title: Ontology-based malaria parasite stage and species identification from peripheral blood smear images

abstract: The diagnosis and treatment of malaria infection requires detecting the presence of the malaria parasite in the patient as well as identification of the parasite species. We present an image processing-based approach to detect parasites in microscope images of a blood smear and an ontology-based classification of the stage of the parasite for identifying the species of infection. This approach is patterned after the diagnosis approach adopted by a pathologist for visual examination, and hence, is expected to deliver similar results. We formulate several rules based on the morphology of the basic components of a parasite, namely, chromatin dot(s) and cytoplasm, to identify the parasite stage and species. Numerical results are presented for data taken from various patients. A sensitivity of 88% and a specificity of 95% is reported by evaluation of the scheme on 55 images.


title: Design of AES S-box using combinational logic optimization

abstract: Advanced Encryption Standard (AES) is one of the most common symmetric encryption algorithms. The hardware complexity in AES is dominated by AES substitution box (S-box) which is considered as one of the most complicated and costly part of the system because it is the only non-linear structure. The proposed work employs a combinational logic design of S-Box implemented in Virtex II FPGA chip. The architecture employs a Boolean simplification of the truth table of the logic function with the aim of reducing the delay. The S-Box is designed using basic gates such as AND gate, NOT gate, OR gate and multiplexer. Theoretically, the design reduces the overall delay and efficiently for applications with high-speed performance. This approach is suitable for FPGA implementation in term of gate area. The hardware, total area and delay are presented.


title: Tensor Factorization via Matrix Factorization

abstract: Tensor factorization arises in many machine learning applications, such knowledge base modeling and parameter estimation in latent variable models. However, numerical methods for tensor factorization have not reached the level of maturity of matrix factorization methods. In this paper, we propose a new method for CP tensor factorization that uses random projections to reduce the problem to simultaneous matrix diagonalization. Our method is conceptually simple and also applies to non-orthogonal and asymmetric tensors of arbitrary order. We prove that a small number random projections essentially preserves the spectral information in the tensor, allowing us to remove the dependence on the eigengap that plagued earlier tensor-to-matrix reductions. Experimentally, our method outperforms existing tensor factorization methods on both simulated data and two real datasets.


title: Learn and play with interactive TV

abstract: Despite the criticism concerning the value of TV content, research reveals several worthwhile aspects -- one of them is the opportunity to learn. In this article we explore the characteristics of interactive TV applications that facilitate education and interactive entertainment. In doing so we analyze research methods and empirical results from experimental and field studies. The findings suggest that interactive TV applications provide support for education and entertainment for children and young people, as well as continuous education for all. In particular, interactive TV is especially suitable for (1) informal learning and (2) for engaging and motivating its audience. We conclude with an agenda for future interactive TV research in entertainment and education.


title: A Brief Survey of Recent Edge-Preserving Smoothing Algorithms on Digital Images

abstract: Edge preserving filters preserve the edges and its information while blurring an image. In other words they are used to smooth an image, while reducing the edge blurring effects across the edge like halos, phantom etc. They are nonlinear in nature. Examples are bilateral filter, anisotropic diffusion filter, guided filter, trilateral filter etc. Hence these family of filters are very useful in reducing the noise in an image making it very demanding in computer vision and computational photography applications like denoising, video abstraction, demosaicing, optical-flow estimation, stereo matching, tone mapping, style transfer, relighting etc. This paper provides a concrete introduction to edge preserving filters starting from the heat diffusion equation in olden to recent eras, an overview of its numerous applications, as well as mathematical analysis, various efficient and optimized ways of implementation and their interrelationships, keeping focus on preserving the boundaries, spikes and canyons in presence of noise. Furthermore it provides a realistic notion for efficient implementation with a research scope for hardware realization for further acceleration.


title: A self-tuning DVS processor using delay-error detection and correction

abstract: In this paper, we present a dynamic voltage scaling (DVS) technique called Razor which incorporates an in situ error detection and correction mechanism to recover from timing errors. We also present the implementation details and silicon measurements results of a 64-bit processor fabricated in 0.18-/spl mu/m technology that uses Razor for supply voltage control. Traditional DVS techniques require significant voltage safety margins to guarantee computational correctness at the worst case combination of process, voltage and temperature conditions, leading to a loss in energy efficiency. In Razor-based DVS, however, the supply voltage is automatically reduced to the point of first failure using the error detection and correction mechanism, thereby eliminating safety margins while still ensuring correct operation. In addition, the supply voltage can be intentionally scaled below the point of first failure of the processor to achieve an optimal tradeoff between energy savings from further voltage reduction and energy overhead from increased error detection and correction activity. We tested and measured savings due to Razor DVS for 33 different dies and obtained an average energy savings of 50% over worst case operating conditions by scaling supply voltage to achieve a 0.1% targeted error rate, at a fixed frequency of 120 MHz.


title: Who cares about Sarcastic Tweets? Investigating the Impact of Sarcasm on Sentiment Analysis.

abstract: Sarcasm is a common phenomenon in social media, and is inherently difficult to analyse, not just automatically but often for humans ::: too. It has an important effect on sentiment, but is usually ignored in social media analysis, because it is considered too tricky to handle. ::: While there exist a few systems which can detect sarcasm, almost no work has been carried out on studying the effect that sarcasm has on ::: sentiment in tweets, and on incorporating this into automatic tools for sentiment analysis. We perform an analysis of the effect of sarcasm ::: scope on the polarity of tweets, and have compiled a number of rules which enable us to improve the accuracy of sentiment analysis when ::: sarcasm is known to be present. We consider in particular the effect of sentiment and sarcasm contained in hashtags, and have developed ::: a hashtag tokeniser for GATE, so that sentiment and sarcasm found within hashtags can be detected more easily. According to our experiments, ::: the hashtag tokenisation achieves 98% Precision, while the sarcasm detection achieved 91% Precision and polarity detection 80%.


title: Dialogue management systems: a survey and overview

abstract: This report provides an overview of the current issues and techniques for the modelling of dialogues using a computer. A dialogue management system can manage a dialogue between two or more agents, be they human or computer. Recently, increasingly complex dialogues are being modelled which allow a range of discourse phenomena including ellipsis and anaphoric reference. Such dialogues are thought to be similar to those between two humans, and accurate modelling of these phenomena leads to "pleasant", i.e. easy to talk to, and natural human-computer dialogues. Dialogue management can be classified into three often overlapping approaches: discourse grammars, plan-based and collaborative approaches. The design of a system often begins by eliciting the language used initially between two humans and later by Wizard of Oz experiments. Special issues relating to dialogue management systems are discussed including recovery strategies from different types of errors and the coding of dialogue in corpora. Lastly, approaches to evaluation are briefly discussed from the qualitative and quantitative viewpoints, recognising the importance and size of this sub-field.


title: epiC: an Extensible and Scalable System for Processing Big Data

abstract: The Big Data problem is characterized by the so called 3V features: Volume - a huge amount of data, Velocity - a high data ingestion rate, and Variety - a mix of structured data, semi-structured data, and unstructured data. The state-of-the-art solutions to the Big Data problem are largely based on the MapReduce framework (aka its open source implementation Hadoop). Although Hadoop handles the data volume challenge successfully, it does not deal with the data variety well since the programming interfaces and its associated data processing model is inconvenient and inefficient for handling structured data and graph data. ::: ::: This paper presents epiC, an extensible system to tackle the Big Data's data variety challenge. epiC introduces a general Actor-like concurrent programming model, independent of the data processing models, for specifying parallel computations. Users process multi-structured datasets with appropriate epiC extensions, the implementation of a data processing model best suited for the data type and auxiliary code for mapping that data processing model into epiC's concurrent programming model. Like Hadoop, programs written in this way can be automatically parallelized and the runtime system takes care of fault tolerance and inter-machine communications. We present the design and implementation of epiC's concurrent programming model. We also present two customized data processing model, an optimized MapReduce extension and a relational model, on top of epiC. Experiments demonstrate the effectiveness and efficiency of our proposed epiC.


