precision quantization scheme that outperforms the accuracy of traditional quantization methods under the same compression rate.


title: A Survey of Available Corpora for Building Data-Driven Dialogue Systems

abstract: During the past decade, several areas of speech and language understanding have witnessed substantial breakthroughs from the use of data-driven models. In the area of dialogue systems, the trend is less obvious, and most practical systems are still built through significant engineering and expert knowledge. Nevertheless, several recent results suggest that data-driven approaches are feasible and quite promising. To facilitate research in this area, we have carried out a wide survey of publicly available datasets suitable for data-driven learning of dialogue systems. We discuss important characteristics of these datasets and how they can be used to learn diverse dialogue strategies. We also describe other potential uses of these datasets, such as methods for transfer learning between datasets and the use of external knowledge, and discuss appropriate choice of evaluation metrics for the learning objective.


title: On the Usefulness of Fibonacci Compression Codes

abstract: Recent publications advocate the use of various variable length codes for which each codeword consists of an integral number of bytes in compression applications using large alphabets. This paper shows that another tradeoff with similar properties can be obtained by Fibonacci codes. These are fixed codeword sets, using binary representations of integers based on Fibonacci numbers of order m ≥ 2. Fibonacci codes have been used before, and this paper extends previous work presenting several novel features. In particular, the compression efficiency is analyzed and compared to that of dense codes, and various table-driven decoding routines are suggested.


title: Discovering regulatory and signalling circuits in molecular interaction networks

abstract: Motivation: In model organisms such as yeast, large databases of protein–protein and protein-DNA interactions have become an extremely important resource for the study of protein function, evolution, and gene regulatory dynamics. In this paper we demonstrate that by integrating these interactions with widely-available mRNA expression data, it is possible to generate concrete hypotheses for the underlying mechanisms governing the observed changes in gene expression. To perform this integration systematically and at large scale, we introduce an approach for screening a molecular interaction network to identify active subnetworks, i.e., connected regions of the network that show significant changes in expression over particular subsets of conditions. The method we present here combines a rigorous statistical measure for scoring subnetworks with a search algorithm for identifying subnetworks with high score. Results: We evaluated our procedure on a small network of 332 genes and 362 interactions and a large network of 4160 genes containing all 7462 protein–protein and protein-DNA interactions in the yeast public databases. In the case of the small network, we identified five significant subnetworks that covered 41 out of 77 (53%) of all significant changes in expression. Both network analyses returned several top-scoring subnetworks with good correspondence to known regulatory mechanisms in the literature. These results demonstrate how large-scale genomic approaches may be used to uncover signalling and regulatory pathways in a systematic, integrative fashion. Availability: The methods presented in this paper are implemented in the Cytoscape software package which is available to the academic community at http://www.


title: Robust airplane detection in satellite images

abstract: Automatic target detection in satellite images remains a challenging problem. The main difficulties lie in the cooccurrence of variations of target type, pose, and size in huge satellite image. In this paper, we propose a new airplane detection approach based on visual saliency computation and symmetry detection. The advantages are twofold. First, saliency and symmetry detection perform stably in obtaining target location and orientation information. Second, independent of target type, pose and size, saliency map and symmetry detection are computed only once. This saves a large amount of computational time but does not miss any targets. Experiments show that our method provides a promising way to detect airplanes in complex airport scenes.


title: Residual Reconstruction for Block-Based Compressed Sensing of Video

abstract: A simple block-based compressed-sensing reconstruction for still images is adapted to video. Incorporating reconstruction from a residual arising from motion estimation and compensation, the proposed technique alternatively reconstructs frames of the video sequence and their corresponding motion fields in an iterative fashion. Experimental results reveal that the proposed technique achieves significantly higher quality than a straightforward reconstruction that applies a still-image reconstruction independently frame by frame, a 3D reconstruction that exploits temporal correlation between frames merely in the form of a motion-agnostic 3D transform, and a similar, yet non-iterative, motion-compensated residual reconstruction.


title: Mathematical Modelling With Young Learners

abstract: Current research is demonstrating that young children can make significant mathematical and social gains from working authentic modelling problems. This paper argues for the implementation of mathematical modelling activities within the elementary and middle school years. The key features of these activities that make them rich learning experiences for children are explored. Some detailed analyses of how children develop and apply generalizable conceptual systems are then presented. It is argued that analogical and case-based reasoning processes play a powerful role in the construction and application of generalized models.


title: Single-trial classification of EEG in a visual object task using ICA and machine learning

abstract: Abstract Presenting different visual object stimuli can elicit detectable changes in EEG recordings, but this is typically observed only after averaging together data from many trials and many participants. We report results from a simple visual object recognition experiment where independent component analysis (ICA) data processing and machine learning classification were able to correctly distinguish presence of visual stimuli at around 87% (0.70 AUC, p Seven subjects observed a series of everyday visual object stimuli while EEG was recorded. The task was to indicate whether or not they recognised each object as familiar to them. EEG or IC data from a subset of initial object presentations was used to train support vector machine (SVM) classifiers, which then generated a label for subsequent data. Task-label classifier accuracy gives a proxy measure of task-related information present in the data used to train. This allows comparison of EEG data processing techniques – here, we found selected single ICs that give higher performance than when classifying from any single scalp EEG channel (0.70 AUC vs 0.65 AUC, p


title: Nucleotide modification of tRNA in. the yeast Saccharomyces cerevisiae is not Affected by the ψ factor which modulates suppression efficiency

abstract: SummaryWe have examined the tRNAs of two related strains of Saccharomyces cerevisiae, ψ+ and ψ−, which differ with respect to an extrachromosomal genetic element that modulates the expression of genotypic and phenotypic suppression. Both the pattern of tRNAs synthesized and the level of nucleotide modification of several selected tRNA species were found to be the same in the ψ+ and ψ− strains.


title: FARMER: a novel approach to file access correlation mining and evaluation reference model for optimizing peta-scale file system performance

abstract: File correlation, which refers to a relationship among related files that can manifest in the form of their common access locality (temporal and/or spatial), has become an increasingly important consideration for performance enhancement in peta-scale storage systems. Previous studies on file correlations mainly concern with two aspects of files: file access sequence and semantic attribute. Based on mining with regard to these two aspects of file systems, various strategies have been proposed to optimize the overall system performance. Unfortunately, all of these studies consider either file access sequences or semantic attribute information separately and in isolation, thus unable to accurately and effectively mine file correlations, especially in large-scale distributed storage systems. This paper introduces a novel File Access coRrelation Mining and Evaluation Reference model (FARMER) for optimizing petascale file system performance that judiciously considers both file access sequences and semantic attributes simultaneously to evaluate the degree of file correlations by leveraging the Vector Space Model (VSM) technique adopted from the Information Retrieval field. We extract the file correlation knowledge from some typical file system traces using FARMER, and incorporate FARMER into a real large-scale object-based storage system as a case study to dynamically infer file correlations and evaluate the benefits and costs of a FARMER-enabled prefetching algorithm for the metadata servers under real file system workloads. Experimental results show that FARMER can mine and evaluate file correlations more accurately and effectively. More significantly, the FARMER-enabled prefetching algorithm is shown to reduce the metadata operations latency by approximately 24-35% when compared to a state-of-the-art metadata prefetching algorithm and a commonly used replacement policy.


