title: Wireless data links for next-generation networked micro-implantables

abstract: Miniaturized minimally-invasive implants with wireless power and communication links have the potential to enable closed-loop treatments and precise diagnostics. As with wireless power transfer, robust wireless communication between implants and external transceivers presents challenges and tradeoffs with miniaturization and increasing depth. Both link efficiency and available bandwidth need to be considered for communication capacity. This paper analyzes and reviews active electromagnetic and ultrasonic communication links for implants. Example transmitter designs are presented for both types of links. Electromagnetic links for mm-sized implants have demonstrated high data rates sufficient for most applications up to Mbps range; nonetheless, they have so far been limited to depths under 5 cm. Ultrasonic links, on the other hand, have shown much deeper transmission depths, but with limited data rate due to their low operating frequency. Spatial multiplexing techniques are proposed to increase ultrasonic data rates without additional power or bandwidth.


title: Customer churn analysis : A case study on the telecommunication industry of Thailand

abstract: Customer churn creates a huge anxiety in highly competitive service sectors especially the telecommunications sector. The objective of this research was to develop a predictive churn model to predict the customers that will be to churn; this is the first step to construct a retention management plan. The dataset was extracted from the data warehouse of the mobile telecommunication company in Thailand. The system generated the customer list, to implement a retention campaign to manage the customers with tendency to leave the company. WEKA software was used to implement the followings techniques: C4.5 decision trees algorithm, the logistic regression algorithm and the neural network algorithm. The C4.5 algorithm of decision trees proved optimal among the models. The findings are unequivocally beneficial to industry and other partners.


title: PAR Sheets, probabilities, and slot machine play: Implications for problem and non-problem gambling

abstract: Through the Freedom of Information and Protection of Privacy Act, we obtained design documents, called PAR Sheets, for slot machine games that are in use in Ontario, Canada. From our analysis of these PAR Sheets and observations from playing and watching others play these games, we report on the design of the structural characteristics of Ontario slots and their implications for problem gambling. We discuss characteristics such as speed of play, stop buttons, bonus modes, hand-pays, nudges, near misses, how some wins are in fact losses, and how two identical looking slot machines can have very different payback percentages. We then discuss how these characteristics can lead to multi-level reinforcement schedules (different reinforcement schedules for frequent and infrequent gamblers playing the same game) and how they may provide an illusion of control and contribute in other ways to irrational thinking, all of which are known risk factors for problem gambling.


title: Fingerprint: DWT, SVD Based Enhancement and Significant Contrast for Ridges and Valleys Using Fuzzy Measures

abstract: Abstract — The performance of the Fingerprint recognition system will be more accurate with respect of enhancement for the fingerprint images. In this paper we develop a novel method for Fingerprint image contrast enhancement technique based on the discrete wavelet transform (DWT) and singular value decomposition (SVD) has been proposed. This technique is compared with conventional image equalization techniques such as standard general histogram equalization and local histogram equalization. An automatic histogram threshold approach based on a fuzziness measure is presented. Then, using an index of fuzziness, a similarity process is started to find the threshold point. A significant contrast between ridges and valleys of the best, medium and poor finger image features to extract from finger images and get maximum recognition rate using fuzzy measures. The experimental results show the recognition of superiority of the proposed method to get maximum performance up gradation to the implementation of this approach.


title: A compact thermal model to predict the junction temperature of high power light emitting diode package

abstract: In this study, the thermal resistance of a typical LED package was analyzed and a compact thermal model (CTM) was obtained by considering the configuration of each layer of LED package. The building of the CTM was presented in detail. Experiments and finite element simulations were conducted to validate the CTM. The error of the thermal resistance obtained by the CTM and simulations were less than 2% when being referred to the experiments. The comparison results demonstrate that the present CTM can be used to obtain the thermal resistance of the package and to predict the junction temperature.


title: Open source ERP selection for small and medium enterprises by using analytic hierarchy process

abstract: Because of the high competition of the market, many small and medium enterprises (SMEs) seek for a suitable Enterprise Resource Planning (ERP) system to enhance the performance of their business, especially open source ERP systems. There are numerous of open source ERP systems today. The challenge is that most of SMEs have the limit of investment so that the appropriate open source ERP is necessary for the successful implementation. The decision making for selecting an appropriate open source ERP is important for SMEs to ensure that it is a valuable investment. This study defines important criteria that affect on decision making for open source ERP selection of SMEs and also applies these criteria with Analytic Hierarchy Process (AHP) to present the framework for selecting an appropriate open source ERP system. The results show that both qualitative and quantitative criteria influence the decision making process of open source ERP selection for SMEs. The results also show that AHP can construct the goal of open source ERP selection for SMEs and criteria affecting decision making as systematically.


Humor Recognition Using Deep Learning


XBRL Usage: A Socio-Economic Perspective


title: Chemical product design: challenges and opportunities

abstract: Abstract This paper highlights for a class of chemical products, the design process, their design with respect to the important issues, the need for appropriate tools and finally, lists some of the challenges and opportunities for the process systems engineering (PSE)/computer-aided process engineering (CAPE) community. The chemical products considered belong to the following types: chemical/biochemical/agrochemical products, coatings and solvents, food (nutraceuticals), HI&I (household, industrial and institutional), personal care, pharmaceuticals and drugs. The challenges and opportunities are highlighted in terms of the needs for multi-level modeling with emphasis on property models that are suitable for computer-aided applications, flexible solution strategies that are able to solve a large range of chemical product design problems and finally, a systems chemical product design framework with the overall objective to reduce the time and cost to market a new or improved product.


title: Beneath the valley of the noncommutative arithmetic-geometric mean inequality: conjectures, case-studies, and consequences

abstract: Randomized algorithms that base iteration-level decisions on samples from some pool are ubiquitous in machine learning and optimization. Examples include stochastic gradient descent and randomized coordinate descent. This paper makes progress at theoretically evaluating the difference in performance between sampling with- and without-replacement in such algorithms. Focusing on least means squares optimization, we formulate a noncommutative arithmetic-geometric mean inequality that would prove that the expected convergence rate of without-replacement sampling is faster than that of with-replacement sampling. We demonstrate that this inequality holds for many classes of random matrices and for some pathological examples as well. We provide a deterministic worst-case bound on the gap between the discrepancy between the two sampling models, and explore some of the impediments to proving this inequality in full generality. We detail the consequences of this inequality for stochastic gradient descent and the randomized Kaczmarz algorithm for solving linear systems.


title: On the Marginal Standard Error Rule and the Testing of Initial Transient Deletion Methods

abstract: In the planning of steady-state simulations, a central issue is the initial transient problem, in which an initial segment of the simulation output is adversely contaminated by initialization bias. Our article makes several contributions toward the analysis of this computational challenge. To begin, we introduce useful ways for measuring the magnitude of the initial transient effect in the single replication setting. We then analyze the marginal standard error rule (MSER) and prove that MSER’s deletion point is determined, as the simulation time horizon tends to infinity, by the minimizer of a certain random walk. We use this insight, together with fluid limit intuition associated with queueing models, to generate two nonpathological examples in which at least one variant of MSER fails to accurately predict the duration of the initial transient. Our results suggest that the efficacy of a deletion procedure is sensitive to the choice of performance measure, and that the set of standard test problems on which initial transient procedures are tested should be significantly broadened.


title: Intelligent security system for residential and industrial automation

abstract: Many accidents due to short circuits, gas leakages, etc. will not allow an ordinary person to enter the accident area so as to reduce further damage1. Such accidents are increasing day-to-day due to lack of awareness, precautionary measures and ignorance. This paper presents an intelligent security system useful for most of the home and industry applications by using remote controlling techniques along with wireless internet applications. This arrangement includes environment detection module, diagnosis module, appliance control modules and robotic modules. Multiple sensors were used for detection process, Wireless RF interface for communication, event condition voice alarms for alerting and a master controller was used as a controller of above mentioned modules. A general user interface (GUI) was designed for intelligent security system, which supervises above said modules. This user interface also supervises the robotic modules with the help of wireless RF interface and GSM based mobile phone applications for remote supervision. Apart from these, the proposed method was also successfully tested an external home security system in the absence of human mobility or can be used as an intruder detection system. Successful outputs of sensory and communication modules were delivered in this project. In case of intruders images were captured by the in-built camera and an alert was raised via SMS and emails. The time taken by this complete process recorded to be within a range of few milliseconds.


title: The Deconfounded Recommender: A Causal Inference Approach to Recommendation

abstract: The goal of a recommender system is to show its users items that they will like. In forming its prediction, the recommender system tries to answer:"what would the rating be if we 'forced' the user to watch the movie?"This is a question about an intervention in the world, a causal question, and so traditional recommender systems are doing causal inference from observational data. This paper develops a causal inference approach to recommendation. Traditional recommenders are likely biased by unobserved confounders, variables that affect both the"treatment assignments"(which movies the users watch) and the"outcomes"(how they rate them). We develop the deconfounded recommender, a strategy to leverage classical recommendation models for causal predictions. The deconfounded recommender uses Poisson factorization on which movies users watched to infer latent confounders in the data; it then augments common recommendation models to correct for potential confounding bias. The deconfounded recommender improves recommendation and it enjoys stable performance against interventions on test sets.


