title: A Waveguide Magic-T With Coplanar Arms for High-Power Solid-State Power Combining

abstract: In this paper, a compact waveguide magic-T for high-power solid-state power combining is proposed. The coplanar arms of the $E$ and $H$ ports are realized by the $E$ -plane power divider and ridge waveguide coupling structure. The input port of the $E$ -plane power divider is used to realize the difference port of the magic-T, and the ridge waveguide port is utilized to realize the sum port. Based on a theoretical analysis, a modified magic-T with two coaxial ports, one ridge port, and one rectangular port is designed and fabricated. Low-power tests show that from 7.8 to 9.4 GHz, when the difference port and the sum port are excited, the insertion loss of the magic-T is less than 0.2 dB. The isolation between the sum/difference ports and the two input ports is better than 40 and 26 dB. As for the in-phase and out-of-phase excitation, the amplitude and phase imbalances are less than ±0.05 dB and 1°. High-power experiments indicate that the power capacity is no less than 14 kW with a 1- $\mu \text{s}$ pulsewidth. The measured results agree with the simulations.


title: A Machine Learning-Empowered System for Long-Term Motion-Tolerant Wearable Monitoring of Blood Pressure and Heart Rate With Ear-ECG/PPG

abstract: In this paper, we propose a fully ear-worn long-term blood pressure (BP) and heart rate (HR) monitor to achieve a higher wearability. Moreover, to enable practical application scenarios, we present a machine learning framework to deal with severe motion artifacts induced by head movements. We suggest situating all electrocardiogram (ECG) and photoplethysmography (PPG) sensors behind two ears to achieve a super wearability, and successfully acquire weak ear-ECG/PPG signals using a semi-customized platform. After introducing head motions toward real-world application scenarios, we apply a support vector machine classifier to learn and identify raw heartbeats from motion artifacts-impacted signals. Furthermore, we propose an unsupervised learning algorithm to automatically filter out residual distorted/faking heartbeats, for ECG-to-PPG pulse transit time (PTT) and HR estimation. Specifically, we introduce a dynamic time warping-based learning approach to quantify distortion conditions of raw heartbeats referring to a high-quality heartbeat pattern, which are then compared with a threshold to perform purification. The heartbeat pattern and the distortion threshold are learned by a K-medoids clustering approach and a histogram triangle method, respectively. Afterward, we perform a comparative analysis on ten PTT or PTT&HR-based BP learning models. Based on an acquired data set, the BP and HR estimation using the proposed algorithm has an error of −1.4±5.2 mmHg and 0.8±2.7 beats/min, respectively, both much lower than the state-of-the-art approaches. These results demonstrate the capability of the proposed machine learning-empowered system in ear-ECG/PPG acquisition and motion-tolerant BP/HR estimation. This proof-of-concept system is expected to illustrate the feasibility of ear-ECG/PPG-based motion-tolerant BP/HR monitoring.


title: Design capital and design moves: the logic of digital business strategy

abstract: As information technology becomes integral to the products and services in a growing range of industries, there has been a corresponding surge of interest in understanding how firms can effectively formulate and execute digital business strategies. This fusion of IT within the business environment gives rise to a strategic tension between investing in digital artifacts for long-term value creation and exploiting them for short-term value appropriation. Further, relentless innovation and competitive pressures dictate that firms continually adapt these artifacts to changing market and technological conditions, but sustained profitability requires scalable architectures that can serve a large customer base and stable interfaces that support integration across a diverse ecosystem of complementary offerings. The study of digital business strategy needs new concepts and methods to examine how these forces are managed in pursuit of competitive advantage. We conceptualize the logic of digital business strategy in terms of two constructs: design capital (i.e., the cumulative stock of designs owned or controlled by a firm) and design moves (i.e., the discrete strategic actions that enlarge, reduce, or modify a firm's stock of designs). We also identify two salient dimensions of design capital, namely, option value and technical debt. Using embedded case studies of four firms, we develop a rich conceptual model and testable propositions to lay out a design-based logic of digital business strategy. This logic highlights the interplay between design moves and design capital in the context of digital business strategy and contributes to a growing body of insights that link the design of digital artifacts to competitive strategy and firm-level performance.


Code Mixed Entity Extraction in Indian Languages using Neural Networks


title: Learning distributed representations for large-scale dynamic social networks

abstract: Learning distributed representations of symbolic data were introduced by Hinton[1], and first developed in modeling networks for learning the node vectors by Perozzi et al (2014). In this work, we proposed Dnps, a novel nodes embedding approach for acquiring distributed representations of large-scale dynamic social networks. Dnps is suitable for many types of social networks: dynamic/static, directed/undirected, and weighted/unweighted. Recently, several works of nodes embedding were proposed. However, they were designed for static networks, such as language networks. To address this problem, first, we develop a damping based positive sampling (DpS) algorithm to learn the hierarchical structure of social networks. Then, we devise a local search based DpS algorithm to obtain incremental information of network evolution. Finally, we show Dnps's potentials on future link prediction task for three real-life large-scale dynamic social networks. The results show that Dnps consistently outperforms all baseline methods and exhibits an improvement of 12%, 6%, 4% on Digg, Flickr and YouTube over the second-highest level, respectively. Moreover, Dnps is also scalable. For example, Dnps can speed up the training process in 2 ∼ 36 times compared with benchmarks on Flickr network. The source codes of the project is available online1.


title: Observing behavior in a vigilance task.

abstract: it has been suggested that level of performance in a vigilance task is accurately reflected by frequency of observing responses. By means of photography it has been demonstrated that under conditions where a decrement in vigilance performance does not occur, the frequency of nonobserving behaviorand general activity increases in time.


title: Microcontroller based DC-DC Cascode Buck-Boost Converter

abstract: In this paper DC-DC Cascode Buck-Boost Converter is proposed by using two buck-boost converter. The structure of the proposed convert is very simple and the switching operation is done by using one active switch IGBT. The Buck-Boost Converters are named as higher semi stage and lower semi stage. The input DC supply to the lower semi stage converter is given by means of the input DC supply and the input to the higher semi stage is given by means of DC supply and the output voltage of the lower semi stage converter. Thus overall output voltage is given by the output sum of individual semi stage converters by cascode. Hence, from this Cascode DC-DC Converter can achieve a very high output voltage gain in a single stage without having an extreme high duty ratio. The input pulse to the active switch IGBT is given by Arduino. The operating principle and steady analysis are discussed in detail in this paper and also the voltage gain is compared with the conventional Boost converter, Buck-Boost converter and Cuk converter. The dynamic analysis of complete circuit is analyzed and MATLAB/Simulink simulation is done and finally to verify the proposed cascode converter laboratory experiment of 50W has been implemented to test both the simulation and experimental result.


title: A Feasibility Study on Autonomous Online Condition Monitoring of High-Voltage Overhead Power Lines

abstract: For electric power transmission, high-voltage overhead power lines play an important role, as the costs for power transmission are comparatively low. However, the sag of the conductors (e.g., due to temperature variations, aging, or icing of conductors as a result of extreme weather conditions) may increase safety margins and limit the operability of these power lines. Furthermore, heavy loads due to icing or vibrations excited by wind streams increase the risk of line breakage. With online condition monitoring of power lines, critical states can be detected early, and appropriate countermeasures can be applied. In this paper, we investigate possibilities for monitoring devices that are directly mounted onto a conductor. The feasibility of powering the device from the electric field, protection of electronic circuitry from strong electric fields, and data transmission by means of a wireless link is demonstrated, as well as its operability during the presence of strong magnetic fields due to high currents and transient signals due to partial and spark discharge events.


title: Slot-Loaded Folded Dipole Antenna With Wideband and Unidirectional Performance for L-Band Applications

abstract: Wideband antennas operating at L-band are rapidly finding their applications in various microwave-based medical diagnostic systems. To increase signal penetration while working in a limited space, these systems require compact unidirectional antennas. To meet these requirements, a three-dimensional folded dipole antenna is presented. It is composed of a slot-loaded coaxially fed printed slab and a U-shaped copper structure that completes the folded dipole loop. An appropriate slot loading enables achieving significant improvements of the proposed design in terms of bandwidth, gain, and front-to-back ratio compared to the conventional folded dipole antenna. A parametric analysis is performed for a deep understanding of the antenna's mechanism. A fabricated prototype demonstrates 57% fractional bandwidth, centered at 1.44 GHz with an average gain of 3.7 dBi over that band. The antenna has a directional radiation pattern with around 9 dBi front-to-back ratio and -10-dB cross-polarization levels along the boresight direction. The overall volume of the antenna is 0.24 × 0.10 × 0.05 λ3, where λ is the wavelength of the lowest operating frequency.


title: Algebraic tensegrity form-finding

abstract: This paper concerns the form-finding problem for general and symmetric tensegrity structures with shape constraints. A number of different geometries are treated and several fundamental properties of tensegrity structures are identified that simplify the form-finding problem. The concept of a tensegrity invariance (similarity) transformation is defined and it is shown that tensegrity equilibrium is preserved under affine node position transformations. This result provides the basis for a new tensegrity form-finding tool. The generality of the problem formulation makes it suitable for the automated generation of the equations and their derivatives. State-of-the-art numerical algorithms are applied to solve several example problems. Examples are given for tensegrity plates, shell-class symmetric tensegrity structures and structures generated by applying similarity transformation. � 2005 Elsevier Ltd. All rights reserved.