title: A case study of 3D stereoscopic vs. 2D monoscopic tele-reality in real-time dexterous teleoperation

abstract: This paper reports a case study of using single 3D stereoscopic visual feedback for real-time teleoperation of dexterous tasks. In traditional teleoperation systems, real-time visual feedbacks of multiple monoscopic views of the robot workspace are provided for remote operator. However, it is difficult for the operator to control remote robot to perform dexterous tasks by looking at multiple video feedbacks at the same time. During teleoperation, remote operators usually find multiple 2D visual feedbacks confusing, especially when performing dexterous tasks that require accurate positioning and orientating of robot end-effectors. In this paper, we propose to provide single real-time 3D stereoscopic visual feedback for remote operators so that they perceive remote robot workspace with the sense of depth. This sense of 3D empowers remote operators to accurately position and orient robot end-effector with confidence. Experiments have been conducted to reveal the usefulness of real-time 3D stereoscopic video feedback over multiple monoscopic video feedback in real-time teleoperation.


title: Learning Context-Dependent Mappings from Sentences to Logical Form

abstract: We consider the problem of learning context-dependent mappings from sentences to logical form. The training examples are sequences of sentences annotated with lambda-calculus meaning representations. We develop an algorithm that maintains explicit, lambda-calculus representations of salient discourse entities and uses a context-dependent analysis pipeline to recover logical forms. The method uses a hidden-variable variant of the perception algorithm to learn a linear model used to select the best analysis. Experiments on context-dependent utterances from the ATIS corpus show that the method recovers fully correct logical forms with 83.7% accuracy.


title: Deep4MalDroid: A Deep Learning Framework for Android Malware Detection Based on Linux Kernel System Call Graphs

abstract: With explosive growth of Android malware and due to its damage to smart phone users (e.g., stealing user credentials, resource abuse), Android malware detection is one of the cyber security topics that are of great interests. Currently, the most significant line of defense against Android malware is anti-malware software products, such as Norton, Lookout, and Comodo Mobile Security, which mainly use the signature-based method to recognize threats. However, malware attackers increasingly employ techniques such as repackaging and obfuscation to bypass signatures and defeat attempts to analyze their inner mechanisms. The increasing sophistication of Android malware calls for new defensive techniques that are harder to evade, and are capable of protecting users against novel threats. In this paper, we propose a novel dynamic analysis method named Component Traversal that can automatically execute the code routines of each given Android application (app) as completely as possible. Based on the extracted Linux kernel system calls, we further construct the weighted directed graphs and then apply a deep learning framework resting on the graph based features for newly unknown Android malware detection. A comprehensive experimental study on a real sample collection from Comodo Cloud Security Center is performed to compare various malware detection approaches. Promising experimental results demonstrate that our proposed method outperforms other alternative Android malware detection techniques. Our developed system Deep4MalDroid has also been integrated into a commercial Android anti-malware software.


title: BAM! The Behance Artistic Media Dataset for Recognition Beyond Photography

abstract: Computer vision systems are designed to work well within the context of everyday photography. However, artists often render the world around them in ways that do not resemble photographs. Artwork produced by people is not constrained to mimic the physical world, making it more challenging for machines to recognize. ::: This work is a step toward teaching machines how to categorize images in ways that are valuable to humans. First, we collect a large-scale dataset of contemporary artwork from Behance, a website containing millions of portfolios from professional and commercial artists. We annotate Behance imagery with rich attribute labels for content, emotions, and artistic media. Furthermore, we carry out baseline experiments to show the value of this dataset for artistic style prediction, for improving the generality of existing object classifiers, and for the study of visual domain adaptation. We believe our Behance Artistic Media dataset will be a good starting point for researchers wishing to study artistic imagery and relevant problems.


title: Few-shot Learning Using a Small-Sized Dataset of High-Resolution FUNDUS Images for Glaucoma Diagnosis

abstract: Deep learning has recently attracted a lot of attention, mainly thanks to substantial gains in terms of effectiveness. However, there is still room for significant improvement, especially when dealing with use cases that come with a limited availability of data, as is often the case in the area of medical image analysis. In this paper, we introduce a novel approach for early diagnosis of glaucoma in high-resolution FUNDUS images, only requiring a small number of training samples. In particular, we developed a predictive model based on a matching neural network architecture, integrating a high-resolution deep convolutional network that allows preserving the high-fidelity nature of the medical images. Our experimental results show that our predictive model is able to obtain higher levels of effectiveness than vanilla deep convolutional neural networks.


title: Performance Analysis of Village-Owned Enterprise Managers as a Basic of Designing Education and Training

abstract: Formation and management of Village-Owned Enterprise (BUMDes) is a manifestation of village productive economic management which is done cooperatively, participative, emancipative, transparently, accountable, and sustainable, which the main goal is to improve village economy. This research focus is aimed to examine the performance of BUMDes managers in Jombang Regency by using three performance indicators as a basic to develop educational model and entrepreneurship training for BUMDes managers. This research uses qualitative approach because it is more natural, descriptive, and inductive in form of case study. This research is done in BUMDes in Jombang Regency. The research subjects are BUMDes manager, headmen, BPD and people who use the service of BUMDes. The data collection technique used is interview, observation and documentation, while the data analysis used is Miles and Huberman model. The preliminary result of this research shows that in: (1) input performance indicator, human resources who handle BUMDes are inexperienced and have background education which is not relevant with their job; (2) process performance indicator, managers are not professional in doing their job; (3) output performance indicator, only some of BUMDes goals have been achieved such as: be able to donate some of BUMDes income to the rural income, creating employment, while the business development is not developed yet. Keyword : BUMDes, Manager Performance, Education and Entrepreneurship Training


title: Extracting structured data from unstructured document with incomplete resources

abstract: We present a method for extracting structured elements of information, called structured data (sdata), from ocr'ed pages. The method first analyzes the layout of the page, building several concurrent layout structures. Then a tagging step is performed in order to tag textual elements based on their content. Combining the layout structures and the tagged elements, layout models for representing the structured data are inferred for the current page. These models are used to correct or tag some elements missed by the tagging step. The final set of structured data is extracted. An evaluation is presented.


title: Penile cancer in patient with a ‘Bouglou’ penile adornment

abstract: Implantation of penile nodules under the foreskin aims at improving male and female sexual pleasures during intercourse. This case discusses the characteristics of the South American penile adornment ‘Bouglou’ as well as the risk factors for penile cancer in this region of Amazonia. This is a case report of a 48-year-old man whose penis presented a destructive gland/penile shaft lesion and three adornments in the penile shaft, reported as ‘Bouglou’. The diagnosis of penile cancer was confirmed after total penectomy. This study is the first that suggests a possible association between squamous cell carcinoma and these penile adornments.


title: Two Wheels Balancing Robot with Line Following Capability

abstract: This project focuses on the development of a line follower algorithm for a Two Wheels Balancing Robot. In this project, ATMEGA32 is chosen as the brain board controller to react towards the data received from Balance Processor Chip on the balance board to monitor the changes of the environment through two infra-red distance sensor to solve the inclination angle problem. Hence, the system will immediately restore to the set point (balance position) through the implementation of internal PID algorithms at the balance board. Application of infra-red light sensors with the PID control is vital, in order to develop a smooth line follower robot. As a result of combination between line follower program and internal self balancing algorithms, we are able to develop a dynamically stabilized balancing robot with line follower function. Keywords—infra-red sensor, PID algorithms, line follower Balancing robot


title: Adversarial Discriminative Heterogeneous Face Recognition

abstract: The gap between sensing patterns of different face modalities remains a challenging problem in heterogeneous face recognition (HFR). This paper proposes an adversarial discriminative feature learning framework to close the sensing gap via adversarial learning on both raw-pixel space and compact feature space. This framework integrates cross-spectral face hallucination and discriminative feature learning into an end-to-end adversarial network. In the pixel space, we make use of generative adversarial networks to perform cross-spectral face hallucination. An elaborate two-path model is introduced to alleviate the lack of paired images, which gives consideration to both global structures and local textures. In the feature space, an adversarial loss and a high-order variance discrepancy loss are employed to measure the global and local discrepancy between two heterogeneous distributions respectively. These two losses enhance domain-invariant feature learning and modality independent noise removing. Experimental results on three NIR-VIS databases show that our proposed approach outperforms state-of-the-art HFR methods, without requiring of complex network or large-scale training dataset.


