title: Implants for elderly patients

abstract: In the developed world, the large birth cohorts of the so-called baby boomer generation have arrived in medical and dental practices. Often, elderly patients are 'young-old' baby boomers in whom partial edentulism is the predominant indication for implant therapy. However, the generation 85+ years of age represents a new challenge for the dental profession, as their lives are frequently dominated by dependency, multimorbidity and frailty. In geriatric implant dentistry, treatment planning is highly individualized, as interindividual differences become more pronounced with age. Nevertheless, there are four typical indications for implant therapy: (i) avoidance of removable partial prostheses; (ii) preservation of existing removable partial prostheses; (iii) stabilization of Kennedy Class I removable partial prostheses; and (iv) stabilization of complete prostheses. From a surgical point of view, two very important aspects must be considered when planning implant surgery in elderly patients: first, the consistent strive to minimize morbidity; and, second, the fact that coexisting medical risk factors are significantly more common in elderly patients. Modern three-dimensional cone beam computed tomography imaging is often indicated in order to plan minimally invasive implant surgery. Computer-assisted implant surgery might allow flapless implant surgery, which offers a low level of postoperative morbidity and a minimal risk of postsurgical bleeding. Short and reduced-diameter implants are now utilized much more often than a decade ago. Two-stage surgical procedures should be avoided in elderly patients. Implant restorations for elderly patients should be designed so that they can be modified to become low-maintenance prostheses, or even be removed, as a strategy to facilitate oral hygiene and comfort in the final stage of life.


title: Internet of Things and Blockchain: Legal Issues and Privacy. The Challenge for a Privacy Standard

abstract: The IoT is innovative and important phenomenon prone to several services ad applications, but it should consider the legal issues related to the data protection law. However, should be taken into account the legal issues related to the data protection and privacy law. Technological solutions are welcome, but it is necessary, before developing applications, to consider the risks which we cannot dismiss. Personal data is a value. In this context is fundamental to evaluate the legal issues and prevent them, adopting in each project the privacy by design approach. Regarding the privacy and security risks, there are some issues with potential consequences for data security and liability. The IoT system allows us to transfer data on the Internet, including personal data. In this context, it is important to consider the new European General Data Protection Regulation (GDPR) - already in force from 24 May 2016 - that will be applicable on 25 May 2018. The GDPR introduces Data Protection Impact Assessment (DPIA), data breach notification and very hard administrative fines in respect of infringements of the Regulation. A correct law analysis allows evaluating risks preventing the wrong use of personal data. The IoT ecosystem is evolving quickly, developing several applications in different sectors. The main topics for the last time are Big Data and the blockchain. People are paying attention to the latest one because of its potential concrete use for services and applications, increasing the security measures to guarantee a secure system. However, it is equally important to analyse the legal issues related to them. Everyone has the right to the protection of personal data concerning him or her. In this context, we cannot dismiss to guarantee an adequate protection of personal data designing any application. The contribution describes the main legal issues related to privacy and data protection especially regarding the blockchain, focusing on the Privacy by Design approach, according to the GDPR. Furthermore, I resolutely believe that is possible to develop a worldwide privacy standard framework that organisations can use for their data protection activities.


title: Integrated circuit testing for quality assurance in manufacturing: history, current status, and future trends

abstract: Integrated circuit (IC) testing for quality assurance is approaching 50% of the manufacturing costs for some complex mixed-signal ICs. For many years the market growth and technology advancements in digital ICs were driving the developments in testing. The increasing trend to integrate information acquisition and digital processing on the same chip has spawned increasing attention to the test needs of mixed-signal ICs. The recent advances in wireless communications indicate a trend toward the integration of the RF and baseband mixed signal technologies. In this paper we examine the developments in IC testing from the historic, current status and future view points. In separate sections we address the testing developments for digital, mixed signal and RF ICs. With these reviews as context, we relate new test paradigms that have the potential to fundamentally alter the methods used to test mixed-signal and RF parts.


title: Alcohol Addiction and the Attachment System. An Empirical Study of Attachment Style, Alexithymia, and Psychiatric Disorders in Alcoholic Inpatients

abstract: This study aims at investigating alcoholic inpatients' attachment system by combining a measurement of adult attachment style (AAQ, . Journal of Personality and Social Psychology, 52(3): 511–524) and the degree of alexithymia (BVAQ, . Bermond-Vorst Alexithymia Questionnaire, Unpublished data). Data were collected from 101 patients (71 men, 30 women) admitted to a psychiatric hospital in Belgium for alcohol use-related problems, between September 2003 and December 2004. To investigate the research question, cluster analyses and regression analyses are performed. We found that it makes sense to distinguish three subgroups of alcoholic inpatients with different degrees of impairment of the attachment system. Our results also reveal a pattern of correspondence between the severity of psychiatric symptoms—personality disorder traits (ADP-IV), anxiety (STAI), and depression (BDI-II-Nl)—and the severity of the attachment system's impairment. Limitations of the study and suggestions for further research are highl...


title: Memory for object-features versus memory for object-location: A positron emission tomography study of encoding and retrieval processes

abstract: Regional cerebral blood flow was measured with positron-emission tomography during two encoding and two retrieval tasks that were designed to compare memory for object features with memory for object locations. Bilateral increases in regional cerebral blood flow were observed in both anterior and posterior regions of inferior temporal cortex and in ventral regions of prestriate cortex, when the condition that required retrieval of object locations was subtracted from the condition that required retrieval of object features. During encoding, these changes were less pronounced and were restricted to the left inferior temporal cortex and right ventral prestriate cortex. In contrast, both encoding and retrieval of object location were associated with bilateral changes in dorsal prestriate and posterior parietal cortex. Finally, the two encoding conditions activated left frontal lobe regions preferentially, whereas the two retrieval conditions activated right frontal lobe regions. These findings confirm that, in human subjects, memory for object features is mediated by a distributed system that includes ventral prestriate cortex and both anterior and posterior regions of the inferior temporal gyrus. In contrast, memory for the locations of objects appears to be mediated by an anatomically distinct system that includes more dorsal regions of prestriate cortex and posterior regions of the parietal lobe.


title: CO2 EMISSION AND CARBON CAPTURE FOR COAL FIRED POWER PLANTS IN MALAYSIA AND INDONESIA

abstract: Coal consumption in Malaysia and Indonesia is growing at the rate of 9.7 and 4.7% per year since 2002, respectively. The increase in coal utilization usually tallies fairly well with the increase in CO2 emission. The present study attempts at predicting the emissions of CO2 from coal fired power plants from 2005 until 2020. The paper also analyzes the potential of carbon capture (CC) program as a source of foreign direct investment in Malaysia and Indonesia. The perceived emission rate is based on the percentage of coal for energy mix and coal consumption for electricity generation. Results from the study shows that CO2 emission from coal fired power plants will grow at 4.1% per year to reach 98 million tons in Malaysia and 171 million tons in Indonesia by 2020. It is learnt that adsorption technology can be applied in coal fired power plants to reduce CO2 emissions in Malaysia and Indonesia. Integrated Gasification Combined Cycle (IGCC) power plants incooperating a pre-combustion capture with the adsorption technology is one of the available options for new plants in Malaysia and Indonesia.


title: DeLiGAN: Generative Adversarial Networks for Diverse and Limited Data

abstract: A class of recent approaches for generating images, called Generative Adversarial Networks (GAN), have been used to generate impressively realistic images of objects, bedrooms, handwritten digits and a variety of other image modalities. However, typical GAN-based approaches require large amounts of training data to capture the diversity across the image modality. In this paper, we propose DeLiGAN – a novel GAN-based architecture for diverse and limited training data scenarios. In our approach, we reparameterize the latent generative space as a mixture model and learn the mixture models parameters along with those of GAN. This seemingly simple modification to the GAN framework is surprisingly effective and results in models which enable diversity in generated samples although trained with limited data. In our work, we show that DeLiGAN can generate images of handwritten digits, objects and hand-drawn sketches, all using limited amounts of data. To quantitatively characterize intra-class diversity of generated samples, we also introduce a modified version of inception-score, a measure which has been found to correlate well with human assessment of generated samples.


title: Generating Music using an LSTM Network

abstract: A model of music needs to have the ability to recall past details and have a clear, coherent understanding of musical structure. Detailed in the paper is a neural network architecture that predicts and generates polyphonic music aligned with musical rules. The probabilistic model presented is a Bi-axial LSTM trained with a kernel reminiscent of a convolutional kernel. When analyzed quantitatively and qualitatively, this approach performs well in composing polyphonic music. Link to the code is provided.


title: On the Integration of Cloud Computing and Internet of Things

abstract: Cloud computing and Internet of Things (IoT), two very different technologies, are both already part of our life. Their massive adoption and use is expected to increase further, making them important components of the Future Internet. A novel paradigm where Cloud and IoT are merged together is foreseen as disruptive and an enabler of a large number of application scenarios. In this paper we focus our attention on the integration of Cloud and IoT, which we call the CloudIoT paradigm. Many works in literature have surveyed Cloud and IoT separately: their main properties, features, underlying technologies, and open issues. However, to the best of our knowledge, these works lack a detailed analysis of the CloudIoT paradigm. To bridge this gap, in this paper we review the literature about the integration of Cloud and IoT. We start analyzing and discussing the need for integrating them, the challenges deriving from such integration, and how these issues have been tackled in literature. We then describe application scenarios that have been presented in literature, as well as platforms--both commercial and open source--and projects implementing the CloudIoT paradigm. Finally, we identify open issues, main challenges and future directions in this promising field.