title: Eye gaze tracking techniques for interactive applications

abstract: This paper presents a review of eye gaze tracking technology and focuses on recent advancements that might facilitate its use in general computer applications. Early eye gaze tracking devices were appropriate for scientific exploration in controlled environments. Although it has been thought for long that they have the potential to become important computer input devices as well, the technology still lacks important usability requirements that hinders its applicability. We present a detailed description of the pupil-corneal reflection technique due to its claimed usability advantages, and show that this method is still not quite appropriate for general interactive applications. Finally, we present several recent techniques for remote eye gaze tracking with improved usability. These new solutions simplify or eliminate the calibration procedure and allow free head motion.


title: A Mobile-Phone-Based Breath Carbon Monoxide Meter to Detect Cigarette Smoking

abstract: i ntroduction: Mobile phones hold considerable promise for delivering evidence-based smoking cessation interventions that require frequent and objectiv e assessment of smoking status via breath carbon monoxide (Breath CO) measurement. However, there are currently no commercially available mobile-phone-based Breath CO meters. We developed a mobile-phone-based Breath CO meter prototype that attaches to and communicates with a smartphone through an audio port. The purpose of the current study was to evaluate the reliability and validity of Breath CO measures collected with the mobile meter prototype and assess the usability and acceptability of the meter. Methods: Participants included 20 regular smokers (≥10 cigarettes/day), 20 light smokers (<10 cigarettes/day), and 20 nonsmokers. Expired air samples were collected 4 times from each participant: twice with the mobile meter and twice with a commercially available Breath CO meter. r esults: Measures calculated by the mobile meter correlated strongly with measures calculated by the commercial meter (r = .96, p < .001). In addition, the mobile meter accurately distinguished between smokers and nonsmokers. The area under the receiver-operating characteristic curve for the mobile meter was 94.7%, and the meter had a combined sensitivity and specificity of 1.86 at an abstinence threshold of ≤6 ppm. Responses on an acceptability survey indicated that smokers liked the meter and would be interested in using it during a quit attempt. conclusions: The results of the current study suggest that a mobile-phone-based Breath CO meter is a reliable, valid, and acceptable device for distinguishing between smokers and nonsmokers.


title: "If a person is emailing you, it just doesn't make sense": Exploring Changing Consumer Behaviors in Email

abstract: Much of the existing research literature on email use focuses on productivity or work settings. However, personal use of email has rarely been studied in depth. With the growth of messaging platforms being used for an increasing amount of personal communication, yet email use remaining high, we were interested in learning what Americans are using email for in their daily lives in 2016. To explore this topic, we use qualitative data from over 150 interviews with personal email users as well as quantitative data from several larger survey-based studies. We will show that personal email use is very different from what has been previously studied by workplace researchers and that daily use is largely focused on receiving and viewing B2C messages such as coupons, deals, receipts, and event notifications with personal communication over email diminished to a rarer, less-than-daily occurrence. We discuss the implications of this for the design of email and communications clients and present a design and prototype for an application that seeks to support these more frequent uses of consumer email.


An Adaptive Compensation Algorithm for Temperature Drift of Micro-Electro-Mechanical Systems Gyroscopes Using a Strong Tracking Kalman Filter


title: Compact T-junction orthomode transducer facilitates easy integration and low cost production

abstract: A novel waveguide T-junction is shown to provide the basic building block of a very compact OMT design. It is formed by a short circuited common circular (or square) waveguide section exhibiting two rectangular waveguide ports at the short circuit situated at opposed side walls — one with an E- and the other with an H-plane alignment. Thus the orthogonal modes (polarizations) of the common waveguide are coupled by different field components at the side wall ports, i.e., the E-plane waveguide port couples the H x -fields of the 1st polarization (TE 11s /TE 01 ) while the H-plane port serves the H z -fields of the orthogonal one (TE 11c /TE 10 ). High isolation between the orthogonal polarized signals is obtained due to the symmetry and the port alignment of the structure. The implementation of appropriate discontinuities within the T-junction allows the realization of a single-acting OMT solution with high performance properties. The approach is verified by a realized OMT design at 26GHz that is made from a single block by CNC machining


title: In-Plant Logistics Efficiency Valuation Using Discrete Event Simulation

abstract: The purpose of this paper is to present an applicable approach for the valuation of in-plant logistics efficiency. Therefore, we developed a time-based efficiency concept that considers all relevant time losses when executing production logistics processes. The occurring delays are captured in real-time using modern auto-ID-technologies. For the valuation and improvement of in-plant logistics efficiency, we propose to use simulation modelling to investigate the cause-effect relations in the production system in advance. Thus, a discrete event simulation model has been constructed to model the workflow of a production system with discrete manufacturing processes and its in-plant logistics processes. The aim of the provided simulation study is to prove the functionality and to verify the applicability of the current approach in business practice. (Received in March 2014, accepted in August 2014. This paper was with the authors 1 month for 1 revision.)


title: Do Markets Erode Social Responsibility

abstract: This paper studies socially responsible behavior in markets. We develop a laboratory product market in which low-cost production creates a negative externality for third parties, but where alternative production with higher costs mitigates the externality. Our first study, conducted in Switzerland, reveals a persistent preference among many consumers and firms for avoiding negative social impact in the market, reflected both in the composition of product types and in a price premium for socially responsible products. Socially responsible behavior is generally robust to varying market settings, such as increased seller competition and limited consumer information, and it responds to costs and prices in a manner consistent with a model in which positive social impact is a utility-enhancing feature of a consumer product. In a second study, we investigate whether market social responsibility varies across societies by comparing market behavior in Switzerland and China. While subjects in Switzerland and China do not differ in their degree of social concern in non-market contexts, we find that low-cost production that creates negative externalities is significantly more prevalent in markets in China. Across both studies, consumers in markets exhibit less social concern than subjects in a comparable individual choice context, though the difference is much smaller in Switzerland.


title: Traffic aware placement of interdependent NFV middleboxes

abstract: Network function virtualization enables flexible implementation of network functions, or middleboxes, as virtual machines running on standard servers. However, the flexibility also creates a challenge for efficiently placing such middleboxes, due to the availability of multiple hosting servers, capability of middleboxes to change traffic volumes, and dependency between middleboxes. In this paper, we address the optimal placement challenge of NFV middleboxes, and propose solutions for middleboxes of different traffic changing effects and with different dependency relations. First, we formulate the Traffic Aware Placement of Interdependent Middleboxes problem as a graph optimization problem. When the flow path is predetermined, we design optimal algorithms to place a non-ordered or totally-ordered middlebox set, and propose an efficient heuristic for the general scenario of a partially-ordered middlebox set after proving its NP-hardness. When the flow path is not predetermined, we show that the problem is NP-hard even for a non-ordered middlebox set, and propose a traffic and space aware routing heuristic. We have evaluated the proposed algorithms using large scale simulations and prototype experiments, and present extensive evaluation results to demonstrate the effectiveness of our design.


title: Arousal-mediated memory consolidation: Role of the Medial Temporal Lobe in Humans

abstract: Although the influence of emotional arousal on declara- tive memory has been documented behaviorally, the mechanisms underlying arousal-memory interactions and their representation in the human brain remain uncertain. One route through which arousal achieves its effects on memory performance is by regulating consoli- dation processes. Animal research has revealed that the amygdala strengthens hippocampal-dependent memory consolidation in a limit- ed time window following participation in an arousing task. To exam- ine whether this integrative function of amygdalo-hippocampal structures extends to the human brain, we tested unilateral-temporal- lobectomy patients on an adaptation of a classic paradigm in which levels of physiological arousal at encoding modulate retention over time. Subjects rated emotionally arousing (taboo) and neutral words on an arousal scale while their skin conductance responses (SCRs) were monitored. Recall for the words was assessed immediately and after a 1-hr delay. Both temporal-lobectomy patients and control sub- jects generated enhanced SCRs and arousal ratings for the arousing words at the time of encoding. However, only control subjects exhibit- ed an increase in memory for the arousing words over time. This group difference in the effect of arousal on the rate of forgetting suggests that the role of medial temporal lobe structures in memory consolidation for arousing events is conserved across species. dala's mechanism of action involves both direct interconnections with the hippocampus and indirect feedback via the release of hormones in the periphery. To investigate whether this function of the medial tem- poral lobe extends to the human brain, we tested unilateral-temporal- lobectomy patients on an adaptation of Kleinsmith and Kaplan's (1963) classic arousal memory task. We developed a novel variant to the original task that expanded the number of items to be recalled and tested memory for the studied words themselves rather than single- digit paired associates. We hypothesized that the patients, whose locus of brain damage included the amygdala, hippocampus, and surround- ing cortex, would not exhibit forgetting-resistant memory traces for the arousing words. Such a finding would suggest that the integrity of the medial temporal lobe is essential for consolidating declarative memories for arousing stimuli in the human brain.


title: Teaching Computational Neuroscience

abstract: The problems and beauty of teaching computational neuroscience are discussed by reviewing three new textbooks.


