title: Automatic Detection of Respiration Rate From Ambulatory Single-Lead ECG

abstract: Ambulatory electrocardiography is increasingly being used in clinical practice to detect abnormal electrical behavior of the heart during ordinary daily activities. The utility of this monitoring can be improved by deriving respiration, which previously has been based on overnight apnea studies where patients are stationary, or the use of multilead ECG systems for stress testing. We compared six respiratory measures derived from a single-lead portable ECG monitor with simultaneously measured respiration air flow obtained from an ambulatory nasal cannula respiratory monitor. Ten controlled 1-h recordings were performed covering activities of daily living (lying, sitting, standing, walking, jogging, running, and stair climbing) and six overnight studies. The best method was an average of a 0.2-0.8 Hz bandpass filter and RR technique based on lengthening and shortening of the RR interval. Mean error rates with the reference gold standard were plusmn4 breaths per minute (bpm) (all activities), plusmn2 bpm (lying and sitting), and plusmn1 breath per minute (overnight studies). Statistically similar results were obtained using heart rate information alone (RR technique) compared to the best technique derived from the full ECG waveform that simplifies data collection procedures. The study shows that respiration can be derived under dynamic activities from a single-lead ECG without significant differences from traditional methods.


title: The challenges of mobile computing

abstract: The technical challenges that mobile computing must surmount to achieve its potential are hardly trivial. Some of the challenges in designing software for mobile computing systems are quite different from those involved in the design of software for today's stationary networked systems. The authors focus on the issues pertinent to software designers without delving into the lower level details of the hardware realization of mobile computers. They look at some promising approaches under investigation and also consider their limitations. The many issues to be dealt with stem from three essential properties of mobile computing: communication, mobility, and portability. Of course, special-purpose systems may avoid some design pressures by doing without certain desirable properties. For instance portability would be less of a concern for mobile computers installed in the dashboards of cars than with hand-held mobile computers. However, the authors concentrate on the goal of large-scale, hand-held mobile computing as a way to reveal a wide assortment of issues. >


title: ChEMBL: a large-scale bioactivity database for drug discovery

abstract: ChEMBL is an Open Data database containing binding, functional and ADMET information for a large number of drug-like bioactive compounds. These data are manually abstracted from the primary published literature on a regular basis, then further curated and standardized to maximize their quality and utility across a wide range of chemical biology and drug-discovery research problems. Currently, the database contains 5.4 million bioactivity measurements for more than 1 million compounds and 5200 protein targets. Access is available through a web-based interface, data downloads and web services at: https://www.ebi.ac.uk/chembldb.


title: CEML: Mixing and moving complex event processing and machine learning to the edge of the network for IoT applications

abstract: The Internet of Things (IoT) is a growing field which is expected to generate and collect data everywhere at any time. Highly scalable cloud analytics systems are frequently being used to handle this data explosion. However, the ubiquitous nature of the IoT data imposes new technical and non-technical requirements which are difficult to address with a cloud deployment. To solve these problems, we need a new set of development technologies such as Distributed Data Mining and Ubiquitous Data Mining targeted and optimized towards IoT applications. In this paper, we present the Complex Event Machine Learning framework which proposes a set of tools for automatic distributed machine learning in (near-) real-time, automatic continuous evaluation tools, and automatic rules management for deployment of rules. These features are implemented for a deployment at the edge of the network instead of the cloud. We evaluate and validate our approach with a well-known classification problem.


title: ORIGINAL ARTICLE Political Communication in Media Society: Does Democracy Still Enjoy an Epistemic Dimension? The Impact of Normative Theory on Empirical Research 1

abstract: I first compare the deliberative to the liberal and the republican models of democracy, and consider possible references to empirical research and then examine what empirical evidence there is for the assumption that political deliberation develops a truth-tracking potential. The main parts of the paper serve to dispel prima facie doubts about the empirical content and the applicability of the communication model of deliberative politics. It moreover highlights 2 critical conditions: mediated political communication in the public sphere can facilitate deliberative legitimation processes in complex societies only if a self-regulating media system gains independence from its social environments and if anonymous audiences grant a feedback between an informed elite discourse and a responsive civil society. doi:10.1111/j.1468-2885.2006.00280.x In Aristotle’s Politics, normative theorizing and empirical research go hand in hand. Yet, contemporary theories of liberal democracy express a demanding ‘‘ought’’ that faces the sobering ‘‘is’’ of ever more complex societies. Especially, the deliberative model of democracy, which claims an epistemic dimension for the democratic procedures of legitimation, appears to exemplify the widening gap between normative and empirical approaches toward politics. Let me first compare the deliberative to the liberal and the republican models of democracy, and consider possible references to empirical research. I will then examine what empirical evidence there is for the assumption that political deliberation develops a truth-tracking potential. The main parts of the paper serve to dispel prima facie doubts about the empirical content and the applicability of the deliberative model. The communication model of deliberative politics that I wish to present highlights two critical conditions: Mediated political communication in the public sphere can facilitate deliberative legitimation processes in complex societies only if


title: Representations of Pleasant and Painful Touch in the Human . . .

abstract: The cortical areas that represent affectively positive and negative aspects of touch were investigated using functional magnetic resonance imaging (fMRI) by comparing activations produced by pleasant touch, painful touch produced by a stylus, and neutral touch, to the left hand. It was found that regions of the orbitofrontal cortex were activated more by pleasant touch and by painful stimuli than by neutral touch and that different areas of the orbitofrontal cortex were activated by the pleasant and painful touches. The orbitofrontal cortex activation was related to the affective aspects of the touch, in that the somatosensory cortex (SI) was less activated by the pleasant and painful stimuli than by the neutral stimuli. This dissociation was highly significant for both the pleasant touch (P < 0.006) and for the painful stimulus (P < 0.02). Further, it was found that a rostral part of the anterior cingulate cortex was activated by the pleasant stimulus and that a more posterior and dorsal part was activated by the painful stimulus. Regions of the somatosensory cortex, including SI and part of SII in the mid-insula, were activated more by the neutral touch than by the pleasant and painful stimuli. Part of the posterior insula was activated only in the pain condition and different parts of the brainstem, including the central grey, were activated in the pain, pleasant and neutral touch conditions. The results provide evidence that different areas of the human orbitofrontal cortex are involved in representing both pleasant touch and pain, and that dissociable parts of the cingulate cortex are involved in representing pleasant touch and pain.


title: Vehicular Trajectory Optimization for Cooperative Collision Avoidance at High Speeds

abstract: Traffic safety is a key aspect in new-generation intelligent transportation systems. Among other areas, an active field of research is cooperative collision avoidance, where vehicles cooperatively calculate trajectories under tight time constraints to avoid colliding under specific road-traffic domains (overtaking, intersections, etc.). In this paper, we particularly analyze the problem of collision avoidance in scenarios in which high-speed vehicles need to generate evasive maneuvers within very short time intervals to avoid or at least mitigate a hypothetical (multiple) collision. We pose this as a multiobjective optimization problem and simplify it by considering only lateral motion for the optimization process, thus having to solve a 1-D trajectory generation problem. The routes of vehicles are optimized according to a weighted aggregation functional that: 1) maximizes the lateral distances between vehicle-vehicle and vehicle-obstacle pairs at the time of overcoming the obstacles; 2) minimizes the lateral speeds at the end of the path; and 3) minimizes the instantaneous lateral acceleration (inertia) along the maneuver. In addition, we compute trajectories by following an optimization strategy that divides the problem into a set of independent subproblems, which are optimized in parallel by using a gradient-descent-based methodology. From this set of solutions, the most suitable option, according to our selected criteria, is chosen. Results show the utility of our approach and its flexibility to compute evasive trajectories adapted to different requirements. Additionally, a simulation of the mechanical response of the vehicles during the evasive maneuvers is conducted.


title: iGen: A Realistic Request Generator for Cloud File Systems Benchmarking

abstract: Benchmarking is a traditional approach for system performance evaluation and optimization. Over the past decades, a variety of file systems, e.g., GFS, HDFS and Ceph, have been designed and implemented, serving as the key components in cloud infrastructures. With the mature of those cloud file systems, the demands for performance evaluation and comparison are also rising. However, due to the complexity and heterogeneity of I/O workloads in cloud infrastructures, it is still challenging to generate realistic I/O workloads. System developers often use traditional file system benchmarks and make inaccurate assumptions on workload generation, yielding to misleading results. To address this problem, we investigate the characteristics of I/O requests in a production cloud infrastructure at Alibaba Cloud Computing, which is one of the biggest cloud providers in Asia. We proposed a flexible framework iGen to mimic I/O request arrivals. One of the salient features of the iGen is that the request arrival process is modeled by three statistics properties, request arrival rate, inter-arrival time distribution, and request periodicity. According to these properties, the iGen can determine the sequence of requests and the inter-arrival time between two subsequent requests. We use the iGen to emulate a real workload that collected from Alibaba cloud platform. Experimental results show that high accuracy and flexibility of the iGen.


title: Learning to Classify Hate and Extremism Promoting Tweets

abstract: Research shows that Twitter is being misused as a platform for online radicalization and contains several hate and extremism promoting users and tweets violating the community guidelines of the website. Manual identification of such tweets is practically impossible due to millions of tweets posted every day and hence solutions to automate the task of tweet classification is required for Twitter moderators or an intelligence and security analyst. We formulate the problem of hate and extremism promoting tweet identification as a one-class classification problem and propose several linguistic features. Experimental results on large and real-world dataset demonstrate that the proposed approach is effective.