title: Conceptual modeling and development of an intelligent agent-assisted decision support system for anti-money laundering

abstract: Criminal elements in today's technology-driven society are using every means available at their disposal to launder the proceeds from their illegal activities. In response, international anti-money laundering (AML) efforts are being made. The events of September 11, 2001, highlighted the need for more sophisticated AML and anti-terrorist financing programs across the industry and nation. In the wake of this, regulators are focusing on the role that technology can play in compliance with laws and ultimately in law enforcement. Banks will have to employ or enhance AML tools and technology to satisfy rising regulatory expectations. While many AML solutions have been in place for some time within the banks, they are faced with the challenge of adapting to the ever-changing risks and methods related to money laundering. In order to provide support for AML decisions, we have formulated an AML conceptual model by following Simon [Simon, H. A. (1977). The new science of management decision. Englewood Cliffs, NJ: Prentice-Hall] decision-making process model. Based on this model, a novel and open multi-agent AML system prototype has been designed and developed. Intelligent agents with their properties of autonomy, reactivity, and proactivity are well suited for dynamic, ill-structured, and complex ML prevention controls. The advanced architecture is able to provide more adaptive, intelligent, and flexible solution for AML. This paper is the first attempt at intelligent agent financial application in the AML domain, with a decision-making/problem-solving process model, an innovative agent-based architecture, and a prototype system.


title: 6 Personalized Search on the World Wide Web

abstract: With the exponential growth of the available information on theWorld Wide Web, a traditional search engine, even if based on sophisticated document indexing algorithms, has difficulty meeting efficiency and effectiveness performance demanded by users searching for relevant information. Users surfing the Web in search of resources to satisfy their information needs have less and less time and patience to formulate queries, wait for the results and sift through them. Consequently, it is vital in many applications - for example in an e-commerce Web site or in a scientific one - for the search system to find the right information very quickly. PersonalizedWeb environments that build models of short-term and long-term user needs based on user actions, browsed documents or past queries are playing an increasingly crucial role: they form a winning combination, able to satisfy the user better than unpersonalized search engines based on traditional Information Retrieval (IR) techniques. Several important user personalization approaches and techniques developed for the Web search domain are illustrated in this chapter, along with examples of real systems currently being used on the Internet.


title: Neurolinguists, beware! The bilingual is not two monolinguals in one person

abstract: Abstract Two views of bilingualism are presented—the monolingual or fractional view which holds that the bilingual is (or should be) two monolinguals in one person, and the bilingual or wholistic view which states that the coexistence of two languages in the bilingual has produced a unique and specific speaker-hearer. These views affect how we compare monolinguals and bilinguals, study language learning and language forgetting, and examine the speech modes—monolingual and bilingual—that characterize the bilingual's everyday interactions. The implications of the wholistic view on the neurolinguistics of bilingualism, and in particular bilingual aphasia, are discussed.


title: Design of High Efficiency Integrated Voltage Regulators with Embedded Magnetic Core Inductors

abstract: Integrated voltage regulators (IVRs) are one of the main trends in power delivery networks (PDNs) for electronic systems. A major challenge in the IVR design is to achieve sufficient integration / minimization of the required passive components of the IVR while still maintaining high power efficiency. This paper demonstrates that a buck converter type IVR designed with package embedded magnetic core inductors can achieve a very high efficiency with a substantially reduced area requirement compared to "air core" inductors. For the final design, an analytical evaluation shows 91% power efficiency for a 1.7V:1.05V conversion. Moreover, a stacked topology of the power stage allows a direct 5V:1V conversion at 79.4% efficiency eliminating the need for a separate off-package voltage regulator.


title: Agenda Setting, Religion and New Media: The Chabad Case Study

abstract: Theoretically grounded in agenda setting, a theory focused on the transfer of influence from communication media to a public, this paper examines the media activity of Chabad, an Ultra-Orthodox Jewish movement that is committed both to making Judaism generally accessible and to influencing public discourse. Rabbi M. M. Schneerson, the movement’s late wise and charismatic leader, undertook this dual mission in light of his conception of the theological grounds for the exploitation of mass media. Our examination of Chabad’s agenda-setting communication strategy was guided by two research questions: 1. What is the range of communication media used by Chabad in an effort to achieve its goal? 2. What evidence is there regarding the agenda-setting success of these communication efforts? Although it is obviously difficult to judge the precise degree of success, it is clear that Chabad is involved in a wide range of media and public activity and has already influenced public discourse well beyond the range of the movement itself and of the Jewish religion. The answer to the two questions above will help us understand the various movements that try to influence the agenda on religious grounds, so that we can determine the interface between religion and setting the agenda. It may also assist the efforts of other religious groups that want to influence the media and the political agenda.


title: Understanding the software development practices of blockchain projects: a survey

abstract: Background: The application of the blockchain technology has shown promises in various areas, such as smart-contracts, Internet of Things, land registry management, identity management, etc. Although Github currently hosts more than three thousand active blockchain software (BCS) projects, a few software engineering research has been conducted on their software engineering practices. Aims: To bridge this gap, we aim to carry out the first formal survey to explore the software engineering practices including requirement analysis, task assignment, testing, and verification of blockchain software projects. Method: We sent an online survey to 1,604 active BCS developers identified via mining the Github repositories of 145 popular BCS projects. The survey received 156 responses that met our criteria for analysis. Results: We found that code review and unit testing are the two most effective software development practices among BCS developers. The results suggest that the requirements of BCS projects are mostly identified and selected by community discussion and project owners which is different from requirement collection of general OSS projects. The results also reveal that the development tasks in BCS projects are primarily assigned on voluntary basis, which is the usual task assignment practice for OSS projects. Conclusions: Our findings indicate that standard software engineering methods including testing and security best practices need to be adapted with more seriousness to address unique characteristics of blockchain and mitigate potential threats.


title: CaptureFoundry: a GPU accelerated packet capture analysis tool

abstract: Packet captures are used to support a variety of tasks, including network administration, fault diagnosis and security and network related research. Despite their usefulness, processing packet capture files is a slow and tedious process that impedes the analysis of large, long-term captures. This paper discusses the primary components and observed performance of CaptureFoundry, a stand-alone capture analysis support tool designed to quickly map, filter and extract packets from large capture files using a combination of indexing techniques and GPU accelerated packet classification. All results are persistent, and may be used to rapidly extract small pre-filtered captures on demand that may be analysed quickly in existing capture analysis applications. Performance results show that CaptureFoundry is capable of generating multiple indexes and classification results for large captures at hundreds of megabytes per second, with minimal CPU and memory overhead and only minor additional storage space requirements.


title: Convolutional Neural Network with embedded Fourier Transform for EEG classification

abstract: In BCI (brain - computer interface) systems, brain signals must be processed to identify distinct activities that convey different mental states. We propose a new technique for the classification of electroencephalographic (EEG) steady-state visual evoked potential (SSVEP) activity for non-invasive BCI. The proposed method is based on a convolutional neural network that includes a Fourier transform between hidden layers in order to switch from the time domain to the frequency domain analysis in the network. The first step allows the creation of different channels. The second step is dedicated to the transformation of the signal in the frequency domain. The last step is the classification. It uses a hybrid rejection strategy that uses a junk class for the mental transition states and thresholds for the confidence values. The presented results with offline processing are obtained with 6 electrodes on 2 subjects with a time segment of 1s. The system is reliable for both subjects over 95%, with rejection criterion.