title: Do People Embrace Praise Even When They Feel Unworthy? A Review of Critical Tests of Self-Enhancement Versus Self-Verification:

abstract: Some contemporary theorists contend that the desire for self-enhancement is prepotent and more powerful than rival motives such as self-verification. If so, then even people with negative self-views will embrace positive evaluations. The authors tested this proposition by conducting a meta-analytic review of the relevant literature. The data provided ample evidence of self-enhancement strivings but little evidence of its prepotency. Instead, the evidence suggested that both motives are influential but control different response classes. In addition, other motives may sometimes come into play. For example, when rejection risk is high, people seem to abandon self-verification strivings, apparently in an effort to gratify their desire for communion. However, when rejection risk is low, as is the case in many secure marital relationships, people prefer self-verifying evaluations. The authors conclude that future researchers should broaden the bandwidth of their explanatory frameworks to include motives other ...


title: Moment shadow mapping

abstract: We present moment shadow mapping, a novel technique for fast, filtered hard shadows. Like variance shadow mapping it allows for the application of all kinds of efficient texture filtering and antialiasing to its moment shadow map. However it is designed to provide a substantially higher quality. Moment shadow maps store four moments of the depth within the filter kernel. Using this information, our efficient algorithm computes the sharpest possible lower bound as approximation to the shadow intensity. The choice to compute such a bound using four moments is based upon an automated evaluation of thousands of alternatives and thus known to be optimal. To reduce memory and bandwidth requirements we present an optimized quantization scheme to allow 16-bit quantization of moment shadow maps. Our evaluation demonstrates that moment shadow mapping produces high quality results with a single shadow map sample per fragment using 64 bits per shadow map texel.


title: Sybil-Resistant Pseudonymization and Pseudonym Change without Trusted Third Parties

abstract: The issuing of pseudonyms is an established approach for protecting the privacy of users while limiting access and preventing sybil attacks. To prevent pseudonym deanonymization through continuous observation and correlation, frequent and unlinkable pseudonym changes must be enabled. Existing approaches for realizing sybil-resistant pseudonymization and pseudonym change (PPC) are either inherently dependent on trusted third parties (TTPs) or involve significant computation overhead at end-user devices. In this paper, we investigate a novel, TTP-independent approach towards sybil-resistant PPC. Our proposal is based on the use of cryptocurrency block chains as general-purpose, append-only bulletin boards. We present a general approach as well as BitNym, a specific design based on the unmodified Bitcoin network. We discuss and propose TTP-independent mechanisms for realizing sybil-free initial access control, pseudonym validation and pseudonym mixing. Evaluation results demonstrate the practical feasibility of our approach and show that anonymity sets encompassing nearly the complete user population are easily achievable.


title: A distributed simulator for cell-relay networks developed by using Java

abstract: The design and development of a Java-based distributed simulator, using the virtual time wrap method, for ATM networks is discussed. The case needing no traditional rollbacking processing when causality is violated is analyzed. A mechanism called virtual time tunneling is then proposed to improve the simulation efficiency. Experiments showed that the speedup factor is not smaller than 4, when the simulator executes on 5 networked computers, and the time for simulating one million cells transferring is smaller than 100 s.


High Capacity data hiding using LSB Steganography and Encryption


title: Narratology for Interactive Storytelling: a Critical Introduction

abstract: Most research in Interactive Storytelling (IS) has sought inspiration in narrative theories issued from contemporary narratology to either identify fundamental concepts or derive formalisms for their implementation. In the former case, the theoretical approach gives raise to empirical solutions, while the latter develops Interactive Storytelling as some form of “computational narratology”, modelled on computational linguistics. In this paper, we review the most frequently cited theories from the perspective of IS research. We discuss in particular the extent to which they can actually inspire IS technologies and highlight key issues for the effective use of narratology in IS.


title: A Comparative Study on Approaches of Vector Space Model in Information Retrieval

abstract: The vector space model is one of the classical and widely applied information retrieval models to rank the web page based on similarity values. The retrieval operations consist of cosine similarity function to compute the similarity values between a given query and the set of documents retrieved and then rank the documents according to the relevance. In this paper, we are presenting different approaches of vector space model to compute similarity values of hits from search engine for given queries based on terms weight. In order to achieve the goal of an effective evaluation algorithm, our work intends to extensive analysis of the main aspects of Vector space model, its approaches and provides a comprehensive comparison for TermCount Model, Tf-Idf model and Vector space model based on normalization.


title: Mining Educational Data to Analyze Students' Performance

abstract: The main objective of higher education institutions is to provide quality education to its students. One way to achieve highest level of quality in higher education system is by discovering knowledge for prediction regarding enrolment of students in a particular course, alienation of traditional classroom teaching model, detection of unfair means used in online examination, detection of abnormal values in the result sheets of the students, prediction about students' performance and so on. The knowledge is hidden among the educational data set and it is extractable through data mining techniques. Present paper is designed to justify the capabilities of data mining techniques in context of higher education by offering a data mining model for higher education system in the university. In this research, the classification task is used to evaluate student's performance and as there are many approaches that are used for data classification, the decision tree method is used here. By this task we extract knowledge that describes students' performance in end semester examination. It helps earlier in identifying the dropouts and students who need special attention and allow the teacher to provide appropriate advising/counseling. Keywords-Educational Data Mining (EDM); Classification; Knowledge Discovery in Database (KDD); ID3 Algorithm.


title: 22-nm fully-depleted tri-gate CMOS transistors

abstract: At the 22-nm technology node, fully-depleted tri-gate transistors were introduced for the first time on a high-volume manufacturing process. Fabricated on a bulk silicon substrate, these transistors feature a third-generation high-k + metal-gate technology and a fifth generation of channel strain techniques resulting in the highest drive currents yet reported for NMOS and PMOS. The use of tri-gate transistors provides steep subthreshold slopes (∼70 mV/decade) and very low DIBL (∼50 mV/V) values that are critical for low voltage operation. Self-aligned contacts are implemented along with the tri-gate transistors to eliminate restrictive contact-to-gate registration requirements from scaling the gate pitch. This enables an SRAM cell size of 0.092 μm2. High yield and reliability have been demonstrated on multiple microprocessors.


A Novel Measure for Coherence in Statistical Topic Models


title: Transforming data to satisfy privacy constraints

abstract: Data on individuals and entities are being collected widely. These data can contain information that explicitly identifies the individual (e.g., social security number). Data can also contain other kinds of personal information (e.g., date of birth, zip code, gender) that are potentially identifying when linked with other available data sets. Data are often shared for business or legal reasons. This paper addresses the important issue of preserving the anonymity of the individuals or entities during the data dissemination process. We explore preserving the anonymity by the use of generalizations and suppressions on the potentially identifying portions of the data. We extend earlier works in this area along various dimensions. First, satisfying privacy constraints is considered in conjunction with the usage for the data being disseminated. This allows us to optimize the process of preserving privacy for the specified usage. In particular, we investigate the privacy transformation in the context of data mining applications like building classification and regression models. Second, our work improves on previous approaches by allowing more flexible generalizations for the data. Lastly, this is combined with a more thorough exploration of the solution space using the genetic algorithm framework. These extensions allow us to transform the data so that they are more useful for their intended purpose while satisfying the privacy constraints.


title: A comparison study for job recommendation

abstract: Job recommender is a system that automatically returns a ranked list of suitable, prospective jobs for employees. It plays a significant role in connecting employees and employers. In order to choose a suitable algorithm to build the system, a comparison study of popular recommendation methods is conducted and reported in this paper. The experimental data crawled from vietnamworks.com, itviec.com and careerlink.vn. A subset includes 7623 jobs extracted for running experiment. There are totally 59 users who have joint in rating jobs as well as giving feedback to measure performance of different methods. The experimental results demonstrated that content based approach is outperform than other tradictional ones.


title: How and Why Clinicians Avoid Communicating Prognosis

abstract: Prognosis communication is a significant area of patient dissatisfaction, and one of the most difficult aspects of health care clinicians' roles. The aim of this research was to investigate how prognosis is communicated between medical, nursing and allied health clinicians. A case series method was utilised, sampling patients with haematological malignancies in an acute care setting. Multidisciplinary clincians and patient records provided the data, which were then thematically analysed. Three major findings were revealed: 1. the term prognosis conjures the concept of death; 2. clinicians aren't prepared to discuss prognosis, even with each other, and 3. there is a prescribed pathway for patients, related to clinicians expectations. These findings lead to the conclusion that both the psychosocial and scientific spheres of prognosis can be valued in communication and decision-making by clinicians, and that prognosis communication is better understood as regular discussion, rather than a singular concept. The research informs the field of prognosis communication, patient decision-making, and pre- service health education.


title: The role of mycorrhizae and plant growth promoting rhizobacteria (PGPR) in improving crop productivity under stressful environments.