title: Quantum cryptography: Overview, security issues and future challenges

abstract: In recent years Quantum Cryptography gets more attention as well as becomes most promising cryptographic field for faster, effective and more secure communications. Quantum Cryptography is an art of science which overlap quantum properties of light under quantum mechanics on cryptographic task instead of current state of algorithms based on mathematical computing technology. Major algorithms for public key encryption and some digital signature scheme such as RSA, El Gamal cryptosystem, hash function are vulnerable at quantum adversaries. Most of the factoring problem can be broken by Shore's algorithm and quantum computer threatens other hand discrete logarithm problem. Our paper describes why modern cryptography contributes quantum cryptography, security issues and future goals of modern cryptography.


title: Subword Regularization: Improving Neural Network Translation Models with Multiple Subword Candidates

abstract: Subword units are an effective way to alleviate the open vocabulary problems in neural machine translation (NMT). While sentences are usually converted into unique subword sequences, subword segmentation is potentially ambiguous and multiple segmentations are possible even with the same vocabulary. The question addressed in this paper is whether it is possible to harness the segmentation ambiguity as a noise to improve the robustness of NMT. We present a simple regularization method, subword regularization, which trains the model with multiple subword segmentations probabilistically sampled during training. In addition, for better subword sampling, we propose a new subword segmentation algorithm based on a unigram language model. We experiment with multiple corpora and report consistent improvements especially on low resource and out-of-domain settings.


title: S3ORAM: A Computation-Efficient and Constant Client Bandwidth Blowup ORAM with Shamir Secret Sharing

abstract: Oblivious Random Access Machine (ORAM) enables a client to access her data without leaking her access patterns. Existing client-efficient ORAMs either achieve O(log N) client-server communication blowup without heavy computation, or O(1) blowup but with expensive homomorphic encryptions. It has been shown that O(log N) bandwidth blowup might not be practical for certain applications, while schemes with O(1) communication blowup incur even more delay due to costly homomorphic operations. In this paper, we propose a new distributed ORAM scheme referred to as Shamir Secret Sharing ORAM (S3ORAM), which achieves O(1) client-server bandwidth blowup and O(1) blocks of client storage without relying on costly partial homomorphic encryptions. S3ORAM harnesses Shamir Secret Sharing, tree-based ORAM structure and a secure multi-party multiplication protocol to eliminate costly homomorphic operations and, therefore, achieves O(1) client-server bandwidth blowup with a high computational efficiency. We conducted comprehensive experiments to assess the performance of S3ORAM and its counterparts on actual cloud environments, and showed that S3ORAM achieves three orders of magnitude lower end-to-end delay compared to alternatives with O(1) client communication blowup (Onion-ORAM), while it is one order of magnitude faster than Path-ORAM for a network with a moderate bandwidth quality. We have released the implementation of S3ORAM for further improvement and adaptation.


title: A Suspended-Substrate Ku-Band Symmetric Radial Power Combiner

abstract: This letter presents a novel eight-way radial power combiner that exhibits wide bandwidth and very low insertion loss. The peripheral ports of this power combiner use low loss suspended substrate stripline. A cylindrical cavity is introduced as the power combining structure to obtain high combining efficiency and high power handling capability. The proposed power combining structure successfully integrates planar transmission lines with a 3-D cavity structure to realize a wide band, low loss, and compact eight-way power combiner. Measured results show a bandwidth of 3.6 GHz from 12.1 to 15.7 GHz with return loss better than 19 dB and insertion loss less than 0.17 dB, which agree very well with simulated results.


title: This Place is Swarming: Using a Mobile Social App to Study Human Traffic in Cities

abstract: Being a leading location-based social network (LBSN), Foursquare’s Swarm app allows users to conduct checkins at a specified location and share their real-time locations with friends. This app records a massive set of spatio-temporal information of users around the world. In this paper, we track the evolution of user density of the Swarm app in New York City (NYC) for one entire week. We study the temporal patterns of different venue categories, and investigate how the function of venue categories affects the temporal behavior of visitors. Moreover, by applying time-series analysis, we validate that the temporal patterns can be effectively decomposed into regular parts which represent the regular human behavior and stochastic parts which represent the randomness of human behavior. Finally, we build a model to predict the evolution of the user density, and our results demonstrate an accurate prediction.


title: Trust, self-confidence, and operators' adaptation to automation

abstract: The increasing use of automation to supplant human intervention in controlling complex systems changes the operators' role from active controllers (directly involved with the system) to supervisory controllers (managing the use of different degrees of automatic and manual control). This paper examines the relationship between trust in automatic controllers, self-confidence in manual control abilities, and the use of automatic controllers in operating a simulated semi-automatic pasteurization plant. Trust, combined with self-confidence, predicted the operators' allocation strategy. A Multitrait-multimethod matrix and logit functions showed how trust and self-confidence relate to the use of automation. An ARMAV time series model of the dynamic interaction of trust and self-confidence, combined with individual biases, accounted for 60.9-86.5% of the variance in the use of the three automatic controllers. In general, automation is used when trust exceeds self-confidence, and manual control when the opposite is true. Since trust and self-confidence are two factors that guide operators' interactions with automation, the design of supervisory control systems should include provisions to ensure that operators' trust reflects the capabilities of the automation and operators' self-confidence reflects their abilities to control the system manually.


title: Image Content Analysis for Cardiac 3D Visualizations

abstract: The problem tackled in this work is the semantic interpretation and an attempt at computer automatic understanding of a 3D structure of spatially visualised coronary vessels with the use of AI graph-based linguistic formalisms. At the stage of the initial analysis, it was found that the problem is subject to numerous important limitations. These limitations result, among other things from the serious obstacles encountered in the development of a universal standard, defining the model shape of the healthy or diseased organ that could possibly undergo typical recognition. Due to this difficulties a decision was made to apply the methods of automatic image understanding for the interpretation of the images considered, which consequently leads to their semantic descriptions. For this purpose the linguistic approach was applied.


title: Cement-retained versus screw-retained implant restorations: Achieving optimal occlusion and esthetics in implant dentistry ☆ ☆☆ ★ ★★

abstract: Abstract Statement of problem. Optimal occlusion and esthetics are goals in prosthetic treatment. Implant dentistry is no exception. Purpose of article. The purpose of this article is to discuss how the choice to use screw-retained or cement-retained implants dramatically influences the occlusion and esthetics. (J Prosthet Dent 1997;77:28-35.)


title: On the de-facto standard of event-driven process chains: how EPC is defined in literature

abstract: The Business Process Modelling Notation (BPMN) and the Event-driven Process Chain (EPC) are both frequently used modelling languages to create business process models. While there is a well-defined standard for BPMN, such a standard is missing for EPC. As a standard would be beneficial to improve interoperability among different vendors, this paper aims at providing the means for future EPC standardization. Therefore, we have conducted a structured literature review of the most common EPC variants in IS research. We provide a structured overview of the evolution of different EPC variants, describe means and capabilities and elaborate different criteria for decision-making in regard to including EPC variants in a standardization process.


