title: Variations in normal color vision. VII. Relationships between color naming and hue scaling

abstract: Abstract A longstanding and unresolved question is how observers construct a discrete set of color categories to partition and label the continuous variations in light spectra, and how these categories might reflect the neural representation of color. We explored the properties of color naming and its relationship to color appearance by analyzing individual differences in color-naming and hue-scaling patterns, using factor analysis of individual differences to identify separate and shared processes underlying hue naming (labeling) and hue scaling (color appearance). Observers labeled the hues of 36 stimuli spanning different angles in cone-opponent space, using a set of eight terms corresponding to primary (red, green, blue, yellow) or binary (orange, purple, blue-green, yellow-green) hues. The boundaries defining different terms varied mostly independently, reflecting the influence of at least seven to eight factors. This finding is inconsistent with conventional color-opponent models in which all colors derive from the relative responses of underlying red-green and blue-yellow dimensions. Instead, color categories may reflect qualitatively distinct attributes that are free to vary with the specific spectral stimuli they label. Inter-observer differences in color naming were large and systematic, and we examined whether these differences were associated with differences in color appearance by comparing the hue naming to color percepts assessed by hue scaling measured in the same observers (from Emery et al., 2017). Variability in both tasks again depended on multiple (7 or 8) factors, with some Varimax-rotated factors specific to hue naming or hue scaling, but others common to corresponding stimuli for both judgments. The latter suggests that at least some of the differences in how individuals name or categorize color are related to differences in how the stimuli are perceived.


title: Children's Conscience and Self-Regulation

abstract: ABSTRACT We focus on children's conscience, an inner guiding system responsible for the gradual emergence and maintenance of self-regulation. Drawing from our research program that has encompassed three large longitudinal studies cumulatively covering the first 6 years of life, we discuss two major components of conscience: moral emotions (guilt, discomfort following transgressions) and moral conduct compatible with rules and standards. We discuss the organization of young children's conscience, focusing on relations between moral emotions and moral conduct, and the development of conscience, focusing on its early form: the child's eager, willing stance toward parental socialization. We also review research on two major sets of influences that predict individual differences in moral emotions and moral conduct: biologically based temperament and socialization in the family. We discuss two inhibitory systems of temperament—fearfulness and effortful control—and several features of socialization, including the style of parental discipline and the quality of the parent-child relationship. Early conscience is an important early personality system, coherently organized, relatively stable over time, and subject to individual differences that emerge as a result of a complex interplay between children's temperamental individuality and socialization in the family.


title: Privacy in the Internet of Things: Threats and Challenges

abstract: The Internet of Things paradigm envisions the pervasive interconnection and cooperation of smart things over the current and future Internet infrastructure. The Internet of Things is, thus, the evolution of the Internet to cover the real world, enabling many new services that will improve people's everyday lives, spawn new businesses, and make buildings, cities, and transport smarter. Smart things allow indeed for ubiquitous data collection or tracking, but these useful features are also examples of privacy threats that are already now limiting the success of the Internet of Things vision when not implemented correctly. These threats involve new challenges such as the pervasive privacy-aware management of personal data or methods to control or avoid ubiquitous tracking and profiling. This paper analyzes the privacy issues in the Internet of Things in detail. To this end, we first discuss the evolving features and trends in the Internet of Things with the goal of scrutinizing their privacy implications. Second, we classify and examine privacy threats in this new setting, pointing out the challenges that need to be overcome to ensure that the Internet of Things becomes a reality. Copyright © 2013 John Wiley & Sons, Ltd.


title: Discontinuity-aware video object cutout

abstract: Existing video object cutout systems can only deal with limited cases. They usually require detailed user interactions to segment real-life videos, which often suffer from both inseparable statistics (similar appearance between foreground and background) and temporal discontinuities (e.g. large movements, newly-exposed regions following disocclusion or topology change). In this paper, we present an efficient video cutout system to meet this challenge. A novel directional classifier is proposed to handle temporal discontinuities robustly, and then multiple classifiers are incorporated to cover a variety of cases. The outputs of these classifiers are integrated via another classifier, which is learnt from real examples. The foreground matte is solved by a coherent matting procedure, and remaining errors can be removed easily by additive spatio-temporal local editing. Experiments demonstrate that our system performs more robustly and more intelligently than existing systems in dealing with various input types, thus saving a lot of user labor and time.


title: Community detection in complex networks using extremal optimization.

abstract: The description of the structure of complex networks has been one of the focus of attention of the physicist’s community in the recent years. The levels of description range from the microscopic (degree, clustering coefficient, centrality measures, etc., of individual nodes) to the macroscopic description in terms of statistical properties of the whole network (degree distribution, total clustering coefficient, degree-degree correlations, etc.) [1, 2, 3, 4]. Between these two extremes there is a ”mesoscopic” description of networks that tries to explain its community structure. The general notion of community structure in complex networks was first pointed out in the physics literature by Girvan and Newman [5], and refers to the fact that nodes in many real networks appear to group in subgraphs in which the density of internal connections is larger than the connections with the rest of nodes in the network. The community structure has been empirically found in many real technological, biological and social networks [6, 7, 8, 9, 10] and its emergence seems to be at the heart of the network formation process [11]. The existing methods intended to devise the community structure in complex networks have been recently reviewed in [10]. All these methods require a definition of community that imposes the limit up to which a group should be considered a community. However, the concept of community itself is qualitative: nodes must be more connected within its community than with the rest of the network, and its quantification is still a subject of debate. Some quantitative definitions that came from sociology have been used in recent studies [12], but in general, the physics community has widely accepted a recent measure for the community structure based on the concept of modularity Q introduced by Newman and Girvan [13]:


title: Emotion detection from EEG recordings

abstract: Human brain behavior is very complex and it is difficult to interpret. Human emotion might come from brain activities. However, the relationship between human emotion and brain activities is far from clear. In recent years, more and more researchers are trying to discover this relationship by recording brain signals such as electroencephalogram (EEG) signals with the associated emotion information extracted from other modalities such as facial expression. In this paper, machine learning based methods are used to model this relationship in the publicly available dataset DEAP (Database for Emotional Analysis using Physiological Signals). Different features are extracted from raw EEG recordings. Then Maximum Relevance Minimum Redundancy (mRMR) was used for feature selection. These features are fed into machine learning methods to build the prediction models to extract the emotion information from EEG signals. The models are evaluated on this dataset and satisfactory results are achieved.


title: Analysis of fisher information and the Cramer-Rao bound for nonlinear parameter estimation after compressed sensing

abstract: In this paper, we analyze the impact of compressed sensing with random matrices on Fisher information and the CRB for estimating unknown parameters in the mean value function of a multivariate normal distribution. We consider the class of random compression matrices that satisfy a version of the Johnson-Lindenstrauss lemma, and we derive analytical lower and upper bounds on the CRB for estimating parameters from randomly compressed data. These bounds quantify the potential loss in CRB as a function of Fisher information of the non-compressed data. In our numerical examples, we consider a direction of arrival estimation problem and compare the actual loss in CRB with our bounds.


title: Introducing Contactless Blood Pressure Assessment Using a High Speed Video Camera

abstract: Recent studies demonstrated that blood pressure (BP) can be estimated using pulse transit time (PTT). For PTT calculation, photoplethysmogram (PPG) is usually used to detect a time lag in pulse wave propagation which is correlated with BP. Until now, PTT and PPG were registered using a set of body-worn sensors. In this study a new methodology is introduced allowing contactless registration of PTT and PPG using high speed camera resulting in corresponding image-based PTT (iPTT) and image-based PPG (iPPG) generation. The iPTT value can be potentially utilized for blood pressure estimation however extent of correlation between iPTT and BP is unknown. The goal of this preliminary feasibility study was to introduce the methodology for contactless generation of iPPG and iPTT and to make initial estimation of the extent of correlation between iPTT and BP "in vivo." A short cycling exercise was used to generate BP changes in healthy adult volunteers in three consecutive visits. BP was measured by a verified BP monitor simultaneously with iPTT registration at three exercise points: rest, exercise peak, and recovery. iPPG was simultaneously registered at two body locations during the exercise using high speed camera at 420 frames per second. iPTT was calculated as a time lag between pulse waves obtained as two iPPG's registered from simultaneous recoding of head and palm areas. The average inter-person correlation between PTT and iPTT was 0.85 ± 0.08. The range of inter-person correlations between PTT and iPTT was from 0.70 to 0.95 (p < 0.05). The average inter-person coefficient of correlation between SBP and iPTT was -0.80 ± 0.12. The range of correlations between systolic BP and iPTT was from 0.632 to 0.960 with p < 0.05 for most of the participants. Preliminary data indicated that a high speed camera can be potentially utilized for unobtrusive contactless monitoring of abrupt blood pressure changes in a variety of settings. The initial prototype system was able to successfully generate approximation of pulse transit time and showed high intra-individual correlation between iPTT and BP. Further investigation of the proposed approach is warranted.


title: The Quest for Privacy in the Internet of Things

abstract: The Internet of Things (IoT) is the current evolutionary paradigm of networking and the key driving force toward a smart world. Although privacy in the IoT is highly regarded to ensure the protection of users and personal information from the perspective of individual or cooperative users, it's insufficiently studied. As members of the always-connected paradigm of the massive IoT world, people can scarcely control the disclosure of their personal information. The biggest challenge is to allow users to experience the best utilization of IoT-based products and services with the fewest privacy threats and failures. This article provides a holistic view of the challenges of and issues related to preserving IoT privacy, as well as the existing solutions. Privacy by design (PbD) is identified as the key solution for many IoT privacy issues. The article also discusses hot topics in IoT privacy and future research directions.


