title: Query type classification for web document retrieval

abstract: The heterogeneous Web exacerbates IR problems and short user queries make them worse. The contents of web documents are not enough to find good answer documents. Link information and URL information compensates for the insufficiencies of content information. However, static combination of multiple evidences may lower the retrieval performance. We need different strategies to find target documents according to a query type. We can classify user queries as three categories, the topic relevance task, the homepage finding task, and the service finding task. In this paper, a user query classification scheme is proposed. This scheme uses the difference of distribution, mutual information, the usage rate as anchor texts, and the POS information for the classification. After we classified a user query, we apply different algorithms and information for the better results. For the topic relevance task, we emphasize the content information, on the other hand, for the homepage finding task, we emphasize the Link information and the URL information. We could get the best performance when our proposed classification method with the OKAPI scoring algorithm was used.


title: Where Is This Tweet From? Inferring Home Locations of Twitter Users

abstract: We present a new algorithm for inferring the home locations of Twitter users at different granularities, such as city, state, or time zone, using the content of their tweets and their tweeting behavior. Unlike existing approaches, our algorithm uses an ensemble of statistical and heuristic classifiers to predict locations. We find that a hierarchical classification approach can improve prediction accuracy. Experimental evidence suggests that our algorithm works well in practice and outperforms the best existing algorithms for predicting the location of Twitter users.


title: Training and analyzing deep recurrent neural networks

abstract: Time series often have a temporal hierarchy, with information that is spread out over multiple time scales. Common recurrent neural networks, however, do not explicitly accommodate such a hierarchy, and most research on them has been focusing on training algorithms rather than on their basic architecture. In this paper we study the effect of a hierarchy of recurrent neural networks on processing time series. Here, each layer is a recurrent network which receives the hidden state of the previous layer as input. This architecture allows us to perform hierarchical processing on difficult temporal tasks, and more naturally capture the structure of time series. We show that they reach state-of-the-art performance for recurrent networks in character-level language modeling when trained with simple stochastic gradient descent. We also offer an analysis of the different emergent time scales.


title: Crosstalk between SOXB1 proteins and WNT/β-catenin signaling in NT2/D1 cells

abstract: During early vertebrate embryogenesis, the expression of SOXB1 proteins is precisely regulated by a number of different mechanisms, including Wnt/β-catenin signaling. This is essential for controlling the balance between stemness and differentiation in embryonic stem cells. In the present study, we analyzed the molecular mechanism of LiCl action in NT2/D1 cells and examined the crosstalk between SOXB1 proteins and Wnt signaling in this model system. We have shown that LiCl increases β-catenin level, induces its translocation to the nucleus and consequently up-regulates β-catenin/Tcf-dependent transcription in NT2/D1 cells. Our results also suggest that LiCl treatment leads to increased expression of SOX2 and SOX3 proteins in NT2/D1 cells through activation of canonical Wnt signaling. Finally, we have detected a negative feedback loop between β-catenin and SOX2 expression in NT2/D1 cells. Since β-catenin and SOX2 have been linked to processes of self-renewal and pluripotency, our results have implications for future research on the maintenance of stemness and lineage commitment of embryonic stem cells.


title: PMSM Magnetization State Estimation Based on Stator-Reflected PM Resistance Using High-Frequency Signal Injection

abstract: Permanent-magnet (PM) magnetization state estimation in PM synchronous machines (PMSMs) is of great importance for torque control and monitoring purposes. The magnetization state of a PMSM can change due to several reasons, injection of stator current ( $d$ -axis or $q$ -axis) and variation of magnet temperature being the primary reasons. PM magnetization state estimation is not easy once the machine is assembled. Methods based on the back electromotive force can be used, but they require that the machine is rotating. This paper analyzes the use of high-frequency signal injection for PM magnetization state estimation in PMSMs. The magnetization state of the PMs in PMSMs affects the stator-reflected PM high-frequency resistance. The stator-reflected PM high-frequency resistance can be estimated by injecting a high-frequency voltage/current using the inverter. The high-frequency signal is superposed on the fundamental excitation used to produce torque, meaning that the method can operate at any speed, including zero speed, and without interfering with the normal operation of the machine.


title: User participation in decision support systems development: influencing system outcomes

abstract: Despite the widely held view that systems are more likely to be successful if users are involved during systems development, there is uncertainty about the exact nature of the relationship between user participation and system outcomes. There has been considerable research activity in this area but findings are inconclusive. Comparatively little qualitative research has been reported. This paper reports an interpretive study that examined the development process for 38 decision support systems in the Australian agricultural sector. The relationship between user participation and system outcome was explored. The degree of user influence in the design process was found to be an important component of this relationship. Degree of user influence was a result of both the type and depth of user participation. Much previous research has focused on whether users were involved in development without detailed consideration of the degree of influence on design features resulting from this participation. The results from this study are significant in that they yield theoretical insights into the phenomenon of user participation and related system outcomes. In addition, the results have practical significance for practitioners developing decision support software.


title: Domain Control for Neural Machine Translation

abstract: Machine translation systems are very sensitive to the domains they were trained on. Several domain adaptation techniques have been deeply studied. We propose a new technique for neural machine translation (NMT) that we call domain control which is performed at runtime using a unique neural network covering multiple domains. The presented approach shows quality improvements when compared to dedicated domains translating on any of the covered domains and even on out-of-domain data. In addition, model parameters do not need to be re-estimated for each domain, making this effective to real use cases. Evaluation is carried out on English-to-French translation for two different testing scenarios. We first consider the case where an end-user performs translations on a known domain. Secondly, we consider the scenario where the domain is not known and predicted at the sentence level before translating. Results show consistent accuracy improvements for both conditions.


title: The Two-Level Ulnar Collateral Ligament Injury of the Metacarpophalangeal Joint of the Thumb

abstract: Ulnar collateral ligament injuries of the thumb may present as avulsion fractures of the bone or as ligamentous tears. Displaced fracture fragments or Stener lesions demand operative management. Occasionally, both fracture and ligamentous tears may occur simultaneously, and the management of the ligamentous aspect may be overlooked in cases with undisplaced fractures leading to failure of non-operative treatment. It is important in cases with a relatively undisplaced fracture fragment to exclude a simultaneous ligamentous tear and Stener lesion by careful clinical examination and by examining the radiographs for telltale displaced flecks of bone.


title: Low-Sidelobe Design of Microstrip Comb-Line Antennas Using Stub-Integrated Radiating Elements in the Millimeter-Wave Band

abstract: A novel radiating element with a matching circuit was developed for comb-line antennas of traveling-wave operation. In the design of low-sidelobe traveling-wave array antennas, large coupling is required for array elements of the comb-line antenna around the input port. Furthermore, the traveling-wave arrays, whose number of elements is small, also require large coupling because the radiation power for each array element is assigned to be much larger. However, wide radiating elements for large coupling perform poor impedance matching characteristics and radiate large cross polarization. Thus, we proposed a novel radiating element with matching stub in parallel to the radiating element to realize strong radiating power, low reflection, and low cross-polarization, simultaneously. The simulated and measured performances of low-sidelobe 14-element and 6-element antennas were demonstrated in this paper.


title: Odontogenic Myxoma of Maxilla

abstract: Odontogenic myxoma (OM) is a locally aggressive, benign, slowgrowing tumor arising from mesenchyme and/or odontogenic epithelium. It is the third most common odontogenic tumor predominantly affecting females and posterior mandible. Radiographically, OM demonstrates unilocular or multilocular, well or ill-defined radiolucency showing ‘honeycomb’, ‘soap-bubble’ or ‘tennis-racket’ pattern with cortical expansion and tooth displacement. Histologically, OM shows loosely arranged stellate or spindle-shaped cells interspersed in myxoid matrix. Various treatment modalities, such as wide excision, enucleation and curettage, curettage with/without electrical or chemical cautery, enbloc resection and wide resection with/without imme diate grafting can be considered based on the extent of the lesion. Here, we present a case of a 17-year-old female, with OM in the left maxilla and the maxillary sinus.


A Mathematical Approach to Obtain Isoperimetric Shapes for D-Form Construction


title: SRAM for Error-Tolerant Applications With Dynamic Energy-Quality Management in 28 nm CMOS

abstract: In this paper, a voltage-scaled SRAM for both error-free and error-tolerant applications is presented that dynamically manages the energy/quality trade-off based on application need. Two variation-resilient techniques, write assist and Error Correcting Code, are selectively applied to bit positions having larger impact on the overall quality, while jointly performing voltage scaling to improve overall energy efficiency. The impact of process variations, voltage and temperature on the energy-quality tradeoff is investigated. A 28 nm CMOS 32 kb SRAM shows 35% energy savings at iso-quality and operates at a supply 220 mV below a baseline voltage-scaled SRAM, at the cost of 1.5% area penalty. The impact of the SRAM quality at the system level is evaluated by adopting a H.264 video decoder as case study.