abstract: Both biotic and abiotic stresses are major constrains to agricultural production. Under stress conditions, plant growth is affected by a number of factors such as hormonal and nutritional imbalance, ion toxicity, physiological disorders, susceptibility to diseases, etc. Plant growth under stress conditions may be enhanced by the application of microbial inoculation including plant growth promoting rhizobacteria (PGPR) and mycorrhizal fungi. These microbes can promote plant growth by regulating nutritional and hormonal balance, producing plant growth regulators, solubilizing nutrients and inducing resistance against plant pathogens. In addition to their interactions with plants, these microbes also show synergistic as well as antagonistic interactions with other microbes in the soil environment. These interactions may be vital for sustainable agriculture because they mainly depend on biological processes rather than on agrochemicals to maintain plant growth and development as well as proper soil health under stress conditions. A number of research articles can be deciphered from the literature, which shows the role of rhizobacteria and mycorrhizae alone and/or in combination in enhancing plant growth under stress conditions. However, in contrast, a few review papers are available which discuss the synergistic interactions between rhizobacteria and mycorrhizae for enhancing plant growth under normal (non-stress) or stressful environments. Biological interactions between PGPR and mycorrhizal fungi are believed to cause a cumulative effect on all rhizosphere components, and these interactions are also affected by environmental factors such as soil type, nutrition, moisture and temperature. The present review comprehensively discusses recent developments on the effectiveness of PGPR and mycorrhizal fungi for enhancing plant growth under stressful environments. The key mechanisms involved in plant stress tolerance and the effectiveness of microbial inoculation for enhancing plant growth under stress conditions have been discussed at length in this review. Growth promotion by single and dual inoculation of PGPR and mycorrhizal fungi under stress conditions have also been discussed and reviewed comprehensively.


title: Single image dehazing by latent region-segmentation based transmission estimation and weighted L 1-norm regularisation

abstract: Image dehazing is a useful technique which can eliminate the bad effect of haze on images and enhance the performances of image/video processing algorithms in the hazy weather. In this study, a single image dehazing method is proposed. The authors estimate the initial transmission properly based on latent region-segmentation and refine the estimated initial transmission by an objective function with a novel weighted L 1-norm regularisation term. The half-quadratic splitting minimisation method is employed to solve this optimisation problem. They also define an evaluation function to estimate the reliable global atmospheric light. With the refined transmission map and atmospheric light they recover the haze-free image by the haze imaging model. The authors’ method is compared with three state-of-the-art methods and is also validated by two image quality assessment methods. The comparative experimental results and evaluations demonstrate that their method can recover comparable and even better results with clear details, low contrast loss and high contrast in most cases.


Dispersion and Characterization of Coplanar Waveguide Based On Conformal Mapping Technique


title: IBM Research Australia at LifeCLEF2014: Plant Identification Task

abstract: In this paper, we present the system and learning strategies that were applied by the IBM Research team to the plant identication task of LifeCLEF 2014. Plant identication is one of the most popular ne-grained categorization tasks. To ensure high classication accuracy, we have utilised strong visual features together with fusion of robust machine learning techniques. Our proposed system involves automatic delineation of the region of interest (e.g. plant's leaf, ower, etc.) in the given image, followed by extracting multiple complementary low level features. The features have been then encoded into the sophisticated Fisher Vector representation which enables accurate classication with


title: Deep code search

abstract: To implement a program functionality, developers can reuse previously written code snippets by searching through a large-scale codebase. Over the years, many code search tools have been proposed to help developers. The existing approaches often treat source code as textual documents and utilize information retrieval models to retrieve relevant code snippets that match a given query. These approaches mainly rely on the textual similarity between source code and natural language query. They lack a deep understanding of the semantics of queries and source code. In this paper, we propose a novel deep neural network named CODEnn (Code-Description Embedding Neural Network). Instead of matching text similarity, CODEnn jointly embeds code snippets and natural language descriptions into a high-dimensional vector space, in such a way that code snippet and its corresponding description have similar vectors. Using the unified vector representation, code snippets related to a natural language query can be retrieved according to their vectors. Semantically related words can also be recognized and irrelevant/noisy keywords in queries can be handled. As a proof-of-concept application, we implement a code search tool named D eep CS using the proposed CODEnn model. We empirically evaluate D eep CS on a large scale codebase collected from GitHub. The experimental results show that our approach can effectively retrieve relevant code snippets and outperforms previous techniques.


title: A Multi-Scale CNN and Curriculum Learning Strategy for Mammogram Classification

abstract: Screening mammography is an important front-line tool for the early detection of breast cancer, and some 39 million exams are conducted each year in the United States alone. Here, we describe a multi-scale convolutional neural network (CNN) trained with a curriculum learning strategy that achieves high levels of accuracy in classifying mammograms. Specifically, we first train CNN-based patch classifiers on segmentation masks of lesions in mammograms, and then use the learned features to initialize a scanning-based model that renders a decision on the whole image, trained end-to-end on outcome data. We demonstrate that our approach effectively handles the “needle in a haystack” nature of full-image mammogram classification, achieving 0.92 AUROC on the DDSM dataset.


title: A comparison of machine learning algorithms for proactive hard disk drive failure detection

abstract: Failures or unexpected events are inevitable in critical and complex systems. Proactive failure detection is an approach that aims to detect such events in advance so that preventative or recovery measures can be planned, thus improving system availability. Machine learning techniques have been successfully applied to learn patterns from available datasets and to classify or predict to which class a new instance of data belongs. In this paper, we evaluate and compare the performance of 21 machine learning algorithms by using them for proactive hard disk drive failure detection. For this comparison, we use WEKA as an experimentation platform and benchmark publicly available datasets of hard disk drives that are used to predict imminent failures before the actual failures occur. The results show that different algorithms are suitable for different applications based on the desired prediction quality and the tolerated training and prediction time.


title: Bier spots: a benign vascular anomaly

abstract: A 26-year-old woman with a reported history of tinea versicolour presented for persistent hypopigmentation on her bilateral forearms. Detailed examination revealed multiple small (5–10 mm), irregularly shaped white macules on the extensor surfaces of the bilateral forearms overlying slightly erythaematous skin. The surrounding erythaematous skin blanched with pressure and with elevation of the upper extremities the white macules were no longer visible (figures 1 and 2). A clinical diagnosis of Bier spots was made based on the patient's characteristic clinical features. ::: ::: ::: ::: Figure 1 ::: Patient's right forearm (A) and left forearm (B) showing multiple small symmetric hypopigmented macules against slightly erythaematous …


title: Successful law students as models for beginner students?

abstract: The aim of the present study is to gain information to be used for student counselling by comparing first-year students’ (n = 243) spontaneous descriptions of their learning activities at the beginning of their legal studies with fast study pace law students (n = 14) who have already finished their first year. Both groups of students mentioned many of the same generative and non-generative strategies, but the use of the strategies was more versatile among the fast study pace students. It was characteristic of the beginner students to mention modes of learning whereas the fast study pace students emphasised elements of organised effort in studying. The fast study pace students’ descriptions may be seen as a model of best practices in studying law and may be considered as valuable peer exemplifications for first-year students.


title: Toward machines that behave ethically better than humans do

abstract: With the increasing dependence on autonomous operating agents and robots the need for ethical machine behavior rises. This paper presents a moral reasoner that combines connectionism, utilitarianism and ethical theory about moral duties. The moral decision-making matches the analysis of expert ethicists in the health domain. This may be useful in many applications, especially where machines interact with humans in a medical context. Additionally, when connected to a cognitive model of emotional intelligence and affective decision making, it can be explored how moral decision making impacts affective behavior.


title: Big Data Meet Green Challenges: Big Data Toward Green Applications

abstract: Big data are widely recognized as being one of the most powerful drivers to promote productivity, improve efficiency, and support innovation. It is highly expected to explore the power of big data and turn big data into big values. To answer the interesting question whether there are inherent correlations between the two tendencies of big data and green challenges, a recent study has investigated the issues on greening the whole life cycle of big data systems. This paper would like to discover the relations between the trend of big data era and that of the new generation green revolution through a comprehensive and panoramic literature survey in big data technologies toward various green objectives and a discussion on relevant challenges and future directions.


title: Intelligent Products: A Survey

abstract: This paper presents an overview of the field of Intelligent Products. As Intelligent Products have many facets, this paper is mainly focused on the concept behind Intelligent Products, the technical foundations, and the achievable practical goals of Intelligent Products. A novel classification of Intelligent Products is introduced, which distinguishes between three orthogonal dimensions. Furthermore, the technical foundations in the areas of automatic identification and embedded processing, distributed information storage and processing, and agent-based systems are discussed, as well as the achievable practical goals in the contexts of manufacturing, supply chains, asset management, and product life cycle management.


title: The Capacitated Team Orienteering Problem: A Bi-level Filter-and-Fan method

abstract: This paper focuses on vehicle routing problems with profits and addresses the so-called Capacitated Team Orienteering Problem. Given a set of customers with a priori known profits and demands, the objective is to find the subset of customers, for which the collected profit is maximized, and to determine the visiting sequence and assignment to vehicle routes assuming capacity and route duration restrictions. The proposed method adopts a hierarchical bi-level search framework that takes advantage of different search landscapes. At the upper level, the solution space is explored on the basis of the collected profit, using a Filter-and-Fan method and a combination of profit oriented neighborhoods, while at the lower level the routing of customers is optimized in terms of traveling distance via a Variable Neighborhood Descent method. Computational experiments on benchmark data sets illustrate the efficiency and effectiveness of the proposed approach. Compared to existing results, new upper bounds are produced with competitive computational times.


title: Noise-Contrastive Estimation for Answer Selection with Deep Neural Networks

abstract: We study answer selection for question answering, in which given a question and a set of candidate answer sentences, the goal is to identify the subset that contains the answer. Unlike previous work which treats this task as a straightforward pointwise classification problem, we model this problem as a ranking task and propose a pairwise ranking approach that can directly exploit existing pointwise neural network models as base components. We extend the Noise-Contrastive Estimation approach with a triplet ranking loss function to exploit interactions in triplet inputs over the question paired with positive and negative examples. Experiments on TrecQA and WikiQA datasets show that our approach achieves state-of-the-art effectiveness without the need for external knowledge sources or feature engineering.