title: A Design Method for Synthesizing Wideband Band-Stop FSS via Its Equivalent Circuit Model

abstract: An equivalent circuit model-based method for synthesizing wideband band-stop frequency selective surface (FSS) has been proposed in this letter. Compared with previous works, the proposed method solves the problem of acquiring FSS structural parameters from the desired frequency response. In the proposed method, equivalent circuit parameters are first derived from the desired frequency response via the transmission line model, and then FSS structural parameters are determined from the equivalent circuit parameters with curve fitting technique. For verification, a dual-layer band-stop FSS composed of square-loop arrays separated by dielectric substrate has been designed, fabricated, and measured. Simulation results show that the designed FSS can provide a stop band operating at 13.5 GHz with a fractional bandwidth of 88.9%. Good agreements between the simulated and the measured results can be observed.


title: High-dimensional Winding-Augmented Motion Planning with 2D topological task projections and persistent homology

abstract: Recent progress in motion planning has made it possible to determine homotopy inequivalent trajectories between an initial and terminal configuration in a robot configuration space. Current approaches have however either assumed the knowledge of differential one-forms related to a skeletonization of the collision space, or have relied on a simplicial representation of the free space. Both of these approaches are currently however not yet practical for higher dimensional configuration spaces. We propose 2D topological task projections (TTPs): mappings from the configuration space to 2-dimensional spaces where simplicial complex filtrations and persistent homology can identify topological properties of the high-dimensional free configuration space. Our approach only requires the availability of collision free samples to identify winding centers that can be used to determine homotopy inequivalent trajectories. We propose the Winding Augmented RRT and RRT* (WA-RRT/RRT*) algorithms using which homotopy inequivalent trajectories can be found. We evaluate our approach in experiments with configuration spaces of planar linkages with 2–10 degrees of freedom. Results indicate that our approach can reliably identify suitable topological task projections and our proposed WA-RRT and WA-RRT* algorithms were able to identify a collection of homotopy inequivalent trajectories in each considered configuration space dimension.


title: Tocilizumab in patients with active rheumatoid arthritis and inadequate response to disease-modifying antirheumatic drugs or tumor necrosis factor inhibitors: subanalysis of Spanish results of an open-label study close to clinical practice.

abstract: OBJECTIVES ::: To analyze the Spanish experience in an international study which evaluated tocilizumab in patients with rheumatoid arthritis (RA) and an inadequate response to conventional disease-modifying antirheumatic drugs (DMARDs) or tumor necrosis factor inhibitors (TNFis) in a clinical practice setting. ::: ::: ::: MATERIAL AND METHODS ::: Subanalysis of 170 patients with RA from Spain who participated in a phase IIIb, open-label, international clinical trial. Patients presented inadequate response to DMARDs or TNFis. They received 8mg/kg of tocilizumab every 4 weeks in combination with a DMARD or as monotherapy during 20 weeks. Safety and efficacy of tocilizumab were analyzed. Special emphasis was placed on differences between failure to a DMARD or to a TNFi and the need to switch to tocilizumab with or without a washout period in patients who had previously received TNFi. ::: ::: ::: RESULTS ::: The most common adverse events were infections (25%), increased total cholesterol (38%) and transaminases (15%). Five patients discontinued the study due to an adverse event. After six months of tocilizumab treatment, 71/50/30% of patients had ACR 20/50/70 responses, respectively. A higher proportion of TNFi-naive patients presented an ACR20 response: 76% compared to 64% in the TNFi group with previous washout and 66% in the TNFi group without previous washout. ::: ::: ::: CONCLUSIONS ::: Safety results were consistent with previous results in patients with RA and an inadequate response to DMARDs or TNFis. Tocilizumab is more effective in patients who did not respond to conventional DMARDs than in patients who did not respond to TNFis.


title: Degradation products of proteins damaged by glycation, oxidation and nitration in clinical type 1 diabetes

abstract: AIMS/HYPOTHESIS ::: Hyperglycaemia in diabetes is associated with increased glycation, oxidative stress and nitrosative stress. Proteins modified consequently contain glycation, oxidation and nitration adduct residues, and undergo cellular proteolysis with release of corresponding free adducts. These free adducts leak into blood plasma for eventual renal excretion. The aim of this study was to perform a comprehensive quantitative analysis of protein glycation, oxidation and nitration adduct residues in plasma protein and haemoglobin as well as of free adducts in plasma and urine to quantify increased protein damage and flux of proteolytic degradation products in diabetes. ::: ::: ::: METHODS ::: Type 1 diabetic patients (n=21) and normal healthy control subjects (n=12) were studied. Venous blood samples, with heparin anticoagulant, and 24-h urine samples were taken. Samples were analysed for protein glycation, oxidation and nitration adducts by a quantitative comprehensive screening method using liquid chromatography with triple quadrupole mass spectrometric detection. ::: ::: ::: RESULTS ::: In type 1 diabetic patients, the concentrations of protein glycation, oxidation and nitration adduct residues increased up to three-fold in plasma protein and up to one-fold in haemoglobin, except for decreases in pentosidine and 3-nitrotyrosine residues in haemoglobin when compared with normal control subjects. In contrast, the concentrations of protein glycation and oxidation free adducts increased up to ten-fold in blood plasma, and urinary excretion increased up to 15-fold in diabetic patients. ::: ::: ::: CONCLUSIONS/INTERPRETATION ::: We conclude that there are profound increases in proteolytic products of glycated and oxidised proteins in diabetic patients, concurrent with much lower increases in protein glycation and oxidation adduct residues.


title: Cryptographic Key Generation using Retina Biometric Parameter

abstract: Crypto Biometrics system is recently emerging as an effective process to generate a cryptographic key. Conventional cryptographic key generation is using password which can be guessed or cracked. Further, the large size of strong key results in delay while encryption / decryption. Biometric field has however emerged in the recent days to reduce process delay and enhances the level of accuracy. This paper thus aims to analyze various biometric parameters that can be applied in realizing enhanced networking security. The proposed approach further reduces the cost associated with lost key and provides increased security. The key is directly generated from the human biometric information such as retinal blood vessels which is not stored in the database. This mode of operations in network security creates more complexity for hackers to crack or to guess the cryptographic key or biometric key.


title: 2DB: a Proteomics database for storage, analysis, presentation, and retrieval of information from mass spectrometric experiments

abstract: BackgroundThe amount of information stemming from proteomics experiments involving (multi dimensional) separation techniques, mass spectrometric analysis, and computational analysis is ever-increasing. Data from such an experimental workflow needs to be captured, related and analyzed. Biological experiments within this scope produce heterogenic data ranging from pictures of one or two-dimensional protein maps and spectra recorded by tandem mass spectrometry to text-based identifications made by algorithms which analyze these spectra. Additionally, peptide and corresponding protein information needs to be displayed.ResultsIn order to handle the large amount of data from computational processing of mass spectrometric experiments, automatic import scripts are available and the necessity for manual input to the database has been minimized. Information is in a generic format which abstracts from specific software tools typically used in such an experimental workflow. The software is therefore capable of storing and cross analysing results from many algorithms. A novel feature and a focus of this database is to facilitate protein identification by using peptides identified from mass spectrometry and link this information directly to respective protein maps. Additionally, our application employs spectral counting for quantitative presentation of the data. All information can be linked to hot spots on images to place the results into an experimental context. A summary of identified proteins, containing all relevant information per hot spot, is automatically generated, usually upon either a change in the underlying protein models or due to newly imported identifications. The supporting information for this report can be accessed in multiple ways using the user interface provided by the application.ConclusionWe present a proteomics database which aims to greatly reduce evaluation time of results from mass spectrometric experiments and enhance result quality by allowing consistent data handling. Import functionality, automatic protein detection, and summary creation act together to facilitate data analysis. In addition, supporting information for these findings is readily accessible via the graphical user interface provided. The database schema and the implementation, which can easily be installed on virtually any server, can be downloaded in the form of a compressed file from our project webpage.


title: Wearable exoskeletons for the physical treatment of children with quadriparesis

abstract: Quadriparesis, caused by a number of congenital and acquired neuropathologies, affects children with the symptoms of weakness and motor impairment in all four limbs. The physical treatment of this disease could be hypothetically improved by the use of wearable exoskeletons which would contribute to avoiding the side effects of the permanent sitting position: scoliosis, osteoporosis, spasticity, respiratory disorders, blood circulation problems, among others. Overall, the use of a wearable exoskeleton would contribute substantially to the improvement of these children's quality of life. This paper presents the first pediatrie wearable exoskeleton for the physical treatment of this disease. The paper shows the main components of the device and the laboratory proof of concept in two voluntary subjects.


title: An Energy Efficient Charging Technique for Switched Capacitor Voltage Converters With Low-Duty Ratio

abstract: Charging a capacitor array of a switched-capacitor (SC) dc-dc converter, supplying load circuits with a very short active period, can be pivotal to achieve high energy efficiency of its operation. This is because the capacitors may lose most of the stored energy during a long sleep period, and thus every sleep-to-active transition requires full recharging of the capacitors. In this brief, we present an energy efficient capacitor charging technique called split-capacitor charging, which charges a capacitor array in a step-wise fashion. Circuit simulations demonstrate that the proposed technique can reduce the energy wastage during sleep-to-active transition up to 66%. When tested on load circuits with a short active period, a voltage converter employing the proposed charging method shows up to 24% improvement in energy efficiency over conventional SC converters.