title: Enhancing the Accuracy of Biometric Feature Extraction Fusion Using Gabor Filter and Mahalanobis Distance Algorithm

abstract: Biometric recognition systems have advanced significantly in the last decade and their use in specific applications will increase in the near future. The ability to conduct meaningful comparisons and assessments will be crucial to successful deployment and increasing biometric adoption. The best modality used as unimodal biometric systems are unable to fully address the problem of higher recognition rate. Multimodal biometric systems are able to mitigate some of the limitations encountered in unimodal biometric systems, such as non-universality, distinctiveness, non-acceptability, noisy sensor data, spoof attacks, and performance. More reliable recognition accuracy and performance are achievable as different modalities were being combined together and different algorithms or techniques were being used. The work presented in this paper focuses on a bimodal biometric system using face and fingerprint. An image enhancement technique (histogram equalization) is used to enhance the face and fingerprint images. Salient features of the face and fingerprint were extracted using the Gabor filter technique. A dimensionality reduction technique was carried out on both images extracted features using a principal component analysis technique. A feature level fusion algorithm (Mahalanobis distance technique) is used to combine each unimodal feature together. The performance of the proposed approach is validated and is effective.


title: Multi-objective reasoning with constrained goal models

abstract: Goal models have been widely used in computer science to represent software requirements, business objectives, and design qualities. Existing goal modelling techniques, however, have shown limitations of expressiveness and/or tractability in coping with complex real-world problems. In this work, we exploit advances in automated reasoning technologies, notably satisfiability and optimization modulo theories (SMT/OMT), and we propose and formalize: (1) an extended modelling language for goals, namely the constrained goal model (CGM), which makes explicit the notion of goal refinement and of domain assumption, allows for expressing preferences between goals and refinements and allows for associating numerical attributes to goals and refinements for defining constraints and optimization goals over multiple objective functions, refinements, and their numerical attributes; (2) a novel set of automated reasoning functionalities over CGMs, allowing for automatically generating suitable refinements of input CGMs, under user-specified assumptions and constraints, that also maximize preferences and optimize given objective functions. We have implemented these modelling and reasoning functionalities in a tool, named CGM-Tool, using the OMT solver OptiMathSAT as automated reasoning backend. Moreover, we have conducted an experimental evaluation on large CGMs to support the claim that our proposal scales well for goal models with 1000s of elements.


title: Optimal Search Engine Marketing Strategy

abstract: Buyers using search engines to look for information tend to trust and follow links displayed in the editorial section of the search-results page. Most on-line sellers, however, do not invest in search engine optimization (SEO) to get higher search-results rankings for their listings, but instead prefer paid placements. They explain that SEO is more expensive than paid placements, produces results that do not justify its cost, and does not consistently lead to high search-results rankings. This implies that sellers would invest in SEO if it were less expensive and its rankings were more consistent. However, even if SEO and paid placement cost the same, and SEO always produced high rankings, paid placement would still be the search engine marketing (SEM) strategy of choice for most on-line sellers. Interestingly, no optimal SEM strategy includes SEO. These findings are especially significant for advertising professionals who have to justify investing in paid placements despite overwhelming evidence that buyers ignore them and follow links in the editorial section of the search-results page.


title: Data Clustering by Markovian Relaxation and the Information Bottleneck Method

abstract: We introduce a new, non-parametric and principled, distance based clustering method. This method combines a pairwise based approach with a vector-quantization method which provide a meaningful interpretation to the resulting clusters. The idea is based on turning the distance matrix into a Markov process and then examine the decay of mutual-information during the relaxation of this process. The clusters emerge as quasi-stable structures during this relaxation, and then are extracted using the information bottleneck method. These clusters capture the information about the initial point of the relaxation in the most effective way. The method can cluster data with no geometric or other bias and makes no assumption about the underlying distribution.


Recognizing textual entailment : Rational , evaluation and approaches


title: A New Class of Planar Ultrawideband Modular Antenna Arrays With Improved Bandwidth

abstract: The theory, design, fabrication, and measurement of a new class of planar ultrawideband modular antenna (PUMA) arrays are presented. The proposed PUMA array class achieves twice the bandwidth (from 3:1 to 6:1) of the conventional shorted via-based PUMA without using an external matching network and while retaining convenient unbalanced feeding, manufacturing, and assembly characteristics. The chief enabling technical innovation hinges upon the reconfiguration of shorting vias into capacitively-loaded vias that simultaneously: 1) mitigate low-frequency bandwidth-limiting loop modes and 2) shift problematic common-mode resonances out-of-band. A simple theoretical model based on ridged waveguides is proposed that qualitatively and quantitatively explains this novel common-mode mitigation. An infinite array operating over 3.53–21.2 GHz (6:1) is designed to achieve active VSWR < {2, 2.5, 3.8} while scanning to {broadside, 45°, 60°}, respectively, without oversampling the aperture. D-plane cross-polarization is around {−15, −10} dB for {45°, 60°} scans with high efficiency, i.e., 0.5 dB co-polarized gain loss on average. A dual-polarized prototype 256-port (128 elements per polarization) array is fabricated and measured having good agreement with full-wave finite array simulations.


title: AIF: An Artificial Intelligence Framework for Smart Wireless Network Management

abstract: To solve the policy optimizing problem in many scenarios of smart wireless network management using a single universal algorithm, this letter proposes a universal learning framework, which is called AI framework based on deep reinforcement learning (DRL). This framework can also solve the problem that the state is painful to design in traditional RL. This AI framework adopts convolutional neural network and recurrent neural network to model the potential spatial features (i.e., location information) and sequential features from the raw wireless signal automatically. These features can be taken as the state definition of DRL. Meanwhile, this framework is suitable for many scenarios, such as resource management and access control due to DRL. The mean value of throughput, the standard deviation of throughput, and handover counts are used to evaluate its performance on the mobility management problem in the wireless local area network on a practical testbed. The results show that the framework gets significant improvements and learns intuitive features automatically.


title: CNN based spatio-temporal feature extraction for face anti-spoofing

abstract: Authentication of biometric system is vulnerable to impostor attacks. Recent research considers face anti-spoofing as a binary classification problem. To differentiate between genuine access and fake attacks, many systems are trained and the number of counter measures is gradually increasing. In this paper, we propose a novel technique for face anti-spoofing. This method is based on Spatio-temporal information to distinguish between legitimate access and impostor videos or video sequences of picture attacks. The idea is to utilize convolutional neural network (CNN) with handcrafted technique such as LBP-TOP for feature extraction and training of the classifier. Proposed approach requires no preprocessing steps such as face detection and refining face regions or enlarging the original images with particular re-scaling ratios. CNN itself cannot learn temporal features but for face anti-spoofing spatio-temporal features are important. We cascade LBP-TOP with CNN to extract spatio-temporal features from video sequences and capture the most discriminative clues between genuine access and impostor attacks. Extensive experiments are conducted on two very challenging datasets: CASIA and REPLAY-ATTACK which are publically available and achieved high competitive score compared with state-of-art techniques results.


An improved LSB image steganography technique using bit-inverse in 24 bit colour image


title: A Resource-Process Framework of New Service Development

abstract: Motivated by the increasing attention given to the operational importance of developing new services, this paper offers a theoretical framework that integrates both process- and resourceoriented perspectives of new service development (NSD) by defining and organizing 45 practice constructs for NSD-related practices and activities that occur in contemporary service firms. We employ a rigorous procedure whereby both quantitative and qualitative data were gathered through multiple rounds of interviews and card-sorting exercises with senior service managers. This iterative refinement process helps ensure that the construct domains and definitions are consistent and that they are applicable across multiple service sectors. A primary contribution of this research is to provide precise operational definitions of theoretically important NSD practice constructs. Importantly, this study expands on the NSD literature by including both resource- and process-centric perspectives within a single framework. A second contribution is to illustrate a general methodology for developing clear, concise, and consistent construct definitions that may be generally useful for production and operations management scholars interested in new construct development for emerging areas. Empirical results suggest that the resource-process framework can help guide and organize future research on, and provide insight into, a more comprehensive view of new service development.


title: A simulation of priority based earliest deadline first scheduling for cloud computing system

abstract: Cloud is a new trend in the computing. It is the combination of distributed, parallel and grid computing. It is a pay on demand computing. Basically cloud is a shared pool of resources. The resources are shared among users who are geographically distributed. As the area of cloud is increasing, several issues are also increasing. These issues are related to security, resource allocation, performance and cost. The paper has its main focus on the resource allocation problem and to solve the problem we are discussing a task allocation algorithm. The algorithm that we will discuss in this paper uses the two task scheduling algorithm one is priority based and other is earliest deadline first scheduling algorithm. The tasks are allocated on the basis of their priority and the task having higher priority get scheduled first. The simulation is performed on the CloudSim toolkit. CloudSim is a JAVA based simulator. The proposed work will results in higher performance and also improves memory utilization.


title: Generalized state-space model for an n-phase interleaved buck-boost converter

abstract: An n-phase interleaved buck-boost converter (IBBC) with generalized steady state model has been presented in this paper. Development in distributed generation system requires low cost, high efficient converters for power conversion. Paralleling of converters is done so as to minimize the ripples at the output and reduce the size of passive components and improve the overall efficiency and cost of the system. An interleaved buck boost converter finds application in fuel cells, electric vehicles and various solar charging applications. The steady state and small signal analysis of an n phase buck-boost converter in CCM mode have been carried out and average state space equations have been developed. In averaging, parasitic elements have been excluded for simplifying the calculations. A simulation model for a three phase interleaved buck boost converter has been presented initially which is then generalized.


