title: Unified YouTube Video Recommendation via Cross-network Collaboration

abstract: The ever growing number of videos on YouTube makes recommendation an important way to help users explore interesting videos. Similar to general recommender systems, YouTube video recommendation suffers from typical problems like new user, cold-start, data sparsity, etc. In this paper, we propose a unified YouTube video recommendation solution via cross-network collaboration: users' auxiliary information on Twitter are exploited to address the typical problems in single network-based recommendation solutions. The proposed two-stage solution first transfers user preferences from auxiliary network by learning cross-network behavior correlations, and then integrates the transferred preferences with the observed behaviors on target network in an adaptive fashion. Experimental results show that the proposed cross-network collaborative solution achieves superior performance not only in term of accuracy, but also in improving the diversity and novelty of the recommended videos.


title: Multi Layer Perceptron Neural Networks Decoder for LDPC Codes

abstract: A very important and near optimum group of the block codes that have been developed in direction of Shannon's theory concept, is low density parity check (LDPC) code. There have been presented different algorithms for decoding of this class of code such as maximum likelihood (ML), bit flipping (BF), a posteriori probability (APP) and sum product (SP) algorithms that the first two of them decode by hard criterion and two others decode by soft criterion. In this article, considering LDPC codes structure and taking advantage of LDPC codes demonstrated by Tanner graph, we have presented a quite new method based on multi layer perceptron (MLP) neural networks that decodes LDPC codes by soft decision manner. This method opens another new way for LDPC and even other block codes decoding. Simulation results display good performance of this method compared to other known algorithms.


title: A Performance Comparison of CUDA and OpenCL

abstract: CUDA and OpenCL are two different frameworks for GPU programming. OpenCL is an open standard that can be used to program CPUs, GPUs, and other devices from different vendors, while CUDA is specific to NVIDIA GPUs. Although OpenCL promises a portable language for GPU programming, its generality may entail a performance penalty. In this paper, we use complex, near-identical kernels from a Quantum Monte Carlo application to compare the performance of CUDA and OpenCL. We show that when using NVIDIA compiler tools, converting a CUDA kernel to an OpenCL kernel involves minimal modifications. Making such a kernel compile with ATI's build tools involves more modifications. Our performance tests measure and compare data transfer times to and from the GPU, kernel execution times, and end-to-end application execution times for both CUDA and OpenCL.


Mechanisms of Vascular Defects in Diabetes Mellitus


title: Measurement based V2V path loss analysis in urban NLOS scenarios

abstract: The importance of an accurate path loss model of vehicular environments is critical for the vehicular communication system design. However, estimating the path loss in vehicular environments is difficult due to high dynamics and low antenna heights. In this paper, we propose a line-of-sight (LOS) path loss model in vehicle-to-vehicle (V2V) scenarios and provide deep analysis of shadow fading in urban non-LOS (NLOS) scenarios by the deductive method with the proposed LOS model. The results can be utilized as reference material for further analysis of V2V path loss measurements.


title: Noninvasive measurement of heartbeat, respiration, snoring and body movements of a subject in bed via a pneumatic method

abstract: We have developed a noninvasive pneumatics-based system by which to measure heartbeat, respiration, snoring, and body movements of a subject in bed. A thin, air-sealed cushion is placed under the bed mattress of the subject and the small movements attributable to human automatic vital functions are measured as changes in pressure using a pressure sensor having an almost flat frequency response from 0.1 to 5 kHz and a sensitivity of 56 mV/Pa. Using the newly developed system, heartbeat, respiration, apnea, snoring and body movements are clearly measured. In addition, the optimal signal-to-noise (S/N) ratio by which to evaluate the reliability of the heart rate measurement is presented. Heart rates were measured for four different body postures, 13 different subjects, four different bed mattresses, and three different sensor positions. For these measurements, the S/N ratios ranged from 15.9 to 23.5 dB, and so were determined to be reliable.


title: Social Media and Body Image Concerns: Current Research and Future Directions

abstract: This paper provides an overview of research on social media and body image. Correlational studies consistently show that social media usage (particularly Facebook) is associated with body image concerns among young women and men, and longitudinal studies suggest that this association may strengthen over time. Furthermore, appearance comparisons play a role in the relationship between social media and body image. Experimental studies, however, suggest that brief exposure to one's own Facebook account does not negatively impact young women's appearance concerns. Further longitudinal and experimental research is needed to determine which aspects of social media are most detrimental to people's body image concerns. Research is also needed on more diverse samples as well as other social media platforms (e.g., Instagram).


title: C4: the continuously concurrent compacting collector

abstract: C4, the Continuously Concurrent Compacting Collector, an updated generational form of the Pauseless GC Algorithm [7], is introduced and described, along with details of its implementation on modern X86 hardware. It uses a read barrier to support concur- rent compaction, concurrent remapping, and concurrent incremental update tracing. C4 differentiates itself from other generational garbage collectors by supporting simultaneous-generational concurrency: the different generations are collected using concurrent (non stop-the-world) mechanisms that can be simultaneously and independently active. C4 is able to continuously perform concurrent young generation collections, even during long periods of concurrent full heap collection, allowing C4 to sustain high allocation rates and maintain the efficiency typical to generational collectors, without sacrificing response times or reverting to stop-the-world operation. Azul systems has been shipping a commercial implementation of the Pauseless GC mechanism, since 2005. Three successive generations of Azul's Vega series systems relied on custom multi-core processors and a custom OS kernel to deliver both the scale and features needed to support Pauseless GC. In 2010, Azul released its first software-only commercial implementation of C4 for modern commodity X86 hardware, using Linux kernel enhancements to support the required feature set. We discuss implementa- tion details of C4 on X86, including the Linux virtual and physical memory management enhancements that were used to support the high rate of virtual memory operations required for sustained pauseless operation. We discuss updates to the collector's manage- ment of the heap for efficient generational collection and provide throughput and pause time data while running sustained workloads.


title: Web Search as a Linguistic Tool

abstract: Many people rely on web search engines to check the spelling or grammatical correctness of input phrases. For example, one might search [recurring or reoccurring] to decide between these similar words. While language-related queries are common, they have low click-through rates, lack a strong intent signal, and are generally challenging to study. Perhaps for these reasons, they have yet to be characterized in the literature. In this paper we report the results of two surveys that investigate how, when, and why people use web search to support low-level, language-related tasks. The first survey was distributed by email, and asked participants to reflect on a recent search task. The second survey was embedded directly in search result pages, and captured information about searchers' intents in-situ. Our analysis confirms that language-related search tasks are indeed common, accounting for at least 2.7% of all queries posed by our respondents. Survey responses also reveal: (1) the range of language-related tasks people perform with search, (2) the contexts in which these tasks arise, and (3), the reasons why people elect to use web search rather than relying on traditional proofing tools (e.g., spelling and grammar checkers).


title: Time-varying social networks in a graph database: a Neo4j use case

abstract: Representing and efficiently querying time-varying social network data is a central challenge that needs to be addressed in order to support a variety of emerging applications that leverage high-resolution records of human activities and interactions from mobile devices and wearable sensors. In order to support the needs of specific applications, as well as general tasks related to data curation, cleaning, linking, post-processing, and data analysis, data models and data stores are needed that afford efficient and scalable querying of the data. In particular, it is important to design solutions that allow rich queries that simultaneously involve the topology of the social network, temporal information on the presence and interactions of individual nodes, and node metadata. Here we introduce a data model for time-varying social network data that can be represented as a property graph in the Neo4j graph database. We use time-varying social network data collected by using wearable sensors and study the performance of real-world queries, pointing to strengths, weaknesses and challenges of the proposed approach.


An Optimal Method for Lubricating Oil Compounds Properties Estimation - the Computer - Based Approach


title: The short version of the Depression Anxiety Stress Scales (DASS-21): Factor structure in a young adolescent sample

abstract: Abstract This study explored the factor structure of the short form of the Depression Anxiety Stress Scales (DASS-21; Lovibond & Lovibond, 1995b ) in a young adolescent sample. A group of 484 high school students ( Mean age = 13.62 years, Min = 11.83, Max = 15.67 years, 52 % boys) completed the DASS-21. Several models were tested using Confirmatory Factor Analysis. A model consistent with the factor structure of the adult DASS, with correlated error terms, provided good fit to the data. However, correlations among the factors were very high. A ‘quadripartite’ model involving a common ‘Negative Affect’ factor as well as the three specific factors of Depression, Anxiety and Tension/Stress was tested to explain these correlations and was supported by the data. This model suggests that the core symptoms of Depression and Anxiety are similar in adults and adolescents, but the conceptualisation and assessment of Tension/Stress in adolescents needs further refinement.


title: Pigmented Epithelioid Melanocytoma of the Cheek, Orbit, and Intracranial Cavity: A Case Report

abstract: Pigmented epithelioid melanocytoma (PEM) of the skin has been rarely reported in ophthalmology. The purpose of this case report is to present a young male born with a progressive, hyperpigmented lesion involving the orbit and intracranial cavity diagnosed as PEM. The case is unique given the young age and the size, multifocality, and growth of this tumor. Identification of this lesion is paramount due to its low-grade malignant potential.


title: Modeling the Forest or Modeling the Trees A Comparison of System Dynamics and Agent-Based Simulation

abstract: System Dynamics and Agent-based Simulation are two approaches that use computer simulation for investigating non-linear social and socio-economic systems with a focus on the understanding and qualitative prediction of a system’s behavior. Although the two schools have a broad overlap in research topics they have been relatively unnoticed by each other so far. This paper contributes to the cross-study of System Dynamics and Agent-Based Simulation. It uncovers and contrasts the primary conceptual predispositions underlying the two approaches. Moreover, ideas about how the approaches could be integrated are presented.