title: Physical Layer Security in Millimeter Wave Cellular Networks

abstract: Recent researches show that millimeter wave (mmWave) communications can offer orders of magnitude increases in the cellular capacity. However, the secrecy performance of a mmWave cellular network has not been investigated so far. Leveraging the new path-loss and blockage models for mmWave channels, which are significantly different from the conventional microwave channel, this paper comprehensively studies the network-wide physical layer security performance of the downlink transmission in a mmWave cellular network under a stochastic geometry framework. We first study the secure connectivity probability and the average number of perfect communication links per unit area in a noise-limited mmWave network for both non-colluding and colluding eavesdroppers scenarios, respectively. Then, we evaluate the effect of the artificial noise (AN) on the secrecy performance, and derive the analysis result of average number of perfect communication links per unit area in an interference-limited mmWave network. Numerical results are demonstrated to show the network-wide secrecy performance, and provide interesting insights into how the secrecy performance is influenced by various network parameters: antenna array pattern, base station (BS) intensity, and AN power allocation, etc.


title: Total and Full-Fat, but Not Low-Fat, Dairy Product Intakes are Inversely Associated with Metabolic Syndrome in Adults

abstract: Background: Growing evidence suggests that dairy products may have beneficial cardiometabolic effects. The current guidelines, however, limit the intake of full-fat dairy products. Objective: We investigated the association of dairy consumption, types of dairy products, and dairy fat content with metabolic syndrome (MetSyn). Methods:We analyzed baseline data of the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil), a multicenter cohort study of 15,105 adults aged 35‐74 y. We excluded participants with known diabetes, cardiovascular diseases, or other chronic diseases, and those who had extreme values of energy intake, leaving 9835 for analysis. Dairy consumption was assessed by a food-frequency questionnaire. We computed servings per day for total and subgroups of dairy intake. We computed a metabolic risk score (MetScore) as the mean z score of waist circumference, systolic blood pressure, HDL cholesterol (negative z score), fasting triglycerides, and fasting glucose. We performed multivariable linear regression to test the association of servings per day of dairy products with MetScore. Results: In analyses that adjusted for demographics, menopausal status, family history of diabetes, dietary intake, nondietary lifestyle factors, and body mass index, we observed a graded inverse association for MetScore with total dairy (20.044 6 0.01, P = 0.009 for each additional dairy servings per day) and full-fat dairy (20.126 6 0.03, P < 0.001) but not with low-fat dairy intake. Associations were no longer present after additional adjustments for dairy-derived saturated fatty acids. Conclusions: Total and especially full-fat dairy food intakes are inversely and independently associated with metabolic syndrome in middle-aged and older adults, associations that seem to be mediated by dairy saturated fatty acids. Dietary recommendations to avoid full-fat dairy intake are not supported by our findings. JN utrdoi: 10.3945/jn.115.220699.


title: Optimizing the cost-performance tradeoff for geo-distributed data analytics with uncertain demand

abstract: In the era of global-scale services, analytical queries are performed on datasets that span multiple data centers (DCs). Due to the scarce and expensive inter-DC bandwidth, various methods have been proposed to reduce either the traffic cost or the completion time for those analytics queries. However, current methods make no attempt to maximize the number of successfully served query requests. Moreover, most of them rely on unrealistic assumptions — such as analytical queries are repeated or known in advance. In this paper, we target at characterizing and optimizing the cost-performance tradeoff for geo-distributed data analytics. Our objectives are two-fold: (1) we minimize the inter-DC traffic cost when serving geo-distributed analytics with uncertain query demand, and (2) we maximize the system throughput, in terms of the number of query requests that can be successfully served with guaranteed queuing delay. To achieve these objectives, we take advantage of Lyapunov optimization techniques to design a two-timescale online control framework. Without prior knowledge of future query requests, this framework makes online decisions on input data placement and admission control of query requests. Extensive trace-driven simulation results demonstrate that our framework is capable of reducing inter-DC traffic cost, improving system throughput and guaranteeing a maximum delay for each query request.


title: A novel objective function based on a generalized kelly criterion for deep learning

abstract: Neural networks have demonstrated promising results for a wide range of applications. The proposed techniques employ different architectures and objective functions to adapt to the application while enabling a feasible implementation. Commonly used objective functions for network optimization are based on the cross entropy between the empirical distribution of the training data and the model distribution. Another approach uses the Dice coefficient to mitigate class imbalances without a need to sample reweighting. However, use of the Dice coefficient hinders backpropagation from the output of the softmax to the previous network layers. In this paper, we propose a novel objective function based on a generalized Kelly criterion for optimizing feed-forward neural networks. In each forward pass of a mini-batch through the network, this criterion determines the optimal bets (allocation fractions) of each sample for betting on multiple horses (classes) whose win probabilities are known. Then the network parameters get optimized by a minimization of the average unspent wealth (reserve rate) of the samples via backpropagation from the output of the softmax layer to the previous network layers. On a fully convolutional residual neural network, called V-net, the proposed function is evaluated against a weighted cross entropy-based and a Dice-based objective function for automatic volumetric medical image segmentation. These evaluations are done after a dedicated hyperparameter optimization for each objective function. They reveal the superiority of the proposed method with regard to the classification accuracy and the convergence speed of the training.


title: Map-reduce based parallel support vector machine for risk analysis

abstract: Now a days people are enjoying the world of data because size and amount of the data has tremendously increased which acts like an invitation to Big data. But some of the classifier techniques like Support Vector Machine (SVM) is not able to handle the huge amount of data due to it's excessive memory requirement and unreasonable complexity in algorithm tough it is one of the most popularly used classifier in machine learning field. Hence a new technique comes into picture which performs parallel algorithm in a efficient way to work data having large scale called as PSVM. In this paper we are going to discuss a PSVM model for risk analysis which is based on map-reduce, and can easily handle a huge amount of data in a distributed manner.


title: A study on using mid-wave infrared images for face recognition

abstract: The problem of face identication in the Mid-Wave InfraRed (MWIR) spectrum is studied in order to understand the performance of intra-spectral (MWIR to MWIR) and cross-spectral (visible to MWIR) matching. The contributions of this work are two-fold. First, a database of 50 subjects is assembled and used to illustrate the challenges associated with the problem. Second, a set of experiments is performed in order to demonstrate the possibility of MWIR intra-spectral and cross-spectral matching. Experiments show that images captured in the MWIR band can be eciently matched to MWIR images using existing techniques (originally not designed to address such a problem). These results are comparable to the baseline results, i.e., when comparing visible to visible face images. Experiments also show that cross-spectral matching (the heterogeneous problem, where gallery and probe sets have face images acquired in dierent spectral bands) is a very challenging problem. In order to perform cross-spectral matching, we use multiple texture descriptors and demonstrate that fusing these descriptors improves recognition performance. Experiments on a small database, suggests that the problem of cross-spectral matching requires further investigation.© (2012) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.


title: EMD2FNN: A strategy combining empirical mode decomposition and factorization machine based neural network for stock market trend prediction

abstract: Abstract Stock market forecasting is a vital component of financial systems. However, the stock prices are highly noisy and non-stationary due to the fact that stock markets are affected by a variety of factors. Predicting stock market trend is usually subject to big challenges. The goal of this paper is to introduce a new hybrid, end-to-end approach containing two stages, the Empirical Mode Decomposition and Factorization Machine based Neural Network (EMD2FNN), to predict the stock market trend. To illustrate the method, we apply EMD2FNN to predict the daily closing prices from the Shanghai Stock Exchange Composite (SSEC) index, the National Association of Securities Dealers Automated Quotations (NASDAQ) index and the Standard & Poor’s 500 Composite Stock Price Index (S&P 500), which respectively exhibit oscillatory, upward and downward patterns. The results are compared with predictions obtained by other methods, including the neural network (NN) model, the factorization machine based neural network (FNN) model, the empirical mode decomposition based neural network (EMD2NN) model and the wavelet de-noising-based back propagation (WDBP) neural network model. Under the same conditions, the experiments indicate that the proposed methods perform better than the other ones according to the metrics of Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). Furthermore, we compute the profitability with a simple long-short trading strategy to examine the trading performance of our models in the metrics of Average Annual Return (AAR), Maximum Drawdown (MD), Sharpe Ratio (SR) and AAR/MD. The performances in two different scenarios, when taking or not taking the transaction cost into consideration, are found economically significant.


title: Fine-Grained Quran Dataset

abstract: Extracting knowledge from text documents has become one of the main hot topics in the field of Natural Language Processing (NLP) in the era of information explosion. Arabic NLP is considered immature due to several reasons including the low available resources. On the other hand, automatically extracting reliable knowledge from specialized data sources as holy books is considered ultimately a challenging task but of great benefit to all humans. In this context, this paper provides a comprehensive Quranic Dataset as a first part (foundation) of an ongoing research that attempts to lay grounds for approaches and applications to explore the holy Quran. The paper presents the algorithms and approaches that have been designed to extract an aggregative data from massive Arabic text sources including the holy Quran and tightly associated books. Holy Quran text is transferred into structured multi-dimensional data records starting from the chapter level, the word level and then the character level. All these are linked with interpretations and meanings, parsing, translations, intonation roots and stems of words, all from authentic and reliable sources. The final dataset is represented in excel sheets and database records format. Also, the paper presents models of the dataset at all levels. The Quranic dataset presented in this paper was designed to be appropriate for: database, data mining, text mining and Artificial Intelligence applications; it is also designed to serve as a comprehensive encyclopedia of holy Quran and the Quranic Science books.