title: A Novel Wideband Antenna Array with Tightly Coupled Octagonal Ring Elements

abstract: A novel phased array antenna with wide bandwidth and wide scan angle is presented. The radiating aperture of the phased array consists of periodically and closely spaced octagonal ring elements. Tight capacitive coupling between adjacent elements is realized by interdigitating the end portions of the ring elements. To improve the impedance matching of the individual antenna elements over wide frequency band, a novel impedance matching layer consists of periodic octagonal ring element is subtly designed and placed over the radiating aperture. Both of the radiating elements and impedance matching layer are printed on a flexible membrane substrate with a thickness of 0.04mm. Measured results of a 16-element linear array demonstrate that good impedance matching over a 4.4 : 1 bandwidth can be obtained for beam scan angles within ±45◦ from broadside. As compared to conventional wideband phased array such as tapered slot antenna array, the proposed phased array has the features such as low cost, low profile, light weight, and ease of fabrication.


title: Legacy Information Systems: Issues and Directions

abstract: A legacy information system represents a massive, long-term business investment. Unfortunately, such systems are often brittle, slow and non-extensible. Capturing legacy system data in a way that can support organizations into the future is an important but relatively new research area. The authors offer an overview of existing research and present two promising methodologies for legacy information system migration.


title: New Improved Recursive Least-Squares Adaptive-Filtering Algorithms

abstract: Two new improved recursive least-squares adaptive-filtering algorithms, one with a variable forgetting factor and the other with a variable convergence factor are proposed. Optimal forgetting and convergence factors are obtained by minimizing the mean square of the noise-free a posteriori error signal. The determination of the optimal forgetting and convergence factors requires information about the noise-free a priori error which is obtained by solving a known L1-L2 minimization problem. Simulation results in system-identification and channel-equalization applications are presented which demonstrate that improved steady-state misalignment, tracking capability, and readaptation can be achieved relative to those in some state-of-the-art competing algorithms.


title: Face Recognition and Micro-expression Recognition Based on Discriminant Tensor Subspace Analysis Plus Extreme Learning Machine

abstract: In this paper, a novel recognition algorithm based on discriminant tensor subspace analysis (DTSA) and extreme learning machine (ELM) is introduced. DTSA treats a gray facial image as a second order tensor and adopts two-sided transformations to reduce dimensionality. One of the many advantages of DTSA is its ability to preserve the spatial structure information of the images. In order to deal with micro-expression video clips, we extend DTSA to a high-order tensor. Discriminative features are generated using DTSA to further enhance the classification performance of ELM classifier. Another notable contribution of the proposed method includes significant improvements in face and micro-expression recognition accuracy. The experimental results on the ORL, Yale, YaleB facial databases and CASME micro-expression database show the effectiveness of the proposed method.


title: Argumentation Mining: Where are we now, where do we want to be and how do we get there?

abstract: This paper gives a short overview of the state-of-the-art and goals of argumentation mining and it provides ideas for further research. Its content is based on two invited lectures on argumentation mining respectively at the FIRE 2013 conference at the India International Center in New Delhi, India and a lecture given as SICSA distinguished visitor at the University of Dundee, UK in the summer of 2014.


title: Deep Graph Kernels

abstract: In this paper, we present Deep Graph Kernels, a unified framework to learn latent representations of sub-structures for graphs, inspired by latest advancements in language modeling and deep learning. Our framework leverages the dependency information between sub-structures by learning their latent representations. We demonstrate instances of our framework on three popular graph kernels, namely Graphlet kernels, Weisfeiler-Lehman subtree kernels, and Shortest-Path graph kernels. Our experiments on several benchmark datasets show that Deep Graph Kernels achieve significant improvements in classification accuracy over state-of-the-art graph kernels.


title: Matching HTML Tables to DBpedia

abstract: Millions of HTML tables containing structured data can be found on the Web. With their wide coverage, these tables are potentially very useful for filling missing values and extending cross-domain knowledge bases such as DBpedia, YAGO, or the Google Knowledge Graph. As a prerequisite for being able to use table data for knowledge base extension, the HTML tables need to be matched with the knowledge base, meaning that correspondences between table rows/columns and entities/schema elements of the knowledge base need to be found. This paper presents the T2D gold standard for measuring and comparing the performance of HTML table to knowledge base matching systems. T2D consists of 8 700 schema-level and 26 100 entity-level correspondences between the WebDataCommons Web Tables Corpus and the DBpedia knowledge base. In contrast related work on HTML table to knowledge base matching, the Web Tables Corpus (147 million tables), the knowledge base, as well as the gold standard are publicly available. The gold standard is used afterward to evaluate the performance of T2K Match, an iterative matching method which combines schema and instance matching. T2K Match is designed for the use case of matching large quantities of mostly small and narrow HTML tables against large cross-domain knowledge bases. The evaluation using the T2D gold standard shows that T2K Match discovers table-to-class correspondences with a precision of 94%, row-to-entity correspondences with a precision of 90%, and column-to-property correspondences with a precision of 77%.


title: Development of a neuro-feedback game based on motor imagery EEG

abstract: Electroencephalogram (EEG) has widely been used to monitor subjects/patients’ mental states. Using the monitor results as feedback, neuro-feedback enables patients to learn to regulate their physiological and psychological states so that improvements in physical and psychological subjects/patients’ states could be achieved. By analyzing EEG components generated by motor imagery, a mind-controlled game based on motor imagery is developed, including the design of BCI and the design of the video game. In the game, neuro-feedback is realized to in a visual manner, through which the users could learn to improve attention span. Based on motor imagery, EEG signal is classified into two categories, the left and right hand motor imagery. The accuracy of classification is up to 70%. The bandpower analysis results show that users’ attention level improves during the experiment. In this neuro-feedback game system, EEG signal is not only used for monitoring but also used for game control. The game provides an attention state measurements for users. With the neuro-feedback in the BCI, the user and the game form a close loop interactively. The proposed BCI video game could not only be used for entertainment and relaxation purpose, but attention-span training purpose.


Stock Market Prediction using Artificial Neural Networks . Case Study of TAL 1 T , Nasdaq OMX Baltic


title: Comparative evaluation of three-phase isolated matrix-type PFC rectifier concepts for high efficiency 380VDC supplies of future telco and data centers

abstract: Due to the high energy consumption in data and telco centers, the use of 380V or 400V DC facility-level distribution has been proposed as an alternative to the conventional AC distribution for a more efficient power delivery structure. The DC voltage is powered from the three-phase mains by a PFC rectifier and in many cases a mains transformer is used to provide galvanic isolation. In order to achieve a high efficiency in the DC voltage generation and to implement the required isolation, a single-stage concept, such as a matrix-type rectifier that enables PFC functionality and galvanic isolation in a single conversion, can be beneficial. In addition, due to the fact that with the matrix-type rectifier the galvanic isolation is performed with a high-frequency transformer, this results in a more compact rectifier system compared to conventional systems where the mains-frequency isolation transformer is located at the input of the PFC rectifier. In this paper, an overview of isolated matrix-type PFC rectifier topologies is given and a new converter circuit is proposed, analyzed and comparatively evaluated against another promising PFC rectifier concept, the phase-modular IMY-rectifier.


title: Design and Implementation of a New Multilevel Inverter Topology

abstract: Multilevel inverters have been widely accepted for high-power high-voltage applications. Their performance is highly superior to that of conventional two-level inverters due to reduced harmonic distortion, lower electromagnetic interference, and higher dc link voltages. However, it has some disadvantages such as increased number of components, complex pulsewidth modulation control method, and voltage-balancing problem. In this paper, a new topology with a reversing-voltage component is proposed to improve the multilevel performance by compensating the disadvantages mentioned. This topology requires fewer components compared to existing inverters (particularly in higher levels) and requires fewer carrier signals and gate drives. Therefore, the overall cost and complexity are greatly reduced particularly for higher output voltage levels. Finally, a prototype of the seven-level proposed topology is built and tested to show the performance of the inverter by experimental results.


Arthur: Rich Post-Facto Debugging for Production Analytics Applications


title: Review of Silicon Power Semiconductor Technologies for Power Supply on Chip and Power Supply in Package Applications

abstract: This paper provides an overview of the fundamental operating principles of power semiconductor devices and reviews the state-of-the-art power device technologies which are most relevant to the realization of power supply on chip and power supply in package solutions. Power switching devices are divided into four categories low-voltage discrete transistors, high-voltage discrete transistors, low-voltage power ICs, and high-voltage power ICs. Advantages and disadvantages of different device technologies are compared and analyzed. Future technology trends are discussed.


title: An Effective Defect Inspection Method for LCD Using Active Contour Model

abstract: Visual defects in liquid crystal display images often appear as low contrast and blurry contour without distinct intensity difference from their surrounding region. Besides, the background is usually intensity inhomogeneity. All these properties make the machine vision inspection extremely hard. This paper presents an effective machine vision inspection method using a local active contour model to detect defects with different brightness levels as well as diverse sizes and shapes. A modified local binary fitting model which is robust to initial contour is developed to extract defect boundary. Meanwhile, a simple preprocessing scheme is given to compensate the drawback of the two-phase active contour model for detecting objects with wide brightness levels. Experimental results show that the presented method can detect various types of defects effectively and achieves high performance in terms of inspection accuracy (both precision and recall are higher than 0.99).