title: Andro-Dumpsys : Anti-malware system based on the similarity of malware creator and malware centric information

abstract: Our system (Andro-Dumpsys) leverages volatile memory acquisition.Andro-Dumpsys leverages malware creator information and malware information.Andro-Dumpsys is anti-malware system based on similarity matching of footprints.Andro-Dumpsys is capable of detecting zero-day threats. With the fast growth in mobile technologies and the accompanied rise of the integration of such technologies into our everyday life, mobile security is viewed as one of the most prominent areas and is being addressed accordingly. For that, and especially to address the threat associated with malware, various malware-centric analysis methods are developed in the literature to identify, classify, and defend against mobile threats and malicious actors. However, along with this development, anti-malware analysis techniques, such as packing, dynamic loading, and dex encryption, have seen wide adoption, making existing malware-centric analysis methods less effective. In this paper, we propose a feature-rich hybrid anti-malware system, called Andro-Dumpsys, which leverages volatile memory acquisition for accurate malware detection and classification. Andro-Dumpsys is based on similarity matching of malware creator-centric and malware-centric information. Using Andro-Dumpsys, we detect and classify malware samples into similar behavior groups by exploiting their footprints, which are equivalent to unique behavior characteristics. Our experimental results demonstrate that Andro-Dumpsys is scalable, and performs well in detecting malware and classifying malware families with low false positives and false negatives, and is capable of responding zero-day threats.


title: A high efficiency transformerless PV grid-connected inverter with leakage current suppression

abstract: This paper presents a new diode free freewheeling and common-mode voltage (CMV) clamping branches for single phase transformerless grid connected photovoltaic (PV) inverter for complete leakage current elimination and low conduction losses. In the past, various isolation techniques have been proposed for leakage current elimination in transformerless PV inverters. However, galvanic isolation only cannot completely eliminate leakage current due to that a resonant path is created by the switch junction capacitors, which also generate leakage current. The proposed freewheeling branch consists of four MOSFETs along with a clamping branch, which consists of two MOSFETs and a capacitor divider. The divider is connected to the DC side of the converter to keep constant CMV in the freewheeling path. As a result, the improved CMV clamping has been achieved for complete leakage current elimination. The unipolar sinusoidal pulse width modulation (SPWM) technique and modified HERIC topology with AC-decoupling for galvanic isolation is adopted for lower conduction losses. The proposed topology consists of only MOSFET in the freewheeling and clamping path which provides lower conduction losses compared with diode based topologies. The performances of the proposed topology in terms of common mode characteristics, leakage current, total harmonic distortion and conversion efficiency are analyzed and compared with H5, H6, HERIC and HBZBR topologies. The detail analyses are performed using MATLAB/Simulink and PSIM.


title: EMOTION: Clues from the Brain

abstract: CONTENTS INTRODUCTION ......... ........ .... .... .... .... 209 NEURAL BASIS OF EMOTION ..... . . .. ... . . ..... . . . . . .. . . . . . . .. . . . . . . . . . ... . . . . . . .. . . . . . . 210 Neural Basis of Fear ........ 210 Neural Pathways Mediating Fear Conditioning .... 211 Cellular Mechanisms Involved in Fear Conditioning , 216 Extinction of Conditioned Fear 219 Conditioned Fear and Instrumental Action (Coping) 220 Fear Conditioning; Conclusions . . . ... 221 Relation of the Neural Basis of Fear to Other Emotions . . 222 IMPLICATIONS OF THE NEURAL BASIS OF FEAR FOR UNDERSTANDING EMOTION 223 Cognitive-Emotional Interactions . . . . . 223 Conscious versus Unconscious Processes in Emotion ........ ........ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .... 227 Volitional Control of Emotion ..... .... .... .... .... .... .... .... .... 227 Psychopathological Issues . . . . ... . . . . . . .... . . . . . . ... . . . . . . . .... . . . . . . .... .... .... .... 228 CONCLUSIONS .... . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .... .... .... 230


title: MOS: Workload-aware Elasticity for Cloud Object Stores

abstract: The use of cloud object stores has been growing rapidly in recent years as they combine key advantages such as HTTP-based RESTful APIs, high availability, elasticity with a "pay-as-you-go" pricing model that allows applications to scale as needed. The current practice is to either use a single set of configuration parameters or rely on statically configured storage policies for a cloud object store deployment, even when the store is used to support different types of applications with evolving requirements. This crucial mismatch between the different applications requirements and capabilities of the object store is problematic and should be addressed to achieve high efficiency and performance. In this paper, we propose MOS, a Micro Object Storage architecture, which supports independently configured microstores each tuned dynamically to the needs of a particular type of workload. We also design an enhancement, MOS++, that extends MOS's capabilities through fine-grained resource management to effectively meet the tenants' SLAs while maximizing resource efficiency. We have implemented a prototype of MOS ++ in OpenStack Swift using Docker containers. Our evaluation shows that MOS ++ can effectively support heterogeneous workloads across multiple tenants. Compared to default and statically configured object store setups, for a two-tenant setup, MOS++ improves the sustained access bandwidth by up to 79% for a large-object workload, while reducing the 95th percentile latency by up to 70.2% for a small-object workload.


title: ON THE PSYCHOLOGY OF PREDICTION 1

abstract: In this paper, we explore the rules that determine intuitive predictions and judgments of confidence and contrast these rules to the normative principles of statistical prediction. Two classes of prediction are discussed: category prediction and numerical prediction. In a categorical case, the prediction is given in nominal form, for example, the winner in an election, the diagnosis of a patient, or a person's future occupation. In a numerical case, the prediction is given in numerical form, for example, the future value of a particular stock or of a student's grade point average. In making predictions and judgments under uncertainty, people do not appear to follow the calculus of chance or the statistical theory of prediction. Instead, they rely on a limited number of heuristics which sometimes yield reasonable judgments and sometimes lead to severe and systematic errors (Kahneman & Tversky, 1972b, 3; Tversky & Kahneman, 1971, 2; 1973, 11). The present paper is concerned with the role of one of these heuristics – representativeness – in intuitive predictions. Given specific evidence (e.g., a personality sketch), the outcomes under consideration (e.g., occupations or levels of achievement) can be ordered by the degree to which they are representative of that evidence. The thesis of this paper is that people predict by representativeness, that is, they select or order outcomes by the degree to which the outcomes represent the essential features of the evidence.


title: MRI of Internal Impingement of the Shoulder

abstract: OBJECTIVE ::: Internal impingement is a condition that occurs in athletes in which the shoulder is put in extreme abduction and external rotation during overhead movements. During this motion, the posterior fibers of the supraspinatus tendon, anterior fibers of the infraspinatus tendon, or both can get impinged between the humeral head and the posterior glenoid. The purpose of this study was to evaluate the ability of MRI to show the findings of internal impingement of the shoulder. ::: ::: ::: CONCLUSION ::: As opposed to our six patients with clinically and surgically diagnosed internal impingement, the control patients had isolated pathology in the rotator cuff, labrum, or humeral head. We found that the constellation of findings of undersurface tears of the supraspinatus or infraspinatus tendon and cystic changes in the posterior aspect of the humeral head associated with posterosuperior labral pathology is a consistent finding diagnostic of internal impingement.


title: Survey and Analysis of Handoff Decision Strategies for Heterogeneous Mobile Wireless Networks

abstract: Mobility is the important feature of today's wireless network. Every user expects the best service network rather than best connected network. Due to the various heterogeneous wireless network availability, users get a choice of selecting the best service network, based on various preferences. Mobility is possible due to handoff process. The typical decision making before the handoff, to select the best connected network, plays a vital role, which decides service continuity. Entire service should be aware of the roaming events. In this paper we study various proposals of handoff decision strategies, based on various parameter listed below and the state of the art analytical analysis is presented with our notations.


title: Class AB vs. class J 5G power amplifier in 28-nm UTBB FD-SOI technology for high efficiency operation

abstract: Nowadays, technological demands are exponentially and rapidly growing. The telecommunication market examines a growing demand for RF mobile devices where high latency performances are targeted. The power amplifier is a major element of the radio frequency front-end especially if power consumption and bandwidth are considered. This paper presents the design of mm-wave power amplifier for the candidate of 5G using both Common Source Class-AB and Class-J topologies by means of the 28-nm UTBB FD-SOI technology under body bias technique. Upon taking into consideration the parasitic extraction of the transistor, RF pads, and interconnection to ground, a comparison is made and the theoretical effectiveness of Class-J topology for single stage large signal amplification is simulated practically. Moreover, two distinct transistor widths 250 μm and 350 μm are simulated where each has its own topology to study the impact of increasing the width on the performance of the Power Amplifier. While 5G spectral band is not yet specified and determined; recent studies proved that the 28 GHz band is particularly effective for 5G mobile standardization. Thus, the 28 GHz band is chosen as the fundamental frequency of the operation for this work.


title: Study of On-Line Adaptive Discriminant Analysis for EEG-Based Brain Computer Interfaces

abstract: A study of different on-line adaptive classifiers, using various feature types is presented. Motor imagery brain computer interface (BCI) experiments were carried out with 18 naive able-bodied subjects. Experiments were done with three two-class, cue-based, electroencephalogram (EEG)-based systems. Two continuously adaptive classifiers were tested: adaptive quadratic and linear discriminant analysis. Three feature types were analyzed, adaptive autoregressive parameters, logarithmic band power estimates and the concatenation of both. Results show that all systems are stable and that the concatenation of features with continuously adaptive linear discriminant analysis classifier is the best choice of all. Also, a comparison of the latter with a discontinuously updated linear discriminant analysis, carried out in on-line experiments with six subjects, showed that on-line adaptation performed significantly better than a discontinuous update. Finally a static subject-specific baseline was also provided and used to compare performance measurements of both types of adaptation