title: A Survey of Solution Methods for the Continuous Location-Allocation Problem

abstract: In this survey, we examine an important class of facility location problems known as the multisource Weber problem (also referred to as the continuous location-allocation problem). We also show how recent advances in the use of metaheuristic rules have significantly improved the ability to solve problems of this type. The new solution methods are discussed for both the well known multisource Weber problem and its counterpart the capacitated case. Research issues which we believe to be worthwhile exploring in future are also highlighted.


title: Periodontal plastic surgery.

abstract: The aim of the present article is to summarize current knowledge in terms of the etiology, diagnosis, prognosis and surgical treatment of gingival recession. Whilst the main etiological factors (i.e. toothbrushing trauma and bacterial plaque) are well established, challenges still remain to be solved in the diagnostic, prognostic and classification processes of gingival recession, especially when the main reference parameter - the cemento-enamel junction - is no longer detectable on the affected tooth or when there is a slight loss of periodontal interdental attachment. Root coverage in single type gingival recession defects is a very predictable outcome following the use of various surgical techniques. The coronally advanced flap, with or without connective tissue grafting, is the technique of choice. The adjunctive use of connective tissue grafts improves the probability of achieving complete root coverage. Surgical coverage of multiple gingival recessions is also predictable with the coronally advanced flap and the coronally advanced flap plus the connective tissue graft, but no data are available indicating which, and how many, gingival recessions should be treated adjunctively with connective tissue grafting in order to limit patient morbidity and improve the esthetic outcome. None of the allograft materials currently available can be considered as a full substitute for the connective tissue graft, even if some recent results are encouraging. The need for future studies with patient-based outcomes (i.e. esthetics and morbidity) as primary objectives is emphasized in this review.


title: Strategies and Influence of Social Bots in a 2017 German state election - A case study on Twitter

abstract: As social media has permeated large parts of the population it simultaneously has become a way to reach many people e.g. with political messages. One way to efficiently reach those people is the application of automated computer programs that aim to simulate human behaviour - so called social bots. These bots are thought to be able to potentially influence users' opinion about a topic. To gain insight in the use of these bots in the run-up to the German Bundestag elections, we collected a dataset from Twitter consisting of tweets regarding a German state election in May 2017. The strategies and influence of social bots were analysed based on relevant features and network visualization. 61 social bots were identified. Possibly due to the concentration on German language as well as the elections regionality, identified bots showed no signs of collective political strategies and low to none influence. Implications are discussed.


title: Dynamic simulation for analysis of hybrid electric vehicle system and subsystem interactions, including power electronics

abstract: Simulation tools for hybrid electric vehicles (HEVs) can be classified into steady-state and dynamic models, according to their purpose. Tools with steady-state models are useful for system-level analysis. The information gained is helpful for assessing long-term behavior of the vehicle. Tools that utilize dynamic models give in-depth information about the short-term behavior of sublevel components. In this paper, a dynamic model of a hybrid electric vehicle that includes fuel cells, batteries, ultracapacitors, and induction machine drives is presented. Simulation results of vehicle configurations with a battery, a fuel cell-battery combination and a fuel cell-ultracapacitor combination are discussed. The focus of the model is a detailed assessment of different subsystem components, particularly component losses.


title: Fine-grained ultrasound range finding for mobile devices: Sensing way beyond the 24 kHz limit of built-in microphones

abstract: Range finding is a core service in many applications for smart homes and cities. Recently studies have been exploring ultrasound-based range finding with commercial-off-the-shelf (COTS) mobile devices. Yet their accuracy is largely bounded by the limited sample rate (typically up to 48 kHz; i.e. only sensitive to sound below 24 kHz) of their built-in microphones and chips. In this paper, we introduce a novel coprime-sampling-based method that can enable current COTS mobile devices to achieve accurate range finding by sensing very-high-frequency (> 24 kHz) ultrasound. We show that, through smart algorithm design, the hardware limit will not be a barrier for COTS devices to derive such useful information as phase shift and power spectral density (PSD) from very-high-frequency ultrasound signals. We discuss how our method can achieve fine-grained range finding, as well as improve other applications such as motion tracking and data transport. Through evaluations of our prototype on the latest Android tablet model, we show that with an ultrasound signal at 42 kHz, our method can achieve fine-grained range finding with a median error of 1.2 mm and a median accuracy of 97.3%.


title: Scale-up graph processing: a storage-centric view

abstract: The determinant of performance in scale-up graph processing on a single system is the speed at which the graph can be fetched from storage: either from disk into memory or from memory into CPU-cache. Algorithms that follow edges perform random accesses to the storage medium for the graph and this can often be the determinant of performance, regardless of the algorithmic complexity or runtime efficiency of the actual algorithm in use. A storage-centric viewpoint would suggest that the solution to this problem lies in recognizing that graphs represent a unique workload and therefore should be treated as such by adopting novel ways to access graph structured data. We approach this problem from two different aspects and this paper details two different efforts in this direction. One approach is specific to graphs stored on SSDs and accelerates random access using a novel prefetcher called RASP. The second approach takes a fresh look at how graphs are accessed and suggests that trading off the low cost of random access for the approach of sequentially streaming a large set of (potentially unrelated) edges can be a winning proposition under certain circumstances: leading to a system for graphs stored on any medium (main-memory, SSD or magnetic disk) called X-stream. RASP and X-stream therefore take - diametrically opposite - storage centric viewpoints of the graph processing problem. After contrasting the approaches and demonstrating the benefit of each, this paper ends with a description of planned future development of an online algorithm that selects between the two approaches, possibly providing the best of both worlds.


title: Quantification of fatal helium exposure following self-administration

abstract: Helium is nontoxic at standard conditions, plays no biological role, and is found in trace amounts in human blood. Helium can be dangerous if inhaled to excess, since it is a simple tissue hypoxia and so displaces the oxygen needed for normal respiration. This report presents a fatal case of a middle-aged male victim who died from self-administered helium exposure. For the first time, the quantification of the helium levels in gastric and lung air and in blood samples was achieved using gas chromatography-mass spectrometry after airtight sampling. The results of the toxicological investigation showed that death was caused directly by helium exposure. However, based on the pathomorphological changes detected during the forensic autopsy, we suppose that the fatal outcome was the result of the lack of oxygen after inhalation.


title: Facial cosmetics database and impact analysis on automatic face recognition

abstract: Facial cosmetics, also called makeup, may change the appearance of a face that we could perceive. In order to contribute to studies in image processing related to facial cosmetics, a database is built which contains multiple images per person with and without applied cosmetics. Annotations provide detailed information about the amount and location of applied makeup for each picture. Furthermore, a classification approach is presented. The classification approach takes the altering effect of the applied cosmetics and the application area into account. Since automatic face recognition evolved to an important topic over the last decades and is affected by facial cosmetics, preliminary tests are done to evaluate their impact on automatic face recognition. The face as a whole as well as its most significant makeup application areas that are skin, eyes and mouth are investigated separately.


title: Effects of False Chinch Bugs 1 on Spiny Saltbush in the San Joaquin Valley, California

abstract: Abstract. Annual pitfall trapping during a 10-year period indicated that outbreaks of false chinch bugs, Nysius raphanus Howard, occurred in 2005, 2009, and 2010 in the southern San Joaquin Valley of California. During these outbreaks, feeding by false chinch bugs appeared to cause widespread damage to rangeland shrubs. In 2009, a study was initiated to compare survival between shrubs infested by false chinch bugs and shrubs not infested. Fifty-one spiny saltbush, Atriplex spinifera J.F. Macbr. shrubs (<0.5 m in height) were selected, of which 26 had been infested and 25 had not been infested by false chinch bugs. Fifteen months later, survival of the false chinch bug-infested shrubs (22%) was significantly less (P < 0.001) than non-infested shrubs (96%). Results indicated that false chinch bugs (during outbreak years) can significantly depress survival of young saltbush shrubs.