title: Path planning for autonomous mobile robot navigation with ant colony optimization and fuzzy cost function evaluation

abstract: In the Motion Planning research field, heuristic methods have demonstrated to outperform classical approaches gaining popularity in the last 35 years. Several ideas have been proposed to overcome the complex nature of this NP-Complete problem. Ant Colony Optimization algorithms are heuristic methods that have been successfully used to deal with this kind of problems. This paper presents a novel proposal to solve the problem of path planning for mobile robots based on Simple Ant Colony Optimization Meta-Heuristic (SACO-MH). The new method was named SACOdm, where d stands for distance and m for memory. In SACOdm, the decision making process is influenced by the existing distance between the source and target nodes; moreover the ants can remember the visited nodes. The new added features give a speed up around 10 in many cases. The selection of the optimal path relies in the criterion of a Fuzzy Inference System, which is adjusted using a Simple Tuning Algorithm. The path planner application has two operating modes, one is for virtual environments, and the second one works with a real mobile robot using wireless communication. Both operating modes are global planners for plain terrain and support static and dynamic obstacle avoidance.


title: On the Consistency of Top-k Surrogate Losses

abstract: The top-$k$ error is often employed to evaluate performance for challenging classification tasks in computer vision as it is designed to compensate for ambiguity in ground truth labels. This practical success motivates our theoretical analysis of consistent top-$k$ classification. To this end, we define top-$k$ calibration as a necessary and sufficient condition for consistency, for bounded below loss functions. Unlike prior work, our analysis of top-$k$ calibration handles non-uniqueness of the predictor scores, and extends calibration to consistency -- providing a theoretically sound basis for analysis of this topic. Based on the top-$k$ calibration analysis, we propose a rich class of top-$k$ calibrated Bregman divergence surrogates. Our analysis continues by showing previously proposed hinge-like top-$k$ surrogate losses are not top-$k$ calibrated and thus inconsistent. On the other hand, we propose two new hinge-like losses, one which is similarly inconsistent, and one which is consistent. Our empirical results highlight theoretical claims, confirming our analysis of the consistency of these losses.


title: A 125-MHz Mixed-Signal Echo Canceller for Gigabit Ethernet on Copper Wire

abstract: A discrete-time analog echo canceller is described that reduces the echo in the front end of Gigabit Ethernet twisted-pair interfaces. Echo cancellation in the analog domain by means of four taps reduces the complexity of the digital echo canceller and crosstalk cancellers. Designed in a 0.4-/spl mu/m CMOS technology, the circuit employs an LMS algorithm to adapt to the cable length and impedance discontinuities, providing an echo suppression of 10 dB. The design operates at 125 MHz while consuming 43 mW from a 3-V supply.


Evaluating Programs for Community College Students: How Do We Know What Works? A Paper Prepared for the White House Summit on Community Colleges.


title: Adaptive Batch Mode Active Learning

abstract: Active learning techniques have gained popularity to reduce human effort in labeling data instances for inducing a classifier. When faced with large amounts of unlabeled data, such algorithms automatically identify the exemplar and representative instances to be selected for manual annotation. More recently, there have been attempts toward a batch mode form of active learning, where a batch of data points is simultaneously selected from an unlabeled set. Real-world applications require adaptive approaches for batch selection in active learning, depending on the complexity of the data stream in question. However, the existing work in this field has primarily focused on static or heuristic batch size selection. In this paper, we propose two novel optimization-based frameworks for adaptive batch mode active learning (BMAL), where the batch size as well as the selection criteria are combined in a single formulation. We exploit gradient-descent-based optimization strategies as well as properties of submodular functions to derive the adaptive BMAL algorithms. The solution procedures have the same computational complexity as existing state-of-the-art static BMAL techniques. Our empirical results on the widely used VidTIMIT and the mobile biometric (MOBIO) data sets portray the efficacy of the proposed frameworks and also certify the potential of these approaches in being used for real-world biometric recognition applications.


title: Working memory in developing and applying mental models from spatial descriptions

abstract: Four dual-task experiments examined visuospatial, articulatory, and central executive working memory involvement during the development and application of spatial mental models. In Experiments 1 and 2 participants read route and survey spatial descriptions while undertaking one of four secondary tasks targeting working memory components. Converging evidence from map drawing and statement verification tasks indicates that while articulatory mechanisms are involved in processing the language itself, visuospatial and central executive mechanisms are involved in developing spatial mental models, particularly during route description reading. In Experiments 3 and 4 participants undertook the same working memory tasks, but did so during testing; results from memory and secondary task performance converge to demonstrate that using spatial mental models is a visuospatially and centrally demanding process, particularly following route description learning. Taken together, results demonstrate that spatial mental model development and application are contingent upon multiple working memory systems and interact with representational formats.


title: Motivational factors that influence the acceptance of Moodle using TAM

abstract: Moodle and other virtual teaching platforms have bolstered the ability and motivation of universities to support distance learning. The aim of our study is to improve understanding of the motivational factors behind student satisfaction, or dissatisfaction, with the Web-based learning platform, Moodle. Our study extends the Technology Acceptance Model (TAM) to include technical support and perceived self-efficacy, with the expectation that they influence usage of Moodle. We surveyed 226 students of the Business Administration and Management (LADE) and Infant and Primary School Teaching degree courses of the University of Huelva. The data showed that technical support has a direct effect on perceived ease of use and perceived usefulness. Moodle usage was also directly influenced by perceived ease of use and attitude. The results reveal the importance of perceived ease of use and perceived usefulness on attitude.


title: A COMPARATIVE STUDY AMONG THE THREE WHEELER AUTOMOBILE DRIVERS ON PULMONARY FUNCTION TESTS IN ADULT MALES OF GULBARGA CITY

abstract: Background: Development of our country has led to rapid urbanization and there is increasing use of automobiles that is aggravating environmental pollution. Occupational exposure to automobile exhaust and industrial smokes has been shown to affect functioning of different systems of the body. The present study was taken up to assess the Pulmonary Function Tests (PFT) in auto rickshaw drivers of Gulbarga city. Methods: Fifty non –smoker male auto drivers in the age group of 20–50 years for more than 5 years of auto driving experience formed the study group. Age and sex matched individuals not exposed to auto rickshaw driving [administrative staff] formed the control group. Pulmonary function parameters FVC, FEV1, FEV1%, PEFR, PIFR, FEF25-75, FEF50 and MVV were assessed using a computerized Spiro meter during their working hours and were statistically analyzed. Results: There was a highly significant decrease in FVC and FEV1 in the study group compared to control group. The decrease in FEV1%, PIFR, FEF25-75 and FEF50 were statistically significant but the decrease in PEFR and MVV were statistically nonsignificant. Conclusion: Our findings point towards the adverse effects of vehicle exhaust on lung functions, mainly on lower airways with restrictive pattern of disease.


Modeling for Recommending GitHub Repositories


title: Supervised Machine Learning Model for High Dimensional Gene Data in Colon Cancer Detection

abstract: With well-developed methods in gene level data extraction, there are huge amount of gene expression data, including normal composition and abnormal ones. Therefore, mining gene expression data is currently an urgent research question, for detecting a corresponding pattern, such as cancer species, quickly and accurately. Since gene expression data classification problem has been widely studied accompanying with the development of gene technology, by far numerous methods, mainly neural network related, have been deployed in medical data analysis, which is mainly dealing with the high dimension and small quantity. A lot of research has been conducted on clustering approaches, extreme learning machine and so on. They are usually applied in a shallow neural network model. Recently deep learning has shown its power and good performance on high dimensional datasets. Unlike current popular deep neural network, we will continue to apply shallow neural network but develop an innovative algorithm for shallow neural network. In the supervised model, we demonstrate a shallow neural network model with a batch of parameters, and narrow its computational process into several positive parts, which process smoothly for a better result and finally achieve an optimal goal. It shows a stable and excellent result comparable to deep neural network. An analysis of the algorithm is also presented in this paper.


title: Cold-start, warm-start and everything in between: An autoencoder based approach to recommendation

abstract: This work addresses the problem of cold and warm start arising in recommender systems. Usually a latent factor model based on matrix factorization is used for collaborative filtering (warm start recommender system). Only in recent times, a handful of papers have been published that uses autoencoders for the same task; these studies have shown to yield better results than matrix factorization. This is the first work that proposes a comprehensive autoencoder based formulation to address both the cold and warm start problem. It makes use of both available rating's of users on items as well as associated user and item metadata. The proposed method has been compared with state-of-the-art methods and have shown to supersede them.


title: Statistical schema matching across web query interfaces

abstract: Schema matching is a critical problem for integrating heterogeneous information sources. Traditionally, the problem of matching multiple schemas has essentially relied on finding pairwise-attribute correspondence. This paper proposes a different approach, motivated by integrating large numbers of data sources on the Internet. On this "deep Web," we observe two distinguishing characteristics that offer a new view for considering schema matching: First, as the Web scales, there are ample sources that provide structured information in the same domains (e.g., books and automobiles). Second, while sources proliferate, their aggregate schema vocabulary tends to converge at a relatively small size. Motivated by these observations, we propose a new paradigm, statistical schema matching: Unlike traditional approaches using pairwise-attribute correspondence, we take a holistic approach to match all input schemas by finding an underlying generative schema model. We propose a general statistical framework MGS for such hidden model discovery, which consists of hypothesis modeling, generation, and selection. Further, we specialize the general framework to develop Algorithm MGSsd, targeting at synonym discovery, a canonical problem of schema matching, by designing and discovering a model that specifically captures synonym attributes. We demonstrate our approach over hundreds of real Web sources in four domains and the results show good accuracy.