Overview of the 5th Author Profiling Task at PAN 2017: Gender and Language Variety Identification in Twitter


title: Overview of data quality challenges in the context of Big Data

abstract: Data quality management systems are thoroughly researched topics and have resulted in many tools and techniques developed by both academia and industry. However, the advent of Big Data might pose some serious questions pertaining to the applicability of existing data quality concepts. There is a debate concerning the importance of data quality for Big Data; one school of thought argues that high data quality methods are essential for deriving higher level analytics while another school of thought argues that data quality level will not be so important as the volume of Big Data would be used to produce patterns and some amount of dirty data will not mask the analytic results which might be derived. This paper aims to investigate various components and activities forming part of data quality management such as dimensions, metrics, data quality rules, data profiling and data cleansing. The result list existing challenges and future research areas associated with Big Data for data quality management.


title: Data-driven HR - R\'esum\'e Analysis Based on Natural Language Processing and Machine Learning

abstract: Recruiters usually spend less than a minute looking at each r\'esum\'e when deciding whether it's worth continuing the recruitment process with the candidate. Recruiters focus on keywords, and it's almost impossible to guarantee a fair process of candidate selection. The main scope of this paper is to tackle this issue by introducing a data-driven approach that shows how to process r\'esum\'es automatically and give recruiters more time to only examine promising candidates. Furthermore, we show how to leverage Machine Learning and Natural Language Processing in order to extract all required information from the r\'esum\'es. Once the information is extracted, a ranking score is calculated. The score describes how well the candidates fit based on their education, work experience and skills. Later this paper illustrates a prototype application that shows how this novel approach can increase the productivity of recruiters. The application enables them to filter and rank candidates based on predefined job descriptions. Guided by the ranking, recruiters can get deeper insights from candidate profiles and validate why and how the application ranked them. This application shows how to improve the hiring process by giving an unbiased hiring decision support.


title: Neural correlates of the psychedelic state as determined by fMRI studies with psilocybin

abstract: Psychedelic drugs have a long history of use in healing ceremonies, but despite renewed interest in their therapeutic potential, we continue to know very little about how they work in the brain. Here we used psilocybin, a classic psychedelic found in magic mushrooms, and a task-free functional MRI (fMRI) protocol designed to capture the transition from normal waking consciousness to the psychedelic state. Arterial spin labeling perfusion and blood-oxygen level-dependent (BOLD) fMRI were used to map cerebral blood flow and changes in venous oxygenation before and after intravenous infusions of placebo and psilocybin. Fifteen healthy volunteers were scanned with arterial spin labeling and a separate 15 with BOLD. As predicted, profound changes in consciousness were observed after psilocybin, but surprisingly, only decreases in cerebral blood flow and BOLD signal were seen, and these were maximal in hub regions, such as the thalamus and anterior and posterior cingulate cortex (ACC and PCC). Decreased activity in the ACC/medial prefrontal cortex (mPFC) was a consistent finding and the magnitude of this decrease predicted the intensity of the subjective effects. Based on these results, a seed-based pharmaco-physiological interaction/functional connectivity analysis was performed using a medial prefrontal seed. Psilocybin caused a significant decrease in the positive coupling between the mPFC and PCC. These results strongly imply that the subjective effects of psychedelic drugs are caused by decreased activity and connectivity in the brain's key connector hubs, enabling a state of unconstrained cognition.


title: CoMon: a mostly-scalable monitoring system for PlanetLab

abstract: CoMon is an evolving, mostly-scalable monitoring system for PlanetLab that has the goal of presenting environment-tailored information for both the administrators and users of the PlanetLab global testbed. In addition to passively reporting metrics provided by the operating system, CoMon also actively gathers a number of metrics useful for developers of networked systems. Using CoMon, PlanetLab administrators and users can easily spot problematic machines, where the problem may arise from the machine itself, local configuration/environment problems, or the workload running on the machine. Furthermore, users can easily observe many properties of all of the experiments running across multiple PlanetLab nodes, facilitating not only their own experiment monitoring and debugging, but also helping scale the task of finding PlanetLab problems.In this paper we describe CoMon's design and operation, including what kinds of data are gathered, the scale of the processing involved, and the approaches we have taken to keep CoMon running. Our goal is not only to illustrate the kinds of problems faced in this environment, but also to invite others to participate, either by experimenting with the data generated by CoMon, or by building on the CoMon system itself.


title: Using heuristics to evaluate the playability of games

abstract: Heuristics have become an accepted and widely used adjunct method of usability evaluation in Internet and software development. This report introduces Heuristic Evaluation for Playability (HEP), a comprehensive set of heuristics for playability, based on the literature on productivity and playtesting heuristics that were specifically tailored to evaluate video, computer, and board games. These heuristics were tested on an evolving game design to assess their face validity and evaluation effectiveness compared to more standard user testing methodologies. The results suggest that HEP identified qualitative similarities and differences with user testing and that HEP is best suited for evaluating general issues in the early development phases with a prototype or mock-up. Combined with user studies, HEP offers a new method for the HCI game community that can result in a more usable and playable game.


title: Extracting Social Structures from Conversations in Twitter: A Case Study on Health-Related Posts

abstract: Online Social Networks (e.g., Twitter, Facebook) have dramatically grown in usage and popularity in recent years. In addition to keeping track of friends and acquaintances, these networks provide powerful means of exchanging and sharing information on many different topics of interest (e.g., sports, religion, politics, health concerns, etc.). Moreover, the use of these networks has introduced a completely new way of collaboration among people, virtually creating spheres of friends who generally feel comfortable discussing a variety of subjects and even helping each other to grow in knowledge about certain subjects. In this work, we built and analyzed networks of social groups on Twitter related to the top leading causes of death in the United States. Due to space limitations, we present results for the state of Florida and only for the top four leading causes of death. We show that using a concept of time window in the creation of relations between users, we can reconstruct social networks for these conversations and these networks have characteristics that are similar to typical social networks. The argument is that social network information can be extracted even in cases where users are not directly talking to each other (as it is the case in most of Twitter).


title: Characterization of an Alternating-Current Plasma Source for Atomic Emission Spectroscopy

abstract: Results on the characterization of a helium alternating-current plasma (ACP) are presented. Spectroscopic techniques were utilized to obtain diagnostic measurements of the plasma. The characteristics studied include appearance, excitation temperature, ionization and electron temperature, electron number density, and interferences from other concomitants. Sample introduction was achieved with a glass frit nebulizer (GFN). Enhancement of the calcium response was observed while sodium was present in the plasma. Signal depression was noted when calcium and phosphate were simultaneously introduced into the ACP.


title: Widespread distribution of microplastics in subsurface seawater in the NE Pacific Ocean

abstract: Abstract We document the abundance, composition and distribution of microplastics in sub-surface seawaters of the northeastern Pacific Ocean and coastal British Columbia. Samples were acid-digested and plastics were characterized using light microscopy by type (fibres or fragments) and size ( 1000 μm). Microplastics concentrations ranged from 8 to 9200 particles/m 3 ; lowest concentrations were in offshore Pacific waters, and increased 6, 12 and 27-fold in west coast Vancouver Island, Strait of Georgia, and Queen Charlotte Sound, respectively. Fibres accounted for ∼75% of particles on average, although nearshore samples had more fibre content than offshore ( p


title: MP3DG-PCC, Open Source Software Framework for Implementation and Evaluation of Point Cloud Compression

abstract: We present MP3DG-PCC, an open source framework for design, implementation and evaluation of point cloud compression algorithms. The framework includes objective quality metrics, lossy and lossless anchor codecs, and a test bench for consistent comparative evaluation. The framework and proposed methodology is in use for the development of an international point cloud compression standard in MPEG. In addition, the library is integrated with the popular point cloud library, making a large number of point cloud processing available and aligning the work with the broader open source community.


title: Erratum: Area under the Precision-Recall Curve: Point Estimates and Confidence Intervals

abstract: The right hand side of the equation for interpolated area under the PR curve at the bottom of page 455 inadvertently transposes a and b. This typo does not affect the validity of the proof.


title: A new VHDL-AMS simulation framework in Matlab

abstract: In this paper we present SAMSA, a new tool for the simulation of VHDL-AMS systems in MatlabTM. The goal is the definition of a VHDL framework in which analog/digital systems can be designed and simulated and new simulation techniques can be studied, ezploiting both the powerful MatlabTM functions and Toolbozes.


title: User Movement Prediction: The Contribution of Machine Learning Techniques

abstract: Ambient Assisted Living (AAL) aims to increase the time older people or disabled people can live in their home environment by assisting them in performing activities of daily living by the use of intelligent products. Localization and tracking of users in indoor environment are the main components of AAL. Wireless sensor networks is an effective technology to accomplish these services by using Received Signal Strength (RSS) information. This work seeks to investigate the effect of machine learning techniques on the accuracy of user movement prediction. Five base classifiers and two ensemble learning approaches are employed and the results are evaluated in terms of precision recall, and F-measure. A real-life benchmark dataset in the area of AAL is used for evaluation. The results show that J48 is the best performing model compared to the other base-level classifiers. It also shows that Bagged J48 achieves the best performance.