Understanding the CMU Sphinx Speech Recognition System


title: Design of liquid crystal polymer based flexible dual-band microstrip patch antenna

abstract: A dual band microstrip patch antenna is designed using liquid crystal polymer (LCP) as substrate material is presented in this paper. The design of microstrip patch antenna with and without complementary split ring resonator (CSRR) is present for comparison sake. The Microstrip patch antenna without complementary split ring resonator resonates at 5.5GHz. The dual band Microstrip patch antenna resonates at 2.13GHz and 5.4GHz. The new band achieved with help of complementary split ring resonator. Liquid crystal polymer is used as substrate of both antennas.


title: Ontogenetic Ritualization of Primate Gesture as a Case Study in Dyadic Brain Modeling

abstract: This paper introduces dyadic brain modeling – the simultaneous, computational modeling of the brains of two interacting agents – to explore ways in which our understanding of macaque brain circuitry can ground new models of brain mechanisms involved in ape interaction. Specifically, we assess a range of data on gestural communication of great apes as the basis for developing an account of the interactions of two primates engaged in ontogenetic ritualization, a proposed learning mechanism through which a functional action may become a communicative gesture over repeated interactions between two individuals (the ‘dyad’). The integration of behavioral, neural, and computational data in dyadic (or, more generally, social) brain modeling has broad application to comparative and evolutionary questions, particularly for the evolutionary origins of cognition and language in the human lineage. We relate this work to the neuroinformatics challenges of integrating and sharing data to support collaboration between primatologists, neuroscientists and modelers that will help speed the emergence of what may be called comparative neuro-primatology.


title: Cross-Sensor Iris Recognition through Kernel Learning

abstract: Due to the increasing popularity of iris biometrics, new sensors are being developed for acquiring iris images and existing ones are being continuously upgraded. Re-enrolling users every time a new sensor is deployed is expensive and time-consuming, especially in applications with a large number of enrolled users. However, recent studies show that cross-sensor matching, where the test samples are verified using data enrolled with a different sensor, often lead to reduced performance. In this paper, we propose a machine learning technique to mitigate the cross-sensor performance degradation by adapting the iris samples from one sensor to another. We first present a novel optimization framework for learning transformations on iris biometrics. We then utilize this framework for sensor adaptation, by reducing the distance between samples of the same class, and increasing it between samples of different classes, irrespective of the sensors acquiring them. Extensive evaluations on iris data from multiple sensors demonstrate that the proposed method leads to improvement in cross-sensor recognition accuracy. Furthermore, since the proposed technique requires minimal changes to the iris recognition pipeline, it can easily be incorporated into existing iris recognition systems.


title: SECFUZZ: Fuzz-testing security protocols

abstract: We propose a light-weight, yet effective, technique for fuzz-testing security protocols. Our technique is modular, it exercises (stateful) protocol implementations in depth, and handles encrypted traffic. We use a concrete implementation of the protocol to generate valid inputs, and mutate the inputs using a set of fuzz operators. A dynamic memory analysis tool monitors the execution as an oracle to detect the vulnerabilities exposed by fuzz-testing. We provide the fuzzer with the necessary keys and cryptographic algorithms in order to properly mutate encrypted messages. We present a case study on two widely used, mature implementations of the Internet Key Exchange (IKE) protocol and report on two new vulnerabilities discovered by our fuzz-testing tool. We also compare the effectiveness of our technique to two existing model-based fuzz-testing tools for IKE.


title: Information Visualization Exploring Large Graphs in 3D Hyperbolic Space

abstract: Drawing graphs as nodes connected by links is visually compelling but computationally difficult. Hyperbolic space and spanning trees can reduce visual clutter, speed up layout, and provide fluid interaction. This article briefly describes a software system that explicitly attempts to handle much larger graphs than previous systems and support dynamic exploration rather than final presentation. It then discusses the applicability of this system to goals beyond simple exploration. A software system that supports graph exploration should include both a layout and an interactive drawing component. I have developed new algorithms for both layout and drawing (H3 and H3Viewer). The H3Viewer drawing algorithm remains under development, so this article presents preliminary results. I have implemented a software library that uses these algorithms. It can handle graphs of more than 100,000 edges by using a spanning tree as the backbone for the layout and drawing algorithms.


attr2vec: Jointly Learning Word and Contextual Attribute Embeddings with Factorization Machines


title: Environmental impacts of polyvinyl chloride (PVC) production process

abstract: The increasing demand for plastics in the world leads to pressures on the environment generated by the consumption of raw materials based on fossil fuels and the need for plastic waste reduction and absorption to diminish their environmental impacts. In this paper we evaluated the environmental impacts induced by the production of polyvinyl chloride (PVC), one of the most widely used plastics in the manufacture of various goods. The impacts were calculated based on Life Cycle Assessment methodology. The negative environmental impacts of PVC production may be reduced by recycling, a sustainable, effective, currently available alternative of waste management. Our study showed that the use of recyclable materials, such as organic biodegradable waste as raw materials instead of crude oil can lead to significant reduction of environmental impacts.


title: Deep Structured Generative Models

abstract: Deep generative models have shown promising results in generating realistic images, but it is still non-trivial to generate images with complicated structures. The main reason is that most of the current generative models fail to explore the structures in the images including spatial layout and semantic relations between objects. To address this issue, we propose a novel deep structured generative model which boosts generative adversarial networks (GANs) with the aid of structure information. In particular, the layout or structure of the scene is encoded by a stochastic and-or graph (sAOG), in which the terminal nodes represent single objects and edges represent relations between objects. With the sAOG appropriately harnessed, our model can successfully capture the intrinsic structure in the scenes and generate images of complicated scenes accordingly. Furthermore, a detection network is introduced to infer scene structures from a image. Experimental results demonstrate the effectiveness of our proposed method on both modeling the intrinsic structures, and generating realistic images.


title: Mathematical model to assess the control of Aedes aegypti mosquitoes by the sterile insect technique.

abstract: We propose a mathematical model to assess the effects of irradiated (or transgenic) male insects introduction in a previously infested region. The release of sterile male insects aims to displace gradually the natural (wild) insect from the habitat. We discuss the suitability of this release technique when applied to peri-domestically adapted Aedes aegypti mosquitoes which are transmissors of Yellow Fever and Dengue disease.


title: Towards a new possibilistic query translation tool for cross-language information retrieval

abstract: Approaches of query translation in Cross-Language Information Retrieval (CLIR) have frequently used dictionaries which suffer from translation ambiguity. Besides, a word-by-word query translation is not sufficient. In this paper, we propose, evaluate and compare a new possibilistic approach for query translation in order to improve the previous dictionary-based ones. This approach uses a probability-to-possibility transformation as a mean to introduce further tolerance in query translation process. Firstly, we identify noun phrases (NPs) in the source query and translate them as units using translation patterns and a language model. Secondly, source query terms which are not included in any selected NPs are translated word-by-word using our new possibilistic approach of single word translation. Indeed, we take into account all query words and their translations when we choose the suitable translation of a given word. We start from the idea that the correct suitable translations of query terms have a tendency to co-occur in the target language documents unlike unsuitable ones. Finally, to increase the coverage of the bilingual dictionary, additional words and their translations are automatically generated from a parallel bilingual corpus. We tested our approach using the French-English parallel text corpus Europarl and the CLEF-2003 French-English CLIR test collection. The reported experiments showed the performance of the probability-to-possibility transformation-based approach compared to the probabilistic one and to some state-of-the-art CLIR tools.


title: Sensitive and Efficient RF Harvesting Supply for Batteryless Backscatter Sensor Networks

abstract: This work presents an efficient and high-sensitivity radio frequency (RF) energy harvesting supply. The harvester consists of a single-series circuit with one double diode on a low-cost, lossy FR-4 substrate, despite the fact that losses decrease RF harvesting efficiency. The design targeted minimum reflection coefficient and maximum rectification efficiency, taking into account not only the impedance matching network, but also the rectifier microstrip trace dimensions and the load. The simulated and measured rectenna efficiency was 28.4% for $-\hbox{20-dBm}$ power input. In order to increase sensitivity, i.e., ability to harvest energy and operate at low power density, rectennas were connected in series configuration (voltage summing), forming rectenna arrays. The proposed RF harvesting system ability was tested at various input power levels, various sizes of rectenna arrays, with or without a commercial boost converter, allowing operation at RF power density as low as 0.0139 $\mu \hbox{W/cm}^{2}$ . It is emphasized that the boost converter, whenever used, was self-started, without any additional external energy. The system was tested in supplying a scatter radio sensor, showing experimentally the effect of input power density on the operational cold start duration and duty cycle of the sensor.


title: Web-based collaborative big data analytics on big data as a service platform

abstract: As data has been increasing explosively due to development of social networks and cloud computing, there has been a new challenge for storing, processing, and analyzing a large volume of data. The traditional technologies do not become a proper solution to process big data so that a big data platform has begun to emerge. It is certain that big data platform helps users develop analysis service effectively. However, it still takes a long time to collect data, develop algorithms and analytics services. We present a collaborative big data analytics platform for big data as a service. Developers can collaborate with each other on the platform by sharing data, algorithms, and services. Therefore, this paper describes big data analytics platform that effectively supports to manage big data and develop analytics algorithms and services, collaborating with data owners, data scientists, and service developers on the Web. Finally, we introduce a CCTV metadata analytics service developed on the platform.


title: Learning to Use IT in the Workplace: Mechanisms and Masters

abstract: Fluency with information technology (IT), defined as “an ability [to use information technology] to express [oneself] creatively, to reformulate knowledge and to synthesize new information,” (Committee on Information Technology Literacy, 1999, p. ES-1), is an important concern for those who manage workers with jobs that require the use of IT. Training is one mechanism to build fluency, but research about “influential individuals” hints that other mechanisms might also play a role. This article presents an interpretive case study of junior-level knowledge workers at a management consulting firm. To learn to use the IT of their jobs, these workers relied not only on formal training, but also on on-the-job learning through experimentation; reading books, manuals, and online help; and social interaction with their peers. The researcher identified different types of “master users” who were indispensable for this learning to take place. The findings of this study suggest that managers and researchers interested in training users also devote attention to these other mechanisms for learning, especially the “master user” phenomenon.


title: Dynamic resource provisioning in cloud computing: A randomized auction approach

abstract: This work studies resource allocation in a cloud market through the auction of Virtual Machine (VM) instances. It generalizes the existing literature by introducing combinatorial auctions of heterogeneous VMs, and models dynamic VM pro- visioning. Social welfare maximization under dynamic resource provisioning is proven NP-hard, and modeled with a linear inte- ger program. An efficient!-approximation algorithm is designed, with!!2.72in typical scenarios. We then employ this algorithm as a building block for designing a randomized combinatorial auction that is computationally efficient, truthful in expectation, and guarantees the same social welfare approximation factor! .A key technique in the design is to utilize a pair of tailored primal and dual LPs for exploiting the underlying packing structure of the social welfare maximization problem, to decompose its fractional solution into a convex combination of integral solutions. Empirical studies driven by Google Cluster traces verify the efficacy of the randomized auction.