SatiricLR: a Language Resource of Satirical News Articles


title: Towards Open World Recognition

abstract: With the of advent rich classification models and high computational power visual recognition systems have found many operational applications. Recognition in the real world poses multiple challenges that are not apparent in controlled lab environments. The datasets are dynamic and novel categories must be continuously detected and then added. At prediction time, a trained system has to deal with myriad unseen categories. Operational systems require minimal downtime, even to learn. To handle these operational issues, we present the problem of Open World Recognition and formally define it. We prove that thresholding sums of monotonically decreasing functions of distances in linearly transformed feature space can balance “open space risk” and empirical risk. Our theory extends existing algorithms for open world recognition. We present a protocol for evaluation of open world recognition systems. We present the Nearest Non-Outlier (NNO) algorithm that evolves model efficiently, adding object categories incrementally while detecting outliers and managing open space risk. We perform experiments on the ImageNet dataset with 1.2M+ images to validate the effectiveness of our method on large scale visual recognition tasks. NNO consistently yields superior results on open world recognition.


title: An analysis of the greedy algorithm for the submodular set covering problem

abstract: We consider the problem: min\(\{ \mathop \Sigma \limits_{j \in s} f_j :z(S) = z(N),S \subseteqq N\} \) wherez is a nondecreasing submodular set function on a finite setN. Whenz is integer-valued andz(O)=0, it is shown that the value of a greedy heuristic solution never exceeds the optimal value by more than a factor\(H(\mathop {\max }\limits_j z(\{ j\} ))\) where\(H(d) = \sum\limits_{i = 1}^d {\frac{1}{i}} \).


Opportunities and Challenges Presented by Wikidata in the Context of Biocuration


title: ATTITUDES AND SOCIAL COGNITION When Dreaming Is Believing: The (Motivated) Interpretation of Dreams

abstract: This research investigated laypeople's interpretation of their dreams. Participants from both Eastern and Western cultures believed that dreams contain hidden truths (Study 1) and considered dreams to provide more meaningful information about the world than similar waking thoughts (Studies 2 and 3). The meaningfulness attributed to specific dreams, however, was moderated by the extent to which the content of those dreams accorded with participants' preexisting beliefs-from the theories they endorsed to attitudes toward acquaintances, relationships with friends, and faith in God (Studies 3-6). Finally, dream content influenced judgment: Participants reported greater affection for a friend after considering a dream in which a friend protected rather than betrayed them (Study 5) and were equally reluctant to fly after dreaming or learning of a plane crash (Studies 2 and 3). Together, these results suggest that people engage in motivated interpretation of their dreams and that these interpretations impact their everyday lives.


title: Analytic network process in risk assessment and decision analysis

abstract: In risk assessment and decision analysis, the analytical network process (ANP) is widely used to assess the key factors of risks and analyze the impacts and preferences of decision alternatives. There are lots of comparison matrices for a complicated risk assessment problem, but a decision has to be made rapidly in emergency cases. However, in the ANP, the reciprocal pairwise comparison matrices (RPCM) are more complicated and difficult than AHP. The consistency test and the inconsistent elements identification need to be simplified. In this paper, a maximum eigenvalue threshold is proposed as the consistency index for the ANP in risk assessment and decision analysis. The proposed threshold is mathematically equivalent to the consistency ratio (CR). To reduce the times of consistency test, a block diagonal matrix is introduced for the RPCM to conduct consistency tests simultaneously for all comparison matrices. Besides, the inconsistent elements can be identified and adjusted by an induced bias block diagonal comparison matrix. The effectiveness and the simplicity of the proposed maximum eigenvalue threshold consistency test method and the inconsistency identification and adjustment method are shown by two illustrative examples of emergent situations.


Classifying Reddit comments by subreddit


title: Distributed Multi-Task Learning with Shared Representation

abstract: We study the problem of distributed multi-task learning with shared representation, where each machine aims to learn a separate, but related, task in an unknown shared low-dimensional subspaces, i.e. when the predictor matrix has low rank. We consider a setting where each task is handled by a different machine, with samples for the task available locally on the machine, and study communication-efficient methods for exploiting the shared structure.


The Burrows-Wheeler transform: Theory and practice


title: Using Heuristics to Estimate an Appropriate Number of Latent Topics in Source Code Analysis

abstract: Abstract Latent Dirichlet Allocation (LDA) is a data clustering algorithm that performs especially well for text documents. In natural-language applications it automatically finds groups of related words (called “latent topics”) and clusters the documents into sets that are about the same “topic”. LDA has also been applied to source code, where the documents are natural source code units such as methods or classes, and the words are the keywords, operators, and programmer-defined names in the code. The problem of determining a topic count that most appropriately describes a set of source code documents is an open problem. We address this empirically by constructing clusterings with different numbers of topics for a large number of software systems, and then use a pair of measures based on source code locality and topic model similarity to assess how well the topic structure identifies related source code units. Results suggest that the topic count required can be closely approximated using the number of software code fragments in the system. We extend these results to recommend appropriate topic counts for arbitrary software systems based on an analysis of a set of open source systems.


title: Anomaly detection in maritime data based on geometrical analysis of trajectories

abstract: Anomaly detection is an important use of the Automatic Identification Systems (AIS), because it offers support to users to evaluate if a vessel is in trouble or causing trouble. For instance, it can be used to detect if a ship is doing something that may cause an accident or if it has changed its route to avoid bad weather condition. In this work, a new method for finding anomalies in the ships' movements is proposed. The method analyzes the trajectory of ships from a geometrical perspective. The trajectory of the ship is compared with a near-optimal path that is generated by a graph search algorithm. The proposed method extracts some scale-invariant features from the real trajectory and also from the optimal movement pattern, and it compares the two sets of features to generate an abnormality score. The method is unsupervised and it does not require training. Instead of labeling the trajectories as normal/abnormal it calculates a score value that denotes the extent of abnormality. The scoring scheme provides a ranking system in which the user can sort the trajectories based on their abnormality score. This is useful when dealing with large number of trajectories and the user wants to picks the most abnormal cases. For the evaluation, the method was run on three months data of North Pacific Ocean and score values were generated. Among the entire dataset, 100 randomly chosen trajectories were labeled by an expert. After applying a threshold on the score value, the proposed method had 94% accuracy.


title: 39GHz GaN front end MMIC for 5G applications

abstract: This paper presents the design and the measured results of a GaN-based T/R MMIC suitable for millimeter-wave 5G applications. The design successfully integrated a PA, an LNA, and a T/R switch in one IC. At 39GHz, the transmit path achieved an average output power of 26dBm with 9% PAE and −30dBc ACPR under a 64QAM OFDM signal. The receive path achieved 16dB gain with 4dB NF. The fabricated MMIC is packaged in a low cost surface mount package in both single and dual channel versions to support multi-element phased array applications.


title: Review of Printed-Circuit-Board Level EMI/EMC Issues and Tools

abstract: A comprehensive review of printed circuit board (PCB) electromagnetic compatibility (EMC) issues, analysis techniques, and possible solutions would fill a large book or more. This review takes a quick look at where the technology of PCB EMC control has been, where it is today, and where it needs to go for the future. As data rates on PCBs have increased, new problems have arisen, requiring new analysis techniques and new solutions. Further development will be needed to keep up with the ever-increasing data rates and smaller form factors.