Marine Animal Detection and Recognition with Advanced Deep Learning Models


title: The Dysphagia Handicap Index: Development and Validation

abstract: Quality-of-life indicators for dysphagia provide invaluable information to the treating clinician regarding the success or failure of swallowing therapy. The purpose of this study was to develop a clinically efficient, statistically robust patient-reported outcomes tool that measures the handicapping effect of dysphagia on emotional, functional, and physical aspects of individual's lives. 60 statements describing the handicapping effect of dysphagia were collected from patient reports and divided into subscales of physical, emotional, and functional problems. The statements were presented to 77 individuals with dysphagia. Respondents replied never, sometimes, or always to each statement and rated their self-perceived dysphagia severity on a 7-point equal-appearing interval scale. Cronbach's α was performed to assess the internal consistency validation of the items within the questionnaire. The final questionnaire was reduced to 25 items and administered to 214 individuals with dysphagia and 74 controls. Test-retest was performed on 63 individuals with dysphagia. Cronbach's α for the initial and final versions was strong at r = 0.96 and r = 0.94, respectively. Significant differences occurred between the dysphagia and control groups. Test-retest reliability was strong. We present a new, easy-to-complete, statistically robust, patient-reported outcomes measure for assessing the handicapping effect of dysphagia.


title: B9. Equivalent circuit with frequency-independent lumped elements for plasmonic graphene patch antenna using particle swarm optimization technique

abstract: Graphene patch microstrip antenna has been investigated for 600 GHz applications. The graphene material introduces a reconfigurable surface conductivity in terahertz frequency band. The input impedance is calculated using the finite integral technique. A five-lumped elements equivalent circuit for graphene patch microstrip antenna has been investigated. The values of the lumped elements equivalent circuit are optimized using the particle swarm optimization techniques. The optimization is performed to minimize the mean square error between the input impedance of the finite integral technique and that calculated by the equivalent circuit model. The effect of varying the graphene material chemical potential and relaxation time on the radiation characteristics of the graphene patch microstrip antenna has been investigated. An improved new equivalent circuit model has been introduced to best fitting the input impedance using a rational function and PSO. The Cauer's realization method is used to synthesize a new lumped- elements equivalent circuits.


title: A Survey of Audio-Based Music Classification and Annotation

abstract: Music information retrieval (MIR) is an emerging research area that receives growing attention from both the research community and music industry. It addresses the problem of querying and retrieving certain types of music from large music data set. Classification is a fundamental problem in MIR. Many tasks in MIR can be naturally cast in a classification setting, such as genre classification, mood classification, artist recognition, instrument recognition, etc. Music annotation, a new research area in MIR that has attracted much attention in recent years, is also a classification problem in the general sense. Due to the importance of music classification in MIR research, rapid development of new methods, and lack of review papers on recent progress of the field, we provide a comprehensive review on audio-based classification in this paper and systematically summarize the state-of-the-art techniques for music classification. Specifically, we have stressed the difference in the features and the types of classifiers used for different classification tasks. This survey emphasizes on recent development of the techniques and discusses several open issues for future research.


title: Designing the user interface and functions of a search engine development tool

abstract: Search engine development tools have been made to allow users to build their own search engines. However, most of these tools have been designed for advanced computer users. Users without a full understanding of topics such as Web spidering would find these tools difficult to use due to different issues in terms of user interface, performance, and reliability. In view of these issues, we presented a tool called SpidersRUs to strike a balance between usability and functionality. On one hand, beginners should be able to operate the tool by using the basic functions needed to build a search engine. On the other, advanced users should be given the options to exert a higher level of customization while working on the tool. To study the interface design of SpidersRUs, we compared its usability and functionality from the users' perspective with two other development tools, namely Alkaline and Greenstone, in an evaluation study. Our study showed that SpidersRUs was preferred over the other two, particularly in areas of screen layout and sequence, terminology and system information, and learning to use the system.


title: System-level performance analysis of embedded system using behavioral C/C++ model

abstract: Design iteration time in SoC design flow is reduced through performance exploration at a higher level of abstraction. This paper proposes an accurate and fast performance analysis method in early stage of design process using a behavioral model written in C/C++ language. We made a cycle-accurate but fast and flexible compiled instruction set simulator (ISS) and IP models that represent hardware functionality and performance. System performance analyzer configured by the target communication architecture analyzes the performance utilizing event-traces obtained by running the ISS and IP models. This solution is automated and implemented in the tool, HIPA. We obtain diverse performance profiling results and achieve 95% accuracy using an abstracted C model. We also achieve about 20 times speed-up over corresponding co-simulation tools.


title: Overconfidence in case-study judgments.

abstract: It is a common phenomenon of clinical practice that as a psychologist accumulates casestudy material about another human being, he comes to think that he knows that person pretty well. Consequently, sooner or later in the information-gathering process, the psychologist becomes confident enough to make diagnostic conclusions, describe the client's main dynamics, and perhaps even venture to predict his future behavior. Though the psychologist's conclusions may remain tentative, his increase in confidence from the time of first approaching the case to the time of writing his report is usually very marked. This study investigated whether that increase in confidence is justified by a corresponding increase in accuracy of conclusions. Though the psychologist's confidence in his conclusions has often been mentioned as an important subject of scientific inquiry (Meehl, 1957), it has only rarely been studied intensively. Furthermore, when it has been studied, rather surprising findings have often resulted. For instance, Goldberg (1959) and Oskamp (1962) have shown that the diagnostic confidence of experienced psychologists is less than that of less experienced persons. The same studies and many others have also shown that professional psychologists are no better interpersonal judges, and sometimes are worse 1 Revision of a paper presented at the Western Psychological Association annual meeting, April 18, 1964. Thanks are due to Stanley Lunde for his help in constructing the case-study test. ones, than are untrained individuals (Taft, 1955).


title: Congestion control in reliable CoAP communication

abstract: The development of IPv6 stacks for wireless constrained devices that have limited hardware resources has paved the way for many new areas of applications and protocols. The Constrained Application Protocol (CoAP) has been designed by the IETF to enable the manipulation of resources for constrained devices that are capable of connecting to the Internet. Due to the limited radio channel capacities and hardware resources, congestion is a common phenomenon in networks of constrained devices. CoAP implements a basic congestion control mechanism for the transmission of reliable messages. Alternative CoAP congestion control approaches are a recent topic of interest in the IETF CoRE Working Group. New Internet-Drafts discuss the limitations of the default congestion control mechanisms and propose alternative ones, yet, there have been no studies in the literature that compare the original approach to the alternative ones. In this paper, we target this crucial study and perform evaluations that show how the default and alternative congestion control mechanisms compare to each other. We use the Cooja simulation environment, which is part of the Contiki development toolset, to simulate CoAP within a complete protocol stack that uses IETF protocols for constrained networks. Through simulations of different network topologies and varying traffic loads, we demonstrate how the advanced mechanisms proposed in the drafts perform relative to the basic congestion control mechanism.


title: 3D Structure from 2D Motion

abstract: This article motivates the structure from motion (SfM) approaches by describing some current practical applications. This is followed by a brief discussion of the background of the field. Then, several techniques are outlined that show various important approaches and paradigms to the SfM problem. Critical issues, advantages and disadvantages are pointed out. Subsequently, we present our SfM approach for recursive estimation of motion, structure, and camera geometry in a nonlinear dynamic system framework. Results are given for synthetic and real images. These are used to assess the accuracy and stability of the technique. We then discuss some practical and real-time applications we have encountered and the reliability and flexibility of the approach in those settings. Finally, we conclude with results from an independent evaluation study conducted by industry where the proposed SfM algorithm compared favorably to alternative approaches.


title: A new k-harmonic nearest neighbor classifier based on the multi-local means

abstract: K different multi-local means based on the k-nearest neighbors are employed.The harmonic mean distance is firstly introduced in the KNN classification problems.The classification error rates can be significantly reduced.Less sensitive to the choice of neighborhood size k.Easily designed in practice with a little extra computation complexity. The k-nearest neighbor (KNN) rule is a classical and yet very effective nonparametric technique in pattern classification, but its classification performance severely relies on the outliers. The local mean-based k-nearest neighbor classifier (LMKNN) was firstly introduced to achieve robustness against outliers by computing the local mean vector of k nearest neighbors for each class. However, its performances suffer from the choice of the single value of k for each class and the uniform value of k for different classes. In this paper, we propose a new KNN-based classifier, called multi-local means-based k-harmonic nearest neighbor (MLM-KHNN) rule. In our method, the k nearest neighbors in each class are first found, and then used to compute k different local mean vectors, which are employed to compute their harmonic mean distance to the query sample. Finally, MLM-KHNN proceeds in classifying the query sample to the class with the minimum harmonic mean distance. The experimental results, based on twenty real-world datasets from UCI and KEEL repository, demonstrated that the proposed MLM-KHNN classifier achieves lower classification error rate and is less sensitive to the parameter k, when compared to nine related competitive KNN-based classifiers, especially in small training sample size situations.


title: A 2.7V elliptical all–MOS gmC–OTA filter

abstract: The g m C-OTA technique relaxes transconductor output swing and DC-gain requirements, which makes it a suitable candidate for low-voltage circuits. To reduce the power consumption of OTAs an excess-phase shift compensation with automatic tuning circuit is proposed. A floating triode region transconductor with a novel biasing to compensate the effect of channel length modulation is presented. The two independent common-mode levels in a g m C-OTA integrator are utilized to bias a PMOS as a linear capacitor. This enables the realization of the floating transmission zero capacitors with a pure digital CMOS process. The implementation and the experimental results for a 3rd order 2.7V all- MOS elliptical filter with 64dB dynamic range and 0.1% linearity are presented.


title: Fully Distributed Scrum: Replicating Local Productivity and Quality with Offshore Teams

abstract: Scrum was designed for hyperproductive teams where productivity increases by 5-10 times over industry averages and many collocated teams have achieved this effect. The question for this paper is whether distributed, offshored teams can consistently achieve the same level of performance. In particular, can a team establish a localized velocity and quality and then maintain or increase that velocity and quality when distributing teams across continents. Since 2006, Xebia (Netherlands) started localized projects with half Dutch and half Indian team members. After establishing localized hyperproductivity, they move the Indian members of the team to India and show the same velocity with fully distributed teams. After running XP engineering practices inside many distributed Scrum projects, Xebia has systematically productized a model for high performance, distributed, offshore teams with one of the lowest defect rates in the industry.