title: Electric Motor Drive Selection Issues for HEV Propulsion Systems: A Comparative Study

abstract: This paper describes a comparative study allowing the selection of the most appropriate electric-propulsion system for a parallel hybrid electric vehicle (HEV). This paper is based on an exhaustive review of the state of the art and on an effective comparison of the performances of the four main electric-propulsion systems, namely the dc motor, the induction motor (IM), the permanent magnet synchronous motor, and the switched reluctance motor. The main conclusion drawn by the proposed comparative study is that it is the cage IM that better fulfills the major requirements of the HEV electric propulsion


title: A Nonlinear Control Law for Hover to Level Flight for the Quad Tilt-Rotor UAV

abstract: This paper presents recent advances in the project: development of a convertible unmanned aerial vehicle (UAV). This aircraft is able to change its flight configuration from hover to level flight and vice versa by means of a transition maneuver, while maintaining the aircraft in flight. For this purpose a nonlinear control strategy based on Lyapunov design is given. Numerical results are presented showing the effectiveness of the proposed approach.


title: A new exact algorithm for the multi-depot vehicle routing problem under capacity and route length constraints

abstract: Abstract This article presents an exact algorithm for the multi-depot vehicle routing problem (MDVRP) under capacity and route length constraints. The MDVRP is formulated using a vehicle-flow and a set-partitioning formulation, both of which are exploited at different stages of the algorithm. The lower bound computed with the vehicle-flow formulation is used to eliminate non-promising edges, thus reducing the complexity of the pricing sub-problem used to solve the set-partitioning formulation. Several classes of valid inequalities are added to strengthen both formulations, including a new family of valid inequalities used to forbid cycles of an arbitrary length. To validate our approach, we also consider the capacitated vehicle routing problem (CVRP) as a particular case of the MDVRP, and conduct extensive computational experiments on several instances from the literature to show its effectiveness. The computational results show that the proposed algorithm is competitive against state-of-the-art methods for these two classes of vehicle routing problems, and is able to solve to optimality some previously open instances. Moreover, for the instances that cannot be solved by the proposed algorithm, the final lower bounds prove stronger than those obtained by earlier methods.


title: Remembering Our Past: Functional Neuroanatomy of Recollection of Recent and Very Remote Personal Events

abstract: Functional magnetic resonance imaging was used to study brain regions implicated in retrieval of memories that are decades old. To probe autobiographical memory, family photographs were selected by confederates without the participant’s involvement, thereby eliminating many of the variables that potentially confounded previous neuroimaging studies. We found that context-rich memories were associated with activity in lingual and precuneus gyri independently of their age. By contrast, retrosplenial cortex was more active for recent events regardless of memory vividness. Hippocampal activation was related to the richness of re-experiencing (vividness) rather than the age of the memory per se. Remote memories were associated with distributed activation along the rostrocaudal axis of the hippocampus whereas activation associated with recent memories was clustered in the anterior portion. This may explain why circumscribed lesions to the hippocampus disproportionately affect recent memories. These findings are incompatible with theories of longterm memory consolidation, and are more easily accommodated by multiple-trace theory, which posits that detailed memories are always dependent on the hippocampus.


title: Efficient ML training of CDHMM parameters based on prior evolution, posterior intervention and feedback

abstract: We present an efficient maximum likelihood (ML) training procedure for Gaussian mixture continuous density hidden Markov model (CDHMM) parameters. This procedure is proposed using the concept of approximate prior evolution, posterior intervention and feedback (PEPIF). In a series of experiments for training CDHMMs for a continuous Mandarin Chinese speech recognition task, the new PEPIF procedure achieves a 4-fold speed-up in terms of user CPU time over that of the Baum-Welch algorithm in producing models of given likelihood or recognition accuracy.


title: Recognising ‘social’ and ‘non-social’ emotions in self and others: A study of autism:

abstract: Studies of emotion processing in autism have produced mixed results, with fewer studies observing autism-specific deficits than might be imagined. In the current study, 21 individuals with autism and 21 age- and ability-matched, learning disabled comparison participants were tested for their ability to (a) recognise, in others, expressions of ‘social’ emotions (e.g., embarrassment) and ‘non-social’ emotions (e.g., happiness) and; (b) report their own previous experiences of each of these emotions.In line with predictions, amongst both groups of participants, social emotions were more difficult to recognise and report than non-social emotions. Also amongst both groups, the ability to report social emotion-experience was significantly associated with the ability to recognise social emotions in others, independent of age and verbal ability. However, contrary to predictions, no between-group differences in levels or patterns of performance on the experimental tasks were observed.In light of previous research,...


title: Steady-state Selection and Efficient Covariance Matrix Update in the Multi-objective CMA-ES

abstract: The multi-objective covariance matrix adaptation evolution strategy (MO-CMA-ES) combines a mutation operator that adapts its search distribution to the underlying optimization problem with multicriteria selection. Here, a generational and two steady-state selection schemes for the MO-CMA-ES are compared. Further, a recently proposed method for computationally efficient adaptation of the search distribution is evaluated in the context of the MO-CMA-ES.


title: Intellectual and Enhance Digital Solution For Police Station

abstract: Police Station is the most important unit for the citizens of the country (INDIA). Whenever a crime happens in the city a citizen first need to contact to the police station. Police stations in the city are still working manually such as filing an First Information Report (FIR) which are reported on paper due to this there are chances of miss placing those records. The Proposed online system- Intellectual and Enhance Digital Solutions (IEDS) for the Police Stations which will help the policeman and user to digitalize their work. This system will maintain the records in the database. IEDS integrate number of police stations in the city. Records like missing people, murder information, photo report, etc. helps the policeman to keep data in their pocket and search on single touch. IEDS also contains a user part for checking the status report which is accessed more securely by One-Time-Password (OTP). This System will also help lawyer to co-ordinate with policeman and victim by getting access to the documents uploaded by police. This System will also do Analysis of Criminal Records using Apriori Algorithm. The System will reduce the load of the policeman as well as local traffic in police station for queries like missing person, missing vehicle, status of their case etc. It will also reduce paper work which will in turn save trees.


title: Direct and Indirect Effects of Product Innovation and Product Quality on Brand Image: Empirical Evidence from Automotive Industry

abstract: This study examines the effect of product innovation and product quality on brand image with brand trust as mediator. Past studies paid less attention to empirically determine the relationship between the said variables on brand image and using brand trust as mediator between such independent and dependent variables. Therefore, this study aims to fill up this gap and contribute to the body of knowledge by examining the relationship between them. Data is collected using questionnaire from 287 passenger car users at shopping malls in Northern region of Malaysia. Specifically, systematic sampling procedure was followed during data collection. The data is analyzed using structural equation modeling (AMOS). The findings indicate that product innovation and product quality have significant relationship with brand image. The findings also reveal that product innovation and product quality have significant relationship with brand trust. Additionally, brand trust mediates the relationships between product innovation and brand image. It is also found that brand trust mediates the relationship between product quality and brand image. Based on these findings, several conclusions were made with some recommendations for future researches to strengthen our understandings toward the process of developing brand image.


title: Small-Size Multiresonant Octaband Antenna for LTE/WWAN Smartphone Applications

abstract: A compact multiresonant antenna for octaband LTE/WWAN operation in the internal smartphone applications is proposed and discussed in this letter. With a small volume of 15×25×4 mm3, the presented antenna comprises two direct feeding strips and a chip-inductor-loaded two-branch shorted strip. The two direct feeding strips can provide two resonant modes at around 1750 and 2650 MHz, and the two-branch shorted strip can generate a double-resonance mode at about 725 and 812 MHz. Moreover, a three-element bandstop matching circuit is designed to generate an additional resonance for bandwidth enhancement of the lower band. Ultimately, up to five resonances are achieved to cover the desired 704-960- and 1710-2690-MHz bands. Simulated and measured results are presented to demonstrate the validity of the proposed antenna.


title: PERS: A Personalized and Explainable POI Recommender System

abstract: The Location-Based Social Networks (LBSN) (e.g., Facebook) have many factors (for instance, ratings, check-in time, etc.) that play a crucial role for the Point-of-Interest (POI) recommendations. Unlike ratings, the reviews can help users to elaborate their opinion and share the extent of consumption experience in terms of the relevant factors of interest (aspects). Though some of the existing recommendation systems have been using the user reviews, most of them are less transparent and non-interpretable. These reasons have induced considerable attention towards explainable and interpretable recommendation. To the best of our knowledge, this is the first paper to exploit the user reviews to incorporate the sentiment and opinions on different aspects for the personalized and explainable POI recommendation. In this paper, we propose a model termed as PERS (Personalized Explainable POI Recommender System) which models the review-aspect category correlation by exploiting deep neural network, formulates the user-aspect category bipartite relation as a bipartite graph, and models the explainable recommendation using bipartite core-based and ranking-based methods. The major contributions of this paper are: (i) it models users and locations based on the aspects posted by user via reviews, (ii) it exploits a deep neural network to model the review-aspect category correlation, (iii) it provisions the incorporation of multiple contexts (e.g., categorical, spatial, etc.) in the POI recommendation model, (iv) it formulates the preference of users' on aspect category as a bipartite relation, represents it as a location-aspect category bipartite graph, and models the explainable recommendation with the notion of ordered dense subgraph extraction using bipartite core-based and ranking-based approaches, and (v) it evaluates the generated recommendation with three real-world datasets.