title: Edge Computing Resource Management and Pricing for Mobile Blockchain

abstract: The mining process in blockchain requires solving a proof-of-work puzzle, which is resource expensive to implement in mobile devices due to the high computing power and energy needed. In this paper, we, for the first time, consider edge computing as an enabler for mobile blockchain. In particular, we study edge computing resource management and pricing to support mobile blockchain applications in which the mining process of miners can be offloaded to an edge computing service provider. We formulate a two-stage Stackelberg game to jointly maximize the profit of the edge computing service provider and the individual utilities of the miners. In the first stage, the service provider sets the price of edge computing nodes. In the second stage, the miners decide on the service demand to purchase based on the observed prices. We apply the backward induction to analyze the sub-game perfect equilibrium in each stage for both uniform and discriminatory pricing schemes. For the uniform pricing where the same price is applied to all miners, the existence and uniqueness of Stackelberg equilibrium are validated by identifying the best response strategies of the miners. For the discriminatory pricing where the different prices are applied to different miners, the Stackelberg equilibrium is proved to exist and be unique by capitalizing on the Variational Inequality theory. Further, the real experimental results are employed to justify our proposed model.


title: SpeechGraph - a visual programming toolkit for speech-enabled applications

abstract: This paper describes SpeechGraph, a toolkit that puts visual programming (user-friendly graphical interface) and model-driven software development theory together. It allows one to build a simple speech recognition application through a dialogue graph (state machine). Currently, the application generated by the toolkit works only with the NUANCE speech recognition engine.


title: Unsupervised Newspaper Segmentation Using Language Context

abstract: There has been increased interest in digitization of newspaper archives. A major problem that must be solved is that of high accuracy decomposition of the page into its logical structure. In this paper I present an approach that uses a language similarity measure based on OCR results to train geometric layout rules tailored to an arbitrary title. Experiments have shown this approach to be very effective.


title: Organic Packages With Embedded Phased-Array Antennas for 60-GHz Wireless Chipsets

abstract: A multilayer organic package with embedded 60-GHz antennas and fully integrated with a 60-GHz phased-array transmitter or receiver chip is demonstrated. The package includes sixteen phased-array antennas, an open cavity for housing the flip-chip attached RF chip, and interconnects operating at DC-66 GHz. The 28 mm 28 mm ball grid array package is manufactured using printed circuit board processes and uses a combination of liquid-crystal polymer and glass-reinforced laminates, allowing excellent 60-GHz interconnect and antenna performance. The measured return loss and gain of each antenna from 56 to 66 GHz are and , respectively. Finally, the packaged transmitter and receiver chipsets, each working with a heat sink, have demonstrated beam-steered, non-line-of-sight links with data rates up to 5.3 Gb/s using 16-quadrature amplitude modulation single-carrier and orthogonal frequency division multiplexing schemes.


Final Project Report : Real Time Tennis Match Prediction Using Machine Learning


title: In-memory fuzzing for binary code similarity analysis

abstract: Detecting similar functions in binary executables serves as a foundation for many binary code analysis and reuse tasks. By far, recognizing similar components in binary code remains a challenge. Existing research employs either static or dynamic approaches to capture program syntax or semantics-level features for comparison. However, there exist multiple design limitations in previous work, which result in relatively high cost, low accuracy and scalability, and thus severely impede their practical use. In this paper, we present a novel method that leverages in-memory fuzzing for binary code similarity analysis. Our prototype tool IMF-SIM applies in-memory fuzzing to launch analysis towards every function and collect traces of different kinds of program behaviors. The similarity score of two behavior traces is computed according to their longest common subsequence. To compare two functions, a feature vector is generated, whose elements are the similarity scores of the behavior trace-level comparisons. We train a machine learning model through labeled feature vectors; later, for a given feature vector by comparing two functions, the trained model gives a final score, representing the similarity score of the two functions. We evaluate IMF-SIM against binaries compiled by different compilers, optimizations, and commonly-used obfuscation methods, in total over one thousand binary executables. Our evaluation shows that IMF-SIM notably outperforms existing tools with higher accuracy and broader application scopes.


title: Meditation With Yoga, Group Therapy With Hypnosis, and Psychoeducation for Long-Term Depressed Mood: A Randomized Pilot Trial

abstract: This randomized pilot study investigated the effects of meditation with yoga (and psychoeducation) versus group therapy with hypnosis (and psychoeducation) versus psychoeducation alone on diagnostic status and symptom levels among 46 individuals with long-term depressive disorders. Results indicate that significantly more meditation group participants experienced a remission than did controls at 9-month follow-up. Eight hypnosis group participants also experienced a remission, but the difference from controls was not statistically significant. Three control participants, but no meditation or hypnosis participants, developed a new depressive episode during the study, though this difference did not reach statistical significance in any case. Although all groups reported some reduction in symptom levels, they did not differ significantly in that outcome. Overall, these results suggest that these two interventions show promise for treating low- to moderate-level depression.


title: On the regularization of image semantics by modal expansion

abstract: Recent research efforts in semantic representations and context modeling are based on the principle of task expansion: that vision problems such as object recognition, scene classification, or retrieval (RCR) cannot be solved in isolation. The extended principle of modality expansion (that RCR problems cannot be solved from visual information alone) is investigated in this work. A semantic image labeling system is augmented with text. Pairs of images and text are mapped to a semantic space, and the text features used to regularize their image counterparts. This is done with a new cross-modal regularizer, which learns the mapping of the image features that maximizes their average similarity to those derived from text. The proposed regularizer is class-sensitive, combining a set of class-specific denoising transformations and nearest neighbor interpolation of text-based class assignments. Regularization of a state-of-the-art approach to image retrieval is then shown to produce substantial gains in retrieval accuracy, outperforming recent image retrieval approaches.


CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature


title: PSyCo: Manifold Span Reduction for Super Resolution

abstract: The main challenge in Super Resolution (SR) is to discover the mapping between the low-and high-resolution manifolds of image patches, a complex ill-posed problem which has recently been addressed through piecewise linear regression with promising results. In this paper we present a novel regression-based SR algorithm that benefits from an extended knowledge of the structure of both manifolds. We propose a transform that collapses the 16 variations induced from the dihedral group of transforms (i.e. rotations, vertical and horizontal reflections) and antipodality (i.e. diametrically opposed points in the unitary sphere) into a single primitive. The key idea of our transform is to study the different dihedral elements as a group of symmetries within the high-dimensional manifold. We obtain the respective set of mirror-symmetry axes by means of a frequency analysis of the dihedral elements, and we use them to collapse the redundant variability through a modified symmetry distance. The experimental validation of our algorithm shows the effectiveness of our approach, which obtains competitive quality with a dictionary of as little as 32 atoms (reducing other methods' dictionaries by at least a factor of 32) and further pushing the state-of-the-art with a 1024 atoms dictionary.


title: Development of Korean Smartphone Addiction Proneness Scale for Youth

abstract: This study developed a Smartphone Addiction Proneness Scale (SAPS) based on the existing internet and cellular phone addiction scales. For the development of this scale, 29 items (1.5 times the final number of items) were initially selected as preliminary items, based on the previous studies on internet/phone addiction as well as the clinical experience of involved experts. The preliminary scale was administered to a nationally representative sample of 795 students in elementary, middle, and high schools across South Korea. Then, final 15 items were selected according to the reliability test results. The final scale consisted of four subdomains: (1) disturbance of adaptive functions, (2) virtual life orientation, (3) withdrawal, and (4) tolerance. The final scale indicated a high reliability with Cronbach's α of .880. Support for the scale's criterion validity has been demonstrated by its relationship to the internet addiction scale, KS-II (r = .49). For the analysis of construct validity, we tested the Structural Equation Model. The results showed the four-factor structure to be valid (NFI = .943, TLI = .902, CFI = .902, RMSEA = .034). Smartphone addiction is gaining a greater spotlight as possibly a new form of addiction along with internet addiction. The SAPS appears to be a reliable and valid diagnostic scale for screening adolescents who may be at risk of smartphone addiction. Further implications and limitations are discussed.