title: A Survey of Visualization Construction User Interfaces

abstract: We have systematically surveyed the publications on visualization construction user interfaces that have been published in 12 major Visualization and HCI venues. We found six different visualization construction approaches (visual builder, visualization spreadsheet, textual programming, visual dataflow programming, template editor, and shelf configuration). The approaches differ in their flexibility, whether they support presentation or exploration tasks, and the spatial, temporal, and conceptual distance between the user interface (UI) and the visualization. Our results provide guidance to designers of visualization construction UIs.


title: Active front-end for shaft power generation and voltage control in FREMM frigates integrated power system: Modeling and validation

abstract: Italian Navy is being equipping its fleet with the new FREMM class of frigates, developed under a joint program by Italian and French Navies. They are conceptually new war ships endowed with a hybrid propulsion system, composed by a gas turbine and two electric drives, which can be operated also as shaft-generators. University of Trieste has been charged with developing a software simulator of the FREMM integrated electrical power system and its propulsion drives, which has been recently delivered. The simulator will be employed by Italian Navy for virtual tests and personnel training activities. The paper is focused on the shaft-generator operation of the electric propulsion drives and their simulation. Numeric results are displayed in the paper and compared with factory tests in order to validate the simulator.


title: Simba: Efficient In-Memory Spatial Analytics

abstract: Large spatial data becomes ubiquitous. As a result, it is critical to provide fast, scalable, and high-throughput spatial queries and analytics for numerous applications in location-based services (LBS). Traditional spatial databases and spatial analytics systems are disk-based and optimized for IO efficiency. But increasingly, data are stored and processed in memory to achieve low latency, and CPU time becomes the new bottleneck. We present the Simba (Spatial In-Memory Big data Analytics) system that offers scalable and efficient in-memory spatial query processing and analytics for big spatial data. Simba is based on Spark and runs over a cluster of commodity machines. In particular, Simba extends the Spark SQL engine to support rich spatial queries and analytics through both SQL and the DataFrame API. It introduces indexes over RDDs in order to work with big spatial data and complex spatial operations. Lastly, Simba implements an effective query optimizer, which leverages its indexes and novel spatial-aware optimizations, to achieve both low latency and high throughput. Extensive experiments over large data sets demonstrate Simba's superior performance compared against other spatial analytics system.


title: Low profile UWB shorted dipole antenna

abstract: In this paper we present a low profile UWB dipole antenna that consists of two elliptical form plates top loaded by circular disks which are shorted to each other by two symmetrical wires. This dipole antenna has a total height of less than lambda/6 and a top loading disk diameter equal to lambda/3. The antenna shows a very wide bandwidth of 6:1 from 3.1 GHz to 18 GHz with a return loss better than 12 dB.


title: The Correlation Between Feelings and Brand Perception on Purchase Intention

abstract: The goal of this research is to examine the effect of feelings and brand perceptions in purchasing of the same-name fragrances and the rate of familiarity with this concept of perfume. Using stratified random sampling, necessary data was collected from 148 people through a valid questionnaire. Structural equation modeling (SEM) has been used to analyze data and model. This study attempts to review the effective Factors related with feelings, brand perception and purchase decision under a model. The results show that: There is a direct significant relationship between the product feature and feeling during purchase. There is a direct significant relationship between the promotion and feeling during purchase. There is a direct significant relationship between the brand perception and purchase intention. This is the first paper to examine the effect of feelings and brand perceptions in purchasing of the same-name fragrances in a model.


title: CFO: Conditional Focused Neural Question Answering with Large-scale Knowledge Bases

abstract: How can we enable computers to automatically answer questions like "Who created the character Harry Potter"? Carefully built knowledge bases provide rich sources of facts. However, it remains a challenge to answer factoid questions raised in natural language due to numerous expressions of one question. In particular, we focus on the most common questions --- ones that can be answered with a single fact in the knowledge base. We propose CFO, a Conditional Focused neural-network-based approach to answering factoid questions with knowledge bases. Our approach first zooms in a question to find more probable candidate subject mentions, and infers the final answers with a unified conditional probabilistic framework. Powered by deep recurrent neural networks and neural embeddings, our proposed CFO achieves an accuracy of 75.7% on a dataset of 108k questions - the largest public one to date. It outperforms the current state of the art by an absolute margin of 11.8%.


title: European guideline for the management of genital herpes

abstract: First infection with either herpes simplex virus type 1 (HSV-1) or type 2 (HSV-2) is termed primary infection and results in either symptomatic disease at the site of viral entry (i.e. around the mouth or around the genital area) or asymptomatic and hence unrecognized disease. In addition there may be systemic symptoms as with other acute viral illnesses. Following infection, the virus becomes latent in the local sensory ganglion, periodically reactivating to cause symptomatic lesions or asymptomatic, but none the less infectious, viral shedding. Genital herpes can be caused by either HSV-1 (the usual cause of oro-labial herpes) or by HSV-2. Infection with either virus can cause an identical initial illness. However, subsequent recurrence frequency is greater for HSV-2 than HSV1 disease1.


title: Cannabinoids in bipolar affective disorder: a review and discussion of their therapeutic potential

abstract: Bipolar affective disorder is often poorly controlled by prescribed drugs. Cannabis use is common in patients with this disorder and anecdotal reports suggest that some patients take it to alleviate symptoms of both mania and depression. We undertook a literature review of cannabis use by patients with bipolar disorder and of the neuropharmacological properties of cannabinoids suggesting possible therapeutic effects in this condition. No systematic studies of cannabinoids in bipolar disorder were found to exist, although some patients claim that cannabisrelieves symptoms of mania and/or depression. The cannabinoids Δ9-tetrahydrocannabinol (THC) and cannabidiol (CBD) may exert sedative, hypnotic, anxiolytic, antidepressant, antipsychotic and anticonvulsant effects. Pure synthetic cannabinoids, such as dronabinol and nabilone and specific plant extracts containing THC, CBD, or amixture of the two in known concentrations, are available and can be delivered sublingually. Controlled trials of these cannabinoid...


title: Extreme learning machines for intrusion detection

abstract: We consider the problem of intrusion detection in a computer network, and investigate the use of extreme learning machines (ELMs) to classify and detect the intrusions. With increasing connectivity between networks, the risk of information systems to external attacks or intrusions has increased tremendously. Machine learning methods like support vector machines (SVMs) and neural networks have been widely used for intrusion detection. These methods generally suffer from long training times, require parameter tuning, or do not perform well in multi-class classification. We propose a basic ELM method based on random features, and a kernel based ELM method for classification. We compare our methods with commonly used SVM techniques in both binary and multi-class classifications. Simulation results show that the proposed basic ELM approach outperforms SVM in training and testing speed, while the proposed kernel based ELM achieves higher detection accuracy than SVM in multi-class classification case.


title: Privacy-preserving verifiable data aggregation and analysis for cloud-assisted mobile crowdsourcing

abstract: Crowdsourcing is a crowd-based outsourcing, where a requester (task owner) can outsource tasks to workers (public crowd). Recently, mobile crowdsourcing, which can leverage workers' data from smartphones for data aggregation and analysis, has attracted much attention. However, when the data volume is getting large, it becomes a difficult problem for a requester to aggregate and analyze the incoming data, especially when the requester is an ordinary smartphone user or a start-up company with limited storage and computation resources. Besides, workers are concerned about their identity and data privacy. To tackle these issues, we introduce a three-party architecture for mobile crowdsourcing, where the cloud is implemented between workers and requesters to ease the storage and computation burden of the resource-limited requester. Identity privacy and data privacy are also achieved. With our scheme, a requester is able to verify the correctness of computation results from the cloud. We also provide several aggregated statistics in our work, together with efficient data update methods. Extensive simulation shows both the feasibility and efficiency of our proposed solution.


title: Dependency Tree-Based Rules for Concept-Level Aspect-Based Sentiment Analysis

abstract: Over the last few years, the way people express their opinions has changed dramatically with the progress of social networks, web communities, blogs, wikis, and other online collaborative media. Now, people buy a product and express their opinion in social media so that other people can acquire knowledge about that product before they proceed to buy it. On the other hand, for the companies it has become necessary to keep track of the public opinions on their products to achieve customer satisfaction. Therefore, nowadays opinion mining is a routine task for every company for developing a widely acceptable product or providing satisfactory service. Concept-based opinion mining is a new area of research. The key parts of this research involve extraction of concepts from the text, determining product aspects, and identifying sentiment associated with these aspects. In this paper, we address each one of these tasks using a novel approach that takes text as input and use dependency parse tree-based rules to extract concepts and aspects and identify the associated sentiment. On the benchmark datasets, our method outperforms all existing state-of-the-art systems.


title: Structural uncertainty based just noticeable difference estimation

abstract: Just noticeable difference (JND) reveals the minimum visible threshold of the human visual system (HVS), which is useful in visual redundancy reduction. Existing JND models estimate the visible threshold with luminance adaptation and contrast masking. As a result, the smooth and edge regions are effectively estimated, while the disorderly texture regions are always underestimated. The disorderly texture regions possess a large amount of disorderly structures and the HVS cannot fully perceive them. Therefore, in this work, we suggest to consider the disorder degree of structure for JND threshold estimation. According to the correlation among neighboring pixels, the uncertain information is extracted, and the disorder degree of structure is computed, which we called structural uncertainty. Then, taking the effect of background luminance, contrast, and structural uncertainty into account, a novel JND model is deduced. Experimental results demonstrate that the proposed JND can accurately estimate the visible thresholds of different image regions. Moreover, the proposed JND is adopted to remove visual redundancy for JPEG compression, which saves about 14% bit rate while keeping the perceptual quality.