Fuzzy Clustering with a Modified MRF Energy Function for Change Detection in Synthetic Aperture Radar Images


title: Remote Timing Attacks are Practical

abstract: Timing attacks are usually used to attack weak computing devices such as smartcards. We show that timing attacks apply to general software systems. Specifically, we devise a timing attack against OpenSSL. Our experiments show that we can extract private keys from an OpenSSL-based web server running on a machine in the local network. Our results demonstrate that timing attacks against network servers are practical and therefore security systems should defend against them.


title: Who will buy smart glasses? Empirical results of two pre-market-entry studies on the role of personality in individual awareness and intended adoption of Google Glass wearables

abstract: We introduce smart glasses to the consumer literature.We investigate the direct and moderating role of personality.We show that personality plays a significant role smart glass use (intention).We provide pre-market entry knowledge about smart glasses. Recent market studies reveal that augmented reality (AR) devices, such as smart glasses, will substantially influence the media landscape. Yet, little is known about the intended adoption of smart glasses, particularly: Who are the early adopters of such wearables? We contribute to the growing body of research that investigates the role of personality in predicting media usage by analyzing smart glasses, such as Google Glass or Microsoft Hololens. First, we integrate AR devices into the current evolution of media and technologies. Then, we draw on the Big Five Model of human personality and present the results from two studies that investigate the direct and moderating effects of human personality on the awareness and innovation adoption of smart glasses. Our results show that open and emotionally stable consumers tend to be more aware of Google Glass. Consumers who perceive the potential for high functional benefits and social conformity of smart glasses are more likely to adopt such wearables. The strength of these effects is moderated by consumers' individual personality, particularly by their levels of openness to experience, extraversion and neuroticism. This article concludes with a discussion of theoretical and managerial implications for research on technology adoption, and with suggestions for avenues for future research.


title: Degradation behaviour of Lithium-ion batteries based on field measured frequency regulation mission profile

abstract: Energy storage systems based on Lithium-ion batteries have been proposed as an environmental friendly alternative to traditional conventional generating units for providing grid frequency regulation. One major challenge regarding the use of Lithium-ion batteries in such applications is their cost competitiveness in comparison to other storage technologies or with the traditional frequency regulation methods. In order to surpass this challenge and to allow for optimal sizing and proper use of the battery, accurate knowledge about the lifetime of the Lithium-ion battery and its degradation behaviour is required. This paper aims to investigate, based on a laboratory developed lifetime model, the degradation behaviour of the performance parameters (i.e., capacity and power capability) of a Lithium-ion battery cell when it is subjected to a field measured mission profile, which is characteristic to the primary frequency regulation service.


Thailand in 2014: The Trouble with Magic Swords


title: Object detection in VHR optical remote sensing images via learning rotation-invariant HOG feature

abstract: Object detection in very high resolution (VHR) optical remote sensing images is one of the most fundamental but challenging problems in the field of remote sensing image analysis. As object detection is usually carried out in feature space, effective feature representation is very important to construct a high-performance object detection system. During the last decades, a great deal of effort has been made to develop various feature representations for the detection of different types of objects. Among various features developed for visual object detection, the histogram of oriented gradients (HOG) feature is maybe one of the most popular features that has been successfully applied to computer vision community. However, although the HOG feature has achieved great success in nature scene images, it is problematic to directly use it for object detection in optical remote sensing images because it is difficult to effectively handle the problem of object rotation variations. To explore a possible solution to the problem, this paper proposes a novel method to learn rotation-invariant HOG (RIHOG) features for object detection in optical remote sensing images. This is achieved by learning a rotation-invariant transformation model via optimizing a new objective function, which constrains the training samples before and after rotation to share the similar features to achieve rotation-invariance. In the experiments, we evaluate the proposed method on a publicly available 10-class VHR geospatial object detection dataset and comprehensive comparisons with state-of-the-arts demonstrate the effectiveness the proposed method.


title: Haploview: analysis and visualization of LD and haplotype maps

abstract: Summary: Research over the last few years has revealed significant haplotype structure in the human genome. The characterization of these patterns, particularly in the context of medical genetic association studies, is becoming a routine research activity. Haploview is a software package that provides computation of linkage disequilibrium statistics and population haplotype patterns from primary genotype data in a visually appealing and interactive interface. ::: ::: Availability: http://www.broad.mit.edu/mpg/haploview/ ::: ::: Contact: jcbarret@broad.mit.edu


title: Personalised Information Gathering and Recommender Systems: Techniques and Trends

abstract: With the explosive growth of resources available through the Internet, information mismatching and overload have become a severe concern to users. Web users are commonly overwhelmed by huge volume of information and are faced with the challenge of finding the most relevant and reliable information in a timely manner. Personalised information gathering and recommender systems represent state-of-the-art tools for efficient selection of the most relevant and reliable information resources, and the interest in such systems has increased dramatically over the last few years. However, web personalization has not yet been well-exploited; difficulties arise while selecting resources through recommender systems from a technological and social perspective. Aiming to promote high quality research in order to overcome these challenges, this paper provides a comprehensive survey on the recent work and achievements in the areas of personalised web information gathering and recommender systems. The report covers concept-based techniques exploited in personalised information gathering and recommender systems.


title: An LTCC-Based folded Rotman lens for phased array applications

abstract: A novel planar Rotman lens design for LTCC (Low-Temperature-Cofired-Ceramics) integration is introduced. By folding the lens about the middle plane and utilizing the multilayer features (3D) of LTCC, extremely compact Rotman lenses can be achieved which are attractive for applications in the lower microwave range. Simulation results for the folded and unfolded Rotman lens show very similar results.


title: The use of Semantic Web technologies for decision support --a survey

abstract: The Semantic Web shares many goals with Decision Support Systems DSS, e.g., being able to precisely interpret information, in order to deliver relevant, reliable and accurate information to a user when and where it is needed. DSS have in addition more specific goals, since the information need is targeted towards making a particular decision, e.g., making a plan or reacting to a certain situation. When surveying DSS literature, we discover applications ranging from Business Intelligence, via general purpose social networking and collaboration support, Information Retrieval and Knowledge Management, to situation awareness, emergency management, and simulation systems. The unifying element is primarily the purpose of the systems, and their focus on information management and provision, rather than the specific technologies they employ to reach these goals. Semantic Web technologies have been used in DSS during the past decade to solve a number of different tasks, such as information integration and sharing, web service annotation and discovery, and knowledge representation and reasoning. In this survey article, we present the results of a structured literature survey of Semantic Web technologies in DSS, together with the results of interviews with DSS researchers and developers both in industry and research organizations outside the university. The literature survey has been conducted using a structured method, where papers are selected from the publisher databases of some of the most prominent conferences and journals in both fields Semantic Web and DSS, based on sets of relevant keywords representing the intersection of the two fields. Our main contribution is to analyze the landscape of semantic technologies in DSS, and provide an overview of current research as well as open research areas, trends and new directions. An added value is the conclusions drawn from interviews with DSS practitioners, which give an additional perspective on the potential of Semantic Web technologies in this field; including scenarios for DSS, and requirements for Semantic Web technologies that may attempt to support those scenarios.


Intelligent machinery a heretical theory ; reprinted in ( Copeland 2004 ) Universal Turing Machine


title: The Ghost in the Quantum Turing Machine

abstract: In honor of Alan Turing's hundredth birthday, I unwisely set out some thoughts about one of Turing's obsessions throughout his life, the question of physics and free will. I focus relatively narrowly on a notion that I call"Knightian freedom": a certain kind of in-principle physical unpredictability that goes beyond probabilistic unpredictability. Other, more metaphysical aspects of free will I regard as possibly outside the scope of science. I examine a viewpoint, suggested independently by Carl Hoefer, Cristi Stoica, and even Turing himself, that tries to find scope for"freedom"in the universe's boundary conditions rather than in the dynamical laws. Taking this viewpoint seriously leads to many interesting conceptual problems. I investigate how far one can go toward solving those problems, and along the way, encounter (among other things) the No-Cloning Theorem, the measurement problem, decoherence, chaos, the arrow of time, the holographic principle, Newcomb's paradox, Boltzmann brains, algorithmic information theory, and the Common Prior Assumption. I also compare the viewpoint explored here to the more radical speculations of Roger Penrose. The result of all this is an unusual perspective on time, quantum mechanics, and causation, of which I myself remain skeptical, but which has several appealing features. Among other things, it suggests interesting empirical questions in neuroscience, physics, and cosmology; and takes a millennia-old philosophical debate into some underexplored territory.