title: Gender Role Identity and Attitudes Toward Feminism

abstract: In this study we examined relationships among gender role identity, support for feminism, nontraditional gender roles, and willingness to consider oneself a feminist in a sample of college students (N=301). For female participants, we found positive relationships among higher masculinity on the PAQ (Personal Attributes Questionnaire), nontraditional attitudes toward gender roles, and the combined SRAI (Sex Role Attitudinal Inventory). A negative correlation was also found between lower scores on the PAQ masculinity–femininity index and the combined SRAI in women. For male participants, we found positive relationships among high femininity on the SIS (Sexual Identity Scale), willingness to consider oneself a feminist, positive attitudes toward the women's movement, and the combined SRAI. We also found a negative relationship between high masculinity on the PAQ and willingness to consider oneself a feminist in men. The implications of these findings for the feminist movement are discussed.


title: Energy Harvesting for Autonomous Wireless Sensor Networks

abstract: Wireless sensor nodes (WSNs) are employed today in many different application areas, ranging from health and lifestyle to automotive, smart building, predictive maintenance (e.g., of machines and infrastructure), and active RFID tags. Currently these devices have limited lifetimes, however, since they require significant operating power. The typical power requirements of some current portable devices, including a body sensor network, are shown in Figure 1.


title: A novel broadband compact 3dB 180° power divider/combiner derived from the Wilkinson power divider/combiner

abstract: In this paper, a novel 3 dB 180° power divider/combiner is proposed. This device is derived from the classical Wilkinson divider/combiner whose output ports were combined with inverting and non-inverting suspended microstrip lines (SMLs). The proposed structure has a reduced size compared with the conventional and enhanced variants of the 3 dB 180° divider/combiner topologies. Moreover, the new architecture has a greatly improved bandwidth. The measured 0.5 dB insertion loss bandwidth is over the 2–5 GHz band for power combining/dividing. Because of its symmetrical architecture, the presented device shows frequency independent power dividing/combining balance performance. The maximum of 0.5 dB amplitude imbalance and 3° phase imbalance measured over frequency band of 2–5 GHz. Measured port isolation is also about 50 dB at the center frequency.


title: State of the art of zirconia for dental applications

abstract: Abstract Zirconia has been recently introduced in prosthetic dentistry for the fabrication of crowns and fixed partial dentures, in combination with CAD/CAM techniques. This review encompasses the specific types of zirconia available in dentistry, together with their properties. The two main processing techniques, soft and hard machining, are assessed in the light of their possible clinical implications and consequences on the long-term performance of zirconia. An update on the status of clinical trials occurring worldwide is provided.


title: Genetic and clinical factors predict lithium’s effects on PER2 gene expression rhythms in cells from bipolar disorder patients

abstract: Bipolar disorder (BD) is associated with abnormal circadian rhythms. In treatment responsive BD patients, lithium (Li) stabilizes mood and reduces suicide risk. Li also affects circadian rhythms and expression of 'clock genes' that control them. However, the extent to which BD, Li and the circadian clock share common biological mechanisms is unknown, and there have been few direct measurements of clock gene function in samples from BD patients. Hence, the role of clock genes in BD and Li treatment remains unclear. Skin fibroblasts from BD patients (N=19) or healthy controls (N=19) were transduced with Per2::luc, a rhythmically expressed, bioluminescent circadian clock reporter gene, and rhythms were measured for 5 consecutive days. Rhythm amplitude and period were compared between BD cases and controls with and without Li. Baseline period was longer in BD cases than in controls. Li 1 mM increased amplitude in controls by 36%, but failed to do so in BD cases. Li 10 mM lengthened period in both BD cases and controls. Analysis of clock gene variants revealed that PER3 and RORA genotype predicted period lengthening by Li, whereas GSK3β genotype predicted rhythm effects of Li, specifically among BD cases. Analysis of BD cases by clinical history revealed that cells from past suicide attempters were more likely to show period lengthening with Li 1 mM. Finally, Li enhanced the resynchronization of damped rhythms, suggesting a mechanism by which Li could act therapeutically in BD. Our work suggests that the circadian clock's response to Li may be relevant to molecular pathology of BD.


title: Mathematical model for administration of web-conference distributed systems

abstract: The development of Web-technologies has led to the development of tools for distance technologies in education. Every year the market of web conferencing systems is replenished with both proprietary and freely distributed systems. The main «bottleneck» in conducting web conferencing is the server load, which can occur both in the insufficient server performance, and in the insufficient bandwidth of the Internet channel. In the article the Mathematical model of the distributed system with the addition of a distributor, the advantages of which are that the possible options are concealed from the user, and the system chooses the server itself, and the algorithm for choosing the «optimal» node in a distributed web conferencing system. The mathematical model of distributed web conferencing system allows describing the state of the system at any time in the form of a set of server characteristics, created webinars and client applications connected to them. A series of full-scale tests of the developed software complex, which manages web conferencing deployed in a cluster of three servers powered by IBM Lotus Sametime, was conducted. Model experiments of managing a distributed web conference system were conducted.


title: A systematic study of the class imbalance problem in convolutional neural networks

abstract: In this study, we systematically investigate the impact of class imbalance on classification performance of convolutional neural networks (CNNs) and compare frequently used methods to address the issue. Class imbalance is a common problem that has been comprehensively studied in classical machine learning, yet very limited systematic research is available in the context of deep learning. In our study, we use three benchmark datasets of increasing complexity, MNIST, CIFAR-10 and ImageNet, to investigate the effects of imbalance on classification and perform an extensive comparison of several methods to address the issue: oversampling, undersampling, two-phase training, and thresholding that compensates for prior class probabilities. Our main evaluation metric is area under the receiver operating characteristic curve (ROC AUC) adjusted to multi-class tasks since overall accuracy metric is associated with notable difficulties in the context of imbalanced data. Based on results from our experiments we conclude that (i) the effect of class imbalance on classification performance is detrimental; (ii) the method of addressing class imbalance that emerged as dominant in almost all analyzed scenarios was oversampling; (iii) oversampling should be applied to the level that completely eliminates the imbalance, whereas the optimal undersampling ratio depends on the extent of imbalance; (iv) as opposed to some classical machine learning models, oversampling does not cause overfitting of CNNs; (v) thresholding should be applied to compensate for prior class probabilities when overall number of properly classified cases is of interest.


title: Emoticons and social interaction on the Internet: the importance of social context

abstract: Abstract The present study examines the influence of social context on the use of emoticons in Internet communication. Secondary school students ( N = 158) responded to short internet chats. Social context (task-oriented vs. socio-emotional) and valence of the context (positive vs. negative) were manipulated in these chats. Participants were permitted to respond with text, emoticon or a combination of both. Results showed that participants used more emoticons in socio-emotional than in task-oriented social contexts. Furthermore, students used more positive emoticons in positive contexts and more negative emoticons in negative contexts. An interaction was found between valence and kind of context; in negative, task-oriented contexts subjects used the least emoticons. Results are related to research about the expression of emotions in face-to-face interaction.


title: Morphological Inflection Generation with Hard Monotonic Attention

abstract: We present a neural model for morphological inflection generation which employs a hard attention mechanism, inspired by the nearly-monotonic alignment commonly found between the characters in a word and the characters in its inflection. We evaluate the model on three previously studied morphological inflection generation datasets and show that it provides state of the art results in various setups compared to previous neural and non-neural approaches. Finally we present an analysis of the continuous representations learned by both the hard and soft attention \cite{bahdanauCB14} models for the task, shedding some light on the features such models extract.


title: Enhancing Physical Education with Exergames and Wearable Technology

abstract: Increases in the numbers of obese and overweight children are a major issue in post-industrial societies because obesity can lead to severe health-related problems. In addition, many challenges affect the quantity and quality of physical education (PE) provided by schools. Exergames that combine exercise with gaming have been recognized as a possible method for motivating children to become physically active and to make PE more fun. Furthermore, exergames that utilize wearable sensors devices allow players’ movements to be tracked for estimating the efficiency of exercise. In this study, we developed the Running Othello 2 (RO2) exergame, where players wear a smartphone and a smart wrist band to compete in a board game enhanced with physical and pedagogical missions. In physical missions, the game uses inertial sensors and a heart rate meter to detect the physical activities of players. The pedagogical part of the game is based on the South Korean PE curriculum. We evaluated RO2 with 61 South Korean third grade elementary school students, 32 of whom learned curriculum topics by playing the game. The other 29 students comprised a control group who studied the pedagogical content using handouts. The results indicated that learning with RO2 was more efficient, the players were engaged, and their heart rates increased. Based on the evaluation, we identified several issues to be addressed in future research. Finally, we discussed how RO2 supports the educational affordances of wearables and we explained how exergames using wearables can overcome some of the challenges faced by PE.


title: COPE: traffic engineering in dynamic networks

abstract: Traffic engineering plays a critical role in determining the performance and reliability of a network. A major challenge in traffic engineering is how to cope with dynamic and unpredictable changes in traffic demand. In this paper, we propose COPE, a class of traffic engineering algorithms that optimize for the expected scenarios while providing a worst-case guarantee for unexpected scenarios. Using extensive evaluations based on real topologies and traffic traces, we show that COPE can achieve efficient resource utilization and avoid network congestion in a wide variety of scenarios.