title: ACCUMULATION OF BIORESISTANT MICROPOLLUTANTS IN A PARTICULAR CATCHMENT IN HUNGARY

abstract: Abstract - Research work from 1973 to 1975 has resulted in the exploration of the heavy metal pollutants, mercury, cadmium, lead, zinc, iron, etc. in one of the most heavily polluted streams in Hungary, the Sajo River (1-6). At flow rates lower than the mean the bulk of heavy metals was found to accumulate in the bottom silt, to be stirred up, spread, or scoured subsequently at flood discharges. In 1976-1977 the bioresistant substances accumulated in the bottom sediment were studied in the river, in the receiving waters, as well as in the largest reservoir of Hungary situated downstream of the tributary. For describing the heavy metal pollution the distribution of various metals in the −25 μm; 25−50 μm and + 50 μm fractions was determined by settling the bottom silt. Around 80% of mercury and lead were found to be present in the −50 μm fraction, about 80% of cadmium in the 25−50 μm fraction and the greater part of zinc in the + 50 μm fraction. Examining among the bioresistant substances, the carcinogenic, polycyclic aromatic hydrocarbons (3,4-benzpyrene, pyrene, etc.), present in the extractable substances, the amount thereof relative to the extractable matter was found to increase with distance from the polluting source.


Wearable Goniometer and Accelerometer Sensory Fusion for Knee Joint Angle Measurement in Daily Life


title: 3D face recognition: a survey

abstract: 3D face recognition has become a trending research direction in both industry and academia. It inherits advantages from traditional 2D face recognition, such as the natural recognition process and a wide range of applications. Moreover, 3D face recognition systems could accurately recognize human faces even under dim lights and with variant facial positions and expressions, in such conditions 2D face recognition systems would have immense difficulty to operate. This paper summarizes the history and the most recent progresses in 3D face recognition research domain. The frontier research results are introduced in three categories: pose-invariant recognition, expression-invariant recognition, and occlusion-invariant recognition. To promote future research, this paper collects information about publicly available 3D face databases. This paper also lists important open problems.


Situating Social Identity through Language Convergence in Online Groups


title: Transformer shielding technique for common mode noise reduction in isolated converters

abstract: In isolated converter, transformer is a main path of common mode current. Methods of how to reduce the noise through transformer have been widely studied. One effective technique is using shield between primary and secondary winding. In this paper, EMI noise transferring path and EMI model for typical isolated converters are analyzed. And the survey about different methods of shielding is discussed. Their pros and cons are analyzed. Then the balance concept is introduced and our proposed double shielding using balance concept for wire winding transformer is raised. It can control the parasitic capacitance accurately and is easy to manufacturing. Next, a newly proposed single layer shielding for PCB winding transformer is discussed. The experiment results are provided to verify the methods.


title: Sparse Bayesian Inference for Dense Semantic Mapping

abstract: Despite impressive advances in simultaneous localization and mapping, dense robotic mapping remains challenging due to its inherent nature of being a high-dimensional inference problem. In this paper, we propose a dense semantic robotic mapping technique that exploits sparse Bayesian models, in particular, the relevance vector machine, for high-dimensional sequential inference. The technique is based on the principle of automatic relevance determination and produces sparse models that use a small subset of the original dense training set as the dominant basis. The resulting map posterior is continuous, and queries can be made efficiently at any resolution. Moreover, the technique has probabilistic outputs per semantic class through Bayesian inference. We evaluate the proposed relevance vector semantic map using publicly available benchmark datasets, NYU Depth V2 and KITTI; and the results show promising improvements over the state-of-the-art techniques.


title: Global optimization of Lipschitz functions

abstract: The goal of the paper is to design sequential strategies which lead to efficient optimization of an unknown function under the only assumption that it has a finite Lipschitz constant. We first identify sufficient conditions for the consistency of generic sequential algorithms and formulate the expected minimax rate for their performance. We introduce and analyze a first algorithm called LIPO which assumes the Lipschitz constant to be known. Consistency, minimax rates for LIPO are proved, as well as fast rates under an additional Holder like condition. An adaptive version of LIPO is also introduced for the more realistic setup where the Lipschitz constant is unknown and has to be estimated along with the optimization. Similar theoretical guarantees are shown to hold for the adaptive LIPO algorithm and a numerical assessment is provided at the end of the paper to illustrate the potential of this strategy with respect to state-of-the-art methods over typical benchmark problems for global optimization.


Characteristics of Chronic Wounds that Itch


title: Nance-Horan syndrome in females due to a balanced X;1 translocation that disrupts the NHS gene: Familial case report and review of the literature.

abstract: The Nance-Horan syndrome is an X-linked disorder characterized by congenital cataract, facial features, microcornea, microphthalmia, and dental anomalies; most of the cases are due to NHS gene mutations on Xp22.13. Heterozygous carrier females generally present less severe features, and up to 30% of the affected males have intellectual disability. We describe two patients, mother and daughter, manifesting Nance-Horan syndrome. The cytogenetic and molecular analyses demonstrated a 46,X,t(X;1)(p22.13;q22) karyotype in each of them. No copy-number genomic imbalances were detected by high-density microarray analysis. The mother had a preferential inactivation of the normal X chromosome; expression analysis did not detect any mRNA isoform of NHS. This is the first report of Nance-Horan syndrome due to a skewed X chromosome inactivation resulting from a balanced translocation t(X;1) that disrupts the NHS gene expression, with important implications for clinical presentation and genetic counseling.


title: The HEALTHGRAIN Cereal Diversity Screen: concept, results, and prospects.

abstract: One hundred and fifty bread wheat lines and 50 other lines of small-grain cereals (spelt, durum wheat, Triticum monococcum, Triticum dicoccum, oats, rye, and barley) were selected for diversity in their geographical origin, age, and characteristics. They were grown on a single site in Hungary in 2004-2005, harvested, milled, and analyzed for a range of phytochemicals (tocols, sterols, phenolic acids, folates, alkylresorcinols) and fiber components that are considered to have health benefits. Detailed analyses of these components in the different species are reported in a series of accompanying papers. The present paper discusses the comparative levels of the bioactive components in the different species, showing differences in both ranges and mean amounts. Furthermore, detailed comparisons of the bread wheat lines show that it is possible to identify lines in which high levels of phytochemicals and dietary fiber components are combined with good yield and processing quality. This means that commercially competitive lines with high levels of bioactive components are a realistic goal for plant breeders.


title: Detecting Life Events in Feeds from Twitter

abstract: Short posts on micro-blogs are characterized by high ambiguity and non-standard language. We focus on detecting life events from such micro-blogs, a type of event which have not been paid much attention so far. We discuss the corpus we assembled and our experiments. Simpler models based on unigrams perform better than models that include history, number of retweets and semantic roles.


title: A Coordinated Approach to Channel Estimation in Large-scale Multiple-antenna Systems

abstract: This paper addresses the problem of channel estimation in multi-cell interference-limited cellular networks. We consider systems employing multiple antennas and are interested in both the finite and large-scale antenna number regimes (so-called "massive MIMO"). Such systems deal with the multi-cell interference by way of per-cell beamforming applied at each base station. Channel estimation in such networks, which is known to be hampered by the pilot contamination effect, constitutes a major bottleneck for overall performance. We present a novel approach which tackles this problem by enabling a low-rate coordination between cells during the channel estimation phase itself. The coordination makes use of the additional second-order statistical information about the user channels, which are shown to offer a powerful way of discriminating across interfering users with even strongly correlated pilot sequences. Importantly, we demonstrate analytically that in the large-number-of-antennas regime, the pilot contamination effect is made to vanish completely under certain conditions on the channel covariance. Gains over the conventional channel estimation framework are confirmed by our simulations for even small antenna array sizes.


title: “Bricks and Clicks”: The Impact of Product Returns on the Strategies of Multichannel Retailers

abstract: The Internet has increased the flexibility of retailers, allowing them to operate an online arm in addition to their physical stores. The online channel offers potential benefits in selling to customer segments that value the convenience of online shopping, but it also raises new challenges. These include the higher likelihood of costly product returns when customers' ability to “touch and feel” products is important in determining fit. We study competing retailers that can operate dual channels “bricks and clicks” and examine how pricing strategies and physical store assistance levels change as a result of the additional Internet outlet. A central result we obtain is that when differentiation among competing retailers is not too high, having an online channel can actually increase investment in store assistance levels e.g., greater shelf display, more-qualified sales staff, floor samples and decrease profits. Consequently, when the decision to open an Internet channel is endogenized, there can exist an asymmetric equilibrium where only one retailer elects to operate an online arm but earns lower profits than its bricks-only rival. We also characterize equilibria where firms open an online channel, even though consumers only use it for research and learning purposes but buy in stores. A number of extensions are discussed, including retail settings where firms carry multiple product categories, shipping and handling costs, and the role of store assistance in impacting consumer perceived benefits.