title: A Recurrent Neural Network Survival Model: Predicting Web User Return Time

abstract: The size of a website’s active user base directly affects its value. Thus, it is important to monitor and influence a user’s likelihood to return to a site. Essential to this is predicting when a user will return. Current state of the art approaches to solve this problem come in two flavors: (1) Recurrent Neural Network (RNN) based solutions and (2) survival analysis methods. We observe that both techniques are severely limited when applied to this problem. Survival models can only incorporate aggregate representations of users instead of automatically learning a representation directly from a raw time series of user actions. RNNs can automatically learn features, but can not be directly trained with examples of non-returning users who have no target value for their return time. We develop a novel RNN survival model that removes the limitations of the state of the art methods. We demonstrate that this model can successfully be applied to return time prediction on a large e-commerce dataset with a superior ability to discriminate between returning and non-returning users than either method applied in isolation. Code related to this paper is available at: https://github.com/grobgl/rnnsm.


title: X-Band GaN SPDT MMIC with over 25 Watt Linear Power Handling

abstract: Single pole double throw (SPDT) switches are becoming more and more key components in phased-array radar transmit/receive modules. An SPDT switch must be able to handle the output power of a high power amplifier and must provide enough isolation to protect the low noise amplifier in the receive chain when the T/R module is transmitting. Therefore gallium nitride technology seems to become a key technology for high power SPDT switch design. The technology shows good performance on microwave frequencies and is able to handle high power. An X-band SPDT switch, with a linear power handling of over 25 W, has been designed, measured and evaluated. The circuit is designed in the coplanar waveguide AlGaN/GaN technology established at QinetiQ.


title: Design and development of a leg-wheel hybrid robot “HyTRo-I”

abstract: This paper proposes a novel and mechanically decoupled leg and wheel hybrid transformable robot called HyTRo-I that combines two mobility concepts. For example, while wheeled vehicles shares higher speed than legged and tracked machines on a flat ground, they have relatively lower degree of flexibility than the other two on irregular terrain. The HyTRo-I robot evolves three motion modes: wheeled rolling, quadrupedal walking and leg-wheel hybrid mode. Despite the over-whelming complexity of obstacles, only several typical obstacles are selected for the study, which are stairs, large protrusions and ditches. Firstly, the transition locomotion mechanism between wheeled rolling mode and quadrupedal walking mode should be studied in detail. In the course of reciprocal transition locomotion, the static and reversible transformation gait not only guarantees the shifting stability and a small number of transition steps, but also the relatively balanced torque of joints. Secondly, after HyTRo-I converting to effective locomotion mode, the adaptive gait control strategies are proposed to traverse three types of obstacles. Finally, a serial of experiments were performed to verify the validity of the proposed transformation gait and adaptive step-up gaits.


title: A brief history of epigenetics.

abstract: The term "epigenetics" was originally used to denote the poorly understood processes by which a fertilized zygote developed into a mature, complex organism. With the understanding that all cells of an organism carry the same DNA, and with increased knowledge of mechanisms of gene expression, the definition was changed to focus on ways in which heritable traits can be associated not with changes in nucleotide sequence, but with chemical modifications of DNA, or of the structural and regulatory proteins bound to it. Recent discoveries about the role of these mechanisms in early development may make it desirable to return to the original definition of epigenetics.


title: Practitioner Review: Reading disorders: what are the effective interventions and how should they be implemented and evaluated?

abstract: Background: Two developmental reading disorders, dyslexia and reading comprehension impairment, are identified by different behavioural characteristics and traced back to different underlying cognitive impairments. Thus, reading interventions designed to address each of these reading disorders differ in content. Method: This review summarises the nature of dyslexia and reading comprehension impairment, and current understanding of best practice in associated reading interventions. Conclusion: There is strong evidence for the effectiveness of phonological-based reading interventions in supporting children with dyslexic difficulties, and a growing understanding of how to meet the needs of children with readingcomprehensionimpairment,withvocabularyinstructionofferingapromisingapproach.Although the content of interventions must be tailored to an individual’s reader profile, general principles regarding the implementation and evaluation of intervention programmes can be extracted. Keywords: Dyslexia, education, evaluation, comprehension, intervention, reading, reading disorder.


title: Graph Summarization for Entity Relatedness Visualization

abstract: In modern search engines, Knowledge Graphs have become a key component for knowledge discovery. When a user searches for an entity, the existing systems usually provide a list of related entities, but they do not necessarily give explanations of how they are related. However, with the help of knowledge graphs, we can generate relatedness graphs between any pair of existing entities. Existing methods of this problem are either graph-based or list-based, but they all have some limitations when dealing with large complex relatedness graphs of two related entity. In this work, we investigate how to summarize the relatedness graphs and how to use the summarized graphs to assistant the users to retrieve target information. We also implemented our approach in an online query system and performed experiments and evaluations on it. The results show that our method produces much better result than previous work.


title: Deep CNN ensembles and suggestive annotations for infant brain MRI segmentation

abstract: Precise 3D segmentation of infant brain tissues is an essential step towards comprehensive volumetric studies and quantitative analysis of early brain developement. However, computing such segmentations is very challenging, especially for 6-month infant brain, due to the poor image quality, among other difficulties inherent to infant brain MRI, e.g., the isointense contrast between white and gray matter and the severe partial volume effect due to small brain sizes. This study investigates the problem with an ensemble of semi-dense fully convolutional neural networks (CNNs), which employs T1-weighted and T2-weighted MR images as input. We demonstrate that the ensemble agreement is highly correlated with the segmentation errors. Therefore, our method provides measures that can guide local user corrections. To the best of our knowledge, this work is the first ensemble of 3D CNNs for suggesting annotations within images. Furthermore, inspired by the very recent success of dense networks, we propose a novel architecture, SemiDenseNet, which connects all convolutional layers directly to the end of the network. Our architecture allows the efficient propagation of gradients during training, while limiting the number of parameters, requiring one order of magnitude less parameters than popular medical image segmentation networks such as 3D U-Net. Another contribution of our work is the study of the impact that early or late fusions of multiple image modalities might have on the performances of deep architectures. We report evaluations of our method on the public data of the MICCAI iSEG-2017 Challenge on 6-month infant brain MRI segmentation, and show very competitive results among 21 teams, ranking first or second in most metrics.


title: Current and New Developments in Spam Filtering

abstract: This paper provides an overview of current and potential future spam filtering techniques. We examine the problems spam introduces, what spam is and how we can measure it. The paper primarily focuses on automated, non-interactive filters, with a broad review ranging from commercial implementations to ideas confined to current research papers. Both machine learning and non-machine learning based filters are reviewed as potential solutions and a taxonomy of known approaches presented. While a range of different techniques have and continue to be evaluated in academic research, heuristic and Bayesian filtering - along with its variants - provide the greatest potential for future spam prevention.


title: An Overview of the Research on Plant Leaves Disease detection using Image Processing Techniques

abstract: Diseases in plants cause major production and economic losses as well as reduction in both quality and quantity of agricultural products. Now a day's plant diseases detection has received increasing attention in monitoring large field of crops. Farmers experience great difficulties in switching from one disease control policy to another. The naked eye observation of experts is the traditional approach adopted in practice for detection and identification of plant diseases. In this paper we review the need of simple plant leaves disease detection system that would facilitate advancements in agriculture. Early information on crop health and disease detection can facilitate the control of diseases through proper management strategies. This technique will improves productivity of crops. This paper also compares the benefits and limitations of these potential methods. It includes several steps viz. image acquisition, image pre-processing, features extraction and neural network based classification.


title: Quaternion polar representation with a complex modulus and complex argument inspired by the Cayley-Dickson form

abstract: We present a new polar representation of quaternions inspired by the Cayley-Dickson representation. In this new polar representation, a quaternion is represented by a pair of complex numbers as in the Cayley-Dickson form, but here these two complex numbers are a complex ‘modulus’ and a complex ‘argument’. As in the Cayley-Dickson form, the two complex numbers are in the same complex plane (using the same complex root of −1), but the complex phase is multiplied by a different complex root of −1 in the exponential function. We show how to calculate the ‘modulus’ and ‘argument’ from an arbitrary quaternion in Cartesian form.


title: A High Step-Down Isolated Three-Phase AC-DC Converter

abstract: In this paper, a single-stage three-phase isolated ac-dc converter topology utilizing SiC MOSFETs is proposed for power rectification with a stepped-down output voltage. Unlike the conventional two-stage [front-end power factor correction (PFC) stage and isolated dc-dc stage] ac-dc converters, the full/half bridge structure in dc-dc stage is eliminated in this structure. The high-frequency pulsating voltage is obtained directly from the PFC stage and is applied across the high-frequency transformer, leading to a more compact design. In addition, there is an advantage of zero voltage switching (ZVS) in four PFC MOSFETs connected to the high-frequency tank, which is not achievable in the case of a conventional two-staged ac-dc converter. A sine-pulse width modulation (PWM)-based control scheme is applied with the common-mode duty ratio injection method to minimize the current harmonics without affecting the power factor. An LC filter is used after the PFC semistage to suppress the line-frequency voltage ripple. Furthermore, the intermediate dc-link capacitor value can be greatly reduced through no additional ripple constraints. Experimental and simulation results are included for a laboratory prototype, which converts 115-V, 400-Hz three-phase input voltage to 28-V dc output voltage. The experimental results demonstrate a power factor of 0.993 with a conversion efficiency of 95.4%, and total harmonic distortion (THD) as low as 3.5% at 2.1-kW load condition.