title: Power Spectral Density Analysis of WLAN based OFDM System

abstract: is a technique used in modern broadband wireless communications systems. OFDM has been adopted for wireless standards such as IEEE 802.11a. In this paper, different Power Spectral Density (PSD) curves of OFDM signal based on WLAN with various pulse shapes are presented. OFDM waveform is analyzed in frequency domain response for different modulation techniques such as BPSK, QPSK, 16-QAM, 64-QAM and different block sizes such as 50 and 2048. Power spectral density increases with increase in levels of modulation. For high power spectral density of transmitter higher levels of QAM modulation techniques are preferred.


title: Bidiagonalization with Parallel Tiled Algorithms

abstract: We consider algorithms for going from a ``full'' matrix to a condensed ::: ``band bidiagonal'' form using orthogonal transformations. ::: We use the framework of ``algorithms by tiles''. ::: Within this framework, we study: (i) the tiled ::: bidiagonalization algorithm \bidiag, which is a tiled version of the ::: standard scalar bidiagonalization algorithm; and ::: (ii) the R-bidiagonalization algorithm \rbidiag, which ::: is a tiled version of the algorithm which ::: consists in first performing the QR factorization of ::: the initial matrix, then performing the band-bidiagonalization of the R-factor. ::: For both bidiagonalization algorithms \bidiag and \rbidiag, ::: we use four main ::: types of reduction trees, namely \FlatTS, \FlatTT, \Greedy, ::: and a newly introduced auto-adaptive tree, \Auto. ::: We provide a study of critical path lengths for these tiled algorithms, which shows ::: that (i) \rbidiag has a shorter critical path length than ::: \bidiag for tall and skinny matrices, and (ii) \Greedy based schemes are ::: much better than earlier proposed variants with unbounded resources. ::: We provide experiments on a single multicore node, and on a few multicore nodes of a parallel distributed ::: shared-memory system, to show the superiority of the new algorithms on a ::: variety of matrix sizes, matrix shapes and core counts.


title: An assessment of features related to phishing websites using an automated technique

abstract: Corporations that offer online trading can achieve a competitive edge by serving worldwide clients. Nevertheless, online trading faces many obstacles such as the unsecured money orders. Phishing is considered a form of internet crime that is defined as the art of mimicking a website of an honest enterprise aiming to acquire confidential information such as usernames, passwords and social security number. There are some characteristics that distinguish phishing websites from legitimate ones such as long URL, IP address in URL, adding prefix and suffix to domain and request URL, etc. In this paper, we explore important features that are automatically extracted from websites using a new tool instead of relying on an experienced human in the extraction process and then judge on the features importance in deciding website legitimacy. Our research aims to develop a group of features that have been shown to be sound and effective in predicting phishing websites and to extract those features according to new scientific precise rules.


title: Colorful Textile Antennas Integrated into Embroidered Logos

abstract: We present a new methodology to create colorful textile antennas that can be embroidered within logos or other aesthetic shapes. Conductive threads (e-threads) have already been used in former embroidery unicolor approaches as attributed to the corresponding conductive material, viz. silver or copper. But so far, they have not been adapted to ‘print’ colorful textile antennas. For the first time, we propose an approach to create colorful electronic textile shapes. In brief, the embroidery process uses an e-thread in the bobbin case of the sewing machine to embroider the antenna on the back side of the garment. Concurrently, a colorful assistant yarn is threaded through the embroidery needle of the embroidery machine and used to secure or ‘couch’ the e-threads onto the fabric. In doing so, a colorful shape is generated on the front side of the garment. The proposed antennas can be unobtrusively integrated into clothing or other accessories for a wide range of applications (e.g., wireless communications, Radio Frequency IDentification, sensing).


title: Sentiment Analysis using supervised classification algorithms

abstract: The exploitation of social media (forums, blogs and social networks) has become crucial due to the explosive growth of textual data from these new sources of information. Our work focuses on the Sentiment analysis resulting from the messages (SMS, Facebook, Twitter...) using original techniques of search of texts. These messages can be classified as having a positive or negative feeling based on certain aspects in relation to a query based on terms. This paper presents a comparison of five supervised classification algorithms: PART, Support Vector Machine, Decision Tree, Naive Bayes, and Logistic Regression.


title: A 3D passive dynamic biped with yaw and roll compensation

abstract: Autonomous walking bipedal machines, possibly useful for rehabilitation and entertainment purposes, need a high energy efficiency, offered by the concept of ‘Passive Dynamic Walking' (exploitation of the natural dynamics of the robot). 2D passive dynamic bipeds have been shown to be inherently stable, but in the third dimension two problematic degrees of freedom are introduced: yaw and roll.We propose a design for a 3D biped with a pelvic body as a passive dynamic compensator, which will compensate for the undesired yaw and roll motion, and allow the rest of the robot to move as if it were a 2D machine. To test our design, we perform numerical simulations on a multibody model of the robot. With limit cycle analysis we calculate the stability of the robot when walking at its natural speed.The simulation shows that the compensator, indeed, effectively compensates for both the yaw and the roll motion, and that the walker is stable.


title: Advantages of a Model-Based Library for Scientific Experiments

abstract: In many research areas, such as the material sciences, researchers plan and conduct numerous experiments. The corresponding findings are published, while the underlying data remains described haphazardly or not at all within the respective institutes and, therefore, is neither shared nor reused by the scientific community.


title: A Low-Jitter 1.8-kV 100-ps Rise-Time 50-kHz Repetition-Rate Pulsed-Power Generator

abstract: A 1.8-kV 100-ps rise-time pulsed-power generator operating at a repetition frequency of 50 kHz is presented. The generator consists of three compression stages. In the first stage, a power MOSFET produces high voltage by breaking an inductor current. In the second stage, a 3-kV drift-step-recovery diode cuts the reverse current rapidly to create a 1-ns rise-time pulse. In the last stage, a silicon-avalanche shaper is used as a fast 100-ps closing switch. Experimental investigation showed that, by optimizing the generator operating point, the shot-to-shot jitter can be reduced to less than 13 ps. The theoretical model of the pulse-forming circuit is presented.


title: Analysis of a laboratory scale three-phase FC-TCR-based static VAr compensator

abstract: This paper presents the design and steady-state analysis of a Fixed Capacitor-Thyristor Controlled Reactor (FC-TCR)-based Static VAr Compensator (SVC). Reactive power compensation is demonstrated through the fundamental frequency analysis of the samples acquired from the designed system. The performance of the SVC in the presence of line reactance is also discussed. National Instrument (NI) based data acquisition system is used to perform the steady-state analysis. Besides, a few transient responses are also captured using the data acquisition system.


title: An Ontology Model to Support the Automated Evaluation of Software

abstract: Even though previous research has tried to model Software Engineering knowledge, focusing either on the entire discipline or on parts of it, we lack an integrated conceptual model for representing software evaluations, and we also lack the information related to them that supports their definition and enables their automation and reproducibility. This paper presents an extensible ontology model for representing software evaluations and evaluation campaigns, i.e., worldwide activities where a group of tools is evaluated according to a certain evaluation specification using common test data. During the development of the ontologies, we have reused current standards and models and have linked these ontologies with some renowned ones.


title: The caBIG™ Annotation and Image Markup Project

abstract: Image annotation and markup are at the core of medical interpretation in both the clinical and the research setting. Digital medical images are managed with the DICOM standard format. While DICOM contains a large amount of meta-data about whom, where, and how the image was acquired, DICOM says little about the content or meaning of the pixel data. An image annotation is the explanatory or descriptive information about the pixel data of an image that is generated by a human or machine observer. An image markup is the graphical symbols placed over the image to depict an annotation. While DICOM is the standard for medical image acquisition, manipulation, transmission, storage, and display, there are no standards for image annotation and markup. Many systems expect annotation to be reported verbally, while markups are stored in graphical overlays or proprietary formats. This makes it difficult to extract and compute with both of them. The goal of the Annotation and Image Markup (AIM) project is to develop a mechanism, for modeling, capturing, and serializing image annotation and markup data that can be adopted as a standard by the medical imaging community. The AIM project produces both human- and machine-readable artifacts. This paper describes the AIM information model, schemas, software libraries, and tools so as to prepare researchers and developers for their use of AIM.


title: Writing narrative style literature reviews

abstract: AbstractReviews provide a synthesis of published literature on a topic and describe its current state-of-art. Reviews in clinical research are thus useful when designing studies or developing practice guidelines. The two standard types of reviews are (a) systematic and (b) non-systematic or narrative review. Unlike systematic reviews that benefit from guidelines such as PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) statement, there are no acknowledged guidelines for narrative reviews. I have attempted to define the best practice recommendations for the preparation of a narrative review in clinical research. The quality of a narrative review may be improved by borrowing from the systematic review methodologies that are aimed at reducing bias in the selection of articles for review and employing an effective bibliographic research strategy. The dynamics of narrative review writing, the organizational pattern of the text, the analysis, and the synthesis processes are also discussed.


title: User Experience in Mobile Augmented Reality: Emotions, Challenges, Opportunities and Best Practices

abstract: Mobile Augmented Reality (MAR) is gaining a strong momentum to become a major interactive technology that can be applied across domains and purposes. The rapid proliferation of MAR applications in ...