title: Estimating blood pressure using Windkessel model on photoplethysmogram

abstract: Simple and non-invasive methods to estimate vital signs are very important for preventive healthcare. In this paper, we present a methodology to estimate Blood Pressure (BP) using Photoplethysmography (PPG). Instead of directly relating systolic and diastolic BP values with PPG features, our proposed methodology initially maps PPG features with some person specific intermediate latent parameters and later derives BP values from them. The 2-Element Windkessel model has been considered in the current context to estimate total peripheral resistance and arterial compliance of a person using PPG features, followed by linear regression for simulating arterial blood pressure. Experimental results, performed on a standard hospital dataset yielded absolute errors of 0.78±13.1 mmHg and 0.59 ± 10.23 mmHg for systolic and diastolic BP values respectively. Results also indicate that the methodology is more robust than the standard methodologies that directly estimate BP values from PPG signal.


title: A Comparison of Methods for Generating Poisson disk distributions

abstract: Poisson disk distributions have many applications in the field of computer graphics. Besides sampling, Poisson disk distributions are used in object distribution, non-photorealistic rendering and procedural texturing. Over the years, a large number of methods for generating Poisson disk distributions have been proposed, making it difficult to choose the right method for a given application. In this paper, we present a detailed comparison of most techniques for generating Poisson disk distributions. The methods we study include dart throwing, relaxation dart throwing, Lloyd's relaxation, Shade's Poisson disk tiles, tiled blue noise samples, fast hierarchical importance sampling with blue noise properties, edge-based Poisson disk tiles, template Poisson disk tiles, corner-based Poisson disk tiles and recursive Wang tiles for real-time blue noise. Analysing all of these methods within a single framework is one of the major contributions of this work.


title: Efficient grid-based spatial representations for robot navigation in dynamic environments

abstract: In robotics, grid maps are often used for solving tasks like collision checking, path planning, and localization. Many approaches to these problems use Euclidean distance maps (DMs), generalized Voronoi diagrams (GVDs), or configuration space (c-space) maps. A key challenge for their application in dynamic environments is the efficient update after potential changes due to moving obstacles or when mapping a previously unknown area. To this end, this paper presents novel algorithms that perform incremental updates that only visit cells affected by changes. Furthermore, we propose incremental update algorithms for DMs and GVDs in the configuration space of non-circular robots. These approaches can be used to implement highly efficient collision checking and holonomic path planning for these platforms. Our c-space representations benefit from parallelization on multi-core CPUs and can also be integrated with other state-of-the-art path planners such as rapidly-exploring random trees. In various experiments using real-world data we show that our update strategies for DMs and GVDs require substantially less cell visits and computation time compared to previous approaches. Furthermore, we demonstrate that our GVD algorithm deals better with non-convex structures, such as indoor areas. All our algorithms consider actual Euclidean distances rather than grid steps and are easy to implement. An open source implementation is available online.


title: A Theory of Shape by Space Carving

abstract: In this paper we consider the problem of computing the 3D shape of an unknown, arbitrarily-shaped scene from multiple photographs taken at known but arbitrarily-distributed viewpoints. By studying the equivalence class of all 3D shapes that reproduce the input photographs, we prove the existence of a special member of this class, the photo hull, that (1) can be computed directly from photographs of the scene, and (2) subsumes all other members of this class. We then give a provably-correct algorithm, called Space Carving, for computing this shape and present experimental results on complex real-world scenes. The approach is designed to (1) capture photorealistic shapes that accurately model scene appearance from a wide range of viewpoints, and (2) account for the complex interactions between occlusion, parallax, shading, and their view-dependent effects on scene-appearance.


title: An experimental comparison of click position-bias models

abstract: Search engine click logs provide an invaluable source of relevance information, but this information is biased. A key source of bias is presentation order: the probability of click is influenced by a document's position in the results page. This paper focuses on explaining that bias, modelling how probability of click depends on position. We propose four simple hypotheses about how position bias might arise. We carry out a large data-gathering effort, where we perturb the ranking of a major search engine, to see how clicks are affected. We then explore which of the four hypotheses best explains the real-world position effects, and compare these to a simple logistic regression model. The data are not well explained by simple position models, where some users click indiscriminately on rank 1 or there is a simple decay of attention over ranks. A 'cascade' model, where users view results from top to bottom and leave as soon as they see a worthwhile document, is our best explanation for position bias in early ranks


title: Cyberbullying: Using Virtual Scenarios to Educate and Raise Awareness.

abstract: This study examined cyberbullying in three distinct phases to facilitate a multifaceted understanding of cyberbullying. The phases included (a) a quantitative survey, (b) a qualitative focus group, and (c) development of educational scenarios/simulations (within the Second Life virtual environment). Phase III was based on adolescent feedback about cyberbullying from Phases I and II of this study. In all three phases, adolescent reactions to cyberbullying were examined and reported to raise awareness and to educate others about cyberbullying. Results from scenario development indicate that simulations created in a virtual environment are engaging and have the potential to be powerful tools in helping schools address problems such as cyberbullying education and prevention. (Keywords: cyberbullying, virtual worlds, Second Life, teacher education, counselor education)


title: Semantic search during divergent thinking

abstract: Abstract Divergent thinking, as a method of examining creative cognition, has not been adequately analyzed in the context of modern cognitive theories. This article casts divergent thinking responding in the context of theories of memory search. First, it was argued that divergent thinking tasks are similar to semantic fluency tasks, but are more constrained, and less well structured. Next, response time distributions from 54 participants were analyzed for temporal and semantic clustering. Participants responded to two prompts from the alternative uses test: uses for a brick and uses for a bottle, for two minutes each. Participants’ cumulative response curves were negatively accelerating, in line with theories of search of associative memory. However, results of analyses of semantic and temporal clustering suggested that clustering is less evident in alternative uses responding compared to semantic fluency tasks. This suggests either that divergent thinking responding does not involve an exhaustive search through a clustered memory trace, but rather that the process is more exploratory, yielding fewer overall responses that tend to drift away from close associates of the divergent thinking prompt.


title: Low-Power, Adaptive Neuromorphic Systems: Recent Progress and Future Directions

abstract: In this paper, we present a survey of recent works in developing neuromorphic or neuro-inspired hardware systems. In particular, we focus on those systems which can either learn from data in an unsupervised or online supervised manner. We present algorithms and architectures developed specially to support on-chip learning. Emphasis is placed on hardware friendly modifications of standard algorithms, such as backpropagation, as well as novel algorithms, such as structural plasticity, developed specially for low-resolution synapses. We cover works related to both spike-based and more traditional non-spike-based algorithms. This is followed by developments in novel devices, such as floating-gate MOS, memristors, and spintronic devices. CMOS circuit innovations for on-chip learning and CMOS interface circuits for post-CMOS devices, such as memristors, are presented. Common architectures, such as crossbar or island style arrays, are discussed, along with their relative merits and demerits. Finally, we present some possible applications of neuromorphic hardware, such as brain–machine interfaces, robotics, etc., and identify future research trends in the field.


title: What do childhood anxiety disorders predict

abstract: Background: Few longitudinal studies of child and adolescent psychopathology have examined the links between specific childhood anxiety disorders and adolescent psychiatric disorder. In this paper we test the predictive specificity of separation anxiety disorder (SAD), overanxious disorder (OAD), generalized anxiety disorder (GAD), and social phobia. ::: ::: Methods: Data come from the Great Smoky Mountains Study (GSMS). A representative population sample of children – ages 9, 11, and 13 years at intake – was followed to age 19. Diagnoses of both childhood (before age 13 years) and adolescent psychiatric disorders (age 13 to 19 years) were available from 906 participants. ::: ::: Results: Childhood SAD predicted adolescent SAD, whereas OAD was associated with later OAD, panic attacks, depression and conduct disorder (CD). GAD was related only to CD. Social phobia in childhood was associated with adolescent OAD, social phobia, and attention-deficit/hyperactivity disorder (ADHD). ::: ::: Conclusions: Anxiety disorders in childhood are predictors of a range of psychiatric disorders in adolescence. It appears that children meriting a well-defined diagnosis are missed by the current rules for the diagnosis of GAD. Future studies should examine whether OAD deserves reconsideration as a nosological entity.


title: Analysis of three-phase rectifiers with constant-voltage loads

abstract: This paper presents a quantitative analysis of the operating characteristics of three-phase bridge rectifiers with AC-side reactance and constant-voltage loads. We describe the operating characteristics of the three-phase diode bridge rectifier in continuous conduction mode. Simple approximate expressions are derived for the output current characteristic, as well as the input power factor. The output current control characteristic is also derived for the three-phase thyristor bridge converter with constant-voltage load. The derived analytical expressions are applied to a practical example and simulations are utilized to validate the analytical results.


title: Mobile commerce adoption in China and the United States: a cross-cultural study

abstract: Mobile communication technologies have penetrated consumer markets throughout the world. Mobile commerce is likely to make a strong influence on business activities, consumer behavior, as well as national and global markets. Thus the identification of factors that influence mobile commerce adoption has significant value. In a global context, this study identified nine factors affecting mobile commerce adoption based on published research in MIS. These factors were investigated in China and the United States, and a comparative examination was conducted. A survey was conducted on 190 individual mobile commerce users in China and USA. Results show that there are several significant differences among the antecedents and their impacts on consumer intention to use mobile commerce in the two cultural settings. The study provides a number of practical insights and informs vendors seeking to enter the Chinese and the US marketplace with specific information on user perspectives.