title: Using predictive prefetching to improve World Wide Web latency

abstract: The long-term success of the World Wide Web depends on fast response time. People use the Web to access information from remote sites, but do not like to wait long for their results. The latency of retrieving a Web document depends on several factors such as the network bandwidth, propagation time and the speed of the server and client computers. Although several proposals have been made for reducing this latency, it is difficult to push it to the point where it becomes insignificant.This motivates our work, where we investigate a scheme for reducing the latency perceived by users by predicting and prefetching files that are likely to be requested soon, while the user is browsing through the currently displayed page. In our scheme the server, which gets to see requests from several clients, makes predictions while individual clients initiate prefetching. We evaluate our scheme based on trace-driven simulations of prefetching over both high-bandwidth and low-bandwidth links. Our results indicate that prefetching is quite beneficial in both cases, resulting in a significant reduction in the average access time at the cost of an increase in network traffic by a similar fraction. We expect prefetching to be particularly profitable over non-shared (dialup) links and high-bandwidth, high-latency (satellite) links.


title: A 20-b $\pm$ 40-mV Range Read-Out IC With 50-nV Offset and 0.04% Gain Error for Bridge Transducers

abstract: This paper presents a 20-b read-out IC with ±40-mV full-scale range that is intended for use with bridge transducers. It consists of a current-feedback instrumentation amplifier (CFIA) followed by a switched-capacitor incremental ΔΣ ADC. The CFIA's offset and 1/f noise are mitigated by chopping, while its gain accuracy and gain drift are improved by applying dynamic element matching to its input and feedback transconductors. Their mismatch is reduced by a digitally assisted correction loop, which further reduces the CFIA's gain drift. Finally, bulk-biasing and impedance-balancing techniques are used to reduce the common-mode dependency of these transconductors, which would otherwise limit the achievable gain accuracy. The combination of these techniques enables the read-out IC to achieve 140-dB CMRR, a worst-case gain error of 0.04% over a 0-2.5 V common-mode range, a maximum gain drift of 0.7 ppm/°C and an INL of 5 ppm. After applying nested-chopping, the read-out IC achieves 50-nV offset, 6-nV/°C offset drift, a thermal noise floor of 16.2 nV/√Hz and a 0.1-mHz 1/f noise corner. Implemented in a 0.7-μm CMOS technology, the prototype read-out IC consumes 270 μA from a 5-V supply.


title: Workload Characterization of the 1998 World Cup Web Site

abstract: World-Wide Web, workload characterization, performance, servers, caching, World Cup This paper presents a detailed workload characterization study of the 1998 World Cup Web site. Measurements from this site were collected over a three month period. During this time the site received 1.35 billion requests, making this the largest Web workload analyzed to date. By examining this extremely busy site and through comparison with existing characterization studies we are able to determine how Web server workloads are evolving. We find that improvements in the caching architecture of the World-Wide Web are changing the workloads of Web servers, but that major improvements to that architecture are still necessary. In particular, we uncover evidence that a better consistency mechanism is required for World-Wide Web caches.


title: Named Entity Recognition using Hidden Markov Model (HMM)

abstract: Named Entity Recognition (NER)is the subtask of Natural Language Processing (NLP)which is the branch of artificial intelligence. It has many applications mainly inmachine translation, text to speech synthesis, natural language understanding, Information Extraction,Information retrieval, question answeringetc. The aim of NER is to classify words into some predefined categories like location name, person name, organization name, date, time etc. In this paper we describe the Hidden Markov Model (HMM) based approach of machine learning in detail to identify the named entities. The main idea behind the use of HMM model for buildingNER system is that it is language independent and we can apply this system for any language domain. In our NER system the states are not fixed means it is of dynamic in nature one can use it according to their interest. The corpus used by our NER system is also not domain specific.


title: ASP: Learning to Forget with Adaptive Synaptic Plasticity in Spiking Neural Networks

abstract: A fundamental feature of learning in animals is the “ability to forget” that allows an organism to perceive, model, and make decisions from disparate streams of information and adapt to changing environments. Against this backdrop, we present a novel unsupervised learning mechanism adaptive synaptic plasticity (ASP) for improved recognition with spiking neural networks (SNNs) for real time online learning in a dynamic environment. We incorporate an adaptive weight decay mechanism with the traditional spike timing dependent plasticity (STDP) learning to model adaptivity in SNNs. The leak rate of the synaptic weights is modulated based on the temporal correlation between the spiking patterns of the pre- and post-synaptic neurons. This mechanism helps in gradual forgetting of insignificant data while retaining significant, yet old, information. ASP, thus, maintains a balance between forgetting and immediate learning to construct a stable-plastic self-adaptive SNN for continuously changing inputs. We demonstrate that the proposed learning methodology addresses catastrophic forgetting, while yielding significantly improved accuracy over the conventional STDP learning method for digit recognition applications. In addition, we observe that the proposed learning model automatically encodes selective attention toward relevant features in the input data, while eliminating the influence of background noise (or denoising) further improving the robustness of the ASP learning.


title: A Miniaturized Hexagonal-Triangular Fractal Antenna for Wide-Band Applications [Antenna Applications Corner]

abstract: Defense and aerospace communication systems need compact wide-band antennas suitable for multiple-frequency-band operation. In this article, a miniaturized hexagonaltriangular fractal antenna is examined for wide-band applications. The iterations are made from hexagonal rings, where the sides of the rings are connected by triangular elements inside a hexagonal metal patch. Transmissionline-feed technique is used to feed the signal. A triangular slotted symmetrical defective ground structure (DGS) with a rectangular slit at the center is used to obtain a bandwidth ratio of 8.4:1 with an operating bandwidth ranging from 3 to 25.2 GHz. The percentage of impedance bandwidth and gain of the proposed antenna are much superior to the recently reported wide-band antennas, which makes it suitable for numerous wireless applications, such as wireless local area networks, Wi-Fi, ultrawideband (UWB), X band, and Ku band. The performance of the antenna is analyzed by various parametric studies and field and current distribution analysis. The corresponding design details and comparison of simulated and practical results are presented.


title: HOW TO MANAGE THE SEGMENT-OF-ONE? A FRAMEWORK TO REDUCE CUSTOMER COMPLEXITY

abstract: The after-sales business is the most profitable area within the automotive industry. All manufacturers seek to tighten their ties with end customers in order to ensure loyalty and foster economic success. In times of dramatic change in the automotive industry (switch from combustion to electric engines, etc.), digitalization plays an evermore important role for developing sustainable business models. End customers in after-sales are as individual as their cars. In an ideal case, every customer is addressed individually, taking the individual's specific needs into account: the classic, but rather complex segment-of-one. This paper presents a multi-dimensional framework for customer segmentation in the automotive after-sales domain which uses a reduced number of segments. It addresses end customers on a perceived individual basis while reducing the complexity significantly compared to a full segment-of-one approach. It provides a holistic view on customers' characteristics, taking into account their specific vehicle needs for maintenance as well as several factors defining the individual's approach to services delivered by the car manufacturer. As such, a perceived segment-of-one is provided. The concept was evaluated using a qualitative study with industry experts and stakeholder representatives. Based on the findings, recommendations for the application of the framework in practice are provided.


title: Preserving Communities in Anonymized Social Networks

abstract: Social media and social networks are embedded in our society to a point that could not have been imagined only ten years ago. Facebook, LinkedIn, and Twitter are already well known social networks that have a large audience in all age groups. The amount of data that those social sites gather from their users is continually increasing and this data is very valuable for marketing, research, and various other purposes. At the same time, this data usually contain a significant amount of sensitive information which should be protected against unauthorized disclosure. To protect the privacy of individuals, this data must be anonymized such that the risk of re-identification of specific individuals is very low. In this paper we study if anonymized social networks preserve existing communities from the original social networks. To perform this study, we introduce two approaches to measure the community preservation between the initial network and its anonymized version. In the first approach we simply count how many nodes from the original communities remained in the same community after the processes of anonymization and de-anonymization. In the second approach we consider the community preservation for each node individually. Specifically, for each node, we compare the original and final communities to which the node belongs. To anonymize social networks we use two models, namely, k-anonymity for social networks and k-degree anonymity. To determine communities in social networks we use an existing community detection algorithm based on modularity quality function. Our experiments on publically available datasets show that anonymized social networks satisfactorily preserve the community structure of their original networks.


title: Lower protein content in infant formula reduces BMI and obesity risk at school age: follow-up of a randomized trial

abstract: Background: Early nutrition is recognized as a target for the effective prevention of childhood obesity. Protein intake was associated with more rapid weight gain during infancy—a known risk factor for later obesity. Objective: We tested whether the reduction of protein in infant formula reduces body mass index (BMI; in kg/m 2 ) and the prevalence of obesity at 6 y of age. Design: The Childhood Obesity Project was conducted as a European multicenter, double-blind, randomized clinical trial that enrolled healthy infants born between October 2002 and July 2004. Formula-fed infants (n = 1090) were randomly assigned to receive higher protein (HP)– or lower protein (LP)–content formula (within recommended amounts) in the first year of life; breastfed infants (n = 588) were enrolled as an observational reference group. We measured the weight and height of 448 (41%) formula-fed children at 6 y of age. BMI was the primary outcome. Results: HP children had a significantly higher BMI (by 0.51; 95% CI: 0.13, 0.90; P = 0.009) at 6 y of age. The risk of becoming obese in the HP group was 2.43 (95% CI: 1.12, 5.27; P = 0.024) times that in the LP group. There was a tendency for a higher weight in HP children (0.67 kg; 95% CI: 20.04, 1.39; P = 0.064) but no difference in height between the intervention groups. Anthropometric measurements were similar in the LP and breastfed groups. Conclusions: Infant formula with a lower protein content reduces BMI and obesity risk at school age. Avoidance of infant foods that provide excessive protein intakes could contribute to a reduction in childhood obesity. This trial was registered at clinicaltrials.gov as NCT00338689. Am J Clin Nutr doi: 10.3945/ajcn.113.064071.