title: Uptake of organics by aerial plant parts: A call for research

abstract: Abstract The purpose of this paper is to call for additional research on the uptake of organics by aerial plant parts. We establish two preliminary correlations for predicting the air-to-vegetation transfer of organic chemicals. Given the limited data data, however, it is impossible to conclude which relationship is more accurate. Regardless, the correlations demonstrate the potential importance of considering foliar uptake of organic chemicals as a source of vegetative contamination and subsequent human exposure.


title: A fusion approach for managing multi-granularity linguistic term sets in decision making

abstract: The aim of this paper is to present a fusion approach of multi-granularity linguistic information for managing information assessed in different linguistic term sets (multi-granularity linguistic term sets) together with its application in a decision making problem with multiple information sources, assuming that the linguistic performance values given to the alternatives by the different sources are represented in linguistic term sets with different granularity and/or semantic. In this context, a decision process based on two steps is proposed with a view to obtaining the solution set of alternatives. First, the fusion of the multi-granularity linguistic performance values is carried out in order to obtain collective performance evaluations. In this step, on the one hand, the multi-granularity linguistic information is made uniform using a linguistic term set as the uniform representation base, the basic linguistic term set. On the other hand, the collective performance evaluations of the alternatives are obtained by means of an aggregation operator, being fuzzy sets on the basic linguistic term set. Second, the choice of the best alternative(s) from the collective performance evaluations is performed. To do that, a fuzzy preference relation is computed from the collective performance evaluations using a ranking method of pairs of fuzzy sets in the setting of Possibility Theory, applied to fuzzy sets on the basic linguistic term set. Then, a choice degree may be applied on the preference relation in order to rank the alternatives.


title: Wi-Fi Indoor Positioning System Based on RSSI Measurements from Wi-Fi Access Points -A Tri-lateration Approach

abstract: Positioning is the most attractive technology today. Various technologies are used now days for positioning purpose. GPS is mainly used for outdoor environment. Non-suitability of GPS in indoor conditions because of its NLOS conditions and signal attenuation has lead to several other techniques of indoor positioning. This paper compares few indoor positioning methods and proposes indoor positioning system using tri-lateration method which uses RSSI data from wi-fi access points to do localization in indoor environment.


title: Well-being, problematic alcohol consumption and acute subjective drug effects in past-year ayahuasca users: a large, international, self-selecting online survey

abstract: Ayahuasca is a natural psychedelic brew, which contains dimethyltryptamine (DMT). Its potential as a psychiatric medicine has recently been demonstrated and its non-medical use around the world appears to be growing. We aimed to investigate well-being and problematic alcohol use in ayahuasca users, and ayahuasca’s subjective effects. An online, self-selecting, global survey examining patterns of drug use was conducted in 2015 and 2016 (n = 96,901). Questions were asked about: use of ayahuasca, lysergic acid diethylamide (LSD) and magic mushrooms; demographics, current well-being and past-year problematic alcohol use of past-year ayahuasca users and comparison drug users; and subjective effects of ayahuasca and comparison drugs. Ayahuasca users (n = 527) reported greater well-being than both classic psychedelic users (n = 18,138) and non-psychedelic drug-using respondents (n = 78,236). Ayahuasca users reported less problematic drinking than classic psychedelic users, although both groups reported greater problematic drinking than the other respondents. Ayahuasca’s acute subjective effects usually lasted for six hours and were most strongly felt one hour after consumption. Within our online, self-selecting survey, ayahuasca users reported better well-being than comparison groups and less problematic drinking than classic psychedelic users. Future longitudinal studies of international samples and randomised controlled trials are needed to dissect the effects of ayahuasca on these outcomes.


title: On the Theory of Weak Supervision for Information Retrieval

abstract: Neural network approaches have recently shown to be effective in several information retrieval (IR) tasks. However, neural approaches often require large volumes of training data to perform effectively, which is not always available. To mitigate the shortage of labeled data, training neural IR models with weak supervision has been recently proposed and received considerable attention in the literature. In weak supervision, an existing model automatically generates labels for a large set of unlabeled data, and a machine learning model is further trained on the generated "weak" data. Surprisingly, it has been shown in prior art that the trained neural model can outperform the weak labeler by a significant margin. Although these obtained improvements have been intuitively justified in previous work, the literature still lacks theoretical justification for the observed empirical findings. In this paper, we provide a theoretical insight into weak supervision for information retrieval, focusing on learning to rank. We model the weak supervision signal as a noisy channel that introduces noise to the correct ranking. Based on the risk minimization framework, we prove that given some sufficient constraints on the loss function, weak supervision is equivalent to supervised learning under uniform noise. We also find an upper bound for the empirical risk of weak supervision in case of non-uniform noise. Following the recent work on using multiple weak supervision signals to learn more accurate models, we find an information theoretic lower bound on the number of weak supervision signals required to guarantee an upper bound for the pairwise error probability. We empirically verify a set of presented theoretical findings, using synthetic and real weak supervision data.


title: A Wearable Airbag to Prevent Fall Injuries

abstract: We have developed a wearable airbag that incorporates a fall-detection system that uses both acceleration and angular velocity signals to trigger inflation of the airbag. The fall-detection algorithm was devised using a thresholding technique with an accelerometer and gyro sensor. Sixteen subjects mimicked falls, and their acceleration waveforms were monitored. Then, we developed a fall-detection algorithm that could detect signals 300 ms before the fall. This signal was used as a trigger to inflate the airbag to a capacity of 2.4 L. Although the proposed system can help to prevent fall-related injuries, further development is needed to miniaturize the inflation system.


title: Improved Bow-Tie Antenna for Pulse Radiation and Its Implementation in a GPR Survey

abstract: In our previous works we developed an improved UWB bow-tie antenna for high-resolution GPR applications. The antenna has been designed to detect small shallow-buried objects for which it should be able to transmit short pulses with very small late-time ringing. It has been shown theoretically that in comparison with conventional bow-tie or dipole antennas, this antenna radiates significantly stronger pulses in its broadside direction with very small late-time ringing. In this paper, experimentally we compare the antenna with a resistively-loaded planar dipole which is commonly used for GPR. It has been found that the antenna exhibits superior characteristics in terms of significantly larger amplitude of the transmitted pulses and much smaller late-time ringing. Furthermore, the antenna has been implemented in a commercial GPR system and tested in real GPR surveys. In this paper we demonstrate that the implementation of this antenna results in clear B-scan images which allow one to easily observe the detail of the shallow subsurface.


title: State Observer-Based Sensor Fault Detection and Isolation, and Fault Tolerant Control of a Single-Phase PWM Rectifier for Electric Railway Traction

abstract: This paper presents an easy and a robust sensor fault detection and isolation (FDI) and fault tolerant control (FTC) of a single phase PWM rectifier for electrical railway traction application. Catenary current sensor and dc link voltage sensor failures are considered. The FDI method is based on observers and residual generation. The different FDI algorithm steps allow a good detection and isolation of the sensor fault and identify the faulty sensor. The reconfiguration strategy consists of two steps with open loop control and closed loop control working. Simulation results are presented to illustrate the good performance of the FTC procedure. Experimental results are also presented to show the effectiveness of the proposed FDI and FTC algorithms and good performances of the rectifier after the reconfiguration.


title: Converging on the divergent: The history (and future) of the international joint workshops in computational creativity

abstract: We survey the history of studies of Computational Creativity, following the development of the International Conference on Computational Creativity from its beginnings, a decade ago, in two parallel workshop series. We give a brief outline of key issues, and a summary of the various different approaches taken by participants in the research field. The outlook is optimistic: a lot has been achieved in 10 years.


title: Deep visual embedding for image classification

abstract: This paper proposes a new visual embedding method for image classification. It goes further in the analogy with textual data and allows us to read visual sentences in a certain order as in the case of text. The proposed method considers the spatial relations between visual words. It uses a very popular text analysis method called ‘word2vec’. In this method, we learn visual dictionaries based on filters of convolution layers of the convolutional neural network (CNN), which is used to capture the visual context of images. We employee visual embedding to convert words to real vectors. We evaluate many designs of dictionary building methods. To assess the performance of the proposed method, we used CIFAR10 and MNIST datasets. The experimental results show that the proposed visual embedding method outperforms the performance of several image classification methods. Experiments also show that our method can improve image classification regardless the structure of the CNN.