title: A Transformerless Intelligent Power Substation: A three-phase SST enabled by a 15-kV SiC IGBT

abstract: The solid-state transformer (SST) is a promising power electronics solution that provides voltage regulation, reactive power compensation, dc-sourced renewable integration, and communication capabilities, in addition to the traditional step-up/step-down functionality of a transformer. It is gaining widespread attention for medium-voltage (MV) grid interfacing to enable increases in renewable energy penetration, and, commercially, the SST is of interest for traction applications due to its light weight as a result of medium-frequency isolation. The recent advancements in silicon carbide (SiC) power semiconductor device technology are creating a new paradigm with the development of discrete power semiconductor devices in the range of 10-15 kV and even beyond-up to 22 kV, as recently reported. In contrast to silicon (Si) IGBTs, which are limited to 6.5-kV blocking, these high-voltage (HV) SiC devices are enabling much simpler converter topologies and increased efficiency and reliability, with dramatic reductions of the size and weight of the MV power-conversion systems.


title: Integration of open source platform duckietown and gesture recognition as an interactive interface for the museum robotic guide

abstract: In recent years, population aging becomes a serious problem. To decrease the demand for labor when navigating visitors in museums, exhibitions, or libraries, this research designs an automatic museum robotic guide which integrates image and gesture recognition technologies to enhance the guided tour quality of visitors. The robot is a self-propelled vehicle developed by ROS (Robot Operating System), in which we achieve the automatic driving based on the function of lane-following via image recognition. This enables the robot to lead guests to visit artworks following the preplanned route. In conjunction with the vocal service about each artwork, the robot can convey the detailed description of the artwork to the guest. We also design a simple wearable device to perform gesture recognition. As a human machine interface, the guest is allowed to interact with the robot by his or her hand gestures. To improve the accuracy of gesture recognition, we design a two phase hybrid machine learning-based framework. In the first phase (or training phase), k-means algorithm is used to train historical data and filter outlier samples to prevent future interference in the recognition phase. Then, in the second phase (or recognition phase), we apply KNN (k-nearest neighboring) algorithm to recognize the hand gesture of users in real time. Experiments show that our method can work in real time and get better accuracy than other methods.


title: Challenges and Rewards of Implementing and Benchmarking Post-Quantum Cryptography in Hardware

abstract: Practical quantum computers have been recently selected as one of 10 breakthrough technologies of 2017 by the MIT Technology Review. Although various fields of human activity, such as chemistry, medicine, and materials science, are likely to be dramatically affected by practical quantum computers, the most likely immediate impact will take place in the area of cryptography and cyber security. As a result of this potential threat, a new field of science has emerged, called Post-Quantum Cryptography (PQC). PQC is devoted to the design and analysis of cryptographic algorithms that are resistant against any known attacks using quantum computers, but by themselves can be implemented using classical computing platforms, based on traditional modern semiconductor technologies. In this paper, we provide an overview and motivation for the PQC, NIST Standardization Effort, cryptographic competitions, and hardware benchmarking of candidates in cryptographic contests. Five major families of PQC schemes, code-, hash-, isogeny-, lattice-, and multivariate-based, are shortly introduced. The challenges of fair and comprehensive hardware benchmarking of PQC submissions are highlighted, together with the possible ways of overcoming these difficulties, such as the use of a common API, development packages, specialized libraries, and high-level synthesis.


title: http://www.informatics.ed.ac.uk / Ontologies, Multi-Perspective Modelling and Knowledge Auditing

abstract: I have extended an existing ontology for knowledge acquisition to support the task of knowledge auditing. This has required two types of change: the ontology has been extended according to the principles of multi-perspective modelling so that it represents knowledge from the viewpoints of ”who”, ”what”, ”where” and ”how”; further work may add ”when” and ”why” perspectives. It also adds some slots to the class of Publications, which are intended to store information for the purpose of knowledge valuation.


title: The rank-order consistency of personality traits from childhood to old age: A quantitative review of longitudinal studies.

abstract: The present study used meta-analytic techniques to test whether trait consistency maximizes and stabilizes at a specific period in the life course. From 152 longitudinal studies, 3,217 test-retest correlation coefficients were compiled. Meta-analytic estimates of mean population test-retest correlation coefficients showed that trait consistency increased from .31 in childhood to .54 during the college years, to .64 at age 30, and then reached a plateau around .74 between ages 50 and 70 when time interval was held constant at 6.7 years. Analysis of moderators of consistency showed that the longitudinal time interval bad a negative relation to trait consistency and that temperament dimensions were less consistent than adult personality traits. Do personality traits stop changing at some point during the life course? The answer to this question is critical for both basic and applied psychologists. For personality psychologists, it goes to the heart of how personality traits are conceptualized. At the core of most definitions is the assumption that traits remain consistent over time (West & Graziano, 1989). For example, Tellegen (1988) defines a trait as "a psychological (therefore) organismic structure underlying a relatively enduring behavioral disposition, i.e., a tendency to respond in certain ways under certain circumstances" (p. 622; see also Harkness& Hogan, 1995). If age is strongly related to trait consistency, then the construct validity of trait measurements may be affected by the age of the samples studied. For applied psychologists, whether traits are unchanging pertains to whether change efforts should be attempted and whether age should be factored into this decision. For example, if personality traits change before age 18 and not after, then interventions focused on changing traitlike syndromes, such as leadership style or personality disorders, may be shaped by the age of the client (see, e.g., Hellervik, Hazucha, & Schneider, 1992; Linehan & Kehrer, 1993). Despite its obvious importance, the question of when in the life course personality traits reach their peak consistency has received little systematic empirical and quantitative attention since Bloom's (1964) review in 1964. In the present study, we focus on one aspect of personality change, rank-order consistency, by compiling longitudinal studies of personality trait consistency. We address three questions: (a) What is the relation between chronological age and trait consistency, (b) at what age does trait consistency peak, and (c) does trait consistency peak at a level high enough to warrant, as some have


title: A Large-Scale SUMO-Based Emulation Platform

abstract: A hardware-in-the-loop simulation platform for emulating large-scale intelligent transportation systems is presented. The platform embeds a real vehicle into SUMO, a microscopic road traffic simulation package. Emulations, consisting of the real vehicle, and potentially thousands of simulated vehicles, are run in real time. The platform provides an opportunity for real drivers to gain a feel of being in a large-scale connected vehicle scenario. Various applications of the platform are presented.


REAL-TIME SYSTEM IDENTIFICATION OF AIRCRAFT DYNAMICS USING TIME-FREQUENCY WAVELET ANALYSIS


title: Chemical and instrumental approaches to treat hyperpigmentation.

abstract: Many modalities of treatment for acquired skin hyperpigmentation are available including chemical agents or physical therapies, but none are completely satisfactory. Depigmenting compounds should act selectively on hyperactivated melanocytes, without short- or long-term side-effects, and induce a permanent removal of undesired pigment. Since 1961 hydroquinone, a tyrosinase inhibitor, has been introduced and its therapeutic efficacy demonstrated, and other whitening agents specifically acting on tyrosinase by different mechanisms have been proposed. Compounds with depigmenting activity are now numerous and the classification of molecules, based on their mechanism of action, has become difficult. Systematic studies to assess both the efficacy and the safety of such molecules are necessary. Moreover, the evidence that bleaching compounds are fairly ineffective on dermal accumulation of melanin has prompted investigations on the effectiveness of physical therapies, such as lasers. This review which describes the different approaches to obtain depigmentation, suggests a classification of whitening molecules on the basis of the mechanism by which they interfere with melanogenesis, and confirms the necessity to apply standardized protocols to evaluate depigmenting treatments.


title: Understanding user behavior in Spotify

abstract: Spotify is a peer-assisted music streaming service that has gained worldwide popularity in the past few years. Until now, little has been published about user behavior in such services. In this paper, we study the user behavior in Spotify by analyzing a massive dataset collected between 2010 and 2011. Firstly, we investigate the system dynamics including session arrival patterns, playback arrival patterns, and daily variation of session length. Secondly, we analyze individual user behavior on both multiple and single devices. Our analysis reveals the favorite times of day for Spotify users. We also show the correlations between both the length and the downtime of successive user sessions on single devices. In particular, we conduct the first analysis of the device-switching behavior of a massive user base.


title: A mechanistic performance model for superscalar out-of-order processors

abstract: A mechanistic model for out-of-order superscalar processors is developed and then applied to the study of microarchitecture resource scaling. The model divides execution time into intervals separated by disruptive miss events such as branch mispredictions and cache misses. Each type of miss event results in characterizable performance behavior for the execution time interval. By considering an interval's type and length (measured in instructions), execution time can be predicted for the interval. Overall execution time is then determined by aggregating the execution time over all intervals. The mechanistic model provides several advantages over prior modeling approaches, and, when estimating performance, it differs from detailed simulation of a 4-wide out-of-order processor by an average of 7p. The mechanistic model is applied to the general problem of resource scaling in out-of-order superscalar processors. First, we use the model to determine size relationships among microarchitecture structures in a balanced processor design. Second, we use the mechanistic model to study scaling of both pipeline depth and width in balanced processor designs. We corroborate previous results in this area and provide new results. For example, we show that at optimal design points, the pipeline depth times the square root of the processor width is nearly constant. Finally, we consider the behavior of unbalanced, overprovisioned processor designs based on insight gained from the mechanistic model. We show that in certain situations an overprovisioned processor may lead to improved overall performance. Designs where a processor's dispatch width is wider than its issue width are of particular interest.