title: 50GHz Double-Balanced Up-Conversion Mixer Using CMOS 90nm Process

abstract: This paper reports the first millimeter-wave double-balanced up-conversion mixer that is realized using a standard CMOS 90nm process. The circuit integrates passive on-chip baluns for single-ended to differential-ended conversions at the high frequency LO and RF ports and an active balun at the IF port. The passive baluns use a stacked configuration and also provide impedance matching to improve conversion gain. The active balun employs the quasi-symmetric properties of the drain and the source of the MOSFET to provide a balanced output. The mixer has a measured 11dB conversion loss and a LO rejection of 26.5dB. The power consumption is 13.2mW.


title: Simultaneous dynamic optimization : A trajectory planning method for nonholonomic car-like robots

abstract: Our trajectory planner can tackle different requirements or constraints uniformly.Our proposal is systematically tested on a wide range of simulation cases.A Hamiltonian-based index is utilized to judge the optimality of an obtained solution.Differences between min-time and min-length trajectories are investigated. Trajectory planning in robotics refers to the process of finding a motion law that enables a robot to reach its terminal configuration, with some predefined requirements considered at the same time. This study focuses on planning the time-optimal trajectories for car-like robots. We formulate a dynamic optimization problem, where the kinematic principles are accurately described through differential equations and the constraints are strictly expressed using algebraic inequalities. The formulated dynamic optimization problem is then solved by an interior-point-method-based simultaneous approach. Compared with the prevailing methods in the field of trajectory planning, our proposed method can handle various user-specified requirements and different optimization objectives in a unified manner. Simulation results indicate that our proposal efficiently deals with different kinds of physical constraints, terminal conditions and collision-avoidance requirements that are imposed on the trajectory planning mission. Moreover, we utilize a Hamiltonian-based optimality index to evaluate how close an obtained solution is to being optimal.


title: An Efficient MRF Embedded Level Set Method for Image Segmentation

abstract: This paper presents a fast and robust level set method for image segmentation. To enhance the robustness against noise, we embed a Markov random field (MRF) energy function to the conventional level set energy function. This MRF energy function builds the correlation of a pixel with its neighbors and encourages them to fall into the same region. To obtain a fast implementation of the MRF embedded level set model, we explore algebraic multigrid (AMG) and sparse field method (SFM) to increase the time step and decrease the computation domain, respectively. Both AMG and SFM can be conducted in a parallel fashion, which facilitates the processing of our method for big image databases. By comparing the proposed fast and robust level set method with the standard level set method and its popular variants on noisy synthetic images, synthetic aperture radar (SAR) images, medical images, and natural images, we comprehensively demonstrate the new method is robust against various kinds of noises. In particular, the new level set method can segment an image of size 500 × 500 within 3 s on MATLAB R2010b installed in a computer with 3.30-GHz CPU and 4-GB memory.


title: Which Facets of Mindfulness Protect Individuals from the Negative Experiences of Obsessive Intrusive Thoughts?

abstract: Obsessive intrusive thoughts (OITs) are experienced by the majority of the general population, and in their more extreme forms are characteristic of obsessive–compulsive disorder (OCD). These cognitions are said to exist on a continuum that includes differences in their frequency and associated distress. The key factors that contribute to an increased frequency and distress are how the individual appraises and responds to the OIT. Facets of mindfulness, such as nonjudgment and nonreactivity, offer an alternative approach to OITs than the negative appraisals and commonly utilised control strategies that often contribute to distress. Clarifying the role of facets of mindfulness in relation to these cognitions offers a means to elucidate individual characteristics that may offer protection from distress associated with OITs. A sample of nonclinical individuals (n = 583) completed an online survey that assessed their experiences of OITs, including frequency, emotional reaction and appraisals, and trait mindfulness. The findings from a series of multiple regression analyses confirmed that specific facets of mindfulness relating to acting with awareness and acceptance (nonjudgment and nonreactivity) consistently predicted less frequent and distressing experiences of OITs. In contrast, the observe facet emerged as a consistent predictor of negative experiences of OITs. These findings suggest that acting with awareness and acceptance may confer protective characteristics in relation to OITs, but that the observe facet may reflect a hypervigilance to OITs. Mindfulness-based prevention and intervention for OCD should be tailored to take account of the potential differential effects of increasing specific facets of mindfulness.


title: How Smart Does Your Profile Image Look?: Estimating Intelligence from Social Network Profile Images

abstract: Profile images on social networks are users' opportunity to present themselves and to affect how others judge them. We examine what Facebook images say about users' perceived and measured intelligence. 1,122 Facebook users completed a matrices intelligence test and shared their current Facebook profile image. Strangers also rated the images for perceived intelligence. We use automatically extracted image features to predict both measured and perceived intelligence. Intelligence estimation from images is a difficult task even for humans, but experimental results show that human accuracy can be equalled using computing methods. We report the image features that predict both measured and perceived intelligence, and highlight misleading features such as "smiling'' and "wearing glasses'' that are correlated with perceived but not measured intelligence. Our results give insights into inaccurate stereotyping from profile images and also have implications for privacy, especially since in most social networks profile images are public by default.


title: CANAuth - A Simple, Backward Compatible Broadcast Authentication Protocol for CAN bus

abstract: The Controller-Area Network (CAN) bus protocol [1] is a bus protocol invented in 1986 by Robert Bosch GmbH, originally intended for automotive use. By now, the bus can be found in devices ranging from cars and trucks, over lightning setups to industrial looms. Due to its nature, it is a system very much focused on safety, i.e., reliability. Unfortunately, there is no build-in way to enforce security, such as encryption or authentication. In this paper, we investigate the problems associated with implementing a backward compatible message authentication protocol on the CAN bus. We show which constraints such a protocol has to meet and why this eliminates, to the best of our knowledge, all the authentication protocols published so far. Furthermore, we present a message authentication protocol, CANAuth, that meets all of the requirements set forth and does not violate any constraint of the CAN bus. Keywords—CAN bus, embedded networks, broadcast authentication, symmetric cryptography


title: From two-step flow to the Internet: The changing array of sources for genetics information seeking

abstract: The diffusion of the Internet has radically expanded the readily available sources for information of all types. Information that was once obtained second-hand from friends and acquaintances--the traditional "two-step flow"--is now found easily through the Internet. The authors make use of survey data to explore this thesis in regards to information sources about genetic testing and the influence of the Internet on the information seeking behaviors of the public. A telephone survey of a random sample of 882 adults asked them about their knowledge of, concerns about, and interest in genetic testing. Respondents were most likely to first turn to the Internet for information about cancer genetics, second to public libraries, and third to medical doctors. Overall, doctors were the most likely source to be consulted when second and third choices are considered. Age, income, and self-reported understanding of genetics are shown to be predictors of whether someone goes to medical professionals for advice, rather than to the Internet or public library. The results raise questions about the apparent tendency of the public to regard the Internet as the best source of information on complex topics like genetics, for which it may be ill-suited.


title: An Automated Electric Vehicle Prototype Showing New Trends in Automotive Architectures

abstract: The automotive domain is challenged by the increasing importance of Information Technology (IT) based functions. To show the possibilities of modern IT systems, a demonstrator car was developed in RACE (Robust and Reliant Automotive Computing Environment for Future eCars) based on a completely redesigned E/E architecture, which supports the integration of mixed-criticality components and offers features like PlugaPlay. This paper presents the architecture and components of this vehicle prototype, which is equipped with modern systems such as Steer-by-Wire without mechanical fallback. It was designed to support future driver assistance systems, e.g. to carry out autonomous parking maneuvers onto an inductive charging station, a task, which is hard to achieve accurately enough for a human driver. Therefore, a special emphasis lies on the description of the sensor set for automated operation.