title: Continuous-time predictive-maintenance scheduling for a deteriorating system

abstract: A predictive-maintenance structure for a gradually deteriorating single-unit system (continuous time/continuous state) is presented in this paper. The proposed decision model enables optimal inspection and replacement decision in order to balance the cost engaged by failure and unavailability on an infinite horizon. Two maintenance decision variables are considered: the preventive replacement threshold and the inspection schedule based on the system state. In order to assess the performance of the proposed maintenance structure, a mathematical model for the maintained system cost is developed using regenerative and semi-regenerative processes theory. Numerical experiments show that the s-expected maintenance cost rate on an infinite horizon can be minimized by a joint optimization of the replacement threshold and the a periodic inspection times. The proposed maintenance structure performs better than classical preventive maintenance policies which can be treated as particular cases. Using the proposed maintenance structure, a well-adapted strategy can automatically be selected for the maintenance decision-maker depending on the characteristics of the wear process and on the different unit costs. Even limit cases can be reached: for example, in the case of expensive inspection and costly preventive replacement, the optimal policy becomes close to a systematic periodic replacement policy. Most of the classical maintenance strategies (periodic inspection/replacement policy, systematic periodic replacement, corrective policy) can be emulated by adopting some specific inspection scheduling rules and replacement thresholds. In a more general way, the proposed maintenance structure shows its adaptability to different possible characteristics of the maintained single-unit system.


title: Improving Matching Models with Contextualized Word Representations for Multi-turn Response Selection in Retrieval-based Chatbots

abstract: We consider matching with pre-trained contextualized word vectors for multi-turn response selection in retrieval-based chatbots. When directly applied to the task, state-of-the-art models, such as CoVe and ELMo, do not work as well as they do on other tasks, due to the hierarchical nature, casual language, and domain-specific word use of conversations. To tackle the challenges, we propose pre-training a sentence-level and a session-level contextualized word vectors by learning a dialogue generation model from large-scale human-human conversations with a hierarchical encoder-decoder architecture. The two levels of vectors are then integrated into the input layer and the output layer of a matching model respectively. Experimental results on two benchmark datasets indicate that the proposed contextualized word vectors can significantly and consistently improve the performance of existing matching models for response selection.


title: An improved formula for lamination core loss calculations in machines operating with high frequency and high flux density excitation

abstract: For electrical machine designers, core loss data are usually provided in the form of tables or curves of total loss versus flux density or frequency. These can be used to extract the loss coefficients of the core loss formulas. In this paper, three currently available formulas are discussed and compared with the loss data supplied by lamination steel manufacturers. It is found that the dynamic hysteresis loop plays an important role in the total loss calculation, especially at high flux densities and high frequencies, and the loss coefficients should change with frequency. A new modified formula is proposed to represent the coefficient changes. The new curve is applied to the measured manufacturer's data, with acceptable accuracy.


Identity-Based Cryptography for Cloud Security


title: DLIREC: Aspect Term Extraction and Term Polarity Classification System

abstract: This paper describes our system used in the Aspect Based Sentiment Analysis Task 4 at the SemEval-2014. Our system consists of two components to address two of the subtasks respectively: a Conditional Random Field (CRF) based classifier for Aspect Term Extraction (ATE) and a linear classifier for Aspect Term Polarity Classification (ATP). For the ATE subtask, we implement a variety of lexicon, syntactic and semantic features, as well as cluster features induced from unlabeled data. Our system achieves state-of-the-art performances in ATE, ranking 1st (among 28 submissions) and 2rd (among 27 submissions) for the restaurant and laptop domain respectively.


title: Spatio-temporal Variability of Water Deficit in Huang-Huai-Hai Plain During 1961-2013

abstract: Water shortage is recognized as the most serious problem for agriculture production in Huang-Huai-Hai plain( 3H plain) and the changing characteristics for water resources in context of climate change remains unknown yet.Based on measured meteorological data obtained from 41 stations located in 3H plain,potential evapotranspiration was calculated by applying Penman-Monteith equation,and water deficit was described as the difference of precipitation and potential evapotranspiration at the same period. Finally,the spatial and temporal variation characteristics of water deficit in four seasons over 3H plain were analyzed by using Kendall-Theil robust line method,Mann-Kendall trend test and Morlet wavelet function. Results showed that multi-year average water deficit was negative in most part of 3H plain and varied between-650 mm and 100 mm with the spatial variation pattern of decreasing gradually from south to north.Among four seasons,spring was presented as the most serious water situation with the shortage of 210. 61 mm in multiyear average,which was higher than any other seasons. Trend analysis of seasonal multi-year average water deficit among 41 stations generally shown an upward trend except in autumn where only stations surrounding Jing-Jin-Ji district increased. Annual water deficit variation among 6 sub-regions was- 391. 45,- 510. 11,- 560. 42,- 427. 21,- 284. 19 and- 21. 63 mm in respectively and this variation was mainly induced by precipitation. For seasonal difference in 6 sub-regions,spring amounts for the highest water shortage period which occupied annual water shortage at 56. 4%,51. 7%,40. 6%,59. 7%,59. 3% and 66. 1%,respectively. Spring water deficit showed higher period,lower period alternately with the main period of 28 years for these six regions,10 years for region V and VI. The seasonal and spatial difference of water deficit in 3H plain indicated a high drought risks with spatio-temporal variability,especially in spring season. Thus,effective climate changing adoption needs water resources be operated rationally and agriculture irrigation arrangements with seasonal.


title: Tweet, but verify: epistemic study of information verification on Twitter

abstract: While Twitter provides an unprecedented opportunity to learn about breaking news and current events as they happen, it often produces skepticism among users as not all the information is accurate but also hoaxes are sometimes spread. While avoiding the diffusion of hoaxes is a major concern during fast-paced events such as natural disasters, the study of how users trust and verify information from tweets in these contexts has received little attention so far. We survey users on credibility perceptions regarding witness pictures posted on Twitter related to Hurricane Sandy. By examining credibility perceptions on features suggested for information verification in the field of epistemology, we evaluate their accuracy in determining whether pictures were real or fake compared to professional evaluations performed by experts. Our study unveils insight about tweet presentation, as well as features that users should look at when assessing the veracity of tweets in the context of fast-paced events. Some of our main findings include that while author details not readily available on Twitter feeds should be emphasized in order to facilitate verification of tweets, showing multiple tweets corroborating a fact misleads users to trusting what actually is a hoax. We contrast some of the behavioral patterns found on tweets with literature in psychology research.


title: Integrated Optical-Phased Array Nanoantenna System Using a Plasmonic Rotman Lens

abstract: An integrated linear optical-phased array with five switchable beams at a spacing of 15 $^\circ$ operating at the standard telecommunication wavelength of $1.55\ \mu \text{m}$ is proposed, and numerically studied. To realize the desired phase shift between adjacent antenna elements, a plasmonic Rotman lens is designed. In order to conduct power into a selected input port, a graphene-based power splitter with switchable output is proposed. The antenna array is composed of six plasmonic nanoantennas designed in compatibility with the feeding plasmonic waveguides. The proposed integrated system can be used for applications such as optical wireless communication, inter- and intrachip optical interconnects, and low-cost integrated LIDAR systems.


Beyond Binary Labels: Political Ideology Prediction of Twitter Users


title: Revenue optimization in the generalized second-price auction

abstract: We consider the optimization of revenue in advertising auctions based on the generalized second-price (GSP) paradigm, which has become a de facto standard. We examine several different GSP variants (including squashing and different types of reserve prices), and consider how to set their parameters optimally. One intriguing finding is that charging each advertiser the same per-click reserve price ("unweighted reserve prices") yields dramatically more revenue than the quality-weighted reserve prices that have become common practice. This result is robust, arising both from theoretical analysis and from two different kinds of computational experiments. We also identify a new GSP variant that is revenue optimal in restricted settings. Finally, we study how squashing and reserve prices interact, and how equilibrium selection affects the revenue of GSP when features such as reserves or squashing are applied.


Latent Discriminant Analysis with Representative Feature Discovery.


title: Immunotherapy for Chronic Hepatitis B Virus Infection

abstract: While new therapies for chronic hepatitis C virus infection have delivered remarkable cure rates, curative therapies for chronic hepatitis B virus (HBV) infection remain a distant goal. Although current direct antiviral therapies are very efficient in controlling viral replication and limiting the progression to cirrhosis, these treatments require lifelong administration due to the frequent viral rebound upon treatment cessation, and immune modulation with interferon is only effective in a subgroup of patients. Specific immunotherapies can offer the possibility of eliminating or at least stably maintaining low levels of HBV replication under the control of a functional host antiviral response. Here, we review the development of immune cell therapy for HBV, highlighting the potential antiviral efficiency and potential toxicities in different groups of chronically infected HBV patients. We also discuss the chronic hepatitis B patient populations that best benefit from therapeutic immune interventions.