title: Crowdsensing in Smart Cities: Overview, Platforms, and Environment Sensing Issues

abstract: Evidence shows that Smart Cities are starting to materialise in our lives through the gradual introduction of the Internet of Things (IoT) paradigm. In this scope, crowdsensing emerges as a powerful solution to address environmental monitoring, allowing to control air pollution levels in crowded urban areas in a distributed, collaborative, inexpensive and accurate manner. However, even though technology is already available, such environmental sensing devices have not yet reached consumers. In this paper, we present an analysis of candidate technologies for crowdsensing architectures, along with the requirements for empowering users with air monitoring capabilities. Specifically, we start by providing an overview of the most relevant IoT architectures and protocols. Then, we present the general design of an off-the-shelf mobile environmental sensor able to cope with air quality monitoring requirements; we explore different hardware options to develop the desired sensing unit using readily available devices, discussing the main technical issues associated with each option, thereby opening new opportunities in terms of environmental monitoring programs.


title: An Advanced Bio-Inspired PhotoPlethysmoGraphy (PPG) and ECG Pattern Recognition System for Medical Assessment

abstract: Physiological signals are widely used to perform medical assessment for monitoring an extensive range of pathologies, usually related to cardio-vascular diseases. Among these, both PhotoPlethysmoGraphy (PPG) and Electrocardiography (ECG) signals are those more employed. PPG signals are an emerging non-invasive measurement technique used to study blood volume pulsations through the detection and analysis of the back-scattered optical radiation coming from the skin. ECG is the process of recording the electrical activity of the heart over a period of time using electrodes placed on the skin. In the present paper we propose a physiological ECG/PPG “combo” pipeline using an innovative bio-inspired nonlinear system based on a reaction-diffusion mathematical model, implemented by means of the Cellular Neural Network (CNN) methodology, to filter PPG signal by assigning a recognition score to the waveforms in the time series. The resulting “clean” PPG signal exempts from distortion and artifacts is used to validate for diagnostic purpose an EGC signal simultaneously detected for a same patient. The multisite combo PPG-ECG system proposed in this work overpasses the limitations of the state of the art in this field providing a reliable system for assessing the above-mentioned physiological parameters and their monitoring over time for robust medical assessment. The proposed system has been validated and the results confirmed the robustness of the proposed approach.


title: Breast Mass Classification from Mammograms using Deep Convolutional Neural Networks

abstract: Mammography is the most widely used method to screen breast cancer. Because of its mostly manual nature, variability in mass appearance, and low signal-to-noise ratio, a significant number of breast masses are missed or misdiagnosed. In this work, we present how Convolutional Neural Networks can be used to directly classify pre-segmented breast masses in mammograms as benign or malignant, using a combination of transfer learning, careful pre-processing and data augmentation to overcome limited training data. We achieve state-of-the-art results on the DDSM dataset, surpassing human performance, and show interpretability of our model.


title: Information Contagion: an Empirical Study of the Spread of News on Digg and Twitter Social Networks

abstract: Social networks have emerged as a critical factor in information dissemination, search, marketing, expertise and influence discovery, and potentially an important tool for mobilizing people. Social media has made social networks ubiquitous, and also given researchers access to massive quantities of data for empirical analysis. These data sets offer a rich source of evidence for studying dynamics of individual and group behavior, the structure of networks and global patterns of the flow of information on them. However, in most previous studies, the structure of the underlying networks was not directly visible but had to be inferred from the flow of information from one individual to another. As a result, we do not yet understand dynamics of information spread on networks or how the structure of the network affects it. We address this gap by analyzing data from two popular social news sites. Specifically, we extract social networks of active users on Digg and Twitter, and track how interest in news stories spreads among them. We show that social networks play a crucial role in the spread of information on these sites, and that network structure affects dynamics of information flow.


title: Psychopathy increases perceived moral permissibility of accidents.

abstract: Psychopaths are notorious for their antisocial and immoral behavior, yet experimental studies have typically failed to identify deficits in their capacities for explicit moral judgment. We tested 20 criminal psychopaths and 25 criminal nonpsychopaths on a moral judgment task featuring hypothetical scenarios that systematically varied an actor’s intention and the action’s outcome. Participants were instructed to evaluate four classes of actions: accidental harms, attempted harms, intentional harms, and neutral acts. Psychopaths showed a selective difference, compared with nonpsychopaths, in judging accidents, where one person harmed another unintentionally. Specifically, psychopaths judged these actions to be more morally permissible. We suggest that this pattern reflects psychopaths’ failure to appreciate the emotional aspect of the victim’s experience of harm. These findings provide direct evidence of abnormal moral judgment in psychopathy.


title: A review of models and frameworks for designing mobile learning experiences and environments | Un examen des modèles et cadres de travail pour la conception d’expériences et d’environnements d’apprentissage mobile

abstract: Mobile learning has become increasingly popular in the past decade due to the unprecedented technological affordances achieved through the advancement of mobile computing, making ubiquitous and situated learning possible. At the same time, there have been research and implementation projects whose efforts centered on developing mobile learning experiences for various learners’ profiles, accompanied by the development of models and frameworks for designing mobile learning experiences. This paper focuses on categorizing and synthesizing models and frameworks targeted specifically on mobile learning. A total of 17 papers were reviewed, and the models or frameworks were divided into five categories and discussed: 1) pedagogies and learning environment design; 2) platform/system design; 3) technology acceptance; (4) evaluation; and 5) psychological construct. This paper provides a review and synthesis of the models/frameworks. The categorization and analysis can also help inform evaluation, design, and development of curriculum and environments for meaningful mobile learning experiences for learners of various demographics. ::: ::: Mobile learning has become increasingly popular in the past decade due to the unprecedented technological affordances achieved through the advancement of mobile computing, which makes ubiquitous and situated learning possible. At the same time, there have been research and implementation projects whose efforts centered on developing mobile learning experiences for various learners’ profiles, accompanied by the development of models and frameworks for designing mobile learning experiences. This paper focuses on categorizing and synthesizing models and frameworks targeted specifically on mobile learning. A total of 17 papers were reviewed, and the models or frameworks were divided into five categories and discussed: 1) pedagogies and learning environment design; 2) platform/system design; 3) technology acceptance; (4) evaluation; and 5) psychological construct. This paper provides a review and synthesis of the models/frameworks. The categorization and analysis can also help inform evaluation, design, and development of curriculum and environments for meaningful mobile learning experiences for learners of various demographics.


title: EMOTIONAL INTELLIGENCE AND ACADEMIC ACHIEVEMENT AMONG ADOLESCENTS

abstract: The aim of the study is to examine the Emotional Intelligence and Academic Achievement among Adolescents. The sample comprised of 100 college students ranging in age between 16–17 years studying in Junior and Senior Intermediate in Tirupati town. The sample consisted of 50 Boys and 50 Girl Adolescents. The participants were administered with Emotional Intelligence scale developed by Mangal and Mangal (2004). Chi – square test was used for analysis of the data. The results of the study indicate that there is significant difference between Emotional Intelligence and Academic Achievement among Adolescents.


title: IEEE 802.11aa intra-AC prioritization - A new method of increasing the granularity of traffic prioritization in WLANs

abstract: Audio-video streaming in Wi-Fi (IEEE 802.11) wireless local area networks (WLANs) has recently attracted a lot of attention. In order to increase the granularity of traffic prioritization and quality of service (QoS) support defined by EDCA, intra-access category prioritization has recently been introduced in the IEEE 802.11aa amendment. In this paper, we present novel results which show how the new mechanism impacts the performance of an access point (AP). Additionally, we propose a wireless credit-based shaper algorithm (WCBSA), a version of CBSA adjusted to the wireless environment. We also analyze how the different settings of WCBSA impact the prioritization of traffic streams. Finally, we compare the operation of 802.11aa intra-access category prioritization with the legacy inter-access category prioritization defined by EDCA.


title: Graph analysis for detecting fraud, waste, and abuse in healthcare data

abstract: Detection of fraud, waste, and abuse (FWA) is an important yet difficult problem. In this paper, we describe a system to detect suspicious activities in large healthcare claims datasets. Each healthcare dataset is viewed as a heterogeneous network of patients, doctors, pharmacies, and other entities. These networks can be large, with millions of patients, hundreds of thousands of doctors, and tens of thousands of pharmacies, for example. Graph analysis techniques are developed to find suspicious individuals, suspicious relationships between individuals, unusual changes over time, unusual geospatial dispersion, and anomalous networks within the overall graph structure. The system has been deployed on multiple sites and data sets, both government and commercial, to facilitate the work of FWA investigation analysts.


title: A novel hybrid genetic algorithm for solving Sudoku puzzles

abstract: In this article, a novel hybrid genetic algorithm is proposed. The selection operator, crossover operator and mutation operator of the genetic algorithm have effectively been improved according to features of Sudoku puzzles. The improved selection operator has impaired the similarity of the selected chromosome and optimal chromosome in the current population such that the chromosome with more abundant genes is more likely to participate in crossover; such a designed crossover operator has possessed dual effects of self-experience and population experience based on the concept of tactfully combining PSO, thereby making the whole iterative process highly directional; crossover probability is a random number and mutation probability changes along with the fitness value of the optimal solution in the current population such that more possibilities of crossover and mutation could then be considered during the algorithm iteration. The simulation results show that the convergence rate and stability of the novel algorithm has significantly been improved.


title: Trait Emotional Intelligence and the Dark Triad Traits of Personality

abstract: This study presents the first behavioral genetic investigation of the relationships between trait emotional intelligence (trait EI or trait emotional self-efficacy) and the Dark Triad traits of narcissism, Machiavellianism, and psychopathy. In line with trait EI theory, the construct correlated positively with narcissism, but negatively with the other two traits. Generally, the correlations were consistent across the 4 factors and 15 facets of the construct. Cholesky decomposition analysis revealed that the phenotypic associations were primarily due to correlated genetic factors and secondarily due to correlated nonshared environmental factors, with shared environmental factors being nonsignificant in all cases. Results are discussed from the perspective of trait EI theory with particular reference to the issue of adaptive value.