title: AcousticBrainz: A Community Platform for Gathering Music Information Obtained from Audio

abstract: Comunicacio presentada a la 16th International Society for Music Information Retrieval Conference (ISMIR 2015), celebrada els dies 26 a 30 d'octubre de 2015 a Malaga, Espanya.


title: Alexithymia, somatization and negative affect in a community sample

abstract: Whilst alexithymia has been consistently linked to somatization, two prominent theoretical models lead to opposite predictions as to which of two proposed typologies of alexithymia will be particularly associated with somatic symptom reporting. In the present study, participants were recruited from a cross-section of the general adult population (n = 301), and asked to complete a battery of self-report questionnaires which assessed alexithymia, somatization and negative affect. Collapsed across the whole sample, specific facets of alexithymia (enhanced fantasy life and difficulty identifying emotions) were significantly associated with somatization, and these pathways were perfectly mediated by negative affectivity. Further, type II alexithymia (impairment in the cognitive but not the affective dimension of alexithymia) was more predictive of somatization relative to type I alexithymia (impairment in both the cognitive and affective dimensions of alexithymia) and non-alexithymia (unimpaired in the cognitive and affective dimensions of alexithymia). The theoretical and clinical implications of these results are discussed.


title: Power synchronization control for capacitor minimization in Solid State Transformers (SST)

abstract: The Solid State Transformer (SST) has been researched and designed to interface with the distribution system. The SST consists of a multilevel AC/DC rectifier stage, a bidirectional DC/DC converter stage and a DC/AC inverter stage. However, in all of the previous designs, large capacitors are required to limit the DC bus voltage ripple within the allowable range and stabilize the multistage system. The large capacitors, especially at high voltage level, result in a large size and volume, as well as significant component cost. This paper proposes a feed-forward power ripple control and an inverter power synchronization control method to reduce the required DC filter capacitance and the 120Hz voltage ripple for high voltage and low voltage DC buses. The switching model simulations illustrate the DC bus capacitance can be greatly reduced comparing to the conventional designed value and meet the same voltage ripple requirements. The SST prototype experiments also validate the proposed power synchronization controller.


title: Social Computing: An Overview

abstract: A collection of technologies termed social computing is driving a dramatic evolution of the Web, matching the dot-com era in growth, excitement, and investment. All of these share a high degree of community formation, user level content creation, and a variety of other characteristics. We provide an overview of social computing and identify salient characteristics. We argue that social computing holds tremendous disruptive potential in the business world and can significantly impact society, and outline possible changes in organized human action that could be brought about. Social computing can also have deleterious effects associated with it, including security issues. We suggest that social computing should be a priority for researchers and business leaders and illustrate the fundamental shifts in communication, computing, collaboration, and commerce brought about by this trend.


title: Geometric mean decomposition based hybrid precoding for millimeter-wave massive MIMO

abstract: Hybrid precoding can reduce the number of required radio frequency (RF) chains in millimeter-Wave (mmWave) massive MIMO systems. However, existing hybrid pre-coding based on singular value decomposition (SVD) requires the complicated bit allocation to match the different signal-to-noise-ratios (SNRs) of different sub-channels. In this paper, we propose a geometric mean decomposition (GMD)-based hybrid precoding to avoid the complicated bit allocation. Specifically, we seek a pair of analog and digital precoders sufficiently close to the unconstrained fully digital GMD precoder. To achieve this, we fix the analog precoder to design the digital precoder, and vice versa. The analog precoder is designed based on the orthogonal matching pursuit (OMP) algorithm, while GMD is used to obtain the digital precoder. Simulations show that the proposed GMD-based hybrid precoding achieves better performance than the conventional SVD-based hybrid precoding with only a slight increase in complexity.


title: Flight mechanics of a tailless articulated wing aircraft

abstract: This paper investigates the flight mechanics of a micro aerial vehicle without a vertical tail in an effort to reverse-engineer the agility of avian flight. The key to stability and control of such a tailless aircraft lies in the ability to control the incidence angles and dihedral angles of both wings independently. The dihedral angles can be varied symmetrically on both wings to control aircraft speed independently of the angle of attack and flight path angle, while asymmetric dihedral can be used to control yaw in the absence of a vertical stabilizer. It is shown that wing dihedral angles alone can effectively regulate sideslip during rapid turns and generate a wide range of equilibrium turn rates while maintaining a constant flight speed and regulating sideslip. Numerical continuation and bifurcation analysis are used to compute trim states and assess their stability. This paper lays the foundation for design and stability analysis of a flapping wing aircraft that can switch rapidly from flapping to gliding flight for agile manoeuvring in a constrained environment.


title: a member of the Old City Publishing Group. KEEL Data-Mining Software Tool: Data Set Repository, Integration of Algorithms and Experimental Analysis Framework

abstract: (Knowledge Extraction based onEvolutionary Learning) tool, an open source software that supports datamanagement and a designer of experiments. KEEL pays special attentionto the implementation of evolutionary learning and soft computing basedtechniques for Data Mining problems including regression, classiﬁcation,clustering, pattern mining and so on.The aim of this paper is to present three new aspects of KEEL: KEEL-dataset, a data set repository which includes the data set partitions in theKEELformatandshowssomeresultsofalgorithmsinthesedatasets; someguidelines for including new algorithms in KEEL, helping the researcherstomaketheirmethodseasilyaccessibletootherauthorsandtocomparetheresults of many approaches already included within the KEEL software;and a module of statistical procedures developed in order to provide to theresearcher a suitable tool to contrast the results obtained in any experimen-talstudy.Acaseofstudyisgiventoillustrateacompletecaseofapplicationwithin this experimental analysis framework.


title: Analysis of Unstructured Data: Applications of Text Analytics and Sentiment Mining

abstract: The proliferation of textual data in business is overwhelming. Unstructured textual data is being constantly generated via call center logs, emails, documents on the web, blogs, tweets, customer comments, customer reviews, and so on. While the amount of textual data is increasing rapidly, businesses’ ability to summarize, understand, and make sense of such data for making better business decisions remain challenging. This paper takes a quick look at how to organize and analyze textual data for extracting insightful customer intelligence from a large collection of documents and for using such information to improve business operations and performance. Multiple business applications of case studies using real data that demonstrate applications of text analytics and sentiment mining using SAS® Text Miner and SAS® Sentiment Analysis Studio are presented. While SAS® products are used as tools for demonstration only, the topics and theories covered are generic (not tool specific).


title: The Temporal Experience of Pleasure Scale (TEPS): Exploration and Confirmation of Factor Structure in a Healthy Chinese Sample

abstract: Background: The Temporal Experience of Pleasure Scale (TEPS) is a measure specifically designed to capture the anticipatory and consummatory facets of pleasure. However, few studies have examined the structure of the measure in non-Western samples. The current study aimed to evaluate the factor structure and psychometric properties of the TEPS in a Chinese sample.


title: SIFT-based local spectrogram image descriptor: a novel feature for robust music identification

abstract: Music identification via audio fingerprinting has been an active research field in recent years. In the real-world environment, music queries are often deformed by various interferences which typically include signal distortions and time-frequency misalignments caused by time stretching, pitch shifting, etc. Therefore, robustness plays a crucial role in music identification technique. In this paper, we propose to use scale invariant feature transform (SIFT) local descriptors computed from a spectrogram image as sub-fingerprints for music identification. Experiments show that these sub-fingerprints exhibit strong robustness against serious time stretching and pitch shifting simultaneously. In addition, a locality sensitive hashing (LSH)-based nearest sub-fingerprint retrieval method and a matching determination mechanism are applied for robust sub-fingerprint matching, which makes the identification efficient and precise. Finally, as an auxiliary function, we demonstrate that by comparing the time-frequency locations of corresponding SIFT keypoints, the factor of time stretching and pitch shifting that music queries might have experienced can be accurately estimated.


title: Searching for a Needle in a Haystack: Predicting Security Vulnerabilities for Windows Vista

abstract: Many factors are believed to increase the vulnerability of software system; for example, the more widely deployed or popular is a software system the more likely it is to be attacked. Early identification of defects has been a widely investigated topic in software engineering research. Early identification of software vulnerabilities can help mitigate these attacks to a large degree by focusing better security verification efforts in these components. Predicting vulnerabilities is complicated by the fact that vulnerabilities are, most often, few in number and introduce significant bias by creating a sparse dataset in the population. As a result, vulnerability prediction can be thought of us preverbally “searching for a needle in a haystack.” In this paper, we present a large-scale empirical study on Windows Vista, where we empirically evaluate the efficacy of classical metrics like complexity, churn, coverage, dependency measures, and organizational structure of the company to predict vulnerabilities and assess how well these software measures correlate with vulnerabilities. We observed in our experiments that classical software measures predict vulnerabilities with a high precision but low recall values. The actual dependencies, however, predict vulnerabilities with a lower precision but substantially higher recall.