title: Planar Compact LTE/WWAN Monopole Antenna for Tablet Computer Application

abstract: A novel design of a planar multiband monopole antenna with dual parasitic shorted strips for LTE/WWAN operation in the tablet computer is proposed. The obtained impedance bandwidth across the operating bands can reach about 255/980 MHz for the LTE/WWAN bands, respectively. Only with the antenna size of 45 $\,\times\,$ 15 $\,\times\,$ 0.8 mm $^{3}$ , the proposed planar antenna has the compact operation with more than 25% antenna size reduction. The measured peak gains and antenna efficiencies are about 2.5/3.6 dBi and 85/84% for the LTE/WWAN bands, respectively.


title: A Simple Algorithm for Nuclear Norm Regularized Problems

abstract: Optimization problems with a nuclear norm regularization, such as e.g. low norm matrix factorizations, have seen many applications recently. We propose a new approximation algorithm building upon the recent sparse approximate SDP solver of (Hazan, 2008). The experimental efficiency of our method is demonstrated on large matrix completion problems such as the Netflix dataset. The algorithm comes with strong convergence guarantees, and can be interpreted as a first theoretically justified variant of Simon-Funk-type SVD heuristics. The method is free of tuning parameters, and very easy to parallelize.


title: Mixed Initiative In Dialogue: An Investigation Into Discourse Segmentation

abstract: Conversation between two people is usually of MIXED-INITIATIVE, with CONTROL over the conversation being transferred from one person to another. We apply a set of rules for the transfer of control to 4 sets of dialogues consisting of a total of 1862 turns. The application of the control rules lets us derive domain-independent discourse structures. The derived structures indicate that initiative plays a role in the structuring of discourse. In order to explore the relationship of control and initiative to discourse processes like centering, we analyze the distribution of four different classes of anaphora for two data sets. This distribution indicates that some control segments are hierarchically related to others. The analysis suggests that discourse participants often mutually agree to a change of topic. We also compared initiative in Task Oriented and Advice Giving dialogues and found that both allocation of control and the manner in which control is transferred is radically different for the two dialogue types. These differences can be explained in terms of collaborative planning principles.


title: Efficient Online Novelty Detection in News Streams

abstract: Novelty detection in text streams is a challenging task that emerges in quite a few different scenarii, ranging from email threads to RSS news feeds on a cell phone. An efficient novelty detection algorithm can save the user a great deal of time when accessing interesting information. Most of the recent research for the detection of novel documents in text streams uses either geometric distances or distributional similarities with the former typically performing better but being slower as we need to compare an incoming document with all the previously seen ones. In this paper, we propose a new novelty detection algorithm based on the Inverse Document Frequency (IDF) scoring function. Computing novelty based on IDF enables us to avoid similarity comparisons with previous documents in the text stream, thus leading to faster execution times. At the same time, our proposed approach outperforms several commonly used baselines when applied on a real-world news articles dataset.


title: All-solid-state pulse adder with bipolar high voltage fast narrow pulses output

abstract: A newly developed bipolar high voltage adder is proposed for generating fast narrow pulses. The new circuit topology enables the use of typical half-bridge semiconductor structures on the basis of conventional unipolar all-solid-state pulse adder. Magnetic ring transformers were employed for isolating the charge loop from the high-voltage pulse discharge loop, which simplified the isolation arrangement. Certain measures were implemented to resolve the EMI caused by the pulse adder arrangement. A laboratory prototype was assembled with 8 stages and each stage was made of 800 V MOSFETs. The modulator operating at 21 kHz repetition rate gives 2 kV bipolar pulses, with 200 ns positive/negative pulse widths, 37 ns rise time, 50 ns fall time, and 40 ns relaxation time into a 50 Ω resistive load. The adopted design is an all-solid-state approach with a simple control method, long maintenance-free lifetime, and high reliability.


title: Level set evolution without re-initialization: a new variational formulation

abstract: In this paper, we present a new variational formulation for geometric active contours that forces the level set function to be close to a signed distance function, and therefore completely eliminates the need of the costly re-initialization procedure. Our variational formulation consists of an internal energy term that penalizes the deviation of the level set function from a signed distance function, and an external energy term that drives the motion of the zero level set toward the desired image features, such as object boundaries. The resulting evolution of the level set function is the gradient flow that minimizes the overall energy functional. The proposed variational level set formulation has three main advantages over the traditional level set formulations. First, a significantly larger time step can be used for numerically solving the evolution partial differential equation, and therefore speeds up the curve evolution. Second, the level set function can be initialized with general functions that are more efficient to construct and easier to use in practice than the widely used signed distance function. Third, the level set evolution in our formulation can be easily implemented by simple finite difference scheme and is computationally more efficient. The proposed algorithm has been applied to both simulated and real images with promising results.


title: Extension of Planar Bulk n-Channel MOSFET Scaling With Oxygen Insertion Technology

abstract: An experimental and simulation study of short- channel planar bulk nMOSFET performance enhancement achieved with oxygen insertion technology is presented. The benefits of this technology for low-power digital logic circuits make it a promising evolutionary approach to extend bulk MOSFET scaling. Index Terms— Low-power (LP) logic circuits, mobility enhancement, oxygen insertion (OI), super-steep retrograde well (SSRW), variability.


title: A 79-GHz LTCC patch array antenna using a low-loss ceramic tape

abstract: This paper presents a 79-GHz microstrip patch array antenna in a low-temperature co-fired ceramics technology. The ceramic tape used has a low loss tangent (0.0013 at 79 GHz), a good shrinkage-control (with zero-shrinkage process), and it also enables a photolithographic process for the outside layers of metallization. To verify the performance of this material system for applications at 79 GHz, an array antenna was designed with a simulated radiation efficiency of 81% (at the antenna feedingpoint). A hybrid feeding-network was chosen for the antenna. On the top layer the microstrip patch antennas were used as the radiating elements (with two layers of ceramic tapes as the substrate). They were connected by the microstrip lines (seriesfeed). A via guard-ring helps to suppress parasitic surface waves due to the thick substrate. A vertically oriented power divider (parallel-feed) keeps the main-beam direction constant in a given frequency bandwidth. The simulated and the measured halfpower beamwidths are 9° and 7° in the E-plane and H-plane, respectively. The measured bandwidth of 10-dB return loss is around 4 GHz. The measured antenna gain (at WR12-flange) is 15.4 dBi. This antenna has a geometry of 28mm×3.2mm00D71.2mm and is suitable for 79GHz middle/short range radar applications.


title: Profiling users in a 3g network using hourglass co-clustering

abstract: With widespread popularity of smart phones, more and more users are accessing the Internet on the go. Understanding mobile user browsing behavior is of great significance for several reasons. For example, it can help cellular (data) service providers (CSPs) to improve service performance, thus increasing user satisfaction. It can also provide valuable insights about how to enhance mobile user experience by providing dynamic content personalization and recommendation, or location-aware services. In this paper, we try to understand mobile user browsing behavior by investigating whether there exists distinct "behavior patterns" among mobile users. Our study is based on real mobile network data collected from a large 3G CSP in North America. We formulate this user behavior profiling problem as a "co-clustering" problem, i.e., we group both users (who share similar browsing behavior), and browsing profiles (of like-minded users) simultaneously. We propose and develop a scalable co-clustering methodology, Phantom, using a novel hourglass model. The proposed hourglass model first reduces the dimensions of the input data and performs divisive hierarchical co-clustering on the lower dimensional data; it then carries out an expansion step that restores the original dimensions. Applying Phantom to the mobile network data, we find that there exists a number of prevalent and distinct behavior patterns that persist over time, suggesting that user browsing behavior in 3G cellular networks can be captured using a small number of co-clusters. For instance, behavior of most users can be classified as either homogeneous (users with very limited set of browsing interests) or heterogeneous (users with very diverse browsing interests), and such behavior profiles do not change significantly at either short (30-min) or long (6 hour) time scales.


title: Iterative Low-Dose CT Reconstruction With Priors Trained by Artificial Neural Network

abstract: Dose reduction in computed tomography (CT) is essential for decreasing radiation risk in clinical applications. Iterative reconstruction algorithms are one of the most promising way to compensate for the increased noise due to reduction of photon flux. Most iterative reconstruction algorithms incorporate manually designed prior functions of the reconstructed image to suppress noises while maintaining structures of the image. These priors basically rely on smoothness constraints and cannot exploit more complex features of the image. The recent development of artificial neural networks and machine learning enabled learning of more complex features of image, which has the potential to improve reconstruction quality. In this letter, K-sparse auto encoder was used for unsupervised feature learning. A manifold was learned from normal-dose images and the distance between the reconstructed image and the manifold was minimized along with data fidelity during reconstruction. Experiments on 2016 Low-dose CT Grand Challenge were used for the method verification, and results demonstrated the noise reduction and detail preservation abilities of the proposed method.