title: Cleaning Framework for BigData: An Interactive Approach for Data Cleaning

abstract: Data is a valuable resource. Proper use of high-quality data can help people make better predictions, analyses and decisions. However, no matter how much effort we put into collecting a good dataset, errors will inevitably creep into the data, making it necessary for data cleaning. This becomes a concern particularly when large-scale heterogeneous data from multiple sources are integrated for other purposes. Data cleaning can be complicated, time-consuming, and expensive, but it is a necessary step in any data-related system since poor-quality data may not be suitable to achieve the intended purposes. The core of our data cleaning system is data association and repairing. Association aims to identify the same object and link with the most associated objects, and repairing is to make a database reliable by fixing errors in the data. For big data applications, we don't necessarily need to use all the data. In most situations, we only need a small subset of the most relevant data. So the goal of association is to convert big raw data into a small subset of the most relevant data that are most useful for a particular application. After we obtain a small amount of relevant data, we also need to further analyze the data to help people digest the data and turn the data into knowledge. We use a number of techniques to associate the data to get useful knowledge for data repairing. Our research shows that data association can effectively help with data repairing. To capture the interaction, we provide a uniform framework that unifies the association and repairing process seamlessly based on context patterns, usage patterns, metadata, and repairing rules.


title: Internal training load and its longitudinal relationship with seasonal player wellness in elite professional soccer

abstract: Abstract Monitoring internal training load has been extensively used and described within team sport environments, however when compared to internal physiological measures such as heart rate (HR) and rating of perceived exertion (RPE), the literature is sparse. The primary aim of this investigation study was to assess differences of playing position on ITL, session-RPE and wellness across two different training microcycles (1 vs. 2 competitive games), in addition with examining the relationship between ITL and Hooper's Index across an entire season. Thirty-five professional soccer players from the Portuguese premier league participated in the study (25.7 ± 5.0 years; 182.3 ± 6.4 cm; 79.1 ± 7.0 kg). Analysis of variance revealed higher values of DOMS (Means(M): 3.33 vs. 3.10; p = 0.001; effect Size (ES) = 0.087), fatigue (M: 3.18 vs. 2.99; p = 0.001; ES = 0.060) and HI (M: 11.85 vs. 11.56; p = 0.045; ES = 0.034) in 2-game weeks compared with 1-game weeks. Correlation between ITL and HI levels found significant negative correlations between ITL and DOMS (ρ = − 0.156), ITL and sleep (ρ = − 0.109), ITL and fatigue (ρ = − 0.225), ITL and stress (ρ = − 0.188), and ITL and HI (ρ = − 0.238) in 2-game weeks. Results from 1-game microcycle only highlighted negative correlations between ITL and stress (ρ = − 0.080). It was concluded from the study that greater fatigue potential, muscle soreness, stress and ITL was significantly more apparent within a 2-game microcycle. As a result, care should be taken when planning the lead into and out of a 2-game fixture microcycle highlighting key specific recovery strategies to damped the increased stress effect. Additionally, the potential utilization of squad rotation strategies may be a positive approach with aim of managing the fatigue effect.


title: Autonomous motorcycles for agile maneuvers, part I: Dynamic modeling

abstract: Single-track vehicles, such as motorcycles, provide an agile mobile platform. Modeling and control of motorcycles for agile maneuvers, such as those by professional racing riders, are challenging due to motorcycle's unstable platform and complex tire/road interaction. As a first step attempting to understand how racing riders drive a motorcycle, this twopart paper presents a modeling and tracking control design of an autonomous motorcycle. In this first-part paper, we discuss a new dynamics model for the autonomous motorcycle. We consider the existence of lateral sliding velocity at each wheel contact point. Because of the importance of the tire/road interaction for vehicle stability and maneuverability, the dynamic modeling scheme also includes the motorcycle tire models. The new nonlinear dynamic models are used for control systems design in the companion paper.


title: Input impedance analysis of single-phase PFC converters

abstract: The input impedance of single-phase boost power factor corrected (PFC) AC-DC converters is modeled and analyzed in this paper. A large-signal model is presented for the input impedance which overcomes the limitations of traditional piece-wise linearized models. The model is valid at frequencies ranging from the crossover frequency of the output voltage loop to half the switching frequency of the converter. Experimental results from a boost single-phase PFC converter are provided to validate the model. Input characteristics of typical boost PFC converters, such as input impedance dipping, leading phase of the input current, and responses to distorted input voltages are studied by using the model. A simple compensation technique to reduce the dipping in the input impedance, thereby improving converter performance and minimizing the potential for undesirable interactions with the input filter or the ac source, is also presented.


title: Spintronics: Emerging Ultra-Low-Power Circuits and Systems beyond MOS Technology

abstract: Conventional MOS integrated circuits and systems suffer serve power and scalability challenges as technology nodes scale into ultra-deep-micron technology nodes (e.g., below 40nm). Both static and dynamic power dissipations are increasing, caused mainly by the intrinsic leakage currents and large data traffic. Alternative approaches beyond charge-only-based electronics, and in particular, spin-based devices, show promising potential to overcome these issues by adding the spin freedom of electrons to electronic circuits. Spintronics provides data non-volatility, fast data access, and low-power operation, and has now become a hot topic in both academia and industry for achieving ultra-low-power circuits and systems. The ITRS report on emerging research devices identified the magnetic tunnel junction (MTJ) nanopillar (one of the Spintronics nanodevices) as one of the most promising technologies to be part of future micro-electronic circuits. In this review we will give an overview of the status and prospects of spin-based devices and circuits that are currently under intense investigation and development across the world, and address particularly their merits and challenges for practical applications. We will also show that, with a rapid development of Spintronics, some novel computing architectures and paradigms beyond classic Von-Neumann architecture have recently been emerging for next-generation ultra-low-power circuits and systems.


title: A Relationship between the Average Precision and the Area Under the ROC Curve

abstract: For similar evaluation tasks, the area under the receiver operating characteristic curve (AUC) is often used by researchers in machine learning, whereas the average precision (AP) is used more often by the information retrieval community. We establish some results to explain why this is the case. Specifically, we show that, when both the AUC and the AP are rescaled to lie in [0,1], the AP is approximately the AUC times the initial precision of the system.


title: Design of small hand-launched solar-powered UAVs: From concept study to a multi-day world endurance record flight

abstract: We present the development process behind AtlantikSolar, a small 6.9 kg hand-launchable low-altitude solar-powered unmanned aerial vehicle (UAV) that recently completed an 81-hour continuous flight and thereby established a new flight endurance world record for all aircraft below 50 kg mass. The goal of our work is to increase the usability of such solar-powered robotic aircraft by maximizing their perpetual flight robustness to meteorological deteriorations such as clouds or winds. We present energetic system models and a design methodology, implement them in our publicly available conceptual design framework for perpetual flight-capable solar-powered UAVs, and finally apply the framework to the AtlantikSolar UAV. We present the detailed AtlantikSolar characteristics as a practical design example. Airframe, avionics, hardware, state estimation, and control method development for autonomous flight operations are described. Flight data are used to validate the conceptual design framework. Flight results from the continuous 81-hour and 2,338 km covered ground distance flight show that AtlantikSolar achieves 39% minimum state-of-charge, 6.8 h excess time and 6.2 h charge margin. These performance metrics are a significant improvement over previous solar-powered UAVs. A performance outlook shows that AtlantikSolar allows perpetual flight in a 6-month window around June 21 at mid-European latitudes, and that multi-day flights with small optical- or infrared-camera payloads are possible for the first time. The demonstrated performance represents the current state-of-the-art in solar-powered low-altitude perpetual flight performance. We conclude with lessons learned from the three-year AtlantikSolar UAV development process and with a sensitivity analysis that identifies the most promising technological areas for future solar-powered UAV performance improvements.