title: Recommendation of High Quality Representative Reviews in e-commerce

abstract: Many users of e-commerce portals commonly use customer reviews for making purchase decisions. But a product may have tens or hundreds of diverse reviews leading to information overload on the customer. The main objective of our work is to develop a recommendation system to recommend a subset of reviews that have high content score and good coverage over different aspects of the product along with their associated sentiments. We address the challenge which arises due to the fact that similar aspects are mentioned in different reviews using different natural language expressions. We use vector representations to identify mentions of similar aspects and map them with aspects mentioned in product features specifications. Review helpfulness score may act as a proxy for the quality of reviews, but new reviews do not have any helpfulness score. We address the cold start problem by using a dynamic convolutional neural network to estimate the quality score from review content. The system is evaluated on datasets from Amazon and Flipkart and is found to be more effective than the competing methods.


title: A control architecture for physical human-UAV interaction with a fully actuated hexarotor

abstract: Physical human-UAV interaction (PHUI) with Unmanned Aerial Vehicles (UAV) has many possible applications, and a few recent works [1], [2] have shown that it is possible to perform PHUI with a standard quadrotor. However, the intrinsic underactuation of quadrotors may hinder the interaction task and also cause safety issues. In this paper, we present an admittance control-based scheme to perform PHUI with a fully actuated UAV. The system also benefits from the robustness provided by our fully actuated UAV implementation of the adaptive super twisting controller (ASTC). While validating our system in simulation, we also show the superior performance of the fully actuated UAV with respect to a standard quadcopter.


title: Exercise training and protein metabolism: influences of contraction, protein intake, and sex-based differences

abstract: Muscle contraction during exercise, whether resistive or endurance in nature, has profound affects on muscle protein turnover that can persist for up to 72 h. It is well established that feeding during the postexercise period is required to bring about a positive net protein balance (muscle protein synthesis - muscle protein breakdown). There is mounting evidence that the timing of ingestion and the protein source during recovery independently regulate the protein synthetic response and influence the extent of muscle hypertrophy. Minor differences in muscle protein turnover appear to exist in young men and women; however, with aging there may be more substantial sex-based differences in response to both feeding and resistance exercise. The recognition of anabolic signaling pathways and molecules are also enhancing our understanding of the regulation of protein turnover following exercise perturbations. In this review we summarize the current understanding of muscle protein turnover in response to exercise and feeding and highlight potential sex-based dimorphisms. Furthermore, we examine the underlying anabolic signaling pathways and molecules that regulate these processes.


OntoPDF : using a text mining pipeline to generate enriched PDF versions of scientific papers


title: Efficient Processing of Semantic Web Queries in HBase and MySQL Cluster

abstract: With rapid growth of the Semantic Web, the authors' research focuses on designing a scalable resource description framework (RDF) database system that can efficiently process SPARQL queries over large RDF datasets. In this article, they present their design, architecture, storage schemes, and performance evaluation of two efficient RDF data management approaches that use state-of-the-art cloud and relational database technologies-HBase and MySQL Cluster.


title: NILM in an Industrial Setting: A Load Characterization and Algorithm Evaluation

abstract: Industrial buildings are responsible for a large share of the worldwide electricity consumption. Disaggregated information about electricity consumption enables decision- making and feedback tools to reduce and optimize the electricity consumption. In industrial settings, electrical load comes from a variety of equipment and machinery which can be awkward and expensive to monitor individually. We believe that Non-Intrusive Load Monitoring (NILM) can ease the burden of such a monitoring infrastructure. This hypothesis has been evaluated by collecting a rich data set from more than forty sensors measuring power consumption for six months at an industrial cold store. Their electrical equipments includes compressors, industrial fans, evaporators etc. which by earlier work have been hypothesised as too difficult to detect by NILM algorithms. This paper provides a detailed study of how industrial equipment and machinery challenge NILM algorithms. We consider how NILM can be used with different levels of sub- metering for providing breakdowns of the power consumption in an industrial setting. Our results show that changing the level of sub- metering increased the test accuracy(F1 score) with a third from 0.4 to 0.6. We introduce FHMM with day specific training, meaning having a model for each day in the week. The FHMM with day specific training reduced by half the mean normalized error from 0.7 to 0.3. These results thereby open up for the use of NILM in an industrial setting.


title: Context-based person identification framework for smart video surveillance

abstract: Smart video surveillance (SVS) applications enhance situational awareness by allowing domain analysts to focus on the events of higher priority. SVS approaches operate by trying to extract and interpret higher "semantic" level events that occur in video. One of the key challenges of SVS is that of person identification where the task is for each subject that occurs in a video shot to identify the person it corresponds to. The problem of person identification is especially challenging in resource-constrained environments where transmission delay, bandwidth restriction, and packet loss may prevent the capture of high-quality data. Conventional person identification approaches which primarily are based on analyzing facial features are often not sufficient to deal with poor-quality data. To address this challenge, we propose a framework that leverages heterogeneous contextual information together with facial features to handle the problem of person identification for low-quality data. We first investigate the appropriate methods to utilize heterogeneous context features including clothing, activity, human attributes, gait, people co-occurrence, and so on. We then propose a unified approach for person identification that builds on top of our generic entity resolution framework called RelDC, which can integrate all these context features to improve the quality of person identification. This work thus links one well-known problem of person identification from the computer vision research area (that deals with video/images) with another well-recognized challenge known as entity resolution from the database and AI/ML areas (that deals with textual data). We apply the proposed solution to a real-world dataset consisting of several weeks of surveillance videos. The results demonstrate the effectiveness and efficiency of our approach even on low-quality video data.


title: A Guided Approach to Behavioral Authentication

abstract: User's behavioral biometrics are promising as authentication factors in particular if accuracy is sufficiently guaranteed. They can be used to augment security in combination with other authentication factors. A gesture-based pattern lock system is a good example of such multi-factor authentication, using touch dynamics in a smartphone. However, touch dynamics can be significantly affected by a shape of gestures with regard to the performance and accuracy, and our concern is that user-chosen patterns are likely far from producing such a good shape of gestures. In this poster, we raise this problem and show our experimental study conducted in this regard. We investigate if there is a reproducible correlation between shape and accuracy and if we can derive effective attribute values for user guidance, based on the gesture-based pattern lock system. In more general, we discuss a guided approach to behavioral authentication.


title: Application of Social Media Analytics in the Banking Sector to Drive Growth and Sustainability: A Proposed Integrated Framework

abstract: Large amounts of data sets being generated from social media platforms have led to surge in demand for social media analytics (SMA) use in business operations. Strategic operation of SMA yields a positive impact on marketing activities, customer engagement, risk analysis and assessment product or service design, credit rating of customer profile, customer education and competitive analysis. Banks have started tapping into advanced prescriptive and predictive analytics into a bid to develop insights, managing high costs of compliance and non-compliance such as financial risks and reputational risks thereby generating a significant impact in business operations. Optimisation modelling of business portfolios, products and services offerings are contributing significantly towards achieving a sustainable and profitable growth. This is however, against the background of high volatility and weakening demand for traditional products in the banking industry. Systematic literature review was used in examining how banks are applying social media analytics to improve their business operations. Findings reveals that social media analytics provide concrete solutions which helps to improve revenue streams of banks, providing a guarantee for compliance, survival, sustainability and growth objectives. As a result, a integrated framework is proposed to address the gap existing in literature which lacks a focus on an integrated framework that can assist decision makers on the social media analytics to employ in the banking operations.