title: Quantification of the impact of cooking processes on indoor concentrations of volatile organic species and primary and secondary organic aerosols

abstract: Cooking is recognized as an important source of particulate pollution in indoor and outdoor environments. We conducted more than 100 individual experiments to characterize the particulate and non-methane organic gas emissions from various cooking processes, their reaction rates and their secondary organic aerosol yields. We used this emission data to develop a box model, for simulating the cooking emission concentrations in a typical European home and the indoor gas-phase reactions leading to secondary organic aerosol production. Our results suggest that about half of the indoor primary organic aerosol emission rates can be explained by cooking. Emission rates of larger and unsaturated aldehydes likely are dominated by cooking while the emission rates of terpenes are negligible. We found that cooking dominates the particulate and gas phase air pollution in non-smoking European households exceeding 1000 μgm-3 . While frying processes are the main driver of aldehyde emissions, terpenes are mostly emitted due to the use of condiments. The secondary aerosol production is negligible with around 2 μgm-3 . Our results further show, that ambient cooking organic aerosol concentrations can only be explained by super-polluters like restaurants. The model offers a comprehensive framework for identifying the main parameters controlling indoor gas and particle phase concentrations.


title: An Investigation of Predictive Profiling from Handwritten Signature Data

abstract: Although it has long been recognized that non-biometric factors (for example, general demographic characteristics) can have an impact on the performance of automated person identification systems, such information is not routinely adopted in most practical biometric processing. In forensic applications, however, such additional information may be exploited most productively, since typical scenarios require the prediction of a wide range of individual characteristics from a range of available samples, biometric and non-biometric. In many such situations, it may be very useful to predict characteristics short of actual identity, since this type of prediction can significantly increase the amount of evidence available. In this paper we provide some benchmarking data to demonstrate the extent to which typical non-biometric information might be effective in practice in this context, and illustrate the differential effects depending on the classifier adopted. In particular, however, we investigate experimentally how traditionally measured handwritten signature data might be used to predict non-biometric categories in a way which can be exploited a variety of practical application scenarios.


title: Extracting Cybersecurity Related Linked Data from Text

abstract: The Web is typically our first source of information about new software vulnerabilities, exploits and cyber-attacks. Information is found in semi-structured vulnerability databases as well as in text from security bulletins, news reports, cyber security blogs and Internet chat rooms. It can be useful to cyber security systems if there is a way to recognize and extract relevant information and represent it as easily shared and integrated semantic data. We describe such an automatic framework that generates and publishes a RDF linked data representation of cyber security concepts and vulnerability descriptions extracted from the National Vulnerability Database and from text sources. A CRF-based system is used to identify cybersecurity-relatedentities, concepts and relations in text, which are then represented using custom ontologies for the cyber security domain and also mapped to objects in the DBpedia knowledge base. The resulting cyber security linked data collection can be used for many purposes, including automating early vulnerability identification, mitigation and prevention efforts.


title: Maximizing Ethernet Security by Switch-Based Single Secure Domain

abstract: Media Access Control Security (MACsec) is a new segment based link layer security, which is standardized on 2006. It leaves data in clear inside switches and facilitates packet inspection at those connection points. The support for traffic analysis is crucial for Enterprises or Service Providers in secure premises, but when the networking devices are placed in populated areas (e.g., hospitals, airports), this facility can break security of total infrastructure. Also inside attackers can take advantage of this unsecured zone. In this paper, we identify the security requirements that need to be followed for better Ethernet security, and propose MACsec-enable switch-based single secure domain to address the above critical problem. Our design requires no additional cryptographic algorithms and adds the new feature only in switches. The evaluation of our proposal highlights the increased security and maximized performance. Finally, we guide further research in this area.


title: The Microsoft Indoor Localization Competition: Experiences and Lessons Learned

abstract: We present the results, experiences, and lessons learned from comparing a diverse set of indoor location technologies during the Microsoft Indoor Localization Competition. Over the last four years (2014-2017), more than 100 teams from academia and industry deployed their indoor location solutions in realistic, unfamiliar environments, allowing us to directly compare their accuracies and overhead. In this article, we provide an analysis of this four-year-long evaluation study's results and discuss the current state of the art in indoor localization.


title: QuantCloud: Big Data Infrastructure for Quantitative Finance on the Cloud

abstract: In this paper, we present the QuantCloud infrastructure, designed for performing big data analytics in modern quantitative finance. Through analyzing market observations, quantitative finance (QF) utilizes mathematical models to search for subtle patterns and inefficiencies in financial markets to improve prospective profits. To discover profitable signals in anticipation of volatile trading patterns amid a global market, analytics are carried out on Exabyte-scale market metadata with a complex process in pursuit of a microsecond or even a nanosecond of data processing advantage. This objective motivates the development of innovative tools to address challenges for handling high volume, velocity, and variety investment instruments. Inspired by this need, we developed QuantCloud by employing large-scale SSD-backed datastore, various parallel processing algorithms, and portability in Cloud computing. QuantCloud bridges the gap between model computing techniques and financial data-driven research. The large volume of market data is structured in an SSD-backed datastore, and a daemon reacts to provide the Data-on-Demand services. Multiple client services process user requests in a parallel mode and query on-demand datasets from the datastore through Internet connections. We benchmark QuantCloud performance on a 40-core, 1TB-memory computer and a 5-TB SSD-backed datastore. We use NYSE TAQ data from the fourth quarter of 2014 as our market data. The results indicate data-access application latency as low as 3.6 nanoseconds per message, sustained throughput for parallel data processing as high as 74 million messages per second, and completion of 11 petabyte-level data analytics within 53 minutes. Our results demonstrate that the aggregated contributions of our infrastructure, parallel algorithms, and sophisticated implementations offer the algorithmic trading and financial engineering community new hope and numeric insights for their research and development.


title: Analysis and Evaluation of SECOS, a protocol for Energy Efficient and Secure Communication in Sensor Networks

abstract: Wireless sensor networks are increasingly being used in applications where the communication between nodes needs to be protected from eavesdropping and tampering. Such protection is typically provided using techniques from symmetric key cryptography. The protocols in this domain suffer from one or more of the following problems-weak security guarantees if some nodes are compromised, lack of scalability, high energy overhead for key management, and increased end-to-end data latency. In this paper, we propose a protocol called Secos that mitigates these problems in static sensor networks. Secos divides the sensor field into control groups each with a control node. Data exchange between nodes within a control group happens through the mediation of the control head which provides the common key. The keys are refreshed periodically and the control nodes are changed periodically to enhance security. Secos enhances the survivability of the network by handling compromise and failures of control nodes. It provides the guarantee that the communication between any two sensor nodes remains secure despite the compromise of any number of other nodes in the network. The experiments based on a simulation model show a seven time reduction in energy overhead and a 50% reduction in latency compared to SPINS, which is one of the state-of-the-art protocols for key management in sensor networks.


title: The forensic investigation of android private browsing sessions using orweb

abstract: The continued increase in the usage of Small Scale Digital Devices (SSDDs) to browse the web has made mobile devices a rich potential for digital evidence. Issues may arise when suspects attempt to hide their browsing habits using applications like Orweb - which intends to anonymize network traffic as well as ensure that no browsing history is saved on the device. In this work, the researchers conducted experiments to examine if digital evidence could be reconstructed when the Orweb browser is used as a tool to hide web browsing activates on an Android smartphone. Examinations were performed on both a non-rooted and a rooted Samsung Galaxy S2 smartphone running Android 2.3.3. The results show that without rooting the device, no private web browsing traces through Orweb were found. However, after rooting the device, the researchers were able to locate Orweb browser history, and important corroborative digital evidence was found.


title: Automatic Glossary Term Extraction from Large-Scale Requirements Specifications

abstract: Creating glossaries for large corpora of requirments is an important but expensive task. Glossary term extraction methods often focus on achieving a high recall rate and, therefore, favor linguistic proecssing for extracting glossary term candidates and neglect the benefits from reducing the number of candidates by statistical filter methods. However, especially for large datasets a reduction of the likewise large number of candidates may be crucial. This paper demonstrates how to automatically extract relevant domain-specific glossary term candidates from a large body of requirements, the CrowdRE dataset. Our hybrid approach combines linguistic processing and statistical filtering for extracting and reducing glossary term candidates. In a twofold evaluation, we examine the impact of our approach on the quality and quantity of extracted terms. We provide a ground truth for a subset of the requirements and show that a substantial degree of recall can be achieved. Furthermore, we advocate requirements coverage as an additional quality metric to assess the term reduction that results from our statistical filters. Results indicate that with a careful combination of linguistic and statistical extraction methods, a fair balance between later manual efforts and a high recall rate can be achieved.


title: Image Dehazing for Object Recognition using Faster RCNN

abstract: Object recognition in hazy conditions is quite difficult due to illumination variance. The challenge arises in finding out features from such images. Herein, we have proposed the method to deal with such images. The input image needs to be dehazed before applying the recognition algorithms. On the other hand, dehazing a non-hazy image makes it dark resulting in loss of features. Hence, a decision is to be made whether or not the image should be dehazed before recognition. Also, for a very dense haze, even dehazing doesn’t help in object recognition. In order to tackle this issue, this paper presents a novel method to quantitatively estimate the amount of haze in the image – also termed as haze degree – using dark channel prior of the input images. We compared our values with the existing method using FRIDA dataset. The estimated haze degree is used to decide whether input image need to be dehazed or not. We use DehazeNet and Faster RCNN for dehazing and recognition, respectively. We test our method on real time hazy images to set a threshold on haze degree to classify the image as light, moderate or densely hazed. We used the Static Scenes dataset from Color Hazy Images for Comparison (CHIC) database to obtain the threshold values.