title: A Wideband 3.6 GHz Digital ΔΣ Fractional-N PLL With Phase Interpolation Divider and Digital Spur Cancellation

abstract: A digital ΔΣ fractional-N frequency synthesizer for 4G communication standards is presented which is able to achieve wide loop bandwidth while producing low fractional spurs. The loop adopts a fractional-N divider based on a phase interpolator, allowing to shrink the TDC dynamic range and to improve its linearity. A dynamic-element matching algorithm is employed to further improve TDC linearity and an original correlation algorithm is used to correct for the phase interpolator mismatches. Both digital algorithms operate in background and they are demonstrated to be concurrently effective in reducing in-band fractional spurs below -57 dBc. The circuit is fully integrated in a 65 nm CMOS process and it synthesizes a carrier in the 3.0-3.6 GHz range from a 40 MHz crystal reference with 40 Hz resolution. It achieves -104-dBc/Hz phase noise at 400-kHz offset and a 3.2-MHz maximum loop bandwidth. The synthesizer dissipates 80 mW and occupies 0.4 mm2.


title: Fourier-Mellin transform for Robust Image Hashing

abstract: This paper proposes a new perceptual image hashing technique based on the Fourier-Mellin Transform (FMT). The idea consists of exploiting the invariance property of the FMT to extract robust hashes using overlapping blocks in the transform domain. To secure the system, two secrete keys are used in different stages. The results show good robustness against signal processing operations and geometric attacks including translation and rotation and demonstrate the superiority of the proposed technique over related transform based hashing techniques.


title: Rule of Thirds Detection from Photograph

abstract: The rule of thirds is one of the most important composition rules used by photographers to create high-quality photos. The rule of thirds states that placing important objects along the imagery thirds lines or around their intersections often produces highly aesthetic photos. In this paper, we present a method to automatically determine whether a photo respects the rule of thirds. Detecting the rule of thirds from a photo requires semantic content understanding to locate important objects, which is beyond the state of the art. This paper makes use of the recent saliency and generic objectness analysis as an alternative and accordingly designs a range of features. Our experiment with a variety of saliency and generic objectness methods shows that an encouraging performance can be achieved in detecting the rule of thirds from photos.


title: Fast Object Segmentation in Unconstrained Video

abstract: We present a technique for separating foreground objects from the background in a video. Our method is fast, fully automatic, and makes minimal assumptions about the video. This enables handling essentially unconstrained settings, including rapidly moving background, arbitrary object motion and appearance, and non-rigid deformations and articulations. In experiments on two datasets containing over 1400 video shots, our method outperforms a state-of-the-art background subtraction technique [4] as well as methods based on clustering point tracks [6, 18, 19]. Moreover, it performs comparably to recent video object segmentation methods based on object proposals [14, 16, 27], while being orders of magnitude faster.


title: Real-Time Object Pose Estimation with Pose Interpreter Networks

abstract: In this work, we introduce pose interpreter networks for 6-DoF object pose estimation. In contrast to other CNN-based approaches to pose estimation that require expensively annotated object pose data, our pose interpreter network is trained entirely on synthetic pose data. We use object masks as an intermediate representation to bridge real and synthetic. We show that when combined with a segmentation model trained on RGB images, our synthetically trained pose interpreter network is able to generalize to real data. Our end-to-end system for object pose estimation runs in real-time (20 Hz) on live RGB data, without using depth information or ICP refinement.


title: SOFIT: Sociotechnical and Organizational Factors for Insider Threat

abstract: Human behavioral factors have been insufficiently represented in structured models (e.g., ontology frameworks) of insider threat risk. This paper describes the design and development of a structured model that emphasizes individual and organizational sociotechnical factors while incorporating technical indicators from previous work. We compare this model with previous research and describe a use case to demonstrate how the model can be applied as an ontology. We also summarize results of an expert knowledge elicitation study to reveal relationships among indicators and to examine several quantitative models for assessing threat of cases comprising multiple indicators.


title: The errors, insights and lessons of famous AI predictions – and what they mean for the future

abstract: Predicting the development of artificial intelligence (AI) is a difficult project – but a vital one, according to some analysts. AI predictions are already abound: but are they reliable? This paper starts by proposing a decomposition schema for classifying them. Then it constructs a variety of theoretical tools for analysing, judging and improving them. These tools are demonstrated by careful analysis of five famous AI predictions: the initial Dartmouth conference, Dreyfus's criticism of AI, Searle's Chinese room paper, Kurzweil's predictions in the Age of Spiritual Machines, and Omohundro's ‘AI drives’ paper. These case studies illustrate several important principles, such as the general overconfidence of experts, the superiority of models over expert judgement and the need for greater uncertainty in all types of predictions. The general reliability of expert judgement in AI timeline predictions is shown to be poor, a result that fits in with previous studies of expert competence.


title: Deep Reinforcement Learning Radio Control and Signal Detection with KeRLym, a Gym RL Agent

abstract: This paper presents research in progress investigating the viability and adaptation of reinforcement learning using deep neural network based function approximation for the task of radio control and signal detection in the wireless domain. We demonstrate a successful initial method for radio control which allows naive learning of search without the need for expert features, heuristics, or search strategies. We also introduce Kerlym, an open Keras based reinforcement learning agent collection for OpenAI's Gym.


title: Prevention of gut leakiness by a probiotic treatment leads to attenuated HPA response to an acute psychological stress in rats

abstract: Summary Background and aims Intestinal barrier impairment is incriminated in the pathophysiology of intestinal gut disorders associated with psychiatric comorbidity. Increased intestinal permeability associated with upload of lipopolysaccharides (LPS) translocation induces depressive symptoms. Gut microbiota and probiotics alter behavior and brain neurochemistry. Since Lactobacillus farciminis suppresses stress-induced hyperpermeability, we examined whether (i) L. farciminis affects the HPA axis stress response, (ii) stress induces changes in LPS translocation and central cytokine expression which may be reversed by L. farciminis , (iii) the prevention of “leaky” gut and LPS upload are involved in these effects. Methods At the end of the following treatments female rats were submitted to a partial restraint stress (PRS) or sham-PRS: (i) oral administration of L. farciminis during 2 weeks, (ii) intraperitoneal administration of ML-7 (a specific myosin light chain kinase inhibitor), (iii) antibiotic administration in drinking water during 12 days. After PRS or sham-PRS session, we evaluated LPS levels in portal blood, plasma corticosterone and adrenocorticotropic hormone (ACTH) levels, hypothalamic corticotropin releasing factor (CRF) and pro-inflammatory cytokine mRNA expression, and colonic paracellular permeability (CPP). Results PRS increased plasma ACTH and corticosterone; hypothalamic CRF and pro-inflammatory cytokine expression; CPP and portal blood concentration of LPS. L. farciminis and ML-7 suppressed stress-induced hyperpermeability, endotoxemia and prevented HPA axis stress response and neuroinflammation. Antibiotic reduction of luminal LPS concentration prevented HPA axis stress response and increased hypothalamic expression of pro-inflammatory cytokines. Conclusion The attenuation of the HPA axis response to stress by L. farciminis depends upon the prevention of intestinal barrier impairment and decrease of circulating LPS levels.