Spotting Spurious Data with Neural Networks


title: Artistic Style Transfer for Videos and Spherical Images

abstract: Manually re-drawing an image in a certain artistic style takes a professional artist a long time. Doing this for a video sequence single-handedly is beyond imagination. We present two computational approaches that transfer the style from one image (for example, a painting) to a whole video sequence. In our first approach, we adapt to videos the original image style transfer technique by Gatys et al. based on energy minimization. We introduce new ways of initialization and new loss functions to generate consistent and stable stylized video sequences even in cases with large motion and strong occlusion. Our second approach formulates video stylization as a learning problem. We propose a deep network architecture and training procedures that allow us to stylize arbitrary-length videos in a consistent and stable way, and nearly in real time. We show that the proposed methods clearly outperform simpler baselines both qualitatively and quantitatively. Finally, we propose a way to adapt these approaches also to 360 ::: $$^\circ $$ ::: images and videos as they emerge with recent virtual reality hardware.


Fourier Transformation and Linear Differential Equations


title: PESTEL analysis of the development of the waste-to-energy incineration industry in China

abstract: With the upgrading of living standards and rapid urbanization throughout the world, municipal solid waste (MSW) treatment has become a ubiquitous environmental issue. Incineration can reduce the quantity and volume of MSW, save land resources and generate power, and it has been considered an effective means of MSW treatment. However, a variety of obstacles to the development of the waste-to-energy (WTE) incineration industry have emerged. To identify and overcome these obstacles, this paper applies the Political, Economic, Social, Technological, Environmental and Legal (PESTEL) framework to analyse the macro-environment of the WTE incineration industry in China. First, the MSW treatment status and treatment methods in China are summarized. Then, we analyse the PESTEL factors in the WTE incineration industry in China, including relevant legislation and policies, investment modes and intensity, the distribution and scale of investment, difficulties in MSW treatment and classification, public concerns, the various incineration techniques, environmental protection issues and the existing shortcomings. Finally, managerial implications and suggestions for the government and private sector regarding policy changes, the application of the Public-Private Partnership (PPP) mode and efficient project operation are provided. It is expected to pave the way for private investors to finance such projects.


title: Improving Knowledge Graph Embedding Using Simple Constraints

abstract: Embedding knowledge graphs (KGs) into continuous vector spaces is a focus of current research. Early works performed this task via simple models developed over KG triples. Recent attempts focused on either designing more complicated triple scoring models, or incorporating extra information beyond triples. This paper, by contrast, investigates the potential of using very simple constraints to improve KG embedding. We examine non-negativity constraints on entity representations and approximate entailment constraints on relation representations. The former help to learn compact and interpretable representations for entities. The latter further encode regularities of logical entailment between relations into their distributed representations. These constraints impose prior beliefs upon the structure of the embedding space, without negative impacts on efficiency or scalability. Evaluation on WordNet, Freebase, and DBpedia shows that our approach is simple yet surprisingly effective, significantly and consistently outperforming competitive baselines. The constraints imposed indeed improve model interpretability, leading to a substantially increased structuring of the embedding space. Code and data are available at this https URL


title: Kansei Engineering: A study on Perception of Online Clothing Websites

abstract: In the early days of e-Commerce, web designers follow their intuition of how the website should look like. Further development of web design have seen for instance Nielsen’s heuristic, which resulted designers to focus mainly on cognitive functionality and usability. However, as technology advances and e-Commerce matures, there is a growing concern to improve the consumer interface of e-Commerce to improve the persuasive power of eCommerce websites by engineering affective appeal in website design. This paper presents a study in progress involving the adoption of Kansei Engineering in e-Commerce websites as a systematic method to engineer consumer’s affective appeal and incorporate them into new formulae of web design. Principal Component Analysis was performed to analyse structure of Kansei Words, and investigates relations between Kansei and website designs.


title: Case-based recommender systems

abstract: We describe recommender systems and especially case-based recommender systems. We define a framework in which these systems can be understood. The framework contrasts collaborative with case-based, reactive with proactive, single-shot with conversational, and asking with proposing. Within this framework, we review a selection of papers from the case-based recommender systems literature, covering the development of these systems over the last ten years.


title: Scoping studies: advancing the methodology

abstract: BackgroundScoping studies are an increasingly popular approach to reviewing health research evidence. In 2005, Arksey and O'Malley published the first methodological framework for conducting scoping studies. While this framework provides an excellent foundation for scoping study methodology, further clarifying and enhancing this framework will help support the consistency with which authors undertake and report scoping studies and may encourage researchers and clinicians to engage in this process.DiscussionWe build upon our experiences conducting three scoping studies using the Arksey and O'Malley methodology to propose recommendations that clarify and enhance each stage of the framework. Recommendations include: clarifying and linking the purpose and research question (stage one); balancing feasibility with breadth and comprehensiveness of the scoping process (stage two); using an iterative team approach to selecting studies (stage three) and extracting data (stage four); incorporating a numerical summary and qualitative thematic analysis, reporting results, and considering the implications of study findings to policy, practice, or research (stage five); and incorporating consultation with stakeholders as a required knowledge translation component of scoping study methodology (stage six). Lastly, we propose additional considerations for scoping study methodology in order to support the advancement, application and relevance of scoping studies in health research.SummarySpecific recommendations to clarify and enhance this methodology are outlined for each stage of the Arksey and O'Malley framework. Continued debate and development about scoping study methodology will help to maximize the usefulness and rigor of scoping study findings within healthcare research and practice.


title: Effect of Propranolol on Word Fluency in Autism

abstract: Objective and Background: Autism is characterized by repetitive behaviors and impaired socialization and communication. Preliminary evidence showed possible language benefits in autism from the b-adrenergic antagonist propranolol. Earlier studies in other populations suggested propranolol might benefit performance on tasks involving a search of semantic and associative networks under certain conditions. Therefore, we wished to determine whether this benefit of propranolol includes an effect on semantic fluency in autism. Methods: A sample of 14 high-functioning adolescent and adult participants with autism and 14 matched controls were given letter and category word fluency tasks on 2 separate testing sessions; 1 test was given 60 minutes after the administration of 40 mg propranolol orally, and 1 test was given after placebo, administered in a double-blinded, counterbalanced manner. Results: Participants with autism were significantly impaired compared with controls on both fluency tasks. Propranolol significantly improved performance on category fluency, but not letter fluency among autism participants. No drug effect was observed among controls. Expected drug effects on heart rate and blood pressure were observed in both the groups. Conclusions: Results are consistent with a selective beneficial effect of propranolol on flexibility of access to semantic and associative networks in autism, with no observed effect on phonological networks. Further study will be necessary to understand potential clinical implications of this finding.


title: Identity Deception Prevention Using Common Contribution Network Data

abstract: Identity deception in social media applications has negatively impacted online communities and it is likely to increase as the social media user population grows. The ease of generating new accounts on social media has exacerbated the issue. Many previous studies have been posited that focused on both verbal, non-verbal, and network data produced by users in an attempt to detect identity deception. However, although these methods produced a high accuracy, they are mainly reactive to the issue of identity deception. This paper proposes a proactive approach that leverages social network data and it is focused on identity deception prevention for online sub-communities, communities that exist within larger communities (e.g., Facebook groups or Subreddits). The method can be applied to various types of social media applications and produces high accuracy in identifying deceptive accounts at the time of attempted entry to a sub-community. Performance results as well as limitations for the method are presented. A discussion follows on the identification of possible implications of this paper for social media applications and future directions on deception prevention are proposed.


title: Fourier tansform infrared (FT-IR) spectroscopy: A rapid tool for detection and analysis of foodborne pathogenic bacteria

abstract: Fourier transform infrared (FT&IR) spectroscopy is a physico&chemical method based on measurement of vibration of a molecule excited by IR radiation at a specific wavelength range. FT&IR spectra of bacterial cells can be used to analyze their total composition, including proteins, fatty acids, carbohydrates, nucleic acids, and lipopolysacharides. FT&IR techniques coupled with different chemometrics analyses of the spectra offer a wide range of applications for food microbiology, including detection, differentiation, quantification, and taxonomic level classification of bacteria from culture broth or food matrices. FT&IR spectroscopy is a reliable, rapid, and economic technique which could be explored as a routine diagnostic tool for bacterial analysis by the food industry, diagnostic laboratories, and public health authorities. This chapter highlights the principles of FT&IR spectroscopic analysis of bacteria, the advantages and disadvantages of FT& IR applied to bacterial analysis, various sampling techniques, spectral manipulation, statistical analysis of spectra, and applications in pathogen detection.