title: Euthanasia: Another Face of Murder

abstract: Debate over euthanasia is not a recent phenomenon. Over the years, public opinion, decisions of courts, and legal and medical approaches to the issue of euthanasia has been conflicting. The connection between murder and euthanasia has been attempted in a few debates. Although it is widely accepted that murder is a crime, a clearly defined stand has not been taken on euthanasia. This article considers euthanasia from the medical, legal, and global perspectives and discusses the crime of murder in relation to euthanasia, taking into consideration the issue of consent in the law of crime. This article concludes that in the midst of this debate on euthanasia and murder, the important thing is that different countries need to find their own solution to the issue of euthanasia rather than trying to import solutions fromother countries.


title: Analysis of bit error rate characteristic in LD wavelength conversion

abstract: A improved theoretical model for wavelength conversion into which the mutual coupling parameter is introduced is ::: present. The bit error rate of wavelength conversion are analyzed and confirmed by value simulation.


title: Twitter Information Extraction for Smart City

abstract: In Indonesia, Bandung is the second most active Twitter user, which means a lot of tweets have been shared among Bandung people on Twitter. Tweets can be used as a data source to explore information related to the city. One example is information related to traffic congestion, such as information of location, date, and time when the traffic congestion happened. In this study, we proposed a method to filter the tweets related to traffic congestion in Bandung and to extract the information of location, time, date and image (if any). SVM with several variations of the weighting and the selection of features is used for filtering process. The results showed that the greatest accuracy rate is 83% use Binary weighting method in top-2000 features. Meanwhile, information extraction process carried out by a rule-based approach, gave satisfactory results, around 98%–100% for the extraction of date, time and URL. However, the extraction of location information only gave accuracy of about 62%. It was caused by OOV (Out of Vocabulary) and OOR (Out of Rules).


title: Characterizing Facebook's Memcached Workload

abstract: Memcached is one of the world's largest key-value deployments. This article analyzes the Memcached workload at Facebook, looking at server-side performance, request composition, caching efficacy, and key locality. The observations presented here lead to several design insights and new research directions for key-value caches, such as the relative inadequacy of the least recently used (LRU) replacement policy.


title: Layer-Wise Relevance Propagation for Deep Neural Network Architectures

abstract: We present the application of layer-wise relevance propagation to several deep neural networks such as the BVLC reference neural net and googlenet trained on ImageNet and MIT Places datasets. Layer-wise relevance propagation is a method to compute scores for image pixels and image regions denoting the impact of the particular image region on the prediction of the classifier for one particular test image. We demonstrate the impact of different parameter settings on the resulting explanation.


title: Full-range approximation of triangulated polyhedra

abstract: We propose a new algorithm for automatically computing approximations of a given polyhedral object at different levels of details. The applications for this algorithm is the display of very complex scenes. Our approach is similar to the region-merging method used in image segmentation. We iteratively collapse edges based on a measure of the geometric deviation from the initial shape. when edges are merged in the right order, this strategy produces a continuum of valid approximations of the original object, which can be used for faster rendering at vastly different scales.


title: Neural networks for deceptive opinion spam detection

abstract: The products reviews are increasingly used by individuals and organizations for purchase and business decisions. Driven by the desire of profit, spammers produce synthesized reviews to promote some products or demote competitors products. So deceptive opinion spam detection has attracted significant attention from both business and research communities in recent years. Existing approaches mainly focus on traditional discrete features, which are based on linguistic and psychological cues. However, these methods fail to encode the semantic meaning of a document from the discourse perspective, which limits the performance. In this work, we empirically explore a neural network model to learn document-level representation for detecting deceptive opinion spam. First, the model learns sentence representation with convolutional neural network. Then, sentence representations are combined using a gated recurrent neural network, which can model discourse information and yield a document vector. Finally, the document representations are directly used as features to identify deceptive opinion spam. Based on three domains datasets, the results on in-domain and cross-domain experiments show that our proposed method outperforms state-of-the-art methods.


title: Robust Physical-World Attacks on Deep Learning Models

abstract: Recent studies show that the state-of-the-art deep neural networks (DNNs) are vulnerable to adversarial examples, resulting from small-magnitude perturbations added to the input. Given that that emerging physical systems are using DNNs in safety-critical situations, adversarial examples could mislead these systems and cause dangerous situations.Therefore, understanding adversarial examples in the physical world is an important step towards developing resilient learning algorithms. We propose a general attack algorithm,Robust Physical Perturbations (RP2), to generate robust visual adversarial perturbations under different physical conditions. Using the real-world case of road sign classification, we show that adversarial examples generated using RP2 achieve high targeted misclassification rates against standard-architecture road sign classifiers in the physical world under various environmental conditions, including viewpoints. Due to the current lack of a standardized testing method, we propose a two-stage evaluation methodology for robust physical adversarial examples consisting of lab and field tests. Using this methodology, we evaluate the efficacy of physical adversarial manipulations on real objects. Witha perturbation in the form of only black and white stickers,we attack a real stop sign, causing targeted misclassification in 100% of the images obtained in lab settings, and in 84.8%of the captured video frames obtained on a moving vehicle(field test) for the target classifier.


title: A review of possible effects of cognitive biases on interpretation of rule-based machine learning models

abstract: This paper investigates to what extent cognitive biases may affect human understanding of interpretable machine learning models, in particular of rules discovered from data. Twenty cognitive biases are covered, as are possible debiasing techniques that can be adopted by designers of machine learning algorithms and software. Our review transfers results obtained in cognitive psychology to the domain of machine learning, aiming to bridge the current gap between these two areas. It needs to be followed by empirical studies specifically aimed at the machine learning domain.


title: Why musical memory can be preserved in advanced Alzheimer’s disease

abstract: Musical memory is considered to be partly independent from other memory systems. In Alzheimer’s disease and different types of dementia, musical memory is surprisingly robust, and likewise for brain lesions affecting other kinds of memory. However, the mechanisms and neural substrates of musical memory remain poorly understood. In a group of 32 normal young human subjects (16 male and 16 female, mean age of 28.0 ± 2.2 years), we performed a 7 T functional magnetic resonance imaging study of brain responses to music excerpts that were unknown, recently known (heard an hour before scanning), and long-known. We used multivariate pattern classification to identify brain regions that encode long-term musical memory. The results showed a crucial role for the caudal anterior cingulate and the ventral pre-supplementary motor area in the neural encoding of long-known as compared with recently known and unknown music. In the second part of the study, we analysed data of three essential Alzheimer’s disease biomarkers in a region of interest derived from our musical memory findings (caudal anterior cingulate cortex and ventral pre-supplementary motor area) in 20 patients with Alzheimer’s disease (10 male and 10 female, mean age of 68.9 ± 9.0 years) and 34 healthy control subjects (14 male and 20 female, mean age of 68.1 ± 7.2 years). Interestingly, the regions identified to encode musical memory corresponded to areas that showed substantially minimal cortical atrophy (as measured with magnetic resonance imaging), and minimal disruption of glucose-metabolism (as measured with 18F-fluorodeoxyglucose positron emission tomography), as compared to the rest of the brain. However, amyloid-β deposition (as measured with 18F-flobetapir positron emission tomography) within the currently observed regions of interest was not substantially less than in the rest of the brain, which suggests that the regions of interest were still in a very early stage of the expected course of biomarker development in these regions (amyloid accumulation → hypometabolism → cortical atrophy) and therefore relatively well preserved. Given the observed overlap of musical memory regions with areas that are relatively spared in Alzheimer’s disease, the current findings may thus explain the surprising preservation of musical memory in this neurodegenerative disease. ::: ::: * Abbreviations ::: : FDG ::: : 18F-fluorodeoxyglucose ::: MVPA ::: : multivariate pattern analysis ::: SMA ::: : supplementary motor area


title: A modified echo state network based remaining useful life estimation approach

abstract: An approach to estimate the remaining useful life (RUL) by Echo State Network (ESN) is presented, which is a new paradigm in recurrent neural network (RNN). ESN randomly establishes a large sparse reservoir to replace the hidden layer of RNN, which overcomes the shortcomings of complicated computing, difficulties in determining the network topology of traditional RNN. An ESN sub-models strategy composed by classified ESN models matching to the varied training data set by retraining and classification is explored to estimate the RUL of turbofan engine system. The experimental results with the turbofan engine data of NASA Ames Prognostics Data Repository show that the proposed method can achieve better RUL estimation precision compared with the approaches of classical ESN and ESN trained by Kalman Filter and potential prospective in application.


title: Social media as a measurement tool of depression in populations

abstract: Depression is a serious and widespread public health challenge. We examine the potential for leveraging social media postings as a new type of lens in understanding depression in populations. Information gleaned from social media bears potential to complement traditional survey techniques in its ability to provide finer grained measurements over time while radically expanding population sample sizes. We present work on using a crowdsourcing methodology to build a large corpus of postings on Twitter that have been shared by individuals diagnosed with clinical depression. Next, we develop a probabilistic model trained on this corpus to determine if posts could indicate depression. The model leverages signals of social activity, emotion, and language manifested on Twitter. Using the model, we introduce a social media depression index that may serve to characterize levels of depression in populations. Geographical, demographic and seasonal patterns of depression given by the measure confirm psychiatric findings and correlate highly with depression statistics reported by the Centers for Disease Control and Prevention (CDC).