title: Flexible and Scalable State Tracking Framework for Goal-Oriented Dialogue Systems

abstract: Goal-oriented dialogue systems typically rely on components specifically developed for a single task or domain. This limits such systems in two different ways: If there is an update in the task domain, the dialogue system usually needs to be updated or completely re-trained. It is also harder to extend such dialogue systems to different and multiple domains. The dialogue state tracker in conventional dialogue systems is one such component - it is usually designed to fit a well-defined application domain. For example, it is common for a state variable to be a categorical distribution over a manually-predefined set of entities (Henderson et al., 2013), resulting in an inflexible and hard-to-extend dialogue system. In this paper, we propose a new approach for dialogue state tracking that can generalize well over multiple domains without incorporating any domain-specific knowledge. Under this framework, discrete dialogue state variables are learned independently and the information of a predefined set of possible values for dialogue state variables is not required. Furthermore, it enables adding arbitrary dialogue context as features and allows for multiple values to be associated with a single state variable. These characteristics make it much easier to expand the dialogue state space. We evaluate our framework using the widely used dialogue state tracking challenge data set (DSTC2) and show that our framework yields competitive results with other state-of-the-art results despite incorporating little domain knowledge. We also show that this framework can benefit from widely available external resources such as pre-trained word embeddings.


title: Meta Learning Shared Hierarchies

abstract: We develop a metalearning approach for learning hierarchically structured policies, improving sample efficiency on unseen tasks through the use of shared primitives---policies that are executed for large numbers of timesteps. Specifically, a set of primitives are shared within a distribution of tasks, and are switched between by task-specific policies. We provide a concrete metric for measuring the strength of such hierarchies, leading to an optimization problem for quickly reaching high reward on unseen tasks. We then present an algorithm to solve this problem end-to-end through the use of any off-the-shelf reinforcement learning method, by repeatedly sampling new tasks and resetting task-specific policies. We successfully discover meaningful motor primitives for the directional movement of four-legged robots, solely by interacting with distributions of mazes. We also demonstrate the transferability of primitives to solve long-timescale sparse-reward obstacle courses, and we enable 3D humanoid robots to robustly walk and crawl with the same policy.


title: Rehabilitation with Brain-Computer Interface Systems

abstract: BCI systems let users convert thoughts into actions that do not involve voluntary muscle movement. The systems offer a new means of communication for those with paralysis or severe neuromuscular disorders. BCI technology is a relatively new, fast-growing field of research and applications with the potential to improve the quality of life in severely disabled people. To date, several BCI prototypes exist, but most work only in a laboratory environment. Before a BCI can be used for communication and control at home, research must solve several problems. An important next step is to establish protocols for easily setting up and using BCI systems in a practical environment. Many features, such as electrode positions and frequency components, must be automatically selectable for particular motor imagery. The system must use the fewest number of recording electrodes possible, striving for the optimal single EEG channel. Finally, training time must decrease, perhaps through game-like feedback and automatic detection of artifacts, such as uncontrolled muscle activity. With these improvements, which are on the horizon, we expect to see practical BCI systems for a wide range of users and applications.


title: Frame-Based Ontology Population with PIKES

abstract: We present an approach for ontology population from natural language English texts that extracts RDF triples according to FrameBase, a Semantic Web ontology derived from FrameNet. Processing is decoupled in two independently-tunable phases. First, text is processed by several NLP tasks, including Semantic Role Labeling (SRL), whose results are integrated in an RDF graph of mentions , i.e., snippets of text denoting some entity/fact. Then, the mention graph is processed with SPARQL-like rules using a specifically created mapping resource from NomBank/PropBank/FrameNet annotations to FrameBase concepts, producing a knowledge graph whose content is linked to DBpedia and organized around semantic frames , i.e., prototypical descriptions of events and situations. A single RDF/OWL representation is used where each triple is related to the mentions/tools it comes from. We implemented the approach in PIKES, an open source tool that combines two complementary SRL systems and provides a working online demo. We evaluated PIKES on a manually annotated gold standard, assessing precision/recall in (i) populating FrameBase ontology, and (ii) extracting semantic frames modeled after standard predicate models, for comparison with state-of-the-art tools for the Semantic Web. We also evaluated (iii) sampled precision and execution times on a large corpus of 110 K Wikipedia-like pages.


title: Measuring the Value Of Information - An Asset Valuation Approach.

abstract: Information is increasingly being recognised as one of the firm's most valuable assets. However so far it has resisted quantitative measurement. While it consumes vast and ever increasing quantities of organisational resources in its capture, storage and processing, it typically receives no financial recognition on the balance sheet. The objective of this paper is to develop an approach to valuing information which is both practical to apply and consistent with accepted accounting principles. It begins by examining the nature of information as an asset, and defines a number of "laws" that govern its behaviour as an economic good. It then looks at alternative asset valuation models from accounting theory and how they may be applied to measure the value of information. Finally, an approach is proposed which adapts existing asset valuation methods to reflect the unique characteristics of information as an asset. Measures of the value of information may be used to measure IT effectiveness , increase organisational awareness of the value of information, guide IT strategic planning and cost-justify DSS/EIS developments.


title: Cloud Databases: A Paradigm Shift in Databases

abstract: Relational databases ruled the Information Technology (IT) industry for almost 40 years. But last few years have seen sea changes in the way IT is being used and viewed. Stand alone applications have been replaced with web-based applications, dedicated servers with multiple distributed servers and dedicated storage with network storage. Cloud computing has become a reality due to its lesser cost, scalability and pay-as-you-go model. It is one of the biggest changes in IT after the rise of World Wide Web. Cloud databases such as Big Table, Sherpa and SimpleDB are becoming popular. They address the limitations of existing relational databases related to scalability, ease of use and dynamic provisioning. Cloud databases are mainly used for dataintensive applications such as data warehousing, data mining and business intelligence. These applications are read-intensive, scalable and elastic in nature. Transactional data management applications such as banking, airline reservation, online ecommerce and supply chain management applications are writeintensive. Databases supporting such applications require ACID (Atomicity, Consistency, Isolation and Durability) properties, but these databases are difficult to deploy in the cloud. The goal of this paper is to review the state of the art in the cloud databases and various architectures. It further assesses the challenges to develop cloud databases that meet the user requirements and discusses popularly used Cloud databases.


title: Studio2Shop: from studio photo shoots to fashion articles

abstract: Fashion is an increasingly important topic in computer vision, in particular the so-called street-to-shop task of matching street images with shop images containing similar fashion items. Solving this problem promises new means of making fashion searchable and helping shoppers find the articles they are looking for. This paper focuses on finding pieces of clothing worn by a person in full-body or half-body images with neutral backgrounds. Such images are ubiquitous on the web and in fashion blogs, and are typically studio photos, we refer to this setting as studio-to-shop. Recent advances in computational fashion include the development of domain-specific numerical representations. Our model Studio2Shop builds on top of such representations and uses a deep convolutional network trained to match a query image to the numerical feature vectors of all the articles annotated in this image. Top-$k$ retrieval evaluation on test query images shows that the correct items are most often found within a range that is sufficiently small for building realistic visual search engines for the studio-to-shop setting.


Complexity of exact gradient computation algorithms for recurrent neural networks


title: Beyond Locality-Sensitive Hashing

abstract: We present a new data structure for the c-approximate near neighbor problem (ANN) in the Euclidean space. For n points in R^d, our algorithm achieves O(n^{\rho} + d log n) query time and O(n^{1 + \rho} + d log n) space, where \rho <= 7/(8c^2) + O(1 / c^3) + o(1). This is the first improvement over the result by Andoni and Indyk (FOCS 2006) and the first data structure that bypasses a locality-sensitive hashing lower bound proved by O'Donnell, Wu and Zhou (ICS 2011). By a standard reduction we obtain a data structure for the Hamming space and \ell_1 norm with \rho <= 7/(8c) + O(1/c^{3/2}) + o(1), which is the first improvement over the result of Indyk and Motwani (STOC 1998).


title: IoT platform for condition monitoring of industrial motors

abstract: The rapid development of technology currently revolves significantly around Internet of Things (IoT). Numbers of things are efficiently interconnected, especially in industrial automation which leads to condition and controlled monitoring to increase productivity. The aim of this project is to design and implementation of IOT technology to monitor and diagnose the condition of Induction motors by recording key operation indicators. The proposed method comprises of an IoT based platform to collect and process the induction motor parameters. The data collected can be stored in the cloud platform and same can be accessed through the web page. And also timely alerts will be received for any violation in desired limits of parameters under monitoring, So that immediate action can be taken to avoid unwanted downtime of the motor that saves time and money. Advantages of this method includes continuous monitoring of the equipment, receiving alerts, and data availability for predictive maintenance.


Marine fish culture in Britain IX. Growth of cultured plaice to marketable size in the laboratory


title: The nom Profit-Maximizing Operating System

abstract: In the near future, cloud providers will sell their users virtual machines with CPU, memory, network, and storage resources whose prices constantly change according to market-driven supply and demand conditions. Running traditional operating systems in these virtual machines is a poor fit: traditional operating systems are not aware of changing resource prices and their sole aim is to maximize performance with no consideration of costs. Consequently, they yield low profits. We present nom, a profit-maximizing operating system designed for cloud computing platforms with dynamic resource prices. Applications running on nom aim to maximize profits by optimizing simultaneously for performance and resource costs. The nom kernel provides them with direct access to the underlying hardware and full control over their private software stacks. Since nom applications know there is no single ``best'' software stack, they adapt their stacks' behavior on the fly according to the current price of available resources and their private utility from them, which differs between applications. We show that in addition to achieving up to 3.9x better throughput and up to 9.1x better latency, nom applications yield up to 11.1x higher profits when compared with the same applications running on Linux and OSv.


title: Generative Attribute Controller with Conditional Filtered Generative Adversarial Networks

abstract: We present a generative attribute controller (GAC), a novel functionality for generating or editing an image while intuitively controlling large variations of an attribute. This controller is based on a novel generative model called the conditional filtered generative adversarial network (CFGAN), which is an extension of the conventional conditional GAN (CGAN) that incorporates a filtering architecture into the generator input. Unlike the conventional CGAN, which represents an attribute directly using an observable variable (e.g., the binary indicator of attribute presence) so its controllability is restricted to attribute labeling (e.g., restricted to an ON or OFF control), the CFGAN has a filtering architecture that associates an attribute with a multi-dimensional latent variable, enabling latent variations of the attribute to be represented. We also define the filtering architecture and training scheme considering controllability, enabling the variations of the attribute to be intuitively controlled using typical controllers (radio buttons and slide bars). We evaluated our CFGAN on MNIST, CUB, and CelebA datasets and show that it enables large variations of an attribute to be not only represented but also intuitively controlled while retaining identity. We also show that the learned latent space has enough expressive power to conduct attribute transfer and attribute-based image retrieval.