Bioinformatics strategies for taxonomy independent binning and visualization of sequences in shotgun metagenomics


Characterization, analysis and risks of nanomaterials in environmental and food samples


title: Scientific benchmarking of parallel computing systems: twelve ways to tell the masses when reporting performance results

abstract: Measuring and reporting performance of parallel computers constitutes the basis for scientific advancement of high-performance computing (HPC). Most scientific reports show performance improvements of new techniques and are thus obliged to ensure reproducibility or at least interpretability. Our investigation of a stratified sample of 120 papers across three top conferences in the field shows that the state of the practice is lacking. For example, it is often unclear if reported improvements are deterministic or observed by chance. In addition to distilling best practices from existing work, we propose statistically sound analysis and reporting techniques and simple guidelines for experimental design in parallel computing and codify them in a portable benchmarking library. We aim to improve the standards of reporting research results and initiate a discussion in the HPC field. A wide adoption of our minimal set of rules will lead to better interpretability of performance results and improve the scientific culture in HPC.


title: Quasi horn antenna array for Ku band monopulse radiation

abstract: In this paper, a quasi-horn antenna array, which is combination of 4 × 4 slot array and its flares, is designed for Ku band monopulse radiation. The slots are fed by waveguide power divider. In order to obtain sum and difference radiation pattern, a magic T is utilized end of the waveguide feeder. Slot dimensions and distance between each slot are adjusted and optimized to get high gain and low return loss using 3D EM simulation. Short depth flares are added to aperture of each 4 × 1 slots to enhance gain of the array. According to the simulation results, gain of the array at the sum port is 21.9 dBi. At the difference port, monopulse radiation pattern have 19.3 dBi maximum gain and −32 dB null. Return losses at the sum and difference port are lower than −6 dB in the operation band. Total dimensions of the 4 × 4 array are 8.36 cm × 8.4 cm × 7.06 cm.


title: Learning Embedding Adaptation for Few-Shot Learning.

abstract: Learning with limited data is a key challenge for visual recognition. Few-shot learning methods address this challenge by learning an instance embedding function from seen classes and apply the function to instances from unseen classes with limited labels. This style of transfer learning is task-agnostic: the embedding function is not learned optimally discriminative with respect to the unseen classes, where discerning among them is the target task. In this paper, we propose a novel approach to adapt the embedding model to the target classification task, yielding embeddings that are task-specific and are discriminative. To this end, we employ a type of self-attention mechanism called Transformer to transform the embeddings from task-agnostic to task-specific by focusing on relating instances from the test instances to the training instances in both seen and unseen classes. We verify the effectiveness of our model on both the standard few-shot classification benchmark and four extended few-shot learning settings with essential use cases (i.e. cross-domain, transductive, generalized few-shot learning, and large scale low-shot learning). Our approach archived consistent improvements over baseline models and previous state-of-the-art methods.


title: Ranking Scientific Publications Using a Simple Model of Network Traffic

abstract: To account for strong aging characteristics of citation networks, we modify Google's PageRank algorithm by initially distributing random surfers exponentially with age, in favor of more recent publications. The output of this algorithm, which we call CiteRank, is interpreted as approximate traffic to individual publications in a simple model of how researchers find new information. We develop an analytical understanding of traffic flow in terms of an RPA-like model and optimize parameters of our algorithm to achieve the best performance. The results are compared for two rather different citation networks: all American Physical Society publications and the set of high-energy physics theory (hep-th) preprints. Despite major differences between these two networks, we find that their optimal parameters for the CiteRank algorithm are remarkably similar.


title: The WSLA Framework: Specifying and Monitoring Service Level Agreements for Web Services

abstract: We describe a novel framework for specifying and monitoring Service Level Agreements (SLA) for Web Services. SLA monitoring and enforcement become increasingly important in a Web Service environment where enterprise applications and services rely on services that may be subscribed dynamically and on-demand. For economic and practical reasons, we want an automated provisioning process for both the service itself as well as the SLA managment system that measures and monitors the QoS parameters, checks the agreed-upon service levels, and reports violations to the authorized parties involved in the SLA management process. Our approach to these issues is presented in this paper. The Web Service Level Agreement (WSLA) framework is targeted at defining and monitoring SLAs for Web Services. Although WSLA has been designed for a Web Services environment, it is applicable as well to any inter-domain management scenario, such as business process and service management, or the management of networks, systems and applications in general. The WSLA framework consists of a flexible and extensible language based on XML Schema and a runtime architecture comprising several SLA monitoring services, which may be outsourced to third parties to ensure a maximum of objectivity. WSLA enables service customers and providers to unambiguously define a wide variety of SLAs, specify the SLA parameters and the way they are measured, and relate them to managed resource instrumentations. Upon receipt of an SLA specification, the WSLA monitoring services are automatically configured to enforce the SLA. An implementation of the WSLA framework, termed SLA Compliance Monitor, is publicly available as part of the IBM Web Services Toolkit.


title: Mean Actor Critic

abstract: We propose a new algorithm, Mean Actor-Critic (MAC), for discrete-action continuous-state reinforcement learning. MAC is a policy gradient algorithm that uses the agent's explicit representation of all action values to estimate the gradient of the policy, rather than using only the actions that were actually executed. We prove that this approach reduces variance in the policy gradient estimate relative to traditional actor-critic methods. We show empirical results on two control domains and on six Atari games, where MAC is competitive with state-of-the-art policy search algorithms.


title: Deep Adversarial Attention Alignment for Unsupervised Domain Adaptation: the Benefit of Target Expectation Maximization

abstract: In this paper, we make two contributions to unsupervised domain adaptation (UDA) using the convolutional neural network (CNN). First, our approach transfers knowledge in all the convolutional layers through attention alignment. Most previous methods align high-level representations, e.g., activations of the fully connected (FC) layers. In these methods, however, the convolutional layers which underpin critical low-level domain knowledge cannot be updated directly towards reducing domain discrepancy. Specifically, we assume that the discriminative regions in an image are relatively invariant to image style changes. Based on this assumption, we propose an attention alignment scheme on all the target convolutional layers to uncover the knowledge shared by the source domain. Second, we estimate the posterior label distribution of the unlabeled data for target network training. Previous methods, which iteratively update the pseudo labels by the target network and refine the target network by the updated pseudo labels, are vulnerable to label estimation errors. Instead, our approach uses category distribution to calculate the cross-entropy loss for training, thereby ameliorating the error accumulation of the estimated labels. The two contributions allow our approach to outperform the state-of-the-art methods by +2.6% on the Office-31 dataset.


title: IoT Applications on Secure Smart Shopping System

abstract: The Internet of Things (IoT) is changing human lives by connecting everyday objects together. For example, in a grocery store, all items can be connected with each other, forming a smart shopping system. In such an IoT system, an inexpensive radio frequency identification (RFID) tag can be attached to each product which, when placed into a smart shopping cart, can be automatically read by a cart equipped with an RFID reader. As a result, billing can be conducted from the shopping cart itself, preventing customers from waiting in a long queue at checkout. Additionally, smart shelving can be added into this system, equipped with RFID readers, and can monitor stock, perhaps also updating a central server. Another benefit of this kind of system is that inventory management becomes much easier, as all items can be automatically read by an RFID reader instead of manually scanned by a laborer. To validate the feasibility of such a system, in this paper we identify the design requirements of a smart shopping system, build a prototype system to test functionality, and design a secure communication protocol to make the system practical. To the best of our knowledge, this is the first time a smart shopping system is proposed with security under consideration.


title: Potential Electricity Savings by Implementing Energy Efficiency Standards for Household Electrical Appliances in Malaysia

abstract: Publisher Summary Household electrical appliances consume a significant amount of electricity in the domestic sector of Malaysia and have become a target for efficiency improvement. Like other developing countries in southeast Asia, Malaysia is experiencing a dramatic growth in the number of household electrical appliances usage and it will be higher in the future. This chapter attempts to identify energy saving possibilities by implementing energy efficiency standards for selected household appliances. As such, efficiency improvement on these appliances will give a significant impact on future electricity consumption in the country. In the improving household appliances efficiency, a new infrastructure has to be created by government to be able to design and implement minimum energy efficiency standards. The establishment of a working relationship between the industry and the government is crucial. Many countries provide guidance and information about correlation between government and industry.


LINEAR CONVOLUTION OF CRITERIA IN MULTICRITERIA OPTIMIZATION


title: Dynamic windowing mechanism to combine sentiment and N-gram analysis in detecting events from social media

abstract: Social sensing is a new paradigm that inherits the main ideas of sensor networks and considers the users as new sensor types. For instance, by the time the users find out that an event has happened, they start to share the related posts and express their feelings through the social networks. Consequently, these networks are becoming a powerful news media in a wide range of topics. Existing event detection methods mostly focus on either the keyword burst or sentiment of posts, and ignore some natural aspects of social networks such as the dynamic rate of arriving posts. In this paper, we devised Dynamic Social Event Detection approach that exploits a new dynamic windowing method. Besides, we add a mechanism to combine the sentiment of posts with the keywords burst in the dynamic windows. The combination of sentiment analysis and the frequently used keywords enhances our approach to detect events with a different level of user engagement. To analyze the behavior of the devised approach, we use a wide range of metrics including histogram of window sizes, sentiment oscillations of posts, topic recall, keyword precision, and keyword recall on two benchmarked datasets. One of the significant outcomes of the devised method is the topic recall of 100% for FA Cup dataset.


title: Open Logo Detection Challenge

abstract: Existing logo detection benchmarks consider artificial deployment scenarios by assuming that large training data with fine-grained bounding box annotations for each class are available for model training. Such assumptions are often invalid in realistic logo detection scenarios where new logo classes come progressively and require to be detected with little or none budget for exhaustively labelling fine-grained training data for every new class. Existing benchmarks are thus unable to evaluate the true performance of a logo detection method in realistic and open deployments. In this work, we introduce a more realistic and challenging logo detection setting, called Open Logo Detection. Specifically, this new setting assumes fine-grained labelling only on a small proportion of logo classes whilst the remaining classes have no labelled training data to simulate the open deployment. We further create an open logo detection benchmark, called OpenLogo,to promote the investigation of this new challenge. OpenLogo contains 27,083 images from 352 logo classes, built by aggregating/refining 7 existing datasets and establishing an open logo detection evaluation protocol. To address this challenge, we propose a Context Adversarial Learning (CAL) approach to synthesising training data with coherent logo instance appearance against diverse background context for enabling more effective optimisation of contemporary deep learning detection models. Experiments show the performance advantage of CAL over existing state-of-the-art alternative methods on the more realistic and challenging OpenLogo benchmark.