Learning and transferring movie styles


title: Towards Taming Privilege-Escalation Attacks on Android

abstract: Android's security framework has been an appealing subject of research in the last few years. Android has been shown to be vulnerable to application-level privilege escalation attacks, such as confused deputy attacks, and more recently, attacks by colluding applications. While most of the proposed approaches aim at solving confused deputy attacks, there is still no solution that simultaneously addresses collusion attacks. In this paper, we investigate the problem of designing and implementing a practical security framework for Android to protect against confused deputy and collusion attacks. We realize that defeating collusion attacks calls for a rather system-centric solution as opposed to application-dependent policy enforcement. To support our design decisions, we conduct a heuristic analysis of Android's system behavior (with popular apps) to identify attack patterns, classify different adversary models, and point out the challenges to be tackled. Then we propose a solution for a system-centric and policy-driven runtime monitoring of communication channels between applications at multiple layers: 1) at the middleware we control IPCs between applications and indirect communication via Android system components. Moreover, inspired by the approach in QUIRE, we establish semantic links between IPCs and enable the reference monitor to verify the call-chain; 2) at the kernel level we realize mandatory access control on the file system (including Unix domain sockets) and local Internet sockets. To allow for runtime, dynamic low-level policy enforcement, we provide a callback channel between the kernel and the middleware. Finally, we evaluate the efficiency and effectiveness of our framework on known confused deputy and collusion attacks, and discuss future directions.


title: Sleep patterns and disorders among university students in Lebanon.

abstract: Background: Insufficient sleep is a significant public health issue with adverse medical consequences. Sleep disturbances are common among university students and have an effect on this group’s overall health and functioning. The aim of this study was to investigate sleep habits and disorders in a population of university students across Lebanon. Methods: This was a cross-sectional study carried out in 2012 among 735 students aged 18-25 yrs. old, enrolled at six universities across Lebanon. The Pittsburg Sleep Quality Index (PSQI) was used to assess sleep quality and habits. Results: Less than half of the total study population (47.3%) were good sleepers (PSQI<5). Upon bivariate analysis, males experienced more sleep difficulties than females (57.8% vs. 40.8%). The majority (60%) of males vs. 40% of females had trouble performing daily activities more than once per week ( P =0.02). Results of the multivariate analysis revealed that reporting poor sleep quality was strongly associated with daytime dysfunction and sleep- enhancing medication use especially more than once per week. Conclusions: This is the first study to describe the nature of sleep problems among university students in Lebanon. This study suggests that sleep problems among Lebanese college students were common and such problems may interfere with daily performance. Findings from this study have important implications for programs intended to improve academic performance by targeting sleep habits of students.


title: The Visual Brain in Action

abstract: Prologue 1. Introduction: vision from a biological point of view 2. Visual processing in the primate visual cortex 3. 'Cortical blindness' 4. Disorders of spatial perception and the visual control of action 5. Disorders of visual recognition 6. Dissociations between perception and action in normal subjects 7. Attention, consciousness, and the coordination of behaviour 8. Epilogue: twelve years on


title: P-Stim™ auricular electroacupuncture stimulation device for pain relief

abstract: Acupuncture is now accepted as a complementary analgesic treatment. Auricular acupuncture is a distinct form of acupuncture. Electrical stimulation of acupoints (electroacupuncture) increases the effects of acupuncture. Recently, an auricular electroacupuncture device, the P-Stim™, has become available. Clinical studies in outpatients have investigated the P-Stim in chronic musculoskeletal pain and its use for minor surgery. In chronic cervical or low back pain, auricular electroacupuncture was more effective than conventional auricular acupuncture. The results in acute pain were controversial. Auricular electroacupuncture reduced pain and remifentanil consumption during oocyte aspiration when compared with conventional auricular acupuncture or a sham treatment. However, after third molar tooth extraction, auricular electroacupuncture and auricular acupuncture failed to reduce either postoperative pain or analgesic consumption. Further large-scale studies are required to evaluate the analgesic efficacy of auricular electroacupuncture.


title: Design of Optimal High-Rank Line-of-Sight MIMO Channels

abstract: This paper describes a technique for realizing a high-rank channel matrix in a line-of-sight (LOS) multiple-input multiple-output (MIMO) transmission scenario. This is beneficial for systems which are unable to make use of the originally derived MIMO gain given by independent and identically distributed (i.i.d.) flat Rayleigh fading subchannels. The technique is based on optimization of antenna placement in uniform linear arrays with respect to mutual information (MI). By introducing a new and more general 3-D geometrical model than that applied in earlier work, additional insight into the optimal design parameters is gained. We also perform a novel analysis of the sensitivity of the optimal design parameters, and derive analytical expressions for the eigenvalues of the pure LOS channel matrix which are valid also when allowing for non-optimal design. Furthermore, we investigate the approximations introduced in the derivations, in order to reveal when the results are applicable. The LOS matrix is employed in a Ricean fading channel model, and performance is evaluated with respect to the average MI and the MI cumulative distribution function. Our results show that even with some deviation from the optimal design, the LOS MEMO case outperforms the i.i.d. Rayleigh case in terms of MI.


title: Interleaving motion in contact and in free space for planning under uncertainty

abstract: In this paper we present a planner that interleaves free-space motion with motion in contact to reduce uncertainty. The planner finds such motions by growing a search tree in the combined space of collision-free and contact configurations. The planner reasons efficiently about the accumulated uncertainty by factoring the state in a belief over configuration and a fully observable contact state. We show the uncertainty-reducing capabilities of the planner on manipulation benchmark from the POMDP literature. The planner scales up to more complex problems like manipulation under uncertainty in seven-dimensional configuration space. We validate our planner in simulation and on a real robot.


title: Benchmarking Classification Models for Software Defect Prediction: A Proposed Framework and Novel Findings

abstract: Software defect prediction strives to improve software quality and testing efficiency by constructing predictive classification models from code attributes to enable a timely identification of fault-prone modules. Several classification models have been evaluated for this task. However, due to inconsistent findings regarding the superiority of one classifier over another and the usefulness of metric-based classification in general, more research is needed to improve convergence across studies and further advance confidence in experimental results. We consider three potential sources for bias: comparing classifiers over one or a small number of proprietary data sets, relying on accuracy indicators that are conceptually inappropriate for software defect prediction and cross-study comparisons, and, finally, limited use of statistical testing procedures to secure empirical findings. To remedy these problems, a framework for comparative software defect prediction experiments is proposed and applied in a large-scale empirical comparison of 22 classifiers over 10 public domain data sets from the NASA Metrics Data repository. Overall, an appealing degree of predictive accuracy is observed, which supports the view that metric-based classification is useful. However, our results indicate that the importance of the particular classification algorithm may be less than previously assumed since no significant performance differences could be detected among the top 17 classifiers.


title: Direct esthetic restorations based on translucency and opacity of composite resins.

abstract: ABSTRACT ::: Light dynamics is a relevant phenomenon with respect to esthetic restorations, as incorrect analysis of the optical behavior of natural dentition may lead to potential clinical failures. The nature of incident light plays a major role in determining the amount of light transmission or reflection, and how an object is perceived depends on the nature of the light source. Natural teeth demonstrate translucency, opalescence, and fluorescence, all of which must be replicated by restorative materials in order to achieve clinical success. Translucency is the intermediary between complete opacity and complete transparency, making its analysis highly subjective. In nature, the translucency of dental enamel varies from tooth to tooth, and from individual to individual. Therefore, four important factors must be considered when appraising translucency. Presence or absence of color, thickness of the enamel, degree of translucency, and surface texture are essential components when determining translucency. State-of-the-art resin composites provide varying shades and opacities that deliver a more faithful reproduction of the chromaticity and translucency/opacity of enamel and dentin. This enables the attainment of individualized and customized composite restorations. The objective of this article is to provide a review of the phenomena of translucency and opacity in the natural dentition and composite resins, under the scope of optics, and to describe how to implement these concepts in the clinical setting. ::: ::: CLINICAL SIGNIFICANCE ::: Choosing composite resins, based on optical properties alone, in order to mimic the properties of natural tooth structures, does not necessarily provide a satisfactory esthetic outcome. In many instances, failure ensues from incorrect analysis of the optical behaviors of the natural dentition as well as the improper use of restorative materials. Therefore, it is necessary to implement a technique that enables a restorative material to be utilized to its full potential to correctly replicate the natural teeth. ::: ::: (J Esthet Restor Dent 23:73–88, 2011)


title: Discriminative n-gram language modeling 1

abstract: This paper describes discriminative language modeling for a large vocabulary speech recognition task. We contrast two parameter estimation methods: the perceptron algorithm, and a method based on maximizing the regularized conditional log-likelihood. The models are encoded as deterministic weighted finite state automata, and are applied by intersecting the automata with word-lattices that are the output from a baseline recognizer. The perceptron algorithm has the benefit of automatically selecting a relatively small feature set in just a couple of passes over the training data. We describe a method based on regularized likelihood that makes use of the feature set given by the perceptron algorithm, and initialization with the perceptron's weights; this method gives an additional 0.5% reduction in word error rate (WER) over training with the perceptron alone. The final system achieves a 1.8% absolute reduction in WER for a baseline first-pass recognition system (from 39.2% to 37.4%), and a 0.9% absolute reduction in WER for a multi-pass recognition system (from 28.9% to 28.0%).


title: Modified Mecanum Wheels for Traversing Rough Terrains

abstract: Mecanum wheels give vehicles and robots autonomous omni-directional capabilities, while regular wheels don’t. The omni-directionality that such wheels provide makes the vehicle extremely maneuverable, which could be very helpful in different indoor and outdoor applications. However, current Mecanum wheel designs can only operate on flat hard surfaces, and perform very poorly on rough terrains. This paper presents two modified Mecanum wheel designs targeted for complex rough terrains and discusses their advantages and disadvantages in comparison to regular Mecanum wheels. The wheels proposed here are particularly advantageous for overcoming obstacles up to 75% of the overall wheel diameter in lateral motion which significantly facilitates the lateral motion of vehicles on hard rough surfaces and soft soils such as sand which cannot be achieved using other types of wheels. The paper also presents control aspects that need to be considered when controlling autonomous vehicles/robots using the proposed wheels.


title: The Human Element in Autonomous Vehicles

abstract: Autonomous vehicle research has been prevalent for well over a decade but only recently has there been a small amount of research conducted on the human interaction that occurs in autonomous vehicles. Although functional software and sensor technology is essential for safe operation, which has been the main focus of autonomous vehicle research, handling all elements of human interaction is also a very salient aspect of their success. This paper will provide an overview of the importance of human vehicle interaction in autonomous vehicles, while considering relevant related factors that are likely to impact adoption. Particular attention will be given to prior research conducted on germane areas relating to control in the automobile, in addition to the different elements that are expected to affect the likelihood of success for these vehicles initially developed for human operation. This paper will also include a discussion of the limited research conducted to consider interactions with humans and the current state of published functioning software and sensor technology that exists.