Frequency of fungal infections in nasal polypi.


title: Improving speech recognition using dynamic multi-pipeline API

abstract: Speech recognition technology has been helping human more comfortable to input to computer, however, there is a concern about its performance in terms of precision and error rate in noise environment. Moreover, most of speech recognition APIs are model audio dependent. Although they support the model, the error rate is not decreased. This causes a faulty sound recognition and return incorrect results. This research aims to increase the performance of the speech recognition of non-native speaker. The proposed technique, Dynamic Multi-Pipeline API (DMP), integrated two-tier speech recognition using a combination of Microsoft API, English voice model, and Google API, a Thai voice model, to increase speech recognition performance. The experiment also conducted in different noise scenarios which are 46 dB and 70 dB respectively. The results showed the comparison of performance of the proposed technique with famous speech recognition APIs, the Microsoft API and Google API. It can be seen that the Dynamic MultiPipeline API (DMP) performed better by providing less error rate at 6% and 14% in 46 dB and 70 dB noise environment which are better than compared techniques.


title: On the Dangers of Pulling a Fast One: Advertisement Disclaimer Speed, Brand Trust, and Purchase Intention

abstract: Two experiments demonstrated that fast (vs. normal-paced) end-of-advertisement disclaimers undermine consumers’ purchase intention toward untrusted brands (both trust-unknown and not-trusted brands), but that disclaimer speed has no effect on consumers’ purchase intention toward trusted brands. The differential effects of disclaimer speed for untrusted versus trusted brands were not due to differences in consumers’ familiarity with the brands (experiment 1). Consistent with the hypothesis that fast disclaimers adversely affect purchase intention via heuristic rather than elaborative processes, the disclaimer speed × brand trust interaction effect remained robust even when the disclaimer presented positive information about the advertised product (experiment 2).


title: A Bi-Directional Message Passing Model for Salient Object Detection

abstract: Recent progress on salient object detection is beneficial from Fully Convolutional Neural Network (FCN). The saliency cues contained in multi-level convolutional features are complementary for detecting salient objects. How to integrate multi-level features becomes an open problem in saliency detection. In this paper, we propose a novel bi-directional message passing model to integrate multi-level features for salient object detection. At first, we adopt a Multi-scale Context-aware Feature Extraction Module (MCFEM) for multi-level feature maps to capture rich context information. Then a bi-directional structure is designed to pass messages between multi-level features, and a gate function is exploited to control the message passing rate. We use the features after message passing, which simultaneously encode semantic information and spatial details, to predict saliency maps. Finally, the predicted results are efficiently combined to generate the final saliency map. Quantitative and qualitative experiments on five benchmark datasets demonstrate that our proposed model performs favorably against the state-of-the-art methods under different evaluation metrics.


title: Trends in Mean Annual Streamflows in Serra da Mantiqueira Environmental Protection Area

abstract: ABSTRACT The aim of this study was to detect trends in the mean annual streamflow in watersheds of Serra da Mantiqueira Environmental Protection Area, an important Brazilian conservation area located between Minas Gerais, Sao Paulo and Rio de Janeiro States. Historical series of four selected streamgage stations were analyzed for the periods of 1980-1998 and 1980-2009, using the Mann-Kendall and Regional Mann-Kendall tests. The results showed that the mean annual streamflows of Serra da Mantiqueira Environmental Protection Area watersheds did not change during the complete period of 1980-2009. The tests detected statistically significant trends of reduction of mean annual streamflow during the 1980-1998 period. The confrontation of this result with the inexistence of rainfall trends during the same period suggested that the streamflow changes detected were consequences of changes in the watersheds physical characteristics, and/or increases in water abstraction for multiple uses. Key words: streamflow changes, Serra da Mantiqueira Range, Mann-Kendall test


title: A real-time reconstructed 3D environment augmented with virtual objects rendered with correct occlusion

abstract: In this work we present a novel framework for the real-time interaction with 3D models in augmented virtual reality. Our framework incorporates view-dependent stereoscopic rendering of the reconstructed environment together with user's hands and a virtual object, and high-precision gesture recognition to manipulate it. Proposed setup consists of a Creative RGB-D camera, Oculus Rift VR head mounted display (HMD), Leap Motion hands and fingers tracker and an AR marker. The system is capable of augmenting the user's hands in relation to their point of view (POV) using the depth sensor mounted on the HMD, and allows manipulation of the environment through the Leap Motion sensor. The AR marker is used to determine the location of the Leap Motion sensor to help with consolidation of transformations between the Oculus and the Leap Motion sensor. Combined with accurate information from the Oculus HMD, the system is able to track the user's head and fingers, with 6-DOF, to provide a spatially accurate augmentation of the user's virtual hands. Such an approach allows us to achieve high level of user immersion since the augmented objects occlude the user's hands properly; something which is not possible with conventional AR. We hypothesize that users of our system will be able to perform better object manipulation tasks in this particular augmented VR setup as compared to virtual reality (VR) where user's hands are not visible, or if visible, always occlude virtual objects.


title: No pane, no gain: efficient evaluation of sliding-window aggregates over data streams

abstract: Windows queries are proving essential to data-stream processing. In this paper, we present an approach for evaluating sliding-window aggregate queries that reduces both space and computation time for query execution. Our approach divides overlapping windows into disjoint panes, computes sub-aggregates over each pane, and "rolls up" the pane-aggregates to computer window-aggregates. Our experimental study shows that using panes has significant performance benefits.


title: Ultra-Low Complexity Block-Based Lane Detection and Departure Warning System

abstract: This paper proposes an ultra-low complexity block-based lane detection and departure warning system. Based on the distribution of the lane markings in the region close to the vehicle, a parameterized region of interest (ROI) is determined. The lane markings in the ROI are enhanced by increasing the pixel intensity for detection in various environmental conditions. To reduce the computational burden, the ROI is partitioned into non-overlapping blocks and two simplified masks are proposed to obtain the block gradients and block angles. The driving conditions are classified into four classes to simplify the lane detection process and the proposed lane departure warning system is based on the lane detection results. The experimental results reveal that the average lane detection rate and the departure warning rate are 96.12% and 98.60%, respectively. With a $1920\times 1080$ resolution, the average processing time is 4.28 ms per frame.


title: Two-Handed Sign Language Recognition for Bangla Character Using Normalized Cross Correlation

abstract: Sign language detection and recognition (SLDR) using computer vision is a very challenging task. In respect to Bangladesh, sign language users are around 2.4 million [1]. In this paper, we try to focus for communicating with those users by computer vision. In this respect, an efficient method is propose consists of two basic steps: (a) refinement and (b) recognition. Initially in refinement, a Red-Green-Blue (RGB) color model is adopted to select heuristically threshold value for detecting candidate regions (i.e. hand and wrist band sign regions). After the candidate regions are obtained by applying color segmentation, then procedures for refining the candidate region are followed by using two different color wrist band regions and filtering. Finally, statistically based template matching technique is used for recognition of hand sign regions. Various hand sign images are used to test the proposed method and results are presented to provide its effectiveness.


title: Guided Upsampling Network for Real-Time Semantic Segmentation

abstract: Semantic segmentation architectures are mainly built upon an encoder-decoder structure. These models perform subsequent downsampling operations in the encoder. Since operations on high-resolution activation maps are computationally expensive, usually the decoder produces output segmentation maps by upsampling with parameters-free operators like bilinear or nearest-neighbor. We propose a Neural Network named Guided Upsampling Network which consists of a multiresolution architecture that jointly exploits high-resolution and large context information. Then we introduce a new module named Guided Upsampling Module (GUM) that enriches upsampling operators by introducing a learnable transformation for semantic maps. It can be plugged into any existing encoder-decoder architecture with little modifications and low additional computation cost. We show with quantitative and qualitative experiments how our network benefits from the use of GUM module. A comprehensive set of experiments on the publicly available Cityscapes dataset demonstrates that Guided Upsampling Network can efficiently process high-resolution images in real-time while attaining state-of-the art performances.


title: Deep Adaptive Proposal Network for Object Detection in Optical Remote Sensing Images

abstract: Object detection is a fundamental and challenging problem in aerial and satellite image analysis. More recently, a two-stage detector Faster R-CNN is proposed and demonstrated to be a promising tool for object detection in optical remote sensing images, while the sparse and dense characteristic of objects in remote sensing images is complexity. It is unreasonable to treat all images with the same region proposal strategy, and this treatment limits the performance of two-stage detectors. In this paper, we propose a novel and effective approach, named deep adaptive proposal network (DAPNet), address this complexity characteristic of object by learning a new category prior network (CPN) on the basis of the existing Faster R-CNN architecture. Moreover, the candidate regions produced by DAPNet model are different from the traditional region proposal network (RPN), DAPNet predicts the detail category of each candidate region. And these candidate regions combine the object number, which generated by the category prior network to achieve a suitable number of candidate boxes for each image. These candidate boxes can satisfy detection tasks in sparse and dense scenes. The performance of the proposed framework has been evaluated on the challenging NWPU VHR-10 data set. Experimental results demonstrate the superiority of the proposed framework to the state-of-the-art.