title: Automated sugaring-out liquid-liquid extraction based on flow system coupled with HPLC-UV for the determination of procainamide in urine

abstract: Abstract A fully automated sugaring-out assisted liquid-liquid extraction procedure was suggested. The procedure was based on the separation of the acetonitrile phase, containing a target analyte from the homogeneous sample solution after injection of sugaring-out reagent (glucose) into a mixing chamber of the flow system. Air bubbling was used to promote the extraction process and phase separation. After the fast phase separation in the mixing chamber, the acetonitrile phase containing the target analyte was transferred to an HPLC-UV system. Under the optimal conditions, the detector response of procainamide was linear in the concentration range of 6×10−7–4×10−5 mol L−1. The limit of detection, calculated from a blank test based on 3σ, was 2×10−7 mol L−1. The proposed method was successfully applied for the determination of procainamide in human urine samples and the analytical results agreed fairly well with the results obtained by reference CE method.


title: Viewpoint-independent book spine segmentation

abstract: We propose a method to precisely segment books on bookshelves in images taken from general viewpoints. The proposed segmentation algorithm overcomes difficulties due to text and texture on book spines, various book orientations under perspective projection, and book proximity. A shape dependent active contour is used as a first step to establish a set of book spine candidates. A subset of these candidates are selected using spatial constraints on the assembly of spine candidates by formulating the selection problem as the maximal weighted independent set (MWIS) of a graph. The segmented book spines may be used by recognition systems (e.g., library automation), or rendered in computer graphics applications. We also propose a novel application that uses the segmented book spines to assist users in bookshelf reorganization or to modify the image to create a bookshelf with a tidier look. Our method was successfully tested on challenging sets of images.


title: Automatic dental CT image segmentation using mean shift algorithm

abstract: Identifying the structure and arrangement of the teeth is one of the dentists' requirements for performing various procedures such as diagnosing abnormalities, dental implant and orthodontic planning. In this regard, robust segmentation of dental Computerized Tomography (CT) images is required. However, dental CT images present some major challenges for the segmentation that make it difficult process. In this research, we propose a multi-step approach for automatic segmentation of the teeth in dental CT images. The main steps of this method are presented as follows: 1-Primary segmentation to classify bony tissues from nonbony tissues. 2-Separating the general region of the teeth structure from the other bony structures and arc curve fitting in the region. 3-Individual tooth region detection. 4-Final segmentation using mean shift algorithm by defining a new feature space. The proposed algorithm has been applied to several Cone Beam Computed Tomography (CBCT) data sets and quality assessment metrics are used to evaluate the performance of the algorithm. The evaluation indicates that the accuracy of proposed method is more than 97 percent. Moreover, we compared the proposed method with thresholding, watershed, level set and active contour methods and our method shows an improvement in compare with other techniques.


title: Algorithms for Scheduling Runway Operations Under Constrained Position Shifting

abstract: The efficient operation of airports, and runways in particular, is critical to the throughput of the air transportation system as a whole. Scheduling arrivals and departures at runways is a complex problem that needs to address diverse and often competing considerations of efficiency, safety, and equity among airlines. One approach to runway scheduling that arises from operational and fairness considerations is that of constrained position shifting (CPS), which requires that an aircraft's position in the optimized sequence not deviate significantly from its position in the first-come-first-served sequence. This paper presents a class of scalable dynamic programming algorithms for runway scheduling under constrained position shifting and other system constraints. The results from a prototype implementation, which is fast enough to be used in real time, are also presented.


title: Processing XML Streams with Deterministic Automata

abstract: We consider the problem of evaluating a large number of XPath expressions on an XML stream. Our main contribution consists in showing that Deterministic Finite Automata (DFA) can be used effectively for this problem: in our experiments we achieve a throughput of about 5.4MB/s, independent of the number of XPath expressions (up to 1,000,000 in our tests). The major problem we face is that of the size of the DFA. Since the number of states grows exponentially with the number of XPath expressions, it was previously believed that DFAs cannot be used to process large sets of expressions. We make a theoretical analysis of the number of states in the DFA resulting from XPath expressions, and consider both the case when it is constructed eagerly, and when it is constructed lazily. Our analysis indicates that, when the automaton is constructed lazily, and under certain assumptions about the structure of the input XML data, the number of states in the lazy DFA is manageable. We also validate experimentally our findings, on both synthetic and real XML data sets.


title: Surfactant foam flushing for in situ removal of DNAPLs in shallow soils

abstract: Abstract An innovative process combining surfactant foam and surfactant flushing (SF) for the remediation of a shallow saturated soil contaminated with a dense non-aqueous phase liquid (DNAPL) residual was investigated at bench-scale. First, foam was formed by injecting DHSS surfactant and nitrogen in alternation in soil (SAG process) at constant pressure. The effect of pressure gradients set point (≤90 kPa m −1 ) on DNAPL recovery was investigated. Foam front mobilized DNAPL with a piston-like fast propagation (1.8 md −1 ) despite the low pressure gradients. 34–60% of DNAPL residual was extracted with low surfactant consumption ( −1 DNAPL removed). Then, a solubilizing agent (Tergitol) was injected into soil, previously treated with foam, to improve the DNAPL removal. It led a removal up to 95% whereas only 2 porous volume (PV) of solubilizing solution were injected. As a comparison, when the solubilizing agent was injected without foam pre-treatment, 40 PV would have been required to lead the removal up to 90%. Considering the overall treatment, the technology we developed requires 10-times less surfactant than the traditional SF technology. This technology is especially suitable for very shallow soils where injection pressure has to be kept at a low level to avoid soil heaving.


title: Adaptive data rate control in low power wide area networks for long range IoT services

abstract: Abstract Internet of Things (IoT) technologies can provide various intelligent services by collecting information from objects. To collect information, Wireless Sensor Networks (WSNs) are exploited. The Low Power Wide Area Network (LPWAN), one type of WSN, has been designed for long-range IoT services. It consumes low power and uses a low data rate for data transmission. The LPWAN includes several communication standards, and Long Range Wide Area Network (LoRaWAN) is the representative standard of the LPWAN. LoRaWAN provides several data rates for transmission and enables adaptive data rate control in order to maintain network connectivity. In the LoRaWAN, the wireless condition is considered by the reception status of the acknowledgement (ACK) message, and adaptive data rate control is performed according to the wireless condition. Because the judgment of the wireless condition by the reception status of ACK messages does not reflect congestion, adaptive data rate control can lead to inefficiency in data transmission. For efficient data transmission in long-range IoT services, this paper proposes a congestion classifier using logistic regression and modified adaptive data rate control. The proposed scheme controls the data rate according to the congestion estimation. Through extensive analysis, we show the proposed scheme’s efficiency in data transmission.


title: A new approach in single Image Super Resolution

abstract: Super Resolution image reconstruction tries to obtain a high resolution image from one or more observed low resolution images of the same scene, using signal processing techniques. Variety of super resolution methods have been proposed in last decades. In this paper, we propose a new super resolution algorithm based on single low resolution image. As the super resolution reconstruction is an inverse problem, our method consists of three phases up-sampling, deblurring and denoising. Experimental results show the effectiveness of the proposed method.


title: Query-sensitive mutual reinforcement chain and its application in query-oriented multi-document summarization

abstract: Sentence ranking is the issue of most concern in document summarization. Early researchers have presented the mutual reinforcement principle (MR) between sentence and term for simultaneous key phrase and salient sentence extraction in generic single-document summarization. In this work, we extend the MR to the mutual reinforcement chain (MRC) of three different text granularities, i.e., document, sentence and terms. The aim is to provide a general reinforcement framework and a formal mathematical modeling for the MRC. Going one step further, we incorporate the query influence into the MRC to cope with the need for query-oriented multi-document summarization. While the previous summarization approaches often calculate the similarity regardless of the query, we develop a query-sensitive similarity to measure the affinity between the pair of texts. When evaluated on the DUC 2005 dataset, the experimental results suggest that the proposed query-sensitive MRC (Qs-MRC) is a promising approach for summarization.