title: Iteratively re-weighted least squares for sparse signal reconstruction from noisy measurements

abstract: Finding sparse solutions of under-determined systems of linear equations is a problem of significance importance in signal processing and statistics. In this paper we study an iterative reweighted least squares (IRLS) approach to find sparse solutions of underdetermined system of equations based on smooth approximation of the L 0 norm and the method is extended to find sparse solutions from noisy measurements. Analysis of the proposed methods show that weaker conditions on the sensing matrices are required. Simulation results demonstrate that the proposed method requires fewer samples than existing methods, while maintaining a reconstruction error of the same order and demanding less computational complexity.


title: Security and privacy for big data: A systematic literature review

abstract: Big data is currently a hot research topic, with four million hits on Google scholar in October 2016. One reason for the popularity of big data research is the knowledge that can be extracted from analyzing these large data sets. However, data can contain sensitive information, and data must therefore be sufficiently protected as it is stored and processed. Furthermore, it might also be required to provide meaningful, proven, privacy guarantees if the data can be linked to individuals. To the best of our knowledge, there exists no systematic overview of the overlap between big data and the area of security and privacy. Consequently, this review aims to explore security and privacy research within big data, by outlining and providing structure to what research currently exists. Moreover, we investigate which papers connect security and privacy with big data, and which categories these papers cover. Ultimately, is security and privacy research for big data different from the rest of the research within the security and privacy domain? To answer these questions, we perform a systematic literature review (SLR), where we collect recent papers from top conferences, and categorize them in order to provide an overview of the security and privacy topics present within the context of big data. Within each category we also present a qualitative analysis of papers representative for that specific area. Furthermore, we explore and visualize the relationship between the categories. Thus, the objective of this review is to provide a snapshot of the current state of security and privacy research for big data, and to discover where further research is required.


title: A Survey on Modeling and Optimizing Multi-Objective Systems

abstract: Many systems or applications have been developed for distributed environments with the goal of attaining multiple objectives in the face of environmental challenges such as high dynamics/hostility or severe resource constraints (e.g., energy or communications bandwidth). Often the multiple objectives are conflicting with each other, requiring optimal tradeoff analyses between the objectives. This paper is mainly concerned with how to model multiple objectives of a system and how to optimize their performance. We first conduct a comprehensive survey of the state-of-the-art modeling and solution techniques to solve multi-objective optimization problems. In addition, we discuss pros and cons of each modeling and optimization technique for in-depth understanding. Further, we classify existing approaches based on the types of objectives and investigate main problem domains, critical tradeoffs, and key techniques used in each class. We discuss the overall trends of the existing techniques in terms of application domains, objectives, and techniques. Further, we discuss challenging issues based on the inherent nature of multi-objective optimization problems. Finally, we suggest future work directions in terms of what critical design factors should be considered to design and analyze a system with multiple objectives.


title: A Multiplicative Coordinated Stealthy Attack and its Detection for Cyber Physical Systems

abstract: In this paper, we investigate a new stealthy attack simultaneously compromising actuators and sensors. This attack is referred to as coordinated attack. We show that the coordinated attack is capable of deriving the system states far away from the desired without being detected. Furthermore, designing such an attack practically does not require knowledge on target systems, which makes the attack much more dangerous compared to the other known attacks. Also, we present a method to detect the coordinated attack. To validate the effect of the proposed attack, we carry out experiments using a quadrotor.


title: Learning Textual Entailment Classification from a Chinese RITE Dataset Specialized for Linguistic Phenomena

abstract: This paper proposed a textual entailment classification system developed based on a dataset focusing on individual entailment-related linguistic phenomena. Identical and synonymous terms in the text pair were aligned and ignored. Several groups of classification rules have been proposed with respect to the difference between the sentences in the text pair. The set of Wikipedia redirected titles became an important resource of synonyms. A SVM classifier was also constructed based on several groups of features. The experimental results showed that a hybrid system achieved the best performance with a macro-averaged F1-measure of 48.50% and an accuracy of 49.33% which outperformed the best systems in the NTCIR-11 RITE-VAL System Validation Chinese subtasks.


title: A novel method for constrained class association rule mining

abstract: To create a classifier using an associative classification algorithm, a complete set of class association rules (CARs) is obtained from the training dataset. Most generated rules, however, are either redundant or insignificant. They not only confuse end users during decision-making but also decrease the performance of the classification process. Thus, it is necessary to eliminate redundant or unimportant rules as much as possible before they are used. A related problem is the discovery of interesting or useful rules. In existing classification systems, the set of such rules may not be discovered easily. However, in real world applications, end users often consider the rules with consequences that contain one of particular classes. For example, in cancer screening applications, researchers are very interested in rules that classify genes into the "cancer" class. This paper proposes a novel approach for mining relevant CARs that considers constraints on the rule consequent. A tree structure for storing frequent itemsets from the dataset is designed. Then, some theorems for pruning tree nodes that cannot generate rules satisfying the class constraints are provided and proved. Finally, an efficient algorithm for mining constrained CARs is presented. Experiments show that the proposed method is faster than existing methods.


title: Three-dimensional shape measurement with a fast and accurate approach.

abstract: A noncontact, fast, accurate, low-cost, broad-range, full-field, easy-to-implement three-dimensional (3D) shape measurement technique is presented. The technique is based on a generalized fringe projection profilometry setup that allows each system component to be arbitrarily positioned. It employs random phase-shifting, multifrequency projection fringes, ultrafast direct phase unwrapping, and inverse self-calibration schemes to perform 3D shape determination with enhanced accuracy in a fast manner. The relative measurement accuracy can reach 1/10,000 or higher, and the acquisition speed is faster than two 3D views per second. The validity and practicability of the proposed technique have been verified by experiments. Because of its superior capability, the proposed 3D shape measurement technique is suitable for numerous applications in a variety of fields.


title: A Distributed Theatre Experiment with Shakespeare

abstract: This paper reports on an experimental production of The Tempest that was developed in collaboration with Miracle Theatre Company realised as a distributed performance from two separate stages through a dynamically configured telepresence system. The production allowed an exploration of the way a range of technologies, including consumer grade broadband, cameras and projection technologies could affect the development and delivery of live theatre by regional touring company. The architecture of the communication platform used to deliver the performance is introduced as are two novel software tools that are used to describe and control the way the play should be captured and represented. The experimental production was thoroughly evaluated and the feedback from audience and theatre professionals is presented in some detail. A considered observation of the process and the way it differs from film, TV and theatre suggest that distributed theatre can be treated as a new genre of storytelling.


title: Color Image Segmentation Based on Mean Shift and Normalized Cuts

abstract: In this correspondence, we develop a novel approach that provides effective and robust segmentation of color images. By incorporating the advantages of the mean shift (MS) segmentation and the normalized cut (Ncut) partitioning methods, the proposed method requires low computational complexity and is therefore very feasible for real-time image segmentation processing. It preprocesses an image by using the MS algorithm to form segmented regions that preserve the desirable discontinuity characteristics of the image. The segmented regions are then represented by using the graph structures, and the Ncut method is applied to perform globally optimized clustering. Because the number of the segmented regions is much smaller than that of the image pixels, the proposed method allows a low-dimensional image clustering with significant reduction of the complexity compared to conventional graph-partitioning methods that are directly applied to the image pixels. In addition, the image clustering using the segmented regions, instead of the image pixels, also reduces the sensitivity to noise and results in enhanced image segmentation performance. Furthermore, to avoid some inappropriate partitioning when considering every region as only one graph node, we develop an improved segmentation strategy using multiple child nodes for each region. The superiority of the proposed method is examined and demonstrated through a large number of experiments using color natural scene images.


title: Therapeutic embolization of arteriovenous malformations: the case for and against.