IR-Cologne at TREC 2017 OpenSearch Track: Rerunning Popularity Ranking Experiments in a Living Lab


title: Microstructure of selective laser melted CM247LC nickel-based superalloy and its evolution through heat treatment

abstract: Abstract The selective laser melting of high temperature alloys is of great interest to the aerospace industry as it offers the prospect of producing more complex geometries than can be achieved with other manufacturing methods. In this study, the microstructure of the nickel-based superalloy, CM247LC, has been characterised following selective laser melting and after a post deposition heat treatment below the γʹ solvus temperature. In the as-deposited state, scanning electron microscopy with electron backscatter diffraction revealed a fine, cellular microstructure with preferential alignment of 〈001〉 along the build direction. A high dislocation density was seen at the periphery of the cells, indicating substantial localised deformation of the material. Fine primary MC carbides were also observed in the inter-cellular regions. High-resolution transmission electron microscopy identified the occurrence of very fine γʹ precipitates, approximately 5 nm in diameter, dispersed within the gamma phase. After heat treatment, the elongated cell colonies were observed to partially coalesce, accompanied by a decrease in dislocation density, producing columnar grains along the build direction. Cuboidal γʹ precipitates approximately 500 nm in diameter were observed to form in the recrystallised grains, accompanied by larger γʹ precipitates on the grain boundaries.


title: MR video fusion: interactive 3D modeling and stitching on wide-baseline videos

abstract: A major challenge facing camera networks today is how to effectively organizing and visualizing videos in the presence of complicated network connection and overwhelming and even increasing amount of data. Previous works focus on 2D stitching or dynamic projection to 3D models, such as panorama and Augmented Virtual Environment (AVE), and haven't given an ideal solution. We present a novel method of multiple video fusion in 3D environment, which produces a highly comprehensive imagery and yields a spatio-temporal consistent scene. User initially interact with a newly designed background model named video model to register and stitch videos' background frames offline. The method then fuses the offline results to render videos in a real time manner. We demonstrate our system on 3 real scenes, each of which contains dozens of wide-baseline videos. The experimental results show that, our 3D modeling interface developed with the our presented model and method can efficiently assist the users to seamlessly integrate videos by comparing to commercial-off-the-shelf software with less operating complexity and more accurate 3D environment. The stitching method proposed by us is much more robust against the position, orientation, attribute differences among videos than the start-of-the-art methods. More importantly, this study sheds light on how to use the 3D techniques to solve 2D problems in realistic and we validate its feasibility.


title: Association of depression and anxiety alone and in combination with chronic musculoskeletal pain in primary care patients.

abstract: OBJECTIVE ::: To assess the relationship between depression and anxiety comorbidity on pain intensity, pain-related disability, and health-related quality of life (HRQL). ::: ::: ::: METHODS ::: Analysis of baseline data from the Stepped Care for Affective Disorders and Musculoskeletal Pain (SCAMP) study. All patients (n = 500) had chronic pain (>or=3-month duration) of the low back, hip, or knee. Patients with depression were oversampled for the clinical trial component of SCAMP and thus represented 50% of the study population. Patients were categorized according to pain comorbid with depression, anxiety, or both. We used analysis of variance and multivariate analysis of variance models to assess the relationships between independent and dependent variables. ::: ::: ::: RESULTS ::: Participants had a mean age of 59 years; they were 55% women, 56% White, and 40% Black. Fifty-four percent (n = 271) reported pain only, 20% (n = 98) had pain and depression, 3% (n = 15) had pain and anxiety, and 23% (n = 116) had pain, depression, and anxiety. Patients with pain and both depression and anxiety experienced the greatest pain severity (p < .0001) and pain-related disability (p < .0001). Psychiatric comorbidity was strongly associated with disability days in the past 3 months (p < .0001), with 18.1 days reported by patients with pain only, 32.2 days by those with pain and anxiety, 38.0 days by those with pain and depression, and 42.6 days in those with all three conditions. We found a similar pattern of poorer HRQL (p < .0001) in those with pain, depression, and anxiety. ::: ::: ::: CONCLUSIONS ::: The added morbidity of depression and anxiety with chronic pain is strongly associated with more severe pain, greater disability, and poorer HRQL.


title: A Survey on Large-Scale Software Defined Networking (SDN) Testbeds: Approaches and Challenges

abstract: Recently, several large-scale software defined networking (SDN) testbeds have been designed and developed. These SDN testbeds have spurred numerous network researchers to run their prototypes and experiments, as well as to set up novel architectures for the future Internet. Based on these efforts, SDN testbeds are actively contributing to future network research, bringing forth the attention of both academia and industry. In addition, researchers envisage these large-scale SDN testbeds to be the future Internet. In this paper, we present a comprehensive survey and research challenges for large-scale SDN testbeds. We first introduce the related work and background knowledge. Then, an overview of SDN testbeds is presented. In addition, five typical implementations of large-scale SDN testbeds around the world are described in detail, including design objectives, key technologies, deployment, and experiments. Moreover, an in-depth comparison of SDN testbeds is given. Finally, challenges and future works of SDN testbeds are discussed.


title: Understanding Android Obfuscation Techniques: A Large-Scale Investigation in the Wild

abstract: In this paper, we seek to better understand Android obfuscation and depict a holistic view of the usage of obfuscation through a large-scale investigation in the wild. In particular, we focus on four popular obfuscation approaches: identifier renaming, string encryption, Java reflection, and packing. To obtain the meaningful statistical results, we designed efficient and lightweight detection models for each obfuscation technique and applied them to our massive APK datasets (collected from Google Play, multiple third-party markets, and malware databases). We have learned several interesting facts from the result. For example, malware authors use string encryption more frequently, and more apps on third-party markets than Google Play are packed. We are also interested in the explanation of each finding. Therefore we carry out in-depth code analysis on some Android apps after sampling. We believe our study will help developers select the most suitable obfuscation approach, and in the meantime help researchers improve code analysis systems in the right direction.


title: Non-surgical treatment of transverse deficiency in adults using Microimplant-assisted Rapid Palatal Expansion (MARPE)

abstract: INTRODUCTION ::: Maxillary transverse deficiency is a highly prevalent malocclusion present in all age groups, from primary to permanent dentition. If not treated on time, it can aggravate and evolve to a more complex malocclusion, hindering facial growth and development. Aside from the occlusal consequences, the deficiency can bring about serious respiratory problems as well, due to the consequent nasal constriction usually associated. In growing patients, this condition can be easily handled with a conventional rapid palatal expansion. However, mature patients are frequently subjected to a more invasive procedure, the surgically-assisted rapid palatal expansion (SARPE). More recently, researches have demonstrated that it is possible to expand the maxilla in grown patients without performing osteotomies, but using microimplants anchorage instead. This novel technique is called microimplant-assisted rapid palatal expansion (MARPE). ::: ::: ::: OBJECTIVE ::: The aim of the present article was to demonstrate and discuss a MARPE technique developed by Dr. Won Moon and colleagues at University of California - Los Angeles (UCLA). ::: ::: ::: METHODS ::: All laboratory and clinical steps needed for its correct execution are thoroughly described. For better comprehension, a mature patient case is reported, detailing all the treatment progress and results obtained. ::: ::: ::: CONCLUSION ::: It was concluded that the demonstrated technique could be an interesting alternative to SARPE in the majority of non-growing patients with maxillary transverse deficiency. The present patient showed important occlusal and respiratory benefits following the procedure, without requiring any surgical intervention.


title: Does the autistic child have a “theory of mind” ?

abstract: Abstract We use a new model of metarepresentational development to predict a cognitive deficit which could explain a crucial component of the social impairment in childhood autism. One of the manifestations of a basic metarepresentational capacity is a ‘theory of mind’. We have reason to believe that autistic children lack such a ‘theory’. If this were so, then they would be unable to impute beliefs to others and to predict their behaviour. This hypothesis was tested using Wimmer and Perner's puppet play paradigm. Normal children and those with Down's syndrome were used as controls for a group of autistic children. Even though the mental age of the autistic children was higher than that of the controls, they alone failed to impute beliefs to others. Thus the dysfunction we have postulated and demonstrated is independent of mental retardation and specific to autism.


title: The Key Technology Research on Integration between Electric Vehicles and Smart Grid

abstract: Expounded China's electric vehicle (EV) scale trends and energy supply technology status. Research on smart AC charging pile orderly charge control strategy execution method, having a guide control function and on-board charger monitoring function of the charging pile intelligent controller, communication method and communication protocol between AC charging piles and vehicle terminal; Research on AC/ DC combined charging interface, AC/ DC combined charging equipment, communication method, communication protocol and working mode between AC/ DC combined charging device and EV; Research on non-contact power transmission scheme and principle, non-contact power transmission circuit topology and the control method; Research on the running characteristics and charging characteristics of EVs, EV charging demand model and the key influence factor and its interaction mechanism; Research on the operation coordinated control scheme between virtual charging stations and grid, research on the optimization control strategy of virtual charging station. Finally, analysis of key technologies research significance.


title: Measuring password guessability for an entire university

abstract: Despite considerable research on passwords, empirical studies of password strength have been limited by lack of access to plaintext passwords, small data sets, and password sets specifically collected for a research study or from low-value accounts. Properties of passwords used for high-value accounts thus remain poorly understood. We fill this gap by studying the single-sign-on passwords used by over 25,000 faculty, staff, and students at a research university with a complex password policy. Key aspects of our contributions rest on our (indirect) access to plaintext passwords. We describe our data collection methodology, particularly the many precautions we took to minimize risks to users. We then analyze how guessable the collected passwords would be during an offline attack by subjecting them to a state-of-the-art password cracking algorithm. We discover significant correlations between a number of demographic and behavioral factors and password strength. For example, we find that users associated with the computer science school make passwords more than 1.5 times as strong as those of users associated with the business school. while users associated with computer science make strong ones. In addition, we find that stronger passwords are correlated with a higher rate of errors entering them. We also compare the guessability and other characteristics of the passwords we analyzed to sets previously collected in controlled experiments or leaked from low-value accounts. We find more consistent similarities between the university passwords and passwords collected for research studies under similar composition policies than we do between the university passwords and subsets of passwords leaked from low-value accounts that happen to comply with the same policies.


title: A Multi-Level Typology of Abstract Visualization Tasks

abstract: The considerable previous work characterizing visualization usage has focused on low-level tasks or interactions and high-level tasks, leaving a gap between them that is not addressed. This gap leads to a lack of distinction between the ends and means of a task, limiting the potential for rigorous analysis. We contribute a multi-level typology of visualization tasks to address this gap, distinguishing why and how a visualization task is performed, as well as what the task inputs and outputs are. Our typology allows complex tasks to be expressed as sequences of interdependent simpler tasks, resulting in concise and flexible descriptions for tasks of varying complexity and scope. It provides abstract rather than domain-specific descriptions of tasks, so that useful comparisons can be made between visualization systems targeted at different application domains. This descriptive power supports a level of analysis required for the generation of new designs, by guiding the translation of domain-specific problems into abstract tasks, and for the qualitative evaluation of visualization usage. We demonstrate the benefits of our approach in a detailed case study, comparing task descriptions from our typology to those derived from related work. We also discuss the similarities and differences between our typology and over two dozen extant classification systems and theoretical frameworks from the literatures of visualization, human-computer interaction, information retrieval, communications, and cartography.