title: Yield gaps, nutrient use efficiencies and response to fertilisers by maize across heterogeneous smallholder farms of western Kenya

abstract: The need to promote fertiliser use by African smallholder farmers to counteract the current decline in per capita food production is widely recognised. But soil heterogeneity results in variable responses of crops to fertilisers within single farms. We used existing databases on maize production under farmer (F-M) and researcher management (R-M) to analyse the effect of soil heterogeneity on the different components of nutrient use efficiency by maize growing on smallholder farms in western Kenya: nutrient availability, capture and conversion efficiencies and crop biomass partitioning. Subsequently, we used the simple model QUEFTS to calculate nutrient recovery efficiencies from the R-M plots and to calculate attainable yields with and without fertilisers based on measured soil properties across heterogeneous farms. The yield gap of maize between F-M and R-M varied from 0.5 to 3 t grain ha−1 season−1 across field types and localities. Poor fields under R-M yielded better than F-M, even without fertilisers. Such differences, of up to 1.1 t ha−1 greater yields under R-M conditions are attributable to improved agronomic management and germplasm. The relative response of maize to N–P–K fertilisers tended to decrease with increasing soil quality (soil C and extractable P), from a maximum of 4.4-fold to −0.5-fold relative to the control. Soil heterogeneity affected resource use efficiencies mainly through effects on the efficiency of resource capture. Apparent recovery efficiencies varied between 0 and 70% for N, 0 and 15% for P, and 0 to 52% for K. Resource conversion efficiencies were less variable across fields and localities, with average values of 97 kg DM kg−1 N, 558 kg DM kg−1 P and 111 kg DM kg−1 K taken up. Using measured soil chemical properties QUEFTS over-estimated observed yields under F-M, indicating that variable crop performance within and across farms cannot be ascribed solely to soil nutrient availability. For the R-M plots QUEFTS predicted positive crop responses to application of 30 kg P ha−1 and 30 kg P ha−1 + 90 kg N ha−1 for a wide range of soil qualities, indicating that there is room to improve current crop productivity through fertiliser use. To ensure their efficient use in sub-Saharan Africa mineral fertilisers should be: (1) targeted to specific niches of soil fertility within heterogeneous farms; and (2) go hand-in-hand with the implementation of agronomic measures to improve their capture and utilisation.


title: DESCRIPTIVE AND PRESCRIPTIVE MODELS FOR JUDGMENT AND DECISION MAKING: IMPLICATIONS FOR KNOWLEDGE ENGINEERING

abstract: Development of a knowledge base for an expert system often requires an analysis of the inference processes and decision making strategies of one or more experts. Knowledge engineers therefore share the psychologists and operations researcher’s interest in inference and decision making. Psychologists have been interested in understanding and describing how people make inferences under complexity and uncertainty. Operations researchers have developed methods that extend human ability to make decisions about complex problems.


title: Seeing Is Believing: Sharing Real-Time Visual Traffic Information via Vehicular Clouds

abstract: From today’s conventional cars to tomorrow’s self-driving cars, advances in technology will enable vehicles to be equipped with more and more-sophisticated sensing devices, such as cameras. As vehicles gain the ability to act as mobile sensors that carry useful traffic information, people and vehicles are sharing sensing data to enhance the driving experience. This paper describes a vehicular cloud service for route planning, where users collaborate to share traffic images by using their vehicles’ on-board cameras. We present the architecture of a collaborative traffic image-sharing system called social vehicle navigation, which allows drivers in the vehicular cloud to report and share visual traffic information called NaviTweets. A set of NaviTweets is then filtered, refined, and condensed into a concise, user-friendly snapshot summary of the route of interest, called a traffic digest. These digests can provide more pertinent and reliable information about the road situation and can complement predictions, such as estimated time of arrival, thereby supporting users’ route decision making. As proof of concept, this paper presents the system design and a prototype implementation running on the Android smartphone platform, along with its evaluation.


title: Emotion Communication System

abstract: In today’s increasingly rich material life, people are shifting their focus from the physical world to the spiritual world. In order to identify and care for people’s emotions, human-machine interaction systems have been created. The currently available human-machine interaction systems often support the interaction between human and robot under the line-of-sight (LOS) propagation environment, while most communications in terms of human-to-human and human-to-machine are non-LOS (NLOS). In order to break the limitation of the traditional human–machine interaction system, we propose the emotion communication system based on NLOS mode. Specifically, we first define the emotion as a kind of multimedia which is similar to voice and video. The information of emotion can not only be recognized, but can also be transmitted over a long distance. Then, considering the real-time requirement of the communications between the involved parties, we propose an emotion communication protocol, which provides a reliable support for the realization of emotion communications. We design a pillow robot speech emotion communication system, where the pillow robot acts as a medium for user emotion mapping. Finally, we analyze the real-time performance of the whole communication process in the scene of a long distance communication between a mother-child users’ pair, to evaluate the feasibility and effectiveness of emotion communications.


title: Business intelligence modeling in action: a hospital case study

abstract: Business Intelligence (BI) projects are long and painful endeavors that employ a variety of design methodologies, inspired mostly by software engineering and project management lifecycle models. In recent BI research, new design methodologies are emerging founded on conceptual business models that capture business objectives, strategies, and more. Their claim is that they facilitate the description of the problem-at-hand, its analysis towards a solution, and the implementation of that solution. The key question explored in this work is:Are such models actually useful to BI design practitioners? To answer this question, we conducted an in situ empirical evaluation based on an on-going BI project for a Toronto hospital. The lessons learned from the study include: confirmation that the BI implementation is well-supported by models founded on business concepts; evidence that these models enhance communication within the project team and business stakeholders; and, evidence that there is a need for business modeling to capture BI requirements and, from those, derive and implement BI designs.


title: Investigating Loss Functions And Optimization Methods For Discriminative Learning Of Label Sequences

abstract: Discriminative models have been of interest in the NLP community in recent years. Previous research has shown that they are advantageous over generative models. In this paper, we investigate how different objective functions and optimization methods affect the performance of the classifiers in the discriminative learning framework. We focus on the sequence labelling problem, particularly POS tagging and NER tasks. Our experiments show that changing the objective function is not as effective as changing the features included in the model.


title: Decision making for autonomous driving considering interaction and uncertain prediction of surrounding vehicles

abstract: Autonomous driving requires decision making in dynamic and uncertain environments. The uncertainty from the prediction originates from the noisy sensor data and from the fact that the intention of human drivers cannot be directly measured. This problem is formulated as a partially observable Markov decision process (POMDP) with the intention of the other vehicles as hidden variables. The solution of the POMDP is a policy determining the optimal acceleration of the ego vehicle along a preplanned path. Therefore, the policy is optimized for the most likely future scenarios resulting from an interactive, probabilistic motion model for the other vehicles. Considering possible future measurements of the surroundings allows the autonomous car to incorporate the estimated change in future prediction accuracy in the optimal policy. A compact representation allows a low-dimensional state-space so that the problem can be solved online for varying road layouts and number of other vehicles. This is done with a point-based solver in an anytime fashion on a continuous state-space. We show the results with simulations for the crossing of complex (unsignalized) intersections. Our approach performs nearly as good as with full prior information about the intentions of the other vehicles and clearly outperforms reactive approaches.


1 RANDOM FORESTS--RANDOM FEATURES


title: Implications of the Fourth Industrial Age on Higher Education

abstract: Higher education in the fourth industrial revolution, HE 4.0, is a complex, dialectical and exciting opportunity which can potentially transform society for the better. The fourth industrial revolution is powered by artificial intelligence and it will transform the workplace from tasks based characteristics to the human centred characteristics. Because of the convergence of man and machine, it will reduce the subject distance between humanities and social science as well as science and technology. This will necessarily require much more interdisciplinary teaching, research and innovation. This paper explores the impact of HE 4.0 on the mission of a university which is teaching, research (including innovation) and service.


title: Recognizing academic performance, sleep quality, stress level, and mental health using personality traits, wearable sensors and mobile phones

abstract: What can wearable sensors and usage of smart phones tell us about academic performance, self-reported sleep quality, stress and mental health condition? To answer this question, we collected extensive subjective and objective data using mobile phones, surveys, and wearable sensors worn day and night from 66 participants, for 30 days each, totaling 1,980 days of data. We analyzed daily and monthly behavioral and physiological patterns and identified factors that affect academic performance (GPA), Pittsburg Sleep Quality Index (PSQI) score, perceived stress scale (PSS), and mental health composite score (MCS) from SF-12, using these month-long data. We also examined how accurately the collected data classified the participants into groups of high/low GPA, good/poor sleep quality, high/low self-reported stress, high/low MCS using feature selection and machine learning techniques. We found associations among PSQI, PSS, MCS, and GPA and personality types. Classification accuracies using the objective data from wearable sensors and mobile phones ranged from 67–92%.


title: User interest and social influence based emotion prediction for individuals

abstract: Emotions are playing significant roles in daily life, making emotion prediction important. To date, most of state-of-the-art methods make emotion prediction for the masses which are invalid for individuals. In this paper, we propose a novel emotion prediction method for individuals based on user interest and social influence. To balance user interest and social influence, we further propose a simple yet efficient weight learning method in which the weights are obtained from users' behaviors. We perform experiments in real social media network, with 4,257 users and 2,152,037 microblogs. The experimental results demonstrate that our method outperforms traditional methods with significant performance gains.