Relation Extraction and Grounding


title: Ion exclusion by sub-2-nm carbon nanotube pores

abstract: Biological pores regulate the cellular traffic of a large variety of solutes, often with high selectivity and fast flow rates. These pores share several common structural features: the inner surface of the pore is frequently lined with hydrophobic residues, and the selectivity filter regions often contain charged functional groups. Hydrophobic, narrow-diameter carbon nanotubes can provide a simplified model of membrane channels by reproducing these critical features in a simpler and more robust platform. Previous studies demonstrated that carbon nanotube pores can support a water flux comparable to natural aquaporin channels. Here, we investigate ion transport through these pores using a sub-2-nm, aligned carbon nanotube membrane nanofluidic platform. To mimic the charged groups at the selectivity region, we introduce negatively charged groups at the opening of the carbon nanotubes by plasma treatment. Pressure-driven filtration experiments, coupled with capillary electrophoresis analysis of the permeate and feed, are used to quantify ion exclusion in these membranes as a function of solution ionic strength, pH, and ion valence. We show that carbon nanotube membranes exhibit significant ion exclusion that can be as high as 98% under certain conditions. Our results strongly support a Donnan-type rejection mechanism, dominated by electrostatic interactions between fixed membrane charges and mobile ions, whereas steric and hydrodynamic effects appear to be less important.


Enhancing Automatic Wordnet Construction Using Word Embeddings


title: Fully digital, vector-controlled PWM VSI-fed AC drives with an inverter dead-time compensation strategy

abstract: A dead-time compensation method in vector-controlled pulse width modulator (PWM) voltage source inverters (VSIs) is proposed. The method is based on a feedforward approach that produces compensating signals obtained from the I/sub d/-I/sub q/ current and inverter output angular frequency references in the rotating reference (d-q) frame. It provides excellent inverter output voltage distortion correction for both fundamental and harmonic components. The correction is not affected by the magnitude of the inverter output voltage or current distortions. Since this dead-time compensation method allows current loop calculations in the d-q frame at a slower sampling rate, with a conventional microprocessor than calculations in the stationary reference frame, a fully digital. vector-controlled speed regulator with just a component current control loop is realized for PWM VSIs. Simulations and test results obtained for the compensation method are also described. >


Data Mining for Direct Marketing: Problems and Solutions


title: Using Machine Learning Techniques to Earlier Predict Student's Performance

abstract: Education field is rich of data, and machine learning used in this field are increasing lately. Based on their first semester result, using machine learning techniques, the student's final year result (GPA) can be predicted. The data used in this experiment are from the computer science subjects, 6 subjects, 1 laboratories results and the GPA on their graduation year. The techniques used in this experiment are Generalized Linear Model, Deep Learning and Decision Tree. From this result, what are the important factors that impact on the result can be extracted to help the students prepared themselves earlier.


title: Deterministic Coreference Resolution Based on Entity-Centric, Precision-Ranked Rules

abstract: We propose a new deterministic approach to coreference resolution that combines the global information and precise features of modern machine-learning models with the transparency and modularity of deterministic, rule-based systems. Our sieve architecture applies a battery of deterministic coreference models one at a time from highest to lowest precision, where each model builds on the previous model's cluster output. The two stages of our sieve-based architecture, a mention detection stage that heavily favors recall, followed by coreference sieves that are precision-oriented, offer a powerful way to achieve both high precision and high recall. Further, our approach makes use of global information through an entity-centric model that encourages the sharing of features across all mentions that point to the same real-world entity. Despite its simplicity, our approach gives state-of-the-art performance on several corpora and genres, and has also been incorporated into hybrid state-of-the-art coreference systems for Chinese and Arabic. Our system thus offers a new paradigm for combining knowledge in rule-based systems that has implications throughout computational linguistics.


title: Impact of near Shannon-limit codes on wireless communications technology

abstract: Tremendous progress has been made on channel coding techniques during the last decade, starting from the invention of turbo codes and the rediscovery of the low-density parity check (LDPC) codes. These codes essentially closed the gap between the Shannon capacity limit and practical implementation. Commercial adoption of these new coding techniques, however, depends heavily on the introduction of the technology into communication standards. In this paper, we briefly review the considerations when turbo codes and LDPC codes were introduced to the 3/sup rd/ generation wireless standards (3GPP and 3GPP2), and the digital video broadcast for satellite standard (DVB-S2), respectively. We then examine a few possible approaches to take advantage of these powerful codes for future wireless channels.


title: A multi-structural framework for adaptive supply chain planning and operations control with structure dynamics considerations

abstract: A trend in up-to-date developments in supply chain management (SCM) is to make supply chains more agile, flexible, and responsive. In supply chains, different structures (functional, organizational, informational, financial, etc.) are (re)formed. These structures interrelate with each other and change in dynamics. The paper introduces a new conceptual framework for multi-structural planning and operations of adaptive supply chains with structure dynamics considerations. We elaborate a vision of adaptive supply chain management (A-SCM), a new dynamic model and tools for the planning and control of adaptive supply chains. SCM is addressed from perspectives of execution dynamics under uncertainty. Supply chains are modelled in terms of dynamic multi-structural macro-states, based on simultaneous consideration of the management as a function of both states and structures. The research approach is theoretically based on the combined application of control theory, operations research, and agent-based modelling. The findings suggest constructive ways to implement multi-structural supply chain management and to transit from a "one-way" partial optimization to the feedback-based, closed-loop adaptive supply chain optimization and execution management for value chain adaptability, stability and crisis-resistance. The proposed methodology enhances managerial insight into advanced supply chain management.


1 Keyword extraction : a review of methods and approaches


title: T/R "core chips" for S-, C- and, X-band radar systems

abstract: This paper presents the design and measurement results of three MMIC core chips. The core chips; consisting of TIR-switches, multi-hit attenuators, multi-hit phase shifters, amplifiers and LVTTL logic, are targeting phased array radar systems at S-, C-, and X-band. The set of core chips, using standard pHEMT processes, exhibited excellent first-pass results including: small RMS attenuator and phase errors, small gain-variations and a first pass RF spec yield of average 50%. With a few design adjustments and process targeting it is believed that reaching a RF spec yield of 70% is possible for a future chip production ... making the chip-set very attractive for low cost phased array T/R-modules.


title: Behavioral/Systems/Cognitive Binary Spiking in Auditory Cortex

abstract: Neurons are often assumed to operate in a highly unreliable manner: a neuron can signal the same stimulus with a variable number of action potentials. However, much of the experimental evidence supporting this view was obtained in the visual cortex. We have, therefore, assessed trial-to-trial variability in the auditory cortex of the rat. To ensure single-unit isolation, we used cell-attached recording. Tone-evoked responses were usually transient, often consisting of, on average, only a single spike per stimulus. Surprisingly, the majority of responses were not just transient, but were also binary, consisting of 0 or 1 action potentials, but not more, in response to each stimulus; several dramatic examples consisted of exactly one spike on 100% of trials, with no trial-to-trial variability in spike count. The variability of such binary responses differs from comparably transient responses recorded in visual cortical areas such as area MT, and represent the lowest trial-to-trial variability mathematically possible for responses of a given firing rate. Our study thus establishes for the first time that transient responses in auditory cortex can be described as a binary process, rather than as a highly variable Poisson process. These results demonstrate that cortical architecture can support a more precise control of spike number than was previously recognized, and they suggest a re-evaluation of models of cortical processing that assume noisiness to be an inevitable feature of cortical codes.


title: From Genetics to Genomics of Epilepsy

abstract: The introduction of DNA microarrays and DNA sequencing technologies in medical genetics and diagnostics has been a challenge that has significantly transformed medical practice and patient management. Because of the great advancements in molecular genetics and the development of simple laboratory technology to identify the mutations in the causative genes, also the diagnostic approach to epilepsy has significantly changed. However, the clinical use of molecular cytogenetics and high-throughput DNA sequencing technologies, which are able to test an entire genome for genetic variants that are associated with the disease, is preparing a further revolution in the near future. Molecular Karyotype and Next-Generation Sequencing have the potential to identify causative genes or loci also in sporadic or non-familial epilepsy cases and may well represent the transition from a genetic to a genomic approach to epilepsy.


title: Probabilistic Perspectives on Collecting Human Uncertainty in Predictive Data Mining

abstract: In many areas of data mining, data is collected from human beings. In this contribution, we ask the question of how people actually respond to ordinal scales. The main problem observed is that users tend to be volatile in their choices, i.e. complex cognitions do not always lead to the same decisions, but to distributions of possible decision outputs. This human uncertainty may sometimes have quite an impact on common data mining approaches and thus, the question of effective modelling this so called human uncertainty emerges naturally. Our contribution introduces two different approaches for modelling the human uncertainty of user responses. In doing so, we develop techniques in order to measure this uncertainty at the level of user inputs as well as the level of user cognition. With support of comprehensive user experiments and large-scale simulations, we systematically compare both methodologies along with their implications for personalisation approaches. Our findings demonstrate that significant amounts of users do submit something completely different (action) than they really have in mind (cognition). Moreover, we demonstrate that statistically sound evidence with respect to algorithm assessment becomes quite hard to realise, especially when explicit rankings shall be built.