title: Bundle adjustment revisited for SLAM with RGBD sensors

abstract: We present a method of using depth information provided by an RGB-D sensor, for visual simultaneous localization and mapping (SLAM), in order to improve its accuracy. We present a constraint bundle adjustment which allows to easily combine depth and visual data in cost function entirely expressed in pixel. The proposed approach is evaluated on a public benchmark dataset and compared to the state of art methods.


title: Validating Prediction of a CGE Model of India: An Introspection

abstract: Although computable general equilibrium (CGE) models have been used extensively to evaluate the potential impact of economic reforms, few efforts have been made to assess the predictive power of the models. This paper attempts to test the performance of one such model, viz., Chadha, Pohit, Deardorff and Stern’s study of India’s unilateral trade/domestic policy reforms in the 1990s. Our model does not incorporate many of the rigidities/features of the Indian economy. Nevertheless, our model can perform quite well at simulating, if not forecasting, actual changes in sectoral output and exports


title: Road condition analysis using 3-axis accelerometer and GPS sensors

abstract: This work proposes road quality data analysis based on accelerometer and GPS sensors built into smartphone. Given paper particularly addresses the problem of road conditions. In order to study on road patterns we collected sufficient amount of data during the driving on different types of roads. In this paper, we provide visual representation of differences of consecutive accelerometer values. All examined cases consider speed of the moving vehicle.


Automatic Poetry Generation with Mutual Reinforcement Learning


title: Ontology Based Similarity for Information Technology Skills

abstract: Online job search and talent procurement have given rise to challenging match and search problems in the e-recruitment domain. Existing systems perform direct keyword matching of technical skills which misses out a closely matching candidate on account of it not having the exact skills. This results in substandard results which ignores the relationships between technical skills. In an attempt to improve relevancy, this paper proposes a semantic similarity measure between IT skills using a knowledge based approach. The approach builds an ontology using DBpedia and uses it to derive a similarity score using feature based similarity measures. The proposed approach performs better than the Resumatcher system in finding the similarity between skills.


A Coverage-Based Utility Model for Identifying Unknown Unknowns


title: Planning as Heuristic Search

abstract: In the AIPS98 Planning Contest, the HSP planner showed that heuristic search planners can be competitive with state-of-the-art Graphplan and SAT planners. Heuristic search planners like HSP transform planning problems into problems of heuristic search by automatically extracting heuristics from Strips encodings. They differ from specialized problem solvers such as those developed for the 24-Puzzle and Rubik’s Cube in that they use a general declarative language for stating problems and a general mechanism for extracting heuristics from these representations. In this paper, we study a family of heuristic search planners that are based on a simple and general heuristic that assumes that action preconditions are independent. The heuristic is then used in the context of best-first and hill-climbing search algorithms, and is tested over a large collection of domains. We then consider variations and extensions such as reversing the direction of the search for speeding node evaluation, and extracting information about propositional invariants for avoiding dead-ends. We analyze the resulting planners, evaluate their performance, and explain when they do best. We also compare the performance of these planners with two state-of-the-art planners, and show that the simplest planner based on a pure best-first search yields the most solid performance over a large set of problems. We also discuss the strengths and limitations of this approach, establish a correspondence between heuristic search planning and Graphplan, and briefly survey recent ideas that can reduce the current gap in performance between general heuristic search planners and specialized solvers.  2001 Elsevier Science B.V. All rights reserved.


title: Multi-physics optimization for high-frequency air-core permanent-magnet motor of aircraft application

abstract: High-specific-power electrical machines are a key enabling technology of electric aircraft propulsion. The machine specific power (power-to-weight ratio) is usually constrained by the thermal and mechanical properties of material. To maximize machine specific power, electrical, thermal and mechanical capabilities have to be considered together. In this paper, an efficient multi-physics model of a high-frequency air-core Halbach-array PM motor, has been developed for the use of a large-scale genetic-algorithm-based optimization. The detailed electromagnetic (EM) and thermal models, and finite-element-analysis (FEA) validation, are presented and discussed. Effects of key machine sizing parameters, such as speed, rotor volume, magnetic loading, and electric loading, in the multi-disciplines and objectives optimization, will be presented and discussed.


title: Semi-supervised Multitask Learning for Sequence Labeling

abstract: We propose a sequence labeling framework with a secondary training objective, learning to predict surrounding words for every word in the dataset. This language modeling objective incentivises the system to learn general-purpose patterns of semantic and syntactic composition, which are also useful for improving accuracy on different sequence labeling tasks. The architecture was evaluated on a range of datasets, covering the tasks of error detection in learner texts, named entity recognition, chunking and POS-tagging. The novel language modeling objective provided consistent performance improvements on every benchmark, without requiring any additional annotated or unannotated data.


title: Comprehensive Feeding Practices Questionnaire: Validation of a New Measure of Parental Feeding Practices

abstract: Objective Measures of parents’ feeding practices have focused primarily on parental control of feeding and have not sufficiently measured other potentially important practices. The current study validates a new measure of feeding practices, the Comprehensive Feeding Practices Questionnaire (CFPQ). Method The first study validated a 9-factor feeding practice scale for mothers and fathers. In the second study, open-ended questions solicited feeding practices from parents to develop a more comprehensive measure of parental feeding. The third study validated an expanded 12-factor feeding practices measure with mothers of children from 2 to 8 years of age. Results The CFPQ appears to be an adequate tool for measuring the feeding practices of parents of young children. Conclusions Researchers, clinicians, and health educators might use this measure to better understand how parents feed their children, the factors that contribute to these practices, and the implications of these practices on children’s eating behaviors.


Continuous Cloud-Scale Query Optimization and Processing


title: Combination use of Beck Depression Inventory and two-question case-finding instrument as a screening tool for depression in the workplace

abstract: Objectives The present study aimed to validate screening tools that could be used to identify depression among workers. Design Diagnostic test study. Settings Workers from three Japanese companies agreed to participate. Participants Recruitment for the group 1 occurred between January 2001 and February 2004, and 89 participants (81 men and 8 women with a mean age of 38.4±6.6 years) (98.8%) took part in the study. Recruitment for the group 2 occurred between July 2000 and February 2004, and 1500 participants (1408 men and 92 women with a mean age of 40.9±7.2 years) (94.2%) took part in the study. Demographic data are shown in supplementary table 1. Interventions Primary and secondary outcome measures: the Beck Depression Inventory (BDI) and a two-question case-finding instrument (TQI) were administered to 89 workers and Mini-International Neuropsychiatric Interview was conducted to verify the diagnosis of depression. A second group of 1500 workers completed the BDI and TQI to detect possible sample bias for the distribution of depression. Specificity, sensitivity and positive predictive value were calculated in order to obtain the optimal cut-off scores for BDI and TQI and receiver operating characteristic curves, and Youden Index were applied to further refine the optimal cut-off scores. Results When paired together, BDI score ≥10 and TQI score of 2 adequately identified workers who had major depressive disorder and those who had other psychiatric disorders that are frequently comorbid with major depressive disorder. Conclusions The combination of BDI score ≥10 and TQI score of 2 can adequately screen for current and potential cases of depression among workers. Furthermore, BDI and TQI offer the advantage of being relatively easy to administer to a large number of workers. Early detection of depression could improve treatment outcomes and decrease economic burden. Trail registration


title: Information Rate of OFDM-Based Optical Wireless Communication Systems With Nonlinear Distortion

abstract: In this paper, a piecewise polynomial function is proposed as a generalized model for the nonlinear transfer characteristic of the transmitter for optical wireless communications (OWC). The two general multicarrier modulation formats for OWC based on orthogonal frequency-division multiplexing (OFDM), direct-current-biased optical OFDM (DCO-OFDM) and asymmetrically clipped optical OFDM (ACO-OFDM), are studied. The nonlinear distortion of the electrical signal-to-noise ratio (SNR) at the receiver is derived in closed form, and it is verified by means of a Monte Carlo simulation. This flexible and accurate model allows for the application of pre-distortion and linearization of the dynamic range of the transmitter between points of minimum and maximum radiated optical power. Through scaling and DC-biasing the transmitted signal is optimally conditioned in accord with the optical power constraints of the transmitter front-end, i.e., minimum, average and maximum radiated optical power. The mutual information of the optimized optical OFDM (O-OFDM) schemes is presented as a measure of the capacity of these OWC systems under an average electrical power constraint. When the additional DC bias power is neglected, DCO-OFDM is shown to achieve the Shannon capacity when the optimization is employed, while ACO-OFDM exhibits a 3-dB gap which grows with higher information rate targets. When the DC bias power is counted towards the signal power, DCO-OFDM outperforms ACO-OFDM for the majority of average optical power levels with the increase of the information rate target or the dynamic range. The results can be considered as a lower bound on the O-OFDM system capacity.