How Users Choose and Reuse Passwords


Big data, open government and e-government: Issues, policies and recommendations


title: FishEyeRecNet: A Multi-Context Collaborative Deep Network for Fisheye Image Rectification

abstract: Images captured by fisheye lenses violate the pinhole camera assumption and suffer from distortions. Rectification of fisheye images is therefore a crucial preprocessing step for many computer vision applications. In this paper, we propose an end-to-end multi-context collaborative deep network for removing distortions from single fisheye images. In contrast to conventional approaches, which focus on extracting hand-crafted features from input images, our method learns high-level semantics and low-level appearance features simultaneously to estimate the distortion parameters. To facilitate training, we construct a synthesized dataset that covers various scenes and distortion parameter settings. Experiments on both synthesized and real-world datasets show that the proposed model significantly outperforms current state of the art methods. Our code and synthesized dataset will be made publicly available.


title: Guns, swords and data: Clustering of player behavior in computer games in the wild

abstract: Behavioral data from computer games can be exceptionally high-dimensional, of massive scale and cover a temporal segment reaching years of real-time and a varying population of users. Clustering of user behavior provides a way to discover behavioral patterns that are actionable for game developers. Interpretability and reliability of clustering results is vital, as decisions based on them affect game design and thus ultimately revenue. Here case studies are presented focusing on clustering analysis applied to high-dimensionality player behavior telemetry, covering a combined total of 260,000 characters from two major commercial game titles: the Massively Multiplayer Online Role-Playing Game Tera and the multi-player strategy war game Battlefield 2: Bad Company 2. K-means and Simplex Volume Maximization clustering were applied to the two datasets, combined with considerations of the design of the games, resulting in actionable behavioral profiles. Depending on the algorithm different insights into the underlying behavior of the population of the two games are provided.


title: Linear Systems Analysis of Functional Magnetic Resonance Imaging in Human V1

abstract: The linear transform model of functional magnetic resonance imaging (fMRI) hypothesizes that fMRI responses are proportional to local average neural activity averaged over a period of time. This work reports results from three empirical tests that support this hypothesis. First, fMRI responses in human primary visual cortex (V1) depend separably on stimulus timing and stimulus contrast. Second, responses to long-duration stimuli can be predicted from responses to shorter duration stimuli. Third, the noise in the fMRI data is independent of stimulus contrast and temporal period. Although these tests can not prove the correctness of the linear transform model, they might have been used to reject the model. Because the linear transform model is consistent with our data, we proceeded to estimate the temporal fMRI impulse-response function and the underlying (presumably neural) contrast-response function of human V1.


title: Socioeconomic status and health: The challenge of the gradient

abstract: Socioeconomic status (SES) is consistently associated with health outcomes, yet little is known about the psychosocial and behavioral mechanisms that might explain this association. Researchers usually control for SES rather than examine it. When it is studied, only effects of lower, poverty-level SES are generally examined. However, there is evidence of a graded association with health at all levels of SES, an observation that requires new thought about domains through which SES may exert its health effects. Variables are highlighted that show a graded relationship with both SES and health to provide examples of possible pathways between SES and health end points. Examples are also given of new analytic approaches that can better illuminate the complexities of the SES-health gradient.


title: Regionally accelerated batch informed trees (RABIT*): A framework to integrate local information into optimal path planning

abstract: Sampling-based optimal planners, such as RRT*, almost-surely converge asymptotically to the optimal solution, but have provably slow convergence rates in high dimensions. This is because their commitment to finding the global optimum compels them to prioritize exploration of the entire problem domain even as its size grows exponentially. Optimization techniques, such as CHOMP, have fast convergence on these problems but only to local optima. This is because they are exploitative, prioritizing the immediate improvement of a path even though this may not find the global optimum of nonconvex cost functions.


title: The Haar wavelet transform: its status and achievements

abstract: Abstract This paper is a brief survey of basic definitions of the Haar wavelet transform. Different generalizations of this transform are also presented. Sign version of the transform is shown. Efficient symbolic calculation of Haar spectrum is discussed. Some applications of Haar wavelet transform are also mentioned.


title: A Novel Adaptive P&O MPPT Algorithm Considering Sudden Changes in the Irradiance

abstract: In this paper, a short-circuit current-based adaptive perturb and observe maximum power point tracking algorithm is proposed to extract the maximum power from photovoltaic (PV) panel under sudden changes in the irradiance. This scheme is divided into two algorithms: 1) current perturbation algorithm; and 2) adaptive control algorithm. The current perturbation algorithm makes the PV panel operate at maximum power point. The adaptive control algorithm identifies the operating limit violation and sets a new operating point nearer to maximum power point. These limits are derived in terms of changes in the irradiance and current. The new operating point is set by estimating the short-circuit current. This algorithm proposes variable current perturbation, which varies continuously with the irradiance. A boost converter is used to realize the proposed algorithm. The proposed algorithm is compared with a conventional algorithm and validated for sudden changes in the irradiance through the experimental results.


title: Emotions Mediated Through Mid-Air Haptics

abstract: Touch is a powerful vehicle for communication between humans. The way we touch (how) embraces and mediates certain emotions such as anger, joy, fear, or love. While this phenomenon is well explored for human interaction, HCI research is only starting to uncover the fine granularity of sensory stimulation and responses in relation to certain emotions. Within this paper we present the findings from a study exploring the communication of emotions through a haptic system that uses tactile stimulation in mid-air. Here, haptic descriptions for specific emotions (e.g., happy, sad, excited, afraid) were created by one group of users to then be reviewed and validated by two other groups of users. We demonstrate the non-arbitrary mapping between emotions and haptic descriptions across three groups. This points to the huge potential for mediating emotions through mid-air haptics. We discuss specific design implications based on the spatial, directional, and haptic parameters of the created haptic descriptions and illustrate their design potential for HCI based on two design ideas.


title: Conditional CycleGAN for Attribute Guided Face Image Generation

abstract: State-of-the-art techniques in Generative Adversarial Networks (GANs) such as cycleGAN is able to learn the mapping of one image domain $X$ to another image domain $Y$ using unpaired image data. We extend the cycleGAN to ${\it Conditional}$ cycleGAN such that the mapping from $X$ to $Y$ is subjected to attribute condition $Z$. Using face image generation as an application example, where $X$ is a low resolution face image, $Y$ is a high resolution face image, and $Z$ is a set of attributes related to facial appearance (e.g. gender, hair color, smile), we present our method to incorporate $Z$ into the network, such that the hallucinated high resolution face image $Y'$ not only satisfies the low resolution constrain inherent in $X$, but also the attribute condition prescribed by $Z$. Using face feature vector extracted from face verification network as $Z$, we demonstrate the efficacy of our approach on identity-preserving face image super-resolution. Our approach is general and applicable to high-quality face image generation where specific facial attributes can be controlled easily in the automatically generated results.


title: An Efficient Semi-Supervised Clustering Algorithm with Sequential Constraints

abstract: Semi-supervised clustering leverages side information such as pairwise constraints to guide clustering procedures. Despite promising progress, existing semi-supervised clustering approaches overlook the condition of side information being generated sequentially, which is a natural setting arising in numerous real-world applications such as social network and e-commerce system analysis. Given emerged new constraints, classical semi-supervised clustering algorithms need to re-optimize their objectives over all data samples and constraints in availability, which prevents them from efficiently updating the obtained data partitions. To address this challenge, we propose an efficient dynamic semi-supervised clustering framework that casts the clustering problem into a search problem over a feasible convex set, i.e., a convex hull with its extreme points being an ensemble of m data partitions. According to the principle of ensemble clustering, the optimal partition lies in the convex hull, and can thus be uniquely represented by an m-dimensional probability simplex vector. As such, the dynamic semi-supervised clustering problem is simplified to the problem of updating a probability simplex vector subject to the newly received pairwise constraints. We then develop a computationally efficient updating procedure to update the probability simplex vector in O(m2) time, irrespective of the data size n. Our empirical studies on several real-world benchmark datasets show that the proposed algorithm outperforms the state-of-the-art semi-supervised clustering algorithms with visible performance gain and significantly reduced running time.