abstract: The field of endovascular therapy has demonstrated stunning technical achievements in AVM embolization. Embolization has the potential to enhance the safety and efficacy of AVM treatment when applied in carefully considered cases. The utility of embolization, at the present time, is limited by the fact that the procedure may be associated with disabling or fatal complications, and because complete or near-complete AVM nidus occlusion can be achieved only in a minority of cases. Because of these factors, embolization should not be considered a "standard-of-care" for the management of all cerebral AVMs, and careful case selection for embolization, with well-defined treatment goals in mind, is essential. Finally, not all AVMs that can be embolized should be embolized.


title: Theoretical Study of the Adsorption of Ethylene on Alkali-Exchanged Zeolites

abstract: The structures of alkali-exchanged faujasite (X–FAU, X = Li+ or Na+ ion) and ZSM-5 (Li–ZSM-5) zeolites and their interactions with ethylene have been investigated by means of quantum cluster and embedded cluster approaches at the B3LYP/6-31G(d, p) level of theory. Inclusion of the Madelung potential from the zeolite framework has a significant effect on the structure and interaction energies of the adsorption complexes and leads to differentiation of different types of zeolites (ZSM-5 and FAU) that cannot be drawn from a typical quantum cluster model, H3SiO(X)Al(OH)2OSiH3. The Li–ZSM-5 zeolite is predicted to have a higher Lewis acidity and thus higher ethylene adsorption energy than the Li–FAU zeolites (16.4 vs. 14.4 kcal/mol), in good agreement with the known acidity trend of these two zeolites. On the other hand, the cluster models give virtually the same adsorption energies for both zeolite complexes (8.9 vs. 9.1 kcal/mol). For the larger cation-exchanged Na–FAU complex, the adsorption energy (11.6 kcal/mol) is predicted to be lower than that of Li–FAU zeolites, which compares well with the experimental estimate of about 9.6 kcal/mol for ethylene adsorption on a less acidic Na–X zeolite. © 2003 Wiley Periodicals, Inc. Int J Quantum Chem 94: 333–340, 2003


title: Model-based Q-learning for humanoid robots

abstract: This paper is proposal with regarding reinforcement learning and robotics. It contains our study so far about reinforcement learning problem and Q-learning — one of the methods to solve it. The method is tested both by running on a simulation and on NAO robot. Both are written in high programming language. Since the testing are also done on NAO robot. This paper also includes our understanding about NAO robot and Robotic Operating System (ROS), and our approach to apply Q-learning on NAO robot. In the end, we have been successfully tested Q-learning method and apply it to NAO robot in real-world environment.


title: An equalizer-adaptation logic for a 25-Gb/s wireline receiver in 28-nm CMOS

abstract: We present an adaptation scheme for a wireline-receiver equalizer composed of a feed-forward equalizer (FFE) and a decision-feedback equalizer (DFE), where the FFE is a cascaded connection of a high-frequency equalizer (HFEQ) and a low-frequency equalizer (LFEQ). The HFEQ is adjusted by using the conventional filter-pattern method. For the LFEQ adjustment, we used a modified filter-pattern method where the pattern matching is performed based on the mark ratio (i.e., the probability of `1'), to enhance the gain of the parameter-adjusting feedback loop. The adaptation is performed in the background, i.e., while data is being received.


title: A MapReduce solution for associative classification of big data

abstract: Associative classifiers have proven to be very effective in classification problems. Unfortunately, the algorithms used for learning these classifiers are not able to adequately manage big data because of time complexity and memory constraints. To overcome such drawbacks, we propose a distributed association rule-based classification scheme shaped according to the MapReduce programming model. The scheme mines classification association rules (CARs) using a properly enhanced, distributed version of the well-known FP-Growth algorithm. Once CARs have been mined, the proposed scheme performs a distributed rule pruning. The set of survived CARs is used to classify unlabeled patterns. The memory usage and time complexity for each phase of the learning process are discussed, and the scheme is evaluated on seven real-world big datasets on the Hadoop framework, characterizing its scalability and achievable speedup on small computer clusters. The proposed solution for associative classifiers turns to be suitable to practically address big datasets even with modest hardware support. Comparisons with two state-of-the-art distributed learning algorithms are also discussed in terms of accuracy, model complexity, and computation time.


title: A compact multivariate histogram representation for query-driven visualization

abstract: As the size of data continues to increase, distribution-based methods become increasingly more important for data summarization and queries. To represent the distribution from a dataset without relying on a particular parametric model, histograms are widely used in many applications as it is simple to create and efficient to query. For multivariate scientific datasets, however, storing multivariate histograms in the form of multi-dimensional arrays is very expensive as the size of the histogram grows exponentially with the number of variables. In this paper, we present a compact structure to store multivariate histograms to reduce its huge space cost while supporting different kinds of histogram queries efficiently. A data space transformation is employed first to transform the large multi-dimensional array to a much smaller array. Dictionaries are constructed to encode this transformation. Then, the multivariate histogram is represented as a sequence of index and frequency pairs where the indices are represented as bitstrings computed from a space filling curve traversal of the transformed array. With this compact representation, the storage cost for the histograms is reduced. Based on our representation, we also present several common types of queries such as histogram marginalization, bin-merging and computation of conditional probability. We parallelize both the histogram computation and queries to improve its efficiency. We present several query-driven visualization applications to explore and analyze multivariate scientific datasets. Experimental results to study the performance of our framework in terms of scalability and space cost are also discussed.


title: Terrain traversability analysis methods for unmanned ground vehicles: A survey

abstract: Motion planning for unmanned ground vehicles (UGV) constitutes a domain of research where several disciplines meet, ranging from artificial intelligence and machine learning to robot perception and computer vision. In view of the plurality of related applications such as planetary exploration, search and rescue, agriculture, mining and off-road exploration, the aim of the present survey is to review the field of 3D terrain traversability analysis that is employed at a preceding stage as a means to effectively and efficiently guide the task of motion planning. We identify that in the epicenter of all related methodologies, 3D terrain information is used which is acquired from LIDAR, stereo range data, color or other sensory data and occasionally combined with static or dynamic vehicle models expressing the interaction of the vehicle with the terrain. By taxonomizing the various directions that have been explored in terrain perception and analysis, this review takes a step toward agglomerating the dispersed contributions from individual domains by elaborating on a number of key similarities as well as differences, in order to stimulate research in addressing the open challenges and inspire future developments.


title: An experimental study on four models of customer churn prediction

abstract: Decision tree, neural network and logistic regression were applied frequently as models of customer churn prediction, but the application of them has been mature and they are difficult to be improved. In this paper, Bayesian Networks, Support Vector Machines, Rough Sets and Survival Analysis were selected for experimental comparison study. An integrated contrast among the four models from the applicability of model in theory and experimental comparison has been processed. Overall, of the four models the Bayesian network model performed best while the Survival analysis did worst.


title: Micro Geared Ultrasonic Motor

abstract: We propose a micro geared ultrasonic motor, the smallest set of a micro ultrasonic motor and micro planetary gear. Our micro ultrasonic motor is one of the most powerful motors in one-millimeter-scale motors. The use of a micro planetary gear amplifies the motor torque to several tens of times and the geared motor enables a high torque density that was unachievable by existing miniature motors. The goal of this paper is to analyze the dynamic characteristic of the micro geared ultrasonic motor with a mathematical model and confirm the torque amplification experimentally using a micro planetary gear prototype. To build the microgear components, we employ a micro-injection molding process using metallic glass, a relatively new material, with a low melting temperature. The micro planetary gear assembly with a diameter of 2 mm, a length of 5.9 mm, and a gear ratio of 256 is built and connected to the micro ultrasonic motor. The micro geared ultrasonic motor demonstrates a continuous torque of 0.4 mN $\cdot$ m and a peak torque of 1 mN $\cdot$ m in the load lifting experiments.