STOL Engine Systems and Sensitivity Factors


title: Performance-oriented DevOps: A Research Agenda

abstract: DevOps is a trend towards a tighter integration between development (Dev) and operations (Ops) teams. The need for such an integration is driven by the requirement to continuously adapt enterprise applications (EAs) to changes in the business environment. As of today, DevOps concepts have been primarily introduced to ensure a constant flow of features and bug fixes into new releases from a functional perspective. In order to integrate a non-functional perspective into these DevOps concepts this report focuses on tools, activities, and processes to ensure one of the most important quality attributes of a software system, namely performance. ::: Performance describes system properties concerning its timeliness and use of resources. Common metrics are response time, throughput, and resource utilization. Performance goals for EAs are typically defined by setting upper and/or lower bounds for these metrics and specific business transactions. In order to ensure that such performance goals can be met, several activities are required during development and operation of these systems as well as during the transition from Dev to Ops. Activities during development are typically summarized by the term Software Performance Engineering (SPE), whereas activities during operations are called Application Performance Management (APM). SPE and APM were historically tackled independently from each other, but the newly emerging DevOps concepts require and enable a tighter integration between both activity streams. This report presents existing solutions to support this integration as well as open research challenges in this area.


title: Word-Based Dialog State Tracking with Recurrent Neural Networks

abstract: Recently discriminative methods for tracking the state of a spoken dialog have been shown to outperform traditional generative models. This paper presents a new wordbased tracking method which maps directly from the speech recognition results to the dialog state without using an explicit semantic decoder. The method is based on a recurrent neural network structure which is capable of generalising to unseen dialog state hypotheses, and which requires very little feature engineering. The method is evaluated on the second Dialog State Tracking Challenge (DSTC2) corpus and the results demonstrate consistently high performance across all of the metrics.


title: A weighted least-norm solution based scheme for avoiding joint limits for redundant joint manipulators

abstract: It is proposed to use weighted least-norm solution to avoid joint limits for redundant joint manipulators. A comparison is made with the gradient projection method for avoiding joint limits. While the gradient projection method provides the optimal direction for the joint velocity vector within the null space, its magnitude is not unique and is adjusted by a scalar coefficient chosen by trial and error. It is shown in this paper that one fixed value of the scalar coefficient is not suitable even in a small workspace. The proposed manipulation scheme automatically chooses an appropriate magnitude of the self-motion throughout the workspace. This scheme, unlike the gradient projection method, guarantees joint limit avoidance, and also minimizes unnecessary self-motion. It was implemented and tested for real-time control of a seven-degree-of-freedom (7-DOF) Robotics Research Corporation (RRC) manipulator. >


title: Multi-view stereo beyond Lambert

abstract: We consider the problem of estimating the shape and radiance of an object from a calibrated set of views under the assumption that the reflectance of the object is non-Lambertian. Unlike traditional stereo, we do not solve the correspondence problem by comparing image-to-image. Instead, we exploit a rank constraint on the radiance tensor field of the surface in space, and use it to define a discrepancy measure between each image and the underlying model. Our approach automatically returns an estimate of the radiance of the scene, along with its shape, represented by a dense surface. The former can be used to generate novel views that capture the non-Lambertian appearance of the scene.


title: Machine learning, social learning and the governance of self-driving cars

abstract: Self-driving cars, a quintessentially ‘smart’ technology, are not born smart. The algorithms that control their movements are learning as the technology emerges. Self-driving cars represent a high-stakes test of the powers of machine learning, as well as a test case for social learning in technology governance. Society is learning about the technology while the technology learns about society. Understanding and governing the politics of this technology means asking ‘Who is learning, what are they learning and how are they learning?’ Focusing on the successes and failures of social learning around the much-publicized crash of a Tesla Model S in 2016, I argue that trajectories and rhetorics of machine learning in transport pose a substantial governance challenge. ‘Self-driving’ or ‘autonomous’ cars are misnamed. As with other technologies, they are shaped by assumptions about social needs, solvable problems, and economic opportunities. Governing these technologies in the public interest means improving soci...


Evaluating teacher competence through the use of performance assessment tasks: An overview


title: Convolutional neural network-based image representation for visual loop closure detection

abstract: Deep convolutional neural networks (CNN) have recently been shown in many computer vision and pattern recognition applications to outperform by a significant margin state-of-the-art solutions that use traditional hand-crafted features. However, this impressive performance is yet to be fully exploited in robotics. In this paper, we focus one specific problem that can benefit from the recent development of the CNN technology, i.e., we focus on using a pre-trained CNN model as a method of generating an image representation appropriate for visual loop closure detection in SLAM (simultaneous localization and mapping). We perform a comprehensive evaluation of the outputs at the intermediate layers of a CNN as image descriptors, in comparison with state-of-the-art image descriptors, in terms of their ability to match images for detecting loop closures. The main conclusions of our study include: (a) CNN-based image representations perform comparably to state-of-the-art hand-crafted competitors in environments without significant lighting change, (b) they outperform state-of-the-art competitors when lighting changes significantly, and (c) they are also significantly faster to extract than the state-of-the-art hand-crafted features even on a conventional CPU and are two orders of magnitude faster on an entry-level GPU.


title: A guide to knowledge translation theory

abstract: Despite calls over several decades for theory development, there remains no overarching knowledge-translation theory. However, a range of models and theoretical perspectives focused on narrower and related areas have been available for some time. We provide an overview of selected perspectives that we believe are particularly useful for developing testable and useful knowledge-translation interventions. In addition, we discuss adjuvant theories necessary to complement these perspectives. We draw from organizational innovation, health, and social sciences literature to illustrate the similarities and differences of various theoretical perspectives related to the knowledge-translation field. A variety of theoretical perspectives useful to knowledge translation exist. They are often spread across disciplinary boundaries, making them difficult to locate and use. Poor definitional clarity, discipline-specific terminology, and implicit assumptions often hinder the use of complementary perspectives. Health care environments are complex, and assessing the setting prior to selecting a theory should be the first step in knowledge-translation initiatives. Finding a fit between setting (context) and theory is important for knowledge-translation initiatives to succeed. Because one theory will not fit all contexts, it is helpful to understand and use several different theories. Although there are often barriers associated with combining theories from different disciplines, such obstacles can be overcome, and to do so will increase the likelihood that knowledge-translation initiatives will succeed.


title: Small-size CPW-fed Quasi-Yagi antenna with round-ended bow-tie CPW-to-slotline transition

abstract: A compact and wideband CPW-fed Quasi-Yagi antenna is proposed and experimentally demonstrated in this paper. Impedance, radiation, and gain characteristics of this antenna are presented, and a prototype antenna is fabricated and measured. The measured impedance bandwidth, defined by −10dB reflection coefficient, can reach an operating bandwidth of 97% from 3.41 to 9.84GHz, over which the antenna exhibits excellent end-fire radiation with a front-to-back ratio of greater than 14dB and a moderate gain of better than 4.24dBi. Moreover, the overall size of the proposed antenna is only 27 × 30 mm2. In contrast to traditional microstrip CPW-fed quasi-Yagi antenna design, our proposed antenna features a smaller size and wider bandwidth.


title: Sliding-mode control of cable-driven parallel robots with elastic cables

abstract: Cable-driven parallel robot (CDPR) is one of robots hard to dealing with because of their nonlinear characteristics based on the elasticity of cables. This paper suggests a design of sliding-mode controller as a solution to control the over-constrained planar CDPR with consideration of cable stiffness and motor dynamics. The system model has 4 elastic cables with linear stiffness force and motor dynamics is considered to make more precise system model. Then the integrated system model has a special model structure called triangular. A backstepping approach was used to apply sliding-mode control for the integrated system model. Simulation was carried out to verify the performance of the suggested control and the result was analyzed. The suggested controller made end-effector follow various trajectories without tuning.


title: On the Kinematic Error in Harmonic Drive Gears

abstract: Harmonic drive gears are widely used in space applications, robotics, and precision positioning systems because of their attractive attributes including near-zero backlash, high speed reduction ratio, compact size, and small weight. On the other hand, they possess an inherent periodic positioning error known as kinematic error responsible for transmission performance degradation. No definite understanding of the mechanism of kinematic error as well as its characterization is available in the literature. In this paper, we report analytical and experimental results on kinematic error using a dedicated research Harmonic Drive Test Apparatus. We first show that the error referred to in the literature as kinematic error actually consists of a basic component, representing ‘‘pure’’ kinematic error, colored with a second component resulting from inherent torsional flexibility in the harmonic drive gear. The latter component explains the source of variability in published kinematic error profiles. The decomposition of the kinematic error into a basic component and a flexibility related component is demonstrated experimentally as well as analytically by matching a mathematical model to experimental data. We also characterize the dependence of the kinematic error on inertial load, gear assembly, and rotational speed. The results of this paper offer a new perspective in the understanding of the mechanism of kinematic error and will be valuable in the mechanical design of harmonic drive gears as well as in the dynamic modeling and precision control of harmonic drive systems. @DOI: 10.1115/1.1334379#


title: Novel design of a wheelchair with stair climbing capabilities

abstract: Stair climbing wheelchairs were created to help disabled people overcome one of the most common architectural barrier, stairs. Many types of devices have been developed using tracked, leg, leg-wheel and hybrid wheels to climb stairs but they are expensive, thus, out of the reach of the neediest. This paper presents a stair climbing wheelchair with four "X"-shaped wheel that uses its legs to climb and descend stairs. This mechanism maintains contact with the stair during climbing activity to perform better at the climbing task. The seat of the wheelchair provides 1 DOF so that the inclination angle can be changed in order to correct the position of the center of gravity to be close to the center of the supporting polygon. An inertial measurement unit (IMU) sensor was used to detect the angle of the seat in relation to the ground and by using a motor utilizing PID control, automatically balance the seat in the horizontal position. In order to climb stairs smoothly the angular position of the wheels should be the same, thus a PD control was implemented to control the position of the wheel so that they will be synchronized, therefore, capable of performing the climbing task at the same time and at the same position. Simulation and experimental results demonstrate the effectiveness of this mechanism and wheelchair.