title: Two-dimensional higher-diffraction-order optical beam splitter based on phenanthrenequinone-doped poly(methyl methacrylate) photopolymer

abstract: A two-dimensional optical beam splitter has been realized that uses the higher diffraction orders of a refractive-index grating. Gratings were recorded experimentally with light from a semiconductor laser incident at a small angle on phenanthrenequinone-doped poly(methyl methacrylate) photopolymer. The incident signal beam, which was made up of three different wavelengths (632.8, 532.0, and 488.0 nm), was split by the grating into multiple output beams with nearly equal size and separation. Results are given for when the sample grating was placed behind, in front of, and in the focal plane of a Fourier lens. The properties of higher-order-diffraction images have been discussed. The discussion shows that a two-dimensional higher-diffraction-order optical beam splitter provides a practical method for splitting a signal beam.


title: PAPERS Efficient Tempo and Beat Tracking in Audio Recordings *

abstract: Automatic beat tracking consists of estimating the number of beats per minutes at which a music track is played and identifying exactly when these beats occur. Applications range from music analysis, sound-effect synchronization, and audio editing to automatic playlist generation and deejaying. An off-line beat-tracking technique for estimating a time-varying tempo in an audio track is presented. The algorithm uses an MMSE estimation of local tempo and beat location candidates, followed by a dynamic programming stage used to determine the optimum choice of candidate in each analysis frame. The algorithm is efficient in its use of computation resource, yet provides very good results on a wide range of audio tracks. The algorithm details are presented, followed by a discussion of the performance and suggestions for further improvements.


title: Thinking in Terms of Design Decisions When Developing Maturity Models

abstract: To measure dedicated aspects of â€œmaturityâ€ , a range of maturity models have been developed in the field of information systems by practitioners and academics over the past years. Despite its broad proliferation, the concept has not escaped criticism. Unnecessary bureaucracy, poor theoretical foundation, and the impression of a falsified certainty to achieve success are a few examples. As there is a significant lack of knowledge on how to design theoretically sound and widely accepted maturity models, in this paper, the author opens the discussion on design decisions when developing these models. Based on analogy and informed arguments, the author synthesizes a generic but adjuvant framework that consists of five common design steps and eighteen decision parameters that help practitioners as well as researchers in the development of maturity models.


title: Ontologies and semantics for seamless connectivity

abstract: The goal of having networks of seamlessly connected people, software agents and IT systems remains elusive. Early integration efforts focused on connectivity at the physical and syntactic layers. Great strides were made; there are many commercial tools available, for example to assist with enterprise application integration. It is now recognized that physical and syntactic connectivity is not adequate. A variety of research systems have been developed addressing some of the semantic issues. In this paper, we argue that ontologies in particular and semantics-based technologies in general will play a key role in achieving seamless connectivity. We give a detailed introduction to ontologies, summarize the current state of the art for applying ontologies to achieve semantic connectivity and highlight some key challenges.


title: Traffic Sign Recognition using SURF: Speeded up robust feature descriptor and artificial neural network classifier

abstract: Traffic Sign Recognition (TSR) system is a vital component of intelligent transport system. It plays an important role by enhancing the safety of the drivers, pedestrians and vehicles as traffic signs provide important information of the traffic environment of the road and assist the drivers to drive more safely and easily by guiding and warning. This paper represents road sign detection and recognition system based on speeded up robust feature (SURF) descriptor that is invariant to rotation, skew and occlusion of the sign and artificial neural network (ANN) classifier. The proposed system consists of three steps. In the first step the road scene image is segmented using color based thresholding algorithm followed by some post processing like morphological analysis and filtering using region properties to extract the road sign as a blob of region. Next, shape verification is carried out by using translation, scaling and rotation invariant features vector named Distance to Borders (DtBs) of the segmented blob. Finally the recognition of the traffic sign is implemented using ANN classifier with the training of SURF descriptor. The proposed system simulated on offline road scene images shows a high success rate in the recognition stage. The performance of the classifier is illustrated in terms of cross entropy, confusion matrix and receiver operating characteristic (ROC) curve.


title: SWISS-MODEL: modelling protein tertiary and quaternary structure using evolutionary information

abstract: Protein structure homology modelling has become a routine technique to generate 3D models for proteins when experimental structures are not available. Fully automated servers such as SWISS-MODEL with user-friendly web interfaces generate reliable models without the need for complex software packages or downloading large databases. Here, we describe the latest version of the SWISS-MODEL expert system for protein structure modelling. The SWISS-MODEL template library provides annotation of quaternary structure and essential ligands and co-factors to allow for building of complete structural models, including their oligomeric structure. The improved SWISS-MODEL pipeline makes extensive use of model quality estimation for selection of the most suitable templates and provides estimates of the expected accuracy of the resulting models. The accuracy of the models generated by SWISS-MODEL is continuously evaluated by the CAMEO system. The new web site allows users to interactively search for templates, cluster them by sequence similarity, structurally compare alternative templates and select the ones to be used for model building. In cases where multiple alternative template structures are available for a protein of interest, a user-guided template selection step allows building models in different functional states. SWISS-MODEL is available at http://swissmodel.expasy.org/.


title: Safe Learning of Quadrotor Dynamics Using Barrier Certificates

abstract: To effectively control complex dynamical systems, accurate nonlinear models are typically needed. However, these models are not always known. In this paper, we present a data-driven approach based on Gaussian processes that learns models of quadrotors operating in partially unknown environments. What makes this challenging is that if the learning process is not carefully controlled, the system will go unstable, i.e., the quadcopter will crash. To this end, barrier certificates are employed for safe learning. The barrier certificates establish a non-conservative forward invariant safe region, in which high probability safety guarantees are provided based on the statistics of the Gaussian Process. A learning controller is designed to efficiently explore those uncertain states and expand the barrier certified safe region based on an adaptive sampling scheme. In addition, a recursive Gaussian Process prediction method is developed to learn the complex quadrotor dynamics in real-time. Simulation results are provided to demonstrate the effectiveness of the proposed approach.


title: Detection is the central problem in real-word spelling correction

abstract: Real-word spelling correction differs from non-word spelling correction in its aims and its challenges. Here we show that the central problem in real-word spelling correction is detection. Methods from non-word spelling correction, which focus instead on selection among candidate corrections, do not address detection adequately, because detection is either assumed in advance or heavily constrained. As we demonstrate in this paper, merely discriminating between the intended word and a random close variation of it within the context of a sentence is a task that can be performed with high accuracy using straightforward models. Trigram models are sufficient in almost all cases. The difficulty comes when every word in the sentence is a potential error, with a large set of possible candidate corrections. Despite their strengths, trigram models cannot reliably find true errors without introducing many more, at least not when used in the obvious sequential way without added structure. The detection task exposes weakness not visible in the selection task.


The dialogue breakdown detection challenge: Task description, datasets, and evaluation metrics.


title: Is it time to reassess the BDNF hypothesis of depression?

abstract: The brain-derived neurotrophic factor (BDNF) hypothesis of depression postulates that a loss of BDNF is directly involved in the pathophysiology of depression, and that its restoration may underlie the therapeutic efficacy of antidepressant treatment. While this theory has received considerable experimental support, an increasing number of studies have generated evidence which is not only inconsistent, but also directly contradicts the hypothesis. This article provides a critical review of the clinical and preclinical studies which have been responsible for this controversy, outlining pharmacological, behavioural and genetic evidence which demonstrates the contrasting role of BDNF in regulating mood and antidepressant effects throughout the brain. I will also review key studies, both human and animal, which have investigated the association of a BDNF single-nucleotide polymorphism (Val66Met) with depression pathogenesis, and detail the number of inconsistencies which also afflict this novel area of BDNF research. The article will conclude by discussing why now is a critical time to reassess the original BDNF hypothesis of depression, and look towards the formation of new models that can provide a more valid account of the complex relationships between growth factors, mood disorders and their treatment.


title: A 100-ps time-resolution CMOS time-to-digital converter for positron emission tomography imaging applications

abstract: An integrated CMOS subnanosecond time-to-digital converter (TDC) has been developed and evaluated for positron emission tomography (PET) front-end applications. The TDC architecture combines an accurate digital counter and an analog time interpolation circuit to make the time interval measurement. The dynamic range of the TDC is programmable and can be easily extended without any timing resolution degradation. The converter was designed to operate over a reference clock frequency range of 62.5 MHz up to 100 MHz and can have a bin size as small as 312.5 ps LSB with 100-ns conversion times. Measurements indicate the TDC achieves a DNL of under /spl plusmn/0.20 LSB and INL less than /spl plusmn/0.30 LSB with an rms timing resolution of 0.312 LSB (97.5 ps), very close to the theoretical limit of 0.289 LSB (90 ps). The design is believed to be the first fully integrated CMOS subnanosecond TDC used in PET medical imaging and the first realization of a CMOS TDC that achieves an rms timing resolution below 100 ps within a 100-ns conversion time.