Law and Society in Traditional China


title: Scene text detection based on multi-scale SWT and edge filtering

abstract: This paper presents a text detection method based on multi-scale Stroke Width Transform (SWT). First, an image pyramid is built and SWT is performed on each level of the pyramid. Second, edge components are filtered using two novel features, stroke pair ratio (SPR) and edge density of a connected component (EDC). Next, the remaining edge components on each level are grouped into text lines. And these lines are projected back onto a single image and merged. Finally, candidate text lines are verified by integrating block level features and line level features. The multi-scale mechanism makes it possible to detect text defected by reflection or blurring. And the two features are proved to be both effective and efficient in filtering non-text edges. Moreover, experimental results on the ICDAR Robust Reading Competition datasets show that the proposed text detection method provides promising performance.


title: Low rank tensor recovery via iterative hard thresholding

abstract: We study extensions of compressive sensing and low rank matrix recovery (matrix completion) to the recovery of low rank tensors of higher order from a small number of linear measurements. While the theoretical understanding of low rank matrix recovery is already well-developed, only few contributions on the low rank tensor recovery problem are available so far. In this paper, we introduce versions of the iterative hard thresholding algorithm for several tensor decompositions, namely the higher order singular value decomposition (HOSVD), the tensor train format (TT), and the general hierarchical Tucker decomposition (HT). We provide a partial convergence result for these algorithms which is based on a variant of the restricted isometry property of the measurement operator adapted to the tensor decomposition at hand that induces a corresponding notion of tensor rank. We show that subgaussian measurement ensembles satisfy the tensor restricted isometry property with high probability under a certain almost optimal bound on the number of measurements which depends on the corresponding tensor format. These bounds are extended to partial Fourier maps combined with random sign flips of the tensor entries. Finally, we illustrate the performance of iterative hard thresholding methods for tensor recovery via numerical experiments where we consider recovery from Gaussian random measurements, tensor completion (recovery of missing entries), and Fourier measurements for third order tensors.


title: DLR MiroSurge: a versatile system for research in endoscopic telesurgery

abstract: PURPOSE ::: Research on surgical robotics demands systems for evaluating scientific approaches. Such systems can be divided into dedicated and versatile systems. Dedicated systems are designed for a single surgical task or technique, whereas versatile systems are designed to be expandable and useful in multiple surgical applications. Versatile systems are often based on industrial robots, though, and because of this, are hardly suitable for close contact with humans. ::: ::: ::: METHOD ::: To achieve a high degree of versatility the Miro robotic surgery platform (MRSP) consists of versatile components, dedicated front-ends towards surgery and configurable interfaces for the surgeon. ::: ::: ::: RESULTS ::: This paper presents MiroSurge, a configuration of the MRSP that allows for bimanual endoscopic telesurgery with force feedback. ::: ::: ::: CONCLUSIONS ::: While the components of the MiroSurge system are shown to fulfil the rigid design requirements for robotic telesurgery with force feedback, the system remains versatile, which is supposed to be a key issue for the further development and optimisation.


A Learning-based Neural Network Model for the Detection and Classification of SQL Injection Attacks


title: Towards consistent performance management systems

abstract: Performance indicators (PIs) and performance measurement are popular topics in management literature nowadays. However, hardly ever is attention paid to the consequences of the relationships that often exist between the different PIs for designing effective, consistent performance management systems for organizations as a whole. Presents a concrete method for constructing a consistent set of PIs forming the basis of a consistent performance management system, where explicit attention is paid to the relations between PIs. Discusses the important role played by the new classification of PIs. Provides a case study to illustrate the use of the method in a practical situation.


title: CricAI: A classification based tool to predict the outcome in ODI cricket

abstract: Victory is the ultimate goal in any sport. In this work we address the winning factors in the sport of One Day International (ODI) cricket. Winning an ODI cricket match depends on various factors related to scoring as well as physical strength of the two teams. Some of the factors have been described in the literature but there is scope for further research on analyzing them, especially with reference to predicting victory. Interesting factors include home game advantage, day / night effect, winning the toss and batting first. In this article, we have used artificial intelligence techniques, more specifically Bayesian classifiers in machine learning, to predict how these factors affect the outcome of an ODI cricket match. Based on the emerged results, we have developed a software tool called CricAI. This tool outputs the probability of victory in an ODI cricket match using input factors such as home game advantage available at the beginning of the match. The CricAI tool can be used in real-world applications by teams playing cricket. It can accordingly be helpful in adjusting certain factors in order to maximize the chances of winning the real game.


Preprint ANL/MCS-P1857-0311 A MATRIX-FREE APPROACH FOR SOLVING THE GAUSSIAN PROCESS MAXIMUM LIKELIHOOD PROBLEM


title: Dermoscopy of pyogenic granuloma: a morphological study

abstract: BACKGROUND ::: Pyogenic granuloma is a common, benign, vascular lesion of the skin and mucous membranes which is a simulator of amelanotic/hypomelanotic melanoma and other tumours. ::: ::: ::: OBJECTIVES ::: To determine the diagnostic significance of dermoscopic structures and patterns associated with pyogenic granulomas in a large series of cases. ::: ::: ::: METHODS ::: Digital dermoscopic images of histopathologically proven cases of 122 pyogenic granulomas and 140 other tumours (28 amelanotic melanomas, seven melanoma metastases, 22 basal cell carcinomas and 83 other tumours) were collected from university hospitals in Spain, Italy, Austria and Turkey. The frequency, sensitivity, specificity, positive predictive value, negative predictive value, intraobserver agreement and interobserver agreement of the dermoscopic structures and patterns associated with pyogenic granulomas were calculated. ::: ::: ::: RESULTS ::: Vascular structures were observed in 45% of pyogenic granulomas (sensitivity of 45·1% and specificity of 17·9%; both P < 0·001). Seven exclusive patterns were made up from the combination of the structures 'reddish homogeneous area' (RHA), 'white collarette' (WC), 'white rail lines' (WRL) and 'vascular structures' (VS). The pattern composed of RHA, WC and WRL showed the highest sensitivity (22·1%; P < 0·001) and a specificity of 100% (P < 0·001) for pyogenic granulomas. Two other patterns (RHA + WC and RHA + WC + WRL + VS) showed 100% specificity when compared with melanoma (P < 0·001 and P < 0·05, respectively). ::: ::: ::: CONCLUSIONS ::: Even though some dermoscopic patterns are useful in the recognition of pyogenic granulomas, dermoscopy is not a substitute for histology, mostly when vessels are present, as melanoma cannot be ruled out.


title: DeepFlow: Large Displacement Optical Flow with Deep Matching

abstract: Optical flow computation is a key component in many computer vision systems designed for tasks such as action detection or activity recognition. However, despite several major advances over the last decade, handling large displacement in optical flow remains an open problem. Inspired by the large displacement optical flow of Brox and Malik, our approach, termed Deep Flow, blends a matching algorithm with a variational approach for optical flow. We propose a descriptor matching algorithm, tailored to the optical flow problem, that allows to boost performance on fast motions. The matching algorithm builds upon a multi-stage architecture with 6 layers, interleaving convolutions and max-pooling, a construction akin to deep convolutional nets. Using dense sampling, it allows to efficiently retrieve quasi-dense correspondences, and enjoys a built-in smoothing effect on descriptors matches, a valuable asset for integration into an energy minimization framework for optical flow estimation. Deep Flow efficiently handles large displacements occurring in realistic videos, and shows competitive performance on optical flow benchmarks. Furthermore, it sets a new state-of-the-art on the MPI-Sintel dataset.


title: Emotional Availability: Conceptualization and Research Findings

abstract: The emotional availability construct (based on observations of parent-child interactions) was first reconceptualized for research in 1991 as a way to describe the quality of parent-child interactions. Since then, there has been considerable refinement of the construct. EA refers to several parental dimensions (sensitivity, structuring, nonintrusiveness, nonhostility) and two child dimensions (responsiveness to parent and involvement of parent). The EA empirical link with attachment and parent-child relationship are reviewed and avenues for future research are suggested.