title: Antecedents and Consequences of Market Orientation: a Brazilian Meta-Analysis and an International Mega-Analysis

abstract: Market orientation has emerged as a significant antecedent of organizational performance and is presumed to contribute to the long-term success of a firm. The growing number of academic studies on market orientation and the mixed findings they have reported complicate the efforts among managers and academics to identify the real antecedents and outcomes of this construct. We have conducted a Brazilian meta-analysis, aggregating a sample size of 4,537 in 27 papers. The findings suggest that the relationship between market orientation and business performance is positive and strong (r = .39). We have also conducted an international mega-analysis, aggregating a sample size of seven meta-analyses on market orientation. The results show that there is a positive, strong and consistent international relationship between market orientation and performance across countries (r = .33).


title: A Probabilistic Framework to Detect Buildings in Aerial and Satellite Images

abstract: Detecting buildings from very high resolution (VHR) aerial and satellite images is extremely useful in map making, urban planning, and land use analysis. Although it is possible to manually locate buildings from these VHR images, this operation may not be robust and fast. Therefore, automated systems to detect buildings from VHR aerial and satellite images are needed. Unfortunately, such systems must cope with major problems. First, buildings have diverse characteristics, and their appearance (illumination, viewing angle, etc.) is uncontrolled in these images. Second, buildings in urban areas are generally dense and complex. It is hard to detect separate buildings from them. To overcome these difficulties, we propose a novel building detection method using local feature vectors and a probabilistic framework. We first introduce four different local feature vector extraction methods. Extracted local feature vectors serve as observations of the probability density function (pdf) to be estimated. Using a variable-kernel density estimation method, we estimate the corresponding pdf. In other words, we represent building locations (to be detected) in the image as joint random variables and estimate their pdf. Using the modes of the estimated density, as well as other probabilistic properties, we detect building locations in the image. We also introduce data and decision fusion methods based on our probabilistic framework to detect building locations. We pick certain crops of VHR panchromatic aerial and Ikonos satellite images to test our method. We assume that these crops are detected using our previous urban region detection method. Our test images are acquired by two different sensors, and they have different spatial resolutions. Also, buildings in these images have diverse characteristics. Therefore, we can test our methods on a diverse data set. Extensive tests indicate that our method can be used to automatically detect buildings in a robust and fast manner in Ikonos satellite and our aerial images.


title: A Smart Safety Helmet using IMU and EEG sensors for worker fatigue detection

abstract: It is known that head gesture and brain activity can reflect some human behaviors related to a risk of accident when using machine-tools. The research presented in this paper aims at reducing the risk of injury and thus increase worker safety. Instead of using camera, this paper presents a Smart Safety Helmet (SSH) in order to track the head gestures and the brain activity of the worker to recognize anomalous behavior. Information extracted from SSH is used for computing risk of an accident (a safety level) for preventing and reducing injuries or accidents. The SSH system is an inexpensive, non-intrusive, non-invasive, and non-vision-based system, which consists of an Inertial Measurement Unit (IMU) and dry EEG electrodes. A haptic device, such as vibrotactile motor, is integrated to the helmet in order to alert the operator when computed risk level (fatigue, high stress or error) reaches a threshold. Once the risk level of accident breaks the threshold, a signal will be sent wirelessly to stop the relevant machine tool or process.


title: The History and Status of General Systems Theory

abstract: The article presents a history of general systems theory and discusses several of its various aspects. According to the author, the notion of general systems theory first stemmed from the pre-Socra...


title: Multi-Modal Trajectory Prediction of Surrounding Vehicles with Maneuver based LSTMs

abstract: To safely and efficiently navigate through complex traffic scenarios, autonomous vehicles need to have the ability to predict the future motion of surrounding vehicles. Multiple interacting agents, the multi-modal nature of driver behavior, and the inherent uncertainty involved in the task make motion prediction of surrounding vehicles a challenging problem. In this paper, we present an LSTM model for interaction aware motion prediction of surrounding vehicles on freeways. Our model assigns confidence values to maneuvers being performed by vehicles and outputs a multi-modal distribution over future motion based on them. We compare our approach with the prior art for vehicle motion prediction on the publicly available NGSIM US-101 and I-80 datasets. Our results show an improvement in terms of RMS values of prediction error. We also present an ablative analysis of the components of our proposed model and analyze the predictions made by the model in complex traffic scenarios.


title: Unified IoT ontology to enable interoperability and federation of testbeds

abstract: After a thorough analysis of existing Internet of Things (IoT) related ontologies, in this paper we propose a solution that aims to achieve semantic interoperability among heterogeneous testbeds. Our model is framed within the EU H2020's FIESTA-IoT project, that aims to seamlessly support the federation of testbeds through the usage of semantic-based technologies. Our proposed model (ontology) takes inspiration from the well-known Noy et al. methodology for reusing and interconnecting existing ontologies. To build the ontology, we leverage a number of core concepts from various mainstream ontologies and taxonomies, such as Semantic Sensor Network (SSN), M3-lite (a lite version of M3 and also an outcome of this study), WGS84, IoT-lite, Time, and DUL. In addition, we also introduce a set of tools that aims to help external testbeds adapt their respective datasets to the developed ontology.


title: On the performance of objective metrics for omnidirectional visual content

abstract: Omnidirectional image and video have gained popularity thanks to availability of capture and display devices for this type of content. Recent studies have assessed performance of objective metrics in predicting visual quality of omnidirectional content. These metrics, however, have not been rigorously validated by comparing their prediction results with ground-truth subjective scores. In this paper, we present a set of 360-degree images along with their subjective quality ratings. The set is composed of four contents represented in two geometric projections and compressed with three different codecs at four different bitrates. A range of objective quality metrics for each stimulus is then computed and compared to subjective scores. Statistical analysis is performed in order to assess performance of each objective quality metric in predicting subjective visual quality as perceived by human observers. Results show the estimated performance of the state-of-the-art objective metrics for omnidirectional visual content. Objective metrics specifically designed for 360-degree content do not outperform conventional methods designed for 2D images.


title: P.: Pose Priors for Simultaneously Solving Alignment and Correspondence

abstract: Estimating a camera pose given a set of 3D-object and 2D-image feature points is a well understood problem when correspondences are given. However, when such correspondences cannot be established a priori, one must simultaneously compute them along with the pose. Most current approaches to solving this problem are too computationally intensive to be practical. An interesting exception is the SoftPosit algorithm, that looks for the solution as the minimum of a suitable objective function. It is arguably one of the best algorithms but its iterative nature means it can fail in the presence of clutter, occlusions, or repetitive patterns. In this paper, we propose an approach that overcomes this limitation by taking advantage of the fact that, in practice, some prior on the camera pose is often available. We model it as a Gaussian Mixture Model that we progressively refine by hypothesizing new correspondences. This rapidly reduces the number of potential matches for each 3D point and lets us explore the pose space more thoroughly than SoftPosit at a similar computational cost. We will demonstrate the superior performance of our approach on both synthetic and real data.


title: A Blockchain Research Framework What We ( don ’ t ) Know , Where We Go from Here , and How We Will Get There

abstract: Abstract While blockchain technology is commonly considered potentially disruptive in various regards, there is a lack of understanding where and how blockchain technology is effectively applicable and where it has mentionable practical effects. This issue has given rise to critical voices that judge the technology as over-hyped. Against this backdrop, this study adapts an established research framework to structure the insights of the current body of research on blockchain technology, outline the present research scope as well as disregarded topics, and sketch out multidisciplinary research approaches. The framework differentiates three groups of activities (design and features, measurement and value, management and organization) at four levels of analysis (users and society, intermediaries, platforms, firms and industry). The review shows that research has predominantly focused on technological questions of design and features, while neglecting application, value creation, and governance. In order to foster substantial blockchain research that addresses meaningful questions, this study identifies several avenues for future studies. Given the breadth of open questions, it shows where research can benefit from multidisciplinary collaborations and presents data sources as starting points for empirical investigations.


title: Fast and accurate single-cell RNA-seq analysis by clustering of transcript-compatibility counts

abstract: Current approaches to single-cell transcriptomic analysis are computationally intensive and require assay-specific modeling, which limits their scope and generality. We propose a novel method that compares and clusters cells based on their transcript-compatibility read counts rather than on the transcript or gene quantifications used in standard analysis pipelines. In the reanalysis of two landmark yet disparate single-cell RNA-seq datasets, we show that our method is up to two orders of magnitude faster than previous approaches, provides accurate and in some cases improved results, and is directly applicable to data from a wide variety of assays.


title: An Advanced Statistical Method for Wind Power Forecasting

abstract: This paper presents an advanced statistical method for wind power forecasting based on artificial intelligence techniques. The method requires as input past power measurements and meteorological forecasts of wind speed and direction interpolated at the site of the wind farm. A self-organized map is trained to classify the forecasted local wind speed provided by the meteorological services. A unique feature of the method is that following a preliminary wind power prediction, it provides an estimation of the quality of the meteorological forecasts that is subsequently used to improve predictions. The proposed method is suitable for operational planning of power systems with increased wind power penetration, i.e., forecasting horizon of 48 h ahead and for wind farm operators trading in electricity markets. Application of the forecasting method on the power production of an actual wind farm shows the validity of the method


title: Side Channels in Deduplication: Trade-offs between Leakage and Efficiency

abstract: Deduplication removes redundant copies of files or data blocks stored on the cloud. Client-side deduplication, where the client only uploads the file upon the request of the server, provides major storage and bandwidth savings, but introduces a number of security concerns. Harnik et al. (2010) showed how cross-user client-side deduplication inherently gives the adversary access to a (noisy) side-channel that may divulge whether or not a particular file is stored on the server, leading to leakage of user information. We provide formal definitions for deduplication strategies and their security in terms of adversarial advantage. Using these definitions, we provide a criterion for designing good strategies and then prove a bound characterizing the necessary trade-off between security and efficiency.