title: The Sandbox for analysis: concepts and methods

abstract: The Sandbox is a flexible and expressive thinking environment that supports both ad-hoc and more formal analytical tasks. It is the evidence marshalling and sense-making component for the analytical software environment called nSpace. This paper presents innovative Sandbox human information interaction capabilities and the rationale underlying them including direct observations of analysis work as well as structured interviews. Key capabilities for the Sandbox include "put-this-there" cognition, automatic process model templates, gestures for the fluid expression of thought, assertions with evidence and scalability mechanisms to support larger analysis tasks. The Sandbox integrates advanced computational linguistic functions using a Web Services interface and protocol. An independent third party evaluation experiment with the Sandbox has been completed. The experiment showed that analyst subjects using the Sandbox did higher quality analysis in less time than with standard tools. Usability test results indicated the analysts became proficient in using the Sandbox with three hours of training.


title: A Very Fast Decision Tree Algorithm for Real-Time Data Mining of Imperfect Data Streams in a Distributed Wireless Sensor Network

abstract: Wireless sensor networks (WSNs) are a rapidly emerging technology with a great potential in many ubiquitous applications. Although these sensors can be inexpensive, they are often relatively unreliable when deployed in harsh environments characterized by a vast amount of noisy and uncertain data, such as urban traffic control, earthquake zones, and battlefields. The data gathered by distributed sensors—which serve as the eyes and ears of the system—are delivered to a decision center or a gateway sensor node that interprets situational information from the data streams. Although many other machine learning techniques have been extensively studied, real-time data mining of high-speed and nonstationary data streams represents one of the most promising WSN solutions. This paper proposes a novel stream mining algorithm with a programmable mechanism for handling missing data. Experimental results from both synthetic and real-life data show that the new model is superior to standard algorithms.


title: Signal Representations on Graphs: Tools and Applications

abstract: We present a framework for representing and modeling data on graphs. Based on this framework, we study three typical classes of graph signals: smooth graph signals, piecewise-constant graph signals, and piecewise-smooth graph signals. For each class, we provide an explicit definition of the graph signals and construct a corresponding graph dictionary with desirable properties. We then study how such graph dictionary works in two standard tasks: approximation and sampling followed with recovery, both from theoretical as well as algorithmic perspectives. Finally, for each class, we present a case study of a real-world problem by using the proposed methodology.


title: An overview of energy storage technologies in electric power systems: What is the future?

abstract: Successful operation of electric grid requires continuous real-time balancing of supply and demand including losses. The US grid operates effectively without much storage at present. However, the peak demand and generation facilities are growing at a faster rate in comparison to the transmission facilities. The system has observed some congestion problems. Over the past decade numerous reasons have accelerated the renewable energy applications in the existing grid. However, renewable resources are intermittent and the energy output varies widely during the day. Electricity storage technologies, when properly designed and integrated, can smooth out this variability and allow electricity to be dispatched at a later time. This paper concentrates on the energy storage technologies as a cost-effective technique that can be used in the US to make the grid more efficient, stable and reliable. Classification of energy storage technologies, comparison of various energy storage devices and system requirements are addressed in this paper.


title: An analysis of Internet content delivery systems

abstract: In the span of only a few years, the Internet has experienced an astronomical increase in the use of specialized content delivery systems, such as content delivery networks and peer-to-peer file sharing systems. Therefore, an understanding of content delivery on the lnternet now requires a detailed understanding of how these systems are used in practice.This paper examines content delivery from the point of view of four content delivery systems: HTTP web traffic, the Akamai content delivery network, and Kazaa and Gnutella peer-to-peer file sharing traffic. We collected a trace of all incoming and outgoing network traffic at the University of Washington, a large university with over 60,000 students, faculty, and staff. From this trace, we isolated and characterized traffic belonging to each of these four delivery classes. Our results (1) quantify, the rapidly increasing importance of new content delivery systems, particularly peer-to-peer networks, (2) characterize the behavior of these systems from the perspectives of clients, objects, and servers, and (3) derive implications for caching in these systems.


title: Management is Dead: Long Live Digital Era Governance

abstract: The "new public management" (NPM) wave in public sector organizational change was founded on themes of disaggregation, competition, and incentivization. Although its effects are still working through in countries new to NPM, this wave has now largely stalled or been reversed in some key "leading-edge" countries. This ebbing chiefly reflects the cumulation of adverse indirect effects on citizens' capacities for solving social problems because NPM has radically increased institutional and policy complexity. The character of the post-NPM regime is currently being formed. We set out the case that a range of connected and information technology-centered changes will be critical for the current and next wave of change, and we focus on themes of reintegration, needs-based holism, and digitization changes. The overall movement incorporating these new shifts is toward "digital-era governance" (DEG), which involves reintegrating functions into the governmental sphere, adopting holistic and needs-oriented structures, and progressing digitalization of administrative processes. DEG offers a perhaps unique opportunity to create self-sustaining change, in a broad range of closely connected technological, organizational, cultural, and social effects. But there are alternative scenarios as to how far DEG will be recognized as a coherent phenomenon and implemented successfully.


Forecasting Time Series by SOFNN with Reinforcement Learning


title: Perceptions of Service Quality in Jordanian Hotels

abstract: The importance of service quality for business performance has been recognized in the literature through the direct effect on customer satisfaction and the indirect effect on customer loyalty. The main objective of the study was to measure hotels' service quality performance from the customer perspective. To do so, a performance-only measurement scale (SERVPERF) was administered to customers stayed in three, four and five star hotels in Aqaba and Petra. Although the importance of service quality and service quality measurement has been recognized, there has been limited research that has addressed the structure and antecedents of the concept for the hotel industry. The clarification of the dimensions is important for managers in the hotel industry as it identifies the bundles of service attributes consumers find important. The results of the study demonstrate that SERVPERF is a reliable and valid tool to measure service quality in the hotel industry. The instrument consists of five dimensions, namely "tangibles", "responsiveness", "empathy", "assurance" and "reliability". Hotel customers are expecting more improved services from the hotels in all service quality dimensions. However, hotel customers have the lowest perception scores on empathy and tangibles. In the light of the results, possible managerial implications are discussed and future research subjects are recommended.


title: Guide to Biometrics

abstract: This complete, technical guide details the principles, methods, technologies, and core ideas used in biometric authentication systems. It explains the definition and measurement of performance and examines the factors involved in choosing between different biometrics. It also delves into practical applications and covers a number of topics critical for successful system integration. These include recognition accuracy, total cost of ownership, acquisition and processing speed, intrinsic and system security, privacy and legal requirements, and user acceptance. The "Guide to Biometrics:" * Debunks myths and candidly confronts problems associated with biometrics research * Details relevant issues in choosing between biometrics, as well as defining and measuring performance * Defines and explains how to measure the performance of both verification and identification systems * Addresses challenges in managing tradeoffs between security and convenience Security and financial administrators, computer science professionals, and biometric systems developers will all benefit from an enhanced understanding of this important technology.