title: Text-aware balloon extraction from manga

abstract: Manga, a Japanese word for comics, is a worldwide popular visual entertainment. Nowadays, electronic devices boost the fast development of motion manga for the purpose of visual richness and manga promotion. To convert static manga images into motion mangas, text balloons are usually animated individually for better story telling. This needs the artists to cut out each text balloon meticulously, and therefore it is quite labor-intensive and time-consuming. In this paper, we propose an automatic approach that can extract text balloons from manga images both accurately and effectively. Our approach starts by extracting white areas that contain texts as text blobs. Different from existing text blob extraction methods that only rely on shape properties, we incorporate text properties in order to differentiate text blobs from texture blobs. Instead of heuristic parameter thresholding, we achieve text blob classification via learning-based classifiers. Along with the novel text blob classification method, we also make the first attempt in trying to tackle the boundary issue in balloon extraction. We apply our method on various styles of mangas and comics with texts in different languages, and convincing results are obtained in all cases.


title: Role of Broca's Area in Implicit Motor Skill Learning: Evidence from Continuous Theta-burst Magnetic Stimulation

abstract: Complex actions can be regarded as a concatenation of simple motor acts, arranged according to specific rules. Because the caudal part of the Broca's region (left Brodmann's area 44, BA 44) is involved in processing hierarchically organized behaviors, we aimed to test the hypothesis that this area may also play a role in learning structured motor sequences. To address this issue, we investigated the inhibitory effects of a continuous theta-burst TMS (cTBS) applied over left BA 44 in healthy subjects, just before they performed a serial RT task (SRTT). SRTT has been widely used to study motor skill learning and is also of interest because, for complex structured sequences, subjects spontaneously organize them into smaller subsequences, referred to as chunks. As a control, cTBS was applied over the vertex in another group, which underwent the same experiment. Control subjects showed both a general practice learning effect, evidenced by a progressive decrease in RT across blocks and a sequence-specific learning effect, demonstrated by a significant RT increase in a pseudorandom sequence. In contrast, when cTBS was applied over left BA 44, subjects lacked both the general practice and sequence-specific learning effects. However, surprisingly, their chunking pattern was preserved and remained indistinguishable from controls. The present study indicates that left BA 44 plays a role in motor sequence learning, but without being involved in elementary chunking. This dissociation between chunking and sequence learning could be explained if we postulate that left BA 44 intervenes in high hierarchical level processing, possibly to integrate elementary chunks together.


title: Development of a Funicular Flexible Crawler for Colonoscopy*

abstract: Recently, the number of colorectal cancer and polyp patients has been increasing. Colonoscopies are often used for diagnosis and treatment of rectal and colon diseases. Passing an endoscope into the large intestine is an extremely difficult operation in conventional large intestine endoscopy. Therefore, many robots to facilitate that operation have been developed recently. Nevertheless, they cannot move independently or quickly. We propose a simple and compact crawler mechanism for colonoscopy. It is suitable for propulsion through narrow spaces because it is driven by a geared motor arranged outside of the body through a flexible shaft. Through experimentation with a large intestine phantom, we confirmed that the proposed crawler can run quickly from the anus to the cecum. As described herein, this propulsion mechanism is applicable for self-traveling colonoscopy.


title: FERO: Fast and Efficient Resource Orchestrator for a Data Plane Built on Docker and DPDK

abstract: Future services and applications, such as Tactile Internet, coordinated remote driving or wireless controlled exoskeletons, pose serious challenges on the underlying networks and IT platforms in terms of reliability, latency, or capacity, just to mention a few. Towards those services, virtualization is a key enabler from both technological and economic aspects which significantly reshaped the IT and networking ecosystem. On the one hand, cloud computing and the services based on that are evident results of last years' efforts; on the other hand, networking is in the middle of a momentous revolution and important changes mainly driven by Network Function Virtualization (NFV) and Software Defined Networking (SDN). In order to enable carrier grade network services with strict QoS requirements, we need a novel data plane supporting high performance and flexible, fine granular programmability and control. As the network functions (implemented by virtual machines or containers) use the same hardware resources (cpu, memory) as the components responsible for networking, we need a low-level resource orchestrator which is capable of jointly controlling these resources. In this paper, we propose a novel resource orchestrator (RO) for a data plane making use of open source components such as, Docker, DPDK and OVS. Our goal is threefold. First, we propose a novel data plane resource model which is capable of abstracting several hardware architectures. Second, we provide an adapter module which can automatically discover the underlying hardware and build the model on-the-fly. Third, we design and implement a novel RO building on the aforementioned components and a publicly available Service Graph embedding engine. As a proof of the concept, two software switches (OVS, ERFS) are adapted and different hardware platforms are evaluated.


title: The 2-Repeat Allele of the MAOA Gene Confers an Increased Risk for Shooting and Stabbing Behaviors

abstract: There has been a great deal of research examining the link between a polymorphism in the promoter region of the MAOA gene and antisocial phenotypes. The results of these studies have consistently revealed that low activity MAOA alleles are related to antisocial behaviors for males who were maltreated as children. Recently, though, some evidence has emerged indicating that a rare allele of the MAOA gene—that is, the 2-repeat allele—may have effects on violence that are independent of the environment. The current study builds on this research and examines the association between the 2-repeat allele and shooting and stabbing behaviors in a sample of males drawn from the National Longitudinal Study of Adolescent Health. Analyses revealed that African-American males who carry the 2-repeat allele are significantly more likely than all other genotypes to engage in shooting and stabbing behaviors and to report having multiple shooting and stabbing victims. The limitations of the study are discussed and suggestions for future research are offered.


title: A balanced modularity maximization link prediction model in social networks

abstract: Abstract Link prediction has been becoming an important research topic due to the rapid growth of social networks. Community-based link prediction methods are proposed to incorporate community information in order to achieve accurate prediction. However, the performance of such methods is sensitive to the selection of community detection algorithms, and they also fail to capture the correlation between link formulation and community evolution. In this paper we introduce a balanced Modularity-Maximization Link Prediction (MMLP) model to address this issue. The idea of MMLP is to integrate the formulation of two types of links into a partitioned network generative model. We proposed a probabilistic algorithm to emphasize the role of innerLinks, which correspondingly maximizes the network modularity. Then, a trade-off technique is designed to maintain the network in a stable state of equilibrium. We also present an effective feature aggregation method by exploring two variations of network features. Our proposed method can overcome the limit of several community-based methods and the extensive experimental results on both synthetic and real-world benchmark data demonstrate its effectiveness and robustness.


title: The Adult Sensory Profile: Measuring Patterns of Sensory Processing

abstract: OBJECTIVE ::: This article describes a series of studies designed to evaluate the reliability and validity of the Adult Sensory Profile. ::: ::: ::: METHOD ::: Expert judges evaluated the construct validity of the items. Coefficient alpha, factor analysis, and correlations of items with subscales determined item reliability, using data from 615 adult sensory profiles. A subsample of 20 adults furnished skin conductance data. A heterogeneous group of 93 adults completed the revised Adult Sensory Profile, and item reliability was reexamined. ::: ::: ::: RESULTS ::: Expert judgment indicated that items could be categorized according to Dunn's Model of Sensory Processing. Results suggested reasonable item reliability for all subscales except for the Sensation Avoiding subscale. Skin conductance measures detected distinct patterns of physiological responses consistent with the four-quadrant model. Revision of the Adult Sensory Profile resulted in improved reliability of the Sensation Avoiding subscale. ::: ::: ::: CONCLUSION ::: The series of studies provides evidence to support the four subscales of the Adult Sensory Profile as distinct constructs of sensory processing preferences.


title: Folliculitis decalvans of the scalp: Response to triple therapy with isotretinoin, clindamycin, and prednisolone

abstract: There are two major scalp diseases dominated by neutrophilic inflammation (1): dissecting cellulitis of the scalp, also known as perifolliculitis capitis abscedens et suffodiens (2), and folliculitis decalvans (3). Both are neutrophilic scalp diseases with recurrent, painful, and suppurative follicular inflammation causing scarring hair loss that is often compared to deforestation by forest fire. Dissecting folliculitis of the scalp can be associated with acne conglobata and/or acne keloidalis nuchae (inverse acne). In rare cases, sternocostoclavicular hyperostosis (SAPHO syndrome) (4) or osteomyelitis of the skull (5) can be found. Folliculitis decalvans is thought to be due to an abnormal host response to Staphylococcus aureus. Whether staphylococcal infection is a primary (6) or secondary manifestation is still a matter of debate (7). In a subset of patients with folliculitis decalvans, tufted hair folliculitis is the major presentation (8). The treatment remains a challenge even in view of new developments during the last decade.


title: Multiview Image Completion with Space Structure Propagation

abstract: We present a multiview image completion method that provides geometric consistency among different views by propagating space structures. Since a user specifies the region to be completed in one of multiview photographs casually taken in a scene, the proposed method enables us to complete the set of photographs with geometric consistency by creating or removing structures on the specified region. The proposed method incorporates photographs to estimate dense depth maps. We initially complete color as well as depth from a view, and then facilitate two stages of structure propagation and structure-guided completion. Structure propagation optimizes space topology in the scene across photographs, while structure-guide completion enhances, and completes local image structure of both depth and color in multiple photographs with structural coherence by searching nearest neighbor fields in relevant views. We demonstrate the effectiveness of the proposed method in completing multiview images.