title: Device Requirements for Optical Interconnects to Silicon Chips

abstract: We examine the current performance and future demands of interconnects to and on silicon chips. We compare electrical and optical interconnects and project the requirements for optoelectronic and optical devices if optics is to solve the major problems of interconnects for future high-performance silicon chips. Optics has potential benefits in interconnect density, energy, and timing. The necessity of low interconnect energy imposes low limits especially on the energy of the optical output devices, with a ~ 10 fJ/bit device energy target emerging. Some optical modulators and radical laser approaches may meet this requirement. Low (e.g., a few femtofarads or less) photodetector capacitance is important. Very compact wavelength splitters are essential for connecting the information to fibers. Dense waveguides are necessary on-chip or on boards for guided wave optical approaches, especially if very high clock rates or dense wavelength-division multiplexing (WDM) is to be avoided. Free-space optics potentially can handle the necessary bandwidths even without fast clocks or WDM. With such technology, however, optics may enable the continued scaling of interconnect capacity required by future chips.


title: Enhancement of password authentication system using graphical images

abstract: In this paper, the Enhancement of password authentication system with the help of images is proposed. This paper mainly focuses on the concept of graphical password system. It is supported by the using cued click points for authentication purpose. The basic concept of this system is simply the interaction of user with sequence of 5 images. The basic goal of this system is to achieve higher security with simple technique to use by a user and harder to guess by a hacker. Graphical password authentication system is best alternative for text password. Cued click point (CCP) [1] is best alternative to old graphical password system. CCP is combination of five click points on particular five images. In this this paper, CCP is clubbed with new technologies like mobile phones and E-mail.


title: Comprehensive Review of Opinion Summarization

abstract: The abundance of opinions on the web has kindled the study of opinion summarization over the last few years. People have introduced various techniques and paradigms to solving this special task. This survey attempts to systematically investigate the different techniques and approaches used in opinion summarization. We provide a multi-perspective classification of the approaches used and highlight some of the key weaknesses of these approaches. This survey also covers evaluation techniques and data sets used in studying the opinion summarization problem. Finally, we provide insights into some of the challenges that are left to be addressed as this will help set the trend for future research in this area.


title: The effects of fat talk on body dissatisfaction and eating behavior: The moderating role of dietary restraint

abstract: Abstract Although research suggests that fat talk , the normalized conversations that involve degrading one's body shape/weight and size, can increase body dissatisfaction and disordered eating behavior, the extent to which dietary restraint may moderate these relationships remains uncertain. A pilot study ( N = 30) comparing online videos with researcher-developed vignettes as prospective manipulations for fat talk exposure suggested use of the vignettes as potent yet subtle stimuli. In the main study, women undergraduates ( N = 116) were randomized to read a fat talk or neutral vignette and then completed standardized measures of body dissatisfaction and dietary restraint while being concurrently presented with food stimuli. Results suggest significant moderation effects for dietary restraint: whereas fat talk exposure was associated with increased body dissatisfaction among low dietary restrainers, it appeared to reduce food consumption among high dietary restrainers. Findings highlight the importance of individual differences in shaping responses to fat talk.


title: Visual Language Modeling on CNN Image Representations

abstract: Measuring the naturalness of images is important to generate realistic images or to detect unnatural regions in images. Additionally, a method to measure naturalness can be complementary to Convolutional Neural Network (CNN) based features, which are known to be insensitive to the naturalness of images. However, most probabilistic image models have insufficient capability of modeling the complex and abstract naturalness that we feel because they are built directly on raw image pixels. In this work, we assume that naturalness can be measured by the predictability on high-level features during eye movement. Based on this assumption, we propose a novel method to evaluate the naturalness by building a variant of Recurrent Neural Network Language Models on pre-trained CNN representations. Our method is applied to two tasks, demonstrating that 1) using our method as a regularizer enables us to generate more understandable images from image features than existing approaches, and 2) unnaturalness maps produced by our method achieve state-of-the-art eye fixation prediction performance on two well-studied datasets.


title: Global surface heat flux anomalies from seismic tomography‐based models of mantle flow: Implications for mantle convection

abstract: We investigate the very long-wavelength, global pattern of surface heat flux anomalies within the context of whole-mantle and layered-mantle anelastically compressible internal loading theories. Since the internal loading framework does not yield a direct estimate of the geotherm, we argue that accurate predictions for the surface heat flux may nevertheless be obtained by assuming that it is linearly related to the radial component of flow velocity at shallow depth in the mantle. The mantle convective circulation is assumed to be driven by density heterogeneity inferred from global seismic tomography models. Best results for the pattern of surface heat flux anomalies are obtained for models that significantly impede the circulation at a depth of 670 km. Total variance reductions of 60–65% (degree 1–5) are obtained when the viscosity profile includes a low-viscosity asthenosphere. Within the context of our modeling assumptions, however, whole-mantle circulation models provide best descriptions of the long-wavelength nonhydrostatic gravity data. In order to resolve the gravity-heat flux impasse that is revealed herein, we consider the possibility of modifying the a priori global seismic models employed in the calculations. We show that the rigidly layered-mantle internal loading theory is equivalent to a theory in which no explicit flow-blocking boundary condition is imposed at 670 km but in which the buoyancy field inferred from the a priori tomographic model is supplemented by flow-blocking heterogeneity in the form of an appropriately constrained sheet mass load. We develop a general mathematical formalism describing how the introduction of appropriately constrained sheet mass loads allows the exact reconciliation of a number of a priori constraints or hypotheses concerning the structure of the circulation. Using this formalism, we explore the extreme nonuniqueness that not only characterizes internal loading theory inferences of the depth profile of mantle viscosity but also inferences of the radial style of the circulation. On this basis, we suggest that great caution is warranted with respect to tomography-based inferences of mantle properties. Based on a viscosity profile whose depth dependence is close to that independently inferred within the context of postglacial rebound studies, we present plausible resolutions of the gravity-heat flux impasse effected either within the framework of whole-mantle or layered-mantle circulation models.


title: Tracking website data-collection and privacy practices with the iWatch web crawler

abstract: In this paper we introduce the iWatch web crawler, a tool designed to catalogue and analyze online data practices and the use of privacy related indicators and technologies. Our goal in developing iWatch was to make possible a new type of analysis of trends, the impact of legislation on practices, and geographic and social differences online. In this paper we present preliminary findings from two sets of data collected 15 months apart and analyzed with this tool. Our combined samples included more than 240,000 pages from over 24,000 domains and 47 different countries. In addition to providing useful and needed data on the state of online data practices, we show that iWatch is a promising approach to the study of the web ecosystem.


title: Query expansion through geographical feature types

abstract: This paper introduces a new approach for expansion of queries with geographical context. The proposed strategy is based on a query parser that captures geonames and spatial relationships, and maps geographical features and feature types into concepts of a geographical ontology. Different strategies for query expansion, according to the geographical restrictions given by the user, are compared. The proposed method allows a more versatile and focused expansion towards the geographical information need of the user.


title: Writeprints: A stylometric approach to identity-level identification and similarity detection in cyberspace

abstract: One of the problems often associated with online anonymity is that it hinders social accountability, as substantiated by the high levels of cybercrime. Although identity cues are scarce in cyberspace, individuals often leave behind textual identity traces. In this study we proposed the use of stylometric analysis techniques to help identify individuals based on writing style. We incorporated a rich set of stylistic features, including lexical, syntactic, structural, content-specific, and idiosyncratic attributes. We also developed the Writeprints technique for identification and similarity detection of anonymous identities. Writeprints is a Karhunen-Loeve transforms-based technique that uses a sliding window and pattern disruption algorithm with individual author-level feature sets. The Writeprints technique and extended feature set were evaluated on a testbed encompassing four online datasets spanning different domains: email, instant messaging, feedback comments, and program code. Writeprints outperformed benchmark techniques, including SVM, Ensemble SVM, PCA, and standard Karhunen-Loeve transforms, on the identification and similarity detection tasks with accuracy as high as 94p when differentiating between 100 authors. The extended feature set also significantly outperformed a baseline set of features commonly used in previous research. Furthermore, individual-author-level feature sets generally outperformed use of a single group of attributes.


title: PC-01: Introduction to computational thinking: Educational technology in primary and secondary education

abstract: This article describes the design and implementation of the course Introduction to Computational Thinking (PC-01) for primary and secondary education. The course introduces the “concepts” and core “processes” of Computational Thinking using a visual programming environment. The design of the course PC-01 includes a set of multimedia content, learning tools and technologies that help teachers and students in the teaching-learning process. These tools allow the teacher the successful delivery of the course content as well as the individual/collective monitoring of students' progress in the course. Students have access to their progress throughout the course and participate in creative and collaborative tasks. This course also serves to introduce transversely educational technologies to many students of primary and secondary education. The technological environment integrates the online teaching resources and the methodological tools of the course. The course uses Scratch 2.0 as the programming environment and Moodle as the learning platform. At present the course is being implemented in public schools in the Dominican Republic.