title: Development of a novel robot-mediated adaptive response system for joint attention task for children with autism

abstract: With Centers for Disease Control and Prevention prevalence estimates for children with autism spectrum disorder (ASD) at 9.1 per 1,000 (1 in 110), identification and effective treatment of ASD is often characterized as a public health emergency. Emerging technology, especially robotic technology, has been shown to be appealing to these children and such interest can be harnessed to address the limitations while providing intervention services to young children with ASD. Generally the spectrum nature of autism calls for intensive, individualized intervention. However, existing robot-mediated systems tend to have limited adaptive capability that limits individualization. Our current work seeks to bridge this gap by developing a novel adaptive and individualized robot-mediated technology for children with ASD. The system is composed of a humanoid robot with its vision being augmented by several wall-mounted cameras for real-time head tracking using a distributed architecture. Based on the cues from the child's head movement, the robot intelligently adapts itself in an individualized manner to promote joint attention. The developed system is validated with two typically developing children. The validation results of the head tracker and the closed-loop nature of interaction are presented.


title: Eyes Closeness Detection Using Appearance Based Methods

abstract: Human eye closeness detection has gained wide applications in human computer interface designation, facial expression recognition, driver fatigue detection, and so on. In this work, we present an extensive comparison on several state of art appearance-based eye closeness detection methods, with emphasize on the role played by each crucial component, including geometric normalization, feature extraction, and classification. Three conclusions are highlighted through our experimental results: 1) fusing multiple cues significantly improves the performance of the detection system; 2) the AdaBoost classifier with difference of intensity of pixels is a good candidate scheme in practice due to its high efficiency and good performance; 3) eye alignment is important and influences the detection accuracy greatly. These provide useful lessons for the future investigations on this interesting topic.


title: High-Performance Local Dimming Algorithm and Its Hardware Implementation for LCD Backlight

abstract: The local-dimming backlight has recently been presented for use in LCD TVs. However, the image resolution is low, particularly at weak edges. In this work, a local-dimming backlight is developed to improve the image contrast and reduce power dissipation. The algorithm enhances low-level edge information to improve the perceived image resolution. Based on the algorithm, a 42-in backlight module with white light-emitting diode (LED) devices was driven by a local dimming control core. The block-wise register approach substantially reduced the number of required line-buffers and shortened the latency time. The measurements made in the laboratory indicate that the backlight system reduces power dissipation by an average of 48% and exhibits no visible distortion compared relative to the fixed backlighting system. The system was successfully demonstrated in a 42-in LCD TV, and the contrast ratio was greatly improved by a factor of 100.


title: Phase-Engineered Synthesis of Centimeter-Scale 1T′- and 2H-Molybdenum Ditelluride Thin Films

abstract: We report the synthesis of centimeter-scale, uniform 1T′- and 2H-MoTe2 thin films via the tellurization of Mo thin films. 1T′-MoTe2 was initially grown and converted gradually to 2H-MoTe2 over a prolonged growth time under a Te atmosphere. Maintaining excessive Te was essential for obtaining the stable stoichiometric 2H-MoTe2 phase. Further annealing under a lower partial pressure of Te at the same temperature, followed by a rapid quenching, led to the reverse phase transition from 2H-MoTe2 to 1T′-MoTe2. The orientation of the 2H-MoTe2 film was determined by the tellurization rate. Slow tellurization was the key for obtaining a highly oriented 2H-MoTe2 film over the entire area, while fast tellurization led to a 2H-MoTe2 film with a randomly oriented c-axis.


title: Scaffolding game-based learning: Impact on learning achievements, perceived learning, and game experiences

abstract: One of the central challenges of integrating game-based learning in school settings is helping learners make the connections between the knowledge learned in the game and the knowledge learned at school, while maintaining a high level of engagement with game narrative and gameplay. The current study evaluated the effect of supplementing a business simulation game with an external conceptual scaffold, which introduces formal knowledge representations, on learners' ability to solve financial-mathematical word problems following the game, and on learners' perceptions regarding learning, flow, and enjoyment in the game. Participants (Mage?=?10.10 years) were randomly assigned to three experimental conditions: a "study and play" condition that presented the scaffold first and then the game, a "play and study" condition, and a "play only" condition. Although no significant gains in problem-solving were found following the intervention, learners who studied with the external scaffold before the game performed significantly better in the post-game problem-solving assessment. Adding the external scaffold before the game reduced learners' perceived learning. However, the scaffold did not have a negative impact on reported flow and enjoyment. Flow was found to significantly predict perceived learning and enjoyment. Yet, perceived learning and enjoyment did not predict problem-solving and flow directly predicted problem solving only in the "play and study" condition. We suggest that presenting the scaffold may have "problematized" learners' understandings of the game by connecting them to disciplinary knowledge. Implications for the design of scaffolds for game-based learning are discussed. The effect of augmenting a game with an external conceptual scaffold was studied.Adding the scaffold before the game led to better problem-solving but lowered perceived learning.The scaffold did not reduce reported flow and enjoyment.Flow predicted perceived learning and enjoyment but these did not predict problem-solving.Flow predicted problem-solving only when the scaffold was presented after the game.


title: Efficient and effective retrieval using selective pruning

abstract: Retrieval can be made more efficient by deploying dynamic pruning strategies such as WAND, which do not degrade effectiveness up to a given rank. It is possible to increase the efficiency of such techniques by pruning more 'aggressively'. However, this may reduce effectiveness. In this work, we propose a novel selective framework that determines the appropriate amount of pruning aggressiveness on a per-query basis, thereby increasing overall efficiency without significantly reducing overall effectiveness. We postulate two hypotheses about the queries that should be pruned more aggressively, which generate two approaches within our framework, based on query performance predictors and query efficiency predictors, respectively. We thoroughly experiment to ascertain the efficiency and effectiveness impacts of the proposed approaches, as part of a search engine deploying state-of-the-art learning to rank techniques. Our results on 50 million documents of the TREC ClueWeb09 collection show that by using query efficiency predictors to target inefficient queries, we observe that a 36% reduction in mean response time and a 50% reduction of the response times experienced by the slowest 10% of queries can be achieved while still ensuring effectiveness.


title: Analyzing Oriented Patterns

abstract: Oriented patterns, such as those produced by propagation, accretion, or deformation, are common in nature and therefore an important class for visual analysis. Our approach to understanding such patterns is to decompose them into two parts: a flow field, describing the direction of anisotropy, and the residual pattern obtained by describing the image in a coordinate system built, from the flow field. We develop a method for the local estimation of anisotropy and a method for combining the estimates to construct a flow coordinate system. Several examples of the use of these methods are presented. These included the use of the slow coordinates to provide preferred directions for edge detection, detection of anomalies, fitting simple models to the straightened pattern, and detecting singularities in the flow field.


title: SC3: consensus clustering of single-cell RNA-seq data

abstract: Single-cell consensus clustering (SC3) provides user-friendly, robust and accurate cell clustering as well as downstream analysis for single-cell RNA-seq data.


title: Design of a Multilayer X-/Ka-Band Frequency-Selective Surface-Backed Reflectarray for Satellite Applications

abstract: A dual-band X-/Military Ka-band (MKa-band) single-aperture reflectarray structure that transmits and receives at both MKa- and X-bands is introduced in this paper. Frequency selective surface (FSS) is used as the ground plane for the MKa-band reflectarray. A cascade configuration of FSS-backed reflectarrays is designed for each of the receive and transmit bands of MKa-band to reduce the coupling between the elements of these two bands when they are etched on the same substrate. Additional FSS’s are implemented to further enhance the isolation between the bands. The MKa-band elements are located on top of the X-band reflectarray that is composed of elements etched on a perfect electric conductor (PEC)-backed ground plane. The reflectarray elements also convert circular to linear polarization which results in a simplified feed structure.


Business Model Innovation Paths and Tools


title: Network Verification in the Light of Program Verification

abstract: The fastest tools for network reachability queries use adhoc algorithms to compute all packets from a source S that can reach a destination D. This paper examines whether network reachability can be solved efficiently using existing verification tools. While most verification tools only compute reachability (“Can S reach D?”), we efficiently generalize them to compute all reachable packets. Using new and old benchmarks, we compare model checkers, SAT solvers and various Datalog implementations. The only existing verification method that worked competitively on all benchmarks in seconds was Datalog with a new composite Filter-Project operator and a Difference of Cubes representation. While Datalog is slightly slower than the Hassel C tool, it is far more flexible. We also present new results that more precisely characterize the computational complexity of network verification. This paper also provides a gentle introduction to program verification for the networking community.


title: Supply Chain Sourcing Under Asymmetric Information

abstract: We study a supply chain with two suppliers competing over a contract to supply components to a manufacturer. One of the suppliers is a big company for whom the manufacturer’s business constitutes a small part of his business. The other supplier is a small company for whom the manufacturer’s business constitutes a large portion of his business. We analyze the problem from the perspective of the big supplier and address the following questions: What is the optimal contracting strategy that the big supplier should follow? How does the information about the small supplier’s production cost affect the profits and contracting decision? How does the existence of the small supplier affect profits? By studying various information scenarios regarding the small supplier’s and the manufacturer’s production cost, we show, for example, that the big supplier benefits when the small supplier keeps its production cost private. We quantify the value of information for the big supplier and the manufacturer. We also quantify the cost (value) of the alternative-sourcing option for the big supplier (the manufacturer). We determine when an alternative-sourcing option has more impact on profits than information. We conclude with extensions and numerical examples to shed light on how system parameters affect this supply chain.