title: Memory-Efficient Hash Joins

abstract: We present new hash tables for joins, and a hash join based on them, that consumes far less memory and is usually faster than recently published in-memory joins. Our hash join is not restricted to outer tables that fit wholly in memory. Key to this hash join is a new concise hash table (CHT), a linear probing hash table that has 100% fill factor, and uses a sparse bitmap with embedded population counts to almost entirely avoid collisions. This bitmap also serves as a Bloom filter for use in multi-table joins. ::: ::: We study the random access characteristics of hash joins, and renew the case for non-partitioned hash joins. We introduce a variant of partitioned joins in which only the build is partitioned, but the probe is not, as this is more efficient for large outer tables than traditional partitioned joins. This also avoids partitioning costs during the probe, while at the same time allowing parallel build without latching overheads. Additionally, we present a variant of CHT, called a concise array table (CAT), that can be used when the key domain is moderately dense. CAT is collision-free and avoids storing join keys in the hash table. ::: ::: We perform a detailed comparison of CHT and CAT against leading in-memory hash joins. Our experiments show that we can reduce the memory usage by one to three orders of magnitude, while also being competitive in performance.


title: Deblurring Low-Light Images with Light Streaks

abstract: Images taken in low-light conditions with handheld cameras are often blurry due to the required long exposure time. Although significant progress has been made recently on image deblurring, state-of-the-art approaches often fail on low-light images, as these images do not contain a sufficient number of salient features that deblurring methods rely on. On the other hand, light streaks are common phenomena in low-light images that contain rich blur information, but have not been extensively explored in previous approaches. In this work, we propose a new method that utilizes light streaks to help deblur low-light images. We introduce a non-linear blur model that explicitly models light streaks and their underlying light sources, and poses them as constraints for estimating the blur kernel in an optimization framework. Our method also automatically detects useful light streaks in the input image. Experimental results show that our approach obtains good results on challenging real-world examples that no other methods could achieve before.


title: Handwritten Digit Recognition: A Neural Network Demo

abstract: A handwritten digit recognition system was used in a demonstration project to visualize artificial neural networks, in particular Kohonen's self-organizing feature map. The purpose of this project was to introduce neural networks through a relatively easy-to-understand application to the general public. This paper describes several techniques used for preprocessing the handwritten digits, as well as a number of ways in which neural networks were used for the recognition task. Whereas the main goal was a purely educational one, a moderate recognition rate of 98% was reached on a test set.


title: Approximate Planning in Large POMDPs via Reusable Trajectories

abstract: We consider the problem of reliably choosing a near-best strategy from a restricted class of strategies II in a partially observable Markov decision process (POMDP). We assume we are given the ability to simulate the POMDP, and study what might be called the sample complexity -- that is, the amount of data one must generate in the POMDP in order to choose a good strategy. We prove upper bounds on the sample complexity showing that, even for infinitely large and arbitrarily complex POMDPs, the amount of data needed can be finite, and depends only linearly on the complexity of the restricted strategy class II, and exponentially on the horizon time. This latter dependence can be eased in a variety of ways, including the application of gradient and local search algorithms. Our measure of complexity generalizes the classical supervised learning notion of VC dimension to the settings of reinforcement learning and planning.


title: Dynamic spatio-temporal integration of traffic accident data

abstract: Up to 50% of delay in traffic is due to non-reoccurring events such as traffic accidents. Accidents lead to delays, which can be costly for transport companies. Road authorities are also very interested in warning drivers about accidents, e.g., to reroute them. This paper presents a novel and efficient approach and system for uncovering effects from traffic accidents by dynamic integration of GPS, weather, and traffic-accident data. This integration makes it possible to explore and quantify how accidents affects traffic. Dynamic integration means that data is combined at query time as it becomes available. This is necessary, because data can be missing (weather station down) or late arriving (accident not officially reported by the police yet). Further, the integration can be parameterized by the user, e.g., distance to accident, which is important due to inaccuracy in reporting. We present the integrated data on a map and show the effectiveness of the integration by allowing users to interactively browse all accidents or pick a single accident to study it in very fine-grained details. Using information from 31 433 road accidents and 38 billion GPS records, we show that the proposed dynamic data integration scales so very large data sets.


title: Intrinsic shape signatures: A shape descriptor for 3D object recognition

abstract: This paper presents a new approach for recognition of 3D objects that are represented as 3D point clouds. We introduce a new 3D shape descriptor called Intrinsic Shape Signature (ISS) to characterize a local/semi-local region of a point cloud. An intrinsic shape signature uses a view-independent representation of the 3D shape to match shape patches from different views directly, and a view-dependent transform encoding the viewing geometry to facilitate fast pose estimation. In addition, we present a highly efficient indexing scheme for the high dimensional ISS shape descriptors, allowing for fast and accurate search of large model databases. We evaluate the performance of the proposed algorithm on a very challenging task of recognizing different vehicle types using a database of 72 models in the presence of sensor noise, obscuration and scene clutter.


title: Novel Berger code checker

abstract: This paper presents a novel checking circuit for Berger codes, with any value of k (also k=2/sup r-1/), which is TSC with respect to a wide set of realistic faults including all possible stuck-ats, transistors stuck-on/stuck-open, as well as several likely to occur resistive bridgings. With respect to the other alternative implementations the proposed checker features the advantage of being inherently more testable, and of requiring lower area overhead.


title: Low profile dipole antenna over compact AMC surface

abstract: In this paper, an alternative technique is proposed to miniaturize the Artificial Magnetic Conductors (AMC) unit cell. Novel unit cell achieves 60% surface size reduction at the same operating frequency compared to conventional mushroom-like unit cell. An AMC surface of 5×5 cells is designed under a dipole antenna with a distance of 0.05λ 0 , where λ 0 denotes the free space wavelength at 0.9 GHz, for gain enhancement and radiation control. Both the dipole antenna and the AMC surface are fabricated and measured to demonstrate the potential of this new structure. At 0.92 GHz, a peak realized gain of 4.3 dBi of the antenna is achieved. The front-to-back ratio is under −15 dB and the maximum cross-polarization level, in the main beam, is less than −25 dB for both E and H plane. The simulated and the measured results are in good agreement.


title: SnaPEA: Predictive Early Activation for Reducing Computation in Deep Convolutional Neural Networks

abstract: Deep Convolutional Neural Networks (CNNs) perform billions of operations for classifying a single input. To reduce these computations, this paper offers a solution that leverages a combination of runtime information and the algorithmic structure of CNNs. Specifically, in numerous modern CNNs, the outputs of compute-heavy convolution operations are fed to activation units that output zero if their input is negative. By exploiting this unique algorithmic property, we propose a predictive early activation technique, dubbed SnaPEA. This technique cuts the computation of convolution operations short if it determines that the output will be negative. SnaPEA can operate in two distinct modes, exact and predictive. In the exact mode, with no loss in classification accuracy, SnaPEA statically re-orders the weights based on their signs and periodically performs a single-bit sign check on the partial sum. Once the partial sum drops below zero, the rest of computations can simply be ignored, since the output value will be zero in any case. In the predictive mode, which trades the classification accuracy for larger savings, SnaPEA speculatively cuts the computation short even earlier than the exact mode. To control the accuracy, we develop a multi-variable optimization algorithm that thresholds the degree of speculation. As such, the proposed algorithm exposes a knob to gracefully navigate the trade-offs between the classification accuracy and computation reduction. Compared to a state-of-the-art CNN accelerator, SnaPEA in the exact mode, yields, on average, 28% speedup and 16% energy reduction in various modern CNNs without affecting their classification accuracy. With 3% loss in classification accuracy, on average, 67.8% of the convolutional layers can operate in the predictive mode. The average speedup and energy saving of these layers are 2.02x and 1.89x, respectively. The benefits grow to a maximum of 3.59x speedup and 3.14x energy reduction. Compared to static pruning approaches, which are complimentary to the dynamic approach of SnaPEA, our proposed technique offers up to 63% speedup and 49% energy reduction across the convolution layers with no loss in classification accuracy.


1 ( Language , Function and Cognition , 2011-12 ) Introduction to Systemic Functional Linguistics for Discourse Analysis


title: Marketing Strategy and the Internet: An Organizing Framework

abstract: Competitive strategy is primarily concerned with how a business should deploy resources at its disposal to achieve and maintain defensible competitive positional advantages in the marketplace. Competitive marketing strategy focuses on how a business should deploy marketing resources at its disposal to facilitate the achievement and maintenance of competitive positional advantages in the marketplace. In a growing number of product-markets, the competitive landscape has evolved from a predominantly physical marketplace to one encompassing both the physical and the electronic marketplace. This article presents a conceptual framework delineating the drivers and outcomes of marketing strategy in the context of competing in this broader, evolving marketplace. The proposed framework provides insights into changes in the nature and scope of marketing strategy; specific industry, product, buyer, and buying environment characteristics; and the unique skills and resources of the firm that assume added relevance in the context of competing in the evolving marketplace.


title: Machine learning algorithms for accurate flow-based network traffic classification: Evaluation and comparison

abstract: The task of network management and monitoring relies on an accurate characterization of network traffic generated by different applications and network protocols. We employ three supervised machine learning (ML) algorithms, Bayesian Networks, Decision Trees and Multilayer Perceptrons for the flow-based classification of six different types of Internet traffic including peer-to-peer (P2P) and content delivery (Akamai) traffic. The dependency of the traffic classification performance on the amount and composition of training data is investigated followed by experiments that show that ML algorithms such as Bayesian Networks and Decision Trees are suitable for Internet traffic flow classification at a high speed, and prove to be robust with respect to applications that dynamically change their source ports. Finally, the importance of correctly classified training instances is highlighted by an experiment that is conducted with wrongly labeled training data.


Gabor Filter Visualization