title: Environmental sound classification with convolutional neural networks

abstract: This paper evaluates the potential of convolutional neural networks in classifying short audio clips of environmental sounds. A deep model consisting of 2 convolutional layers with max-pooling and 2 fully connected layers is trained on a low level representation of audio data (segmented spectrograms) with deltas. The accuracy of the network is evaluated on 3 public datasets of environmental and urban recordings. The model outperforms baseline implementations relying on mel-frequency cepstral coefficients and achieves results comparable to other state-of-the-art approaches.


title: Optic nerve head segmentation

abstract: Reliable and efficient optic disk localization and segmentation are important tasks in automated retinal screening. General-purpose edge detection algorithms often fail to segment the optic disk due to fuzzy boundaries, inconsistent image contrast or missing edge features. This paper presents an algorithm for the localization and segmentation of the optic nerve head boundary in low-resolution images (about 20 /spl mu//pixel). Optic disk localization is achieved using specialized template matching, and segmentation by a deformable contour model. The latter uses a global elliptical model and a local deformable model with variable edge-strength dependent stiffness. The algorithm is evaluated against a randomly selected database of 100 images from a diabetic screening programme. Ten images were classified as unusable; the others were of variable quality. The localization algorithm succeeded on all bar one usable image; the contour estimation algorithm was qualitatively assessed by an ophthalmologist as having Excellent-Fair performance in 83% of cases, and performs well even on blurred images.


title: Enabling network programmability in LTE/EPC architecture using OpenFlow

abstract: Nowadays, mobile operators face the challenge to sustain the future data tsunami. In fact, today's increasing data and control traffic generated by new kinds of network usage puts strain on mobile operators', without creating any corresponding equivalent revenue. In our previous work, we analyzed the 3GPP LTE/EPC architecture and showed that a redesign of this architecture is needed to suit future network usages and to provide new revenue generating services. Moreover, we proposed a new control plane based on the OpenFlow (OF) protocol for the LTE/EPC architecture that enables flexibility and programmability aspects. In this paper, we are interested in the programmability aspect. We show how the data plane can be easily configured thanks to OF. In addition, we evaluate the signaling load of our proposed architecture and compare it to that of 3GPP LTE/EPC architecture. The preliminary findings suggest that managing the data plane with OF has little impact on the signaling load while the network programmability is improved.


Behavioral Analysis of Review Fraud: Linking Malicious Crowdsourcing to Amazon and Beyond.


An Access Control Model for NoSQL Databases


title: Combining partial correlation and an information theory approach to the reversed engineering of gene co-expression networks

abstract: Motivation: We present PCIT, an algorithm for the reconstruction of gene co-expression networks (GCN) that combines the concept partial correlation coefficient with information theory to identify significant gene to gene associations defining edges in the reconstruction of GCN. The properties of PCIT are examined in the context of the topology of the reconstructed network including connectivity structure, clustering coefficient and sensitivity. ::: ::: Results: We apply PCIT to a series of simulated datasets with varying levels of complexity in terms of number of genes and experimental conditions, as well as to three real datasets. Results show that, as opposed to the constant cutoff approach commonly used in the literature, the PCIT algorithm can identify and allow for more moderate, yet not less significant, estimates of correlation (r) to still establish a connection in the GCN. We show that PCIT is more sensitive than established methods and capable of detecting functionally validated gene–gene interactions coming from absolute r values as low as 0.3. These bona fide associations, which often relate to genes with low variation in expression patterns, are beyond the detection limits of conventional fixed-threshold methods, and would be overlooked by studies relying on those methods. ::: ::: Availability: FORTRAN 90 source code to perform the PCIT algorithm is available as Supplementary File 1. ::: ::: Contact: tony.reverter-gomez@csiro.au ::: ::: Supplementary information:Supplementary data are available at Bioinformatics online.


title: Lichens as a potential natural source of bioactive compounds: a review

abstract: Biological activity of material whether known in folk medicine or observed in planned screening program has been the starting point in the drug research. The general pattern is the isolation of active principles, elucidation their structures, followed by attempts for modulation of its activity potential by chemical modification. Lichens are valuable plant resources and are used as medicine, food, fodder, perfume, spice, dyes and for miscellaneous purposes throughout the world. Lichens are well known for the diversity of secondary metabolites that they produce. Compounds isolated from various lichen species have been reported to display diverse biological activities. Here we review the medicinal efficacy of lichen substances, which intends to explore the pharmaceutical potential of lichen substances.


title: Slim Embedding Layers for Recurrent Neural Language Models

abstract: Recurrent neural language models are the state-of-the-art models for language modeling. When the vocabulary size is large, the space taken to store the model parameters becomes the bottleneck for the use of recurrent neural language models. In this paper, we introduce a simple space compression method that randomly shares the structured parameters at both the input and output embedding layers of the recurrent neural language models to significantly reduce the size of model parameters, but still compactly represent the original input and output embedding layers. The method is easy to implement and tune. Experiments on several data sets show that the new method can get similar perplexity and BLEU score results while only using a very tiny fraction of parameters.


title: GRAPH MATCHING AND LEARNING IN PATTERN RECOGNITION IN THE LAST 10 YEARS

abstract: In this paper, we examine the main advances registered in the last ten years in Pattern Recognition methodologies based on graph matching and related techniques, analyzing more than 180 papers; the aim is to provide a systematic framework presenting the recent history and the current developments. This is made by introducing a categorization of graph-based techniques and reporting, for each class, the main contributions and the most outstanding research results.


title: Ontology-Based Integrated Information Platform for Digital City

abstract: Information integration develops toward semantic integration on the Semantic Web. Ontology facilitates the integration of heterogeneous data sources by resolving semantic heterogeneity among them. In this paper, we propose the system architecture of integrated information platform for digital city based on ontology, including three main layers: distributed heterogeneous data source layer, information integration layer, and application system layer. Its core is the information integration layer including the ontology server and the integrated database. Then, the domain ontology model for digital city is presented. Furthermore, we realize the semantic information retrieval according to the relations of "synonymy of, "kind of and "part of between concepts. At the end we conclude that ontology is a good tool to integrate information among various heterogeneous urban management systems on semantic level.


title: Global non-rigid alignment of 3-D scans

abstract: A key challenge in reconstructing high-quality 3D scans is registering data from different viewpoints. Existing global (multiview) alignment algorithms are restricted to rigid-body transformations, and cannot adequately handle non-rigid warps frequently present in real-world datasets. Moreover, algorithms that can compensate for such warps between pairs of scans do not easily generalize to the multiview case. We present an algorithm for obtaining a globally optimal alignment of multiple overlapping datasets in the presence of low-frequency non-rigid deformations, such as those caused by device nonlinearities or calibration error. The process first obtains sparse correspondences between views using a locally weighted, stability-guaranteeing variant of iterative closest points (ICP). Global positions for feature points are found using a relaxation method, and the scans are warped to their final positions using thin-plate splines. Our framework efficiently handles large datasets---thousands of scans comprising hundreds of millions of samples---for both rigid and non-rigid alignment, with the non-rigid case requiring little overhead beyond rigid-body alignment. We demonstrate that, relative to rigid-body registration, it improves the quality of alignment and better preserves detail in 3D datasets from a variety of scanners exhibiting non-rigid distortion.


title: Aggregated Search and Interleaving Methods: A survey

abstract: Aggregated search attempts to satisfy user's need by searching and assembling information from variety verticals and placing them into a single result page. Aggregated search has two research directions namely, cross-vertical Aggregated Search (cvAS) and Relational Aggregated Search (RAS). The first one focuses to find relations between the keywords of query. The second one tries to attain the variety results from different information sources. The outcomes (results) given by aggregated search should be evaluated to determine which system is reliable. To achieve this evaluation we use various metrics. Then for improving the ranked list which given by web search engines we must utilize interleaved methods for comparing the relative quality of two ranking systems. In addition to present the perfect result to users. The purpose of this article is to collect and classify the different methods of aggregated search and interleaving methods that exist so far in literature. Furthermore, we conduct some comparisons between interleaved methods and other ranking systems.


title: 3D dental reconstruction from CBCT data

abstract: We will present in this paper a method for the reconstruction of the oral 3D structure using Computed Tomography (CT) sections obtained from Cone Beam Computed Tomography (CBCT). A high-quality visualization of the dental tissues, based nowadays on fine 3D reconstruction, is essential for accurate diagnosis and treatments. Dental CBCT images present several major problems because of the noisiness and of the configuration of the system. We tried several algorithms in order to overcome these issues and to obtain a feasible and efficient method that can be used directly in the clinical procedures. Our approach includes novel procedures like the delimitation of the region of interest and the combination of registration and segmentation steps, which grant faster results and open the route to better representations.