title: Diesel engine performance and exhaust emission analysis using waste cooking biodiesel fuel with an artificial neural network

abstract: This study deals with artificial neural network (ANN) modeling of a diesel engine using waste cooking biodiesel fuel to predict the brake power, torque, specific fuel consumption and exhaust emissions of the engine. To acquire data for training and testing the proposed ANN, a two cylinders, four-stroke diesel engine was fuelled with waste vegetable cooking biodiesel and diesel fuel blends and operated at different engine speeds. The properties of biodiesel produced from waste vegetable oil was measured based on ASTM standards. The experimental results revealed that blends of waste vegetable oil methyl ester with diesel fuel provide better engine performance and improved emission characteristics. Using some of the experimental data for training, an ANN model was developed based on standard Back-Propagation algorithm for the engine. Multi layer perception network (MLP) was used for non-linear mapping between the input and output parameters. Different activation functions and several rules were used to assess the percentage error between the desired and the predicted values. It was observed that the ANN model can predict the engine performance and exhaust emissions quite well with correlation coefficient (R) 0.9487, 0.999, 0.929 and 0.999 for the engine torque, SFC, CO and HC emissions, respectively. The prediction MSE (Mean Square Error) error was between the desired outputs as measured values and the simulated values were obtained as 0.0004 by the model.


title: Position and Force Control Based on Mathematical Models of Pneumatic Artificial Muscles Reinforced by Straight Glass Fibers

abstract: This paper reports on the position and force control of pneumatic artificial muscles reinforced by straight glass fibers. This type of artificial muscle has a greater contraction ratio and power and a longer lifetime than conventional McKibben types. However, these muscles are highly non-linear; hence, it is difficult to use them in a mechanical system. Furthermore, this actuator has a high compliance characteristic. Though this characteristic is useful for human interactions, the position and force of this actuator cannot be easily controlled. In this paper, a mathematical model of this type of artificial muscle is proposed, and the relationship between design parameters and control specifications is realized. In addition, the position and force based on the mathematical model are determined and applied to artificial muscle linearization.


title: Automatic modeling of animatable virtual humans - a survey

abstract: Highly realistic virtual human models are rapidly becoming commonplace in computer graphics. These models, often represented by complex shape and requiring labor-intensive process, challenge the problem of automatic modeling. We study the problem and solutions to automatic modeling of animatable virtual humans. Methods for capturing the shape of real people, parameterization techniques for modeling static shape (the variety of human body shapes) and dynamic shape (how the body shape changes as it moves) of virtual humans are classified, summarized and compared. Finally, methods for clothed virtual humans are reviewed.


Causal models as minimal descriptions of multivariate systems. http://parallel.vub.ac.be/∼jan


title: Bayesian Policy Gradients via Alpha Divergence Dropout Inference

abstract: Policy gradient methods have had great success in solving continuous control tasks, yet the stochastic nature of such problems makes deterministic value estimation difficult. We propose an approach which instead estimates a distribution by fitting the value function with a Bayesian Neural Network. We optimize an $\alpha$-divergence objective with Bayesian dropout approximation to learn and estimate this distribution. We show that using the Monte Carlo posterior mean of the Bayesian value function distribution, rather than a deterministic network, improves stability and performance of policy gradient methods in continuous control MuJoCo simulations.


title: RoadSense: Smartphone Application to Estimate Road Conditions Using Accelerometer and Gyroscope

abstract: Monitoring the road condition has acquired a critical significance during recent years. There are different reasons behind broadening research on this field: to start with, it will guarantee safety and comfort to different road users; second, smooth streets will cause less damage to the car. Our motivation is to create a real-time Android Application RoadSense that automatically predicts the quality of the road based on a tri-axial accelerometer and a gyroscope, show the road location trace on a geographic map using GPS, and save all recorded workout entries. C4.5 Decision tree classifier is applied on training data to classify road segments and to build our model. Our experimental results show consistent accuracy of 98.6%. Using this approach, we expect to visualize a road quality map of a selected region. Hence, we can provide constructive feedback to drivers and local authorities. Besides, road manager can benefit from this system to evaluate the state of their road network and make a checkup on road construction projects, whether they meet or not the required quality.


title: Inverse dynamics based altitude/attitude control and stabilization of a quadrotor UAV

abstract: This paper presents an inclusive study on dynamical modeling, nonlinear angular control, and stabilization of a quadrotor aerial robot. Due to inherent nonlinear and coupled quadrotor dynamics, inverse dynamics based control is designed which renders a decoupled control solution. Third and fourth order Lie derivatives are avoided using static feedback. Designed nonlinear controller stabilizes the angular states from specified aggressive initial conditions, which is not possible using classical linear controllers. This brief also presents successful tracking of desired angular states. In addition, mathematical model of quadrotor aerial robot is recalled and derived using the Newton-Euler method. Simulation results are presented that strongly verify the effectiveness of designed controller.


title: Nutrition for wound healing

abstract: The importance of the role of nutrition in wound healing is an area that has been widely explored over the last decade. It is well recognised that both macronutrients (protein, fat and carbohydrate) and micronutrients (vitamins, minerals and trace elements) play important parts in the healing of both chronic wounds and acute injuries. The term 'wound' encompasses many different situations from leg ulcers to laparostomy wounds. This article provides an overview of the role of different nutrients in the healing of wounds and guidance to nurses on first-line assessments, which can be used to ensure the patient is receiving adequate nutrition for successful wound healing. It will focus on commonly seen wounds in primary and secondary care but will not cover specialist wound management, such as laparostomy sites and burns, as these must always be cared for by experienced and specialist multidisciplinary teams.


title: A Dependency Treebank of Classical Chinese Poems

abstract: As interest grows in the use of linguistically annotated corpora in research and teaching of foreign languages and literature, treebanks of various historical texts have been developed. We introduce the first large-scale dependency treebank for Classical Chinese literature. Derived from the Stanford dependency types, it consists of over 32K characters drawn from a collection of poems written in the 8th century CE. We report on the design of new dependency relations, discuss aspects of the annotation process and evaluation, and illustrate its use in a study of parallelism in Classical Chinese poetry.


title: A Multi-token Authorization Strategy for Secure Mobile Cloud Computing

abstract: Cloud computing is an emerging paradigm shifting the shape of computing models from being a technology to a utility. However, security, privacy and trust are amongst the issues that can subvert the benefits and hence wide deployment of cloud computing. With the introduction of omnipresent mobile-based clients, the ubiquity of the model increases, suggesting a still higher integration in life. Nonetheless, the security issues rise to a higher degree as well. The constrained input methods for credentials and the vulnerable wireless communication links are among factors giving rise to serious security issues. To strengthen the access control of cloud resources, organizations now commonly acquire Identity Management Systems (IdM). This paper presents that the most popular IdM, namely OAuth, working in scope of Mobile Cloud Computing has many weaknesses in authorization architecture. In particular, authors find two major issues in current IdM. First, if the IdM System is compromised through malicious code, it allows a hacker to get authorization of all the protected resources hosted on a cloud. Second, all the communication links among client, cloud and IdM carries complete authorization token, that can allow hacker, through traffic interception at any communication link, an illegitimate access of protected resources. We also suggest a solution to the reported problems, and justify our arguments with experimentation and mathematical modeling.


title: Is Lymphostasis an Aggravant of Lipedema

abstract: A 54-year-old female patient reported that a characteristic of her family was ‘fat legs’ with postural edema since adolescence. Over the years the patient had been gaining weight with an increase in fatty tissue in the legs and arms. At the age of 24 years she started taking oral contraceptives and noted worse swelling and pain in the lower limbs. She was advised to suspend the use of the contraceptives and to start using a transdermal lymphatic system drug and physical exercise which partially improved the symptoms. Three years ago she noted that the swelling was increasing without improvement and sought a physician who raised the hypothesis of lymphedema and referred her to a specialized center. Lipedema and lymphedema was diagnosed in the physical examination. A 3-day intensive treatment program (8 h daily) was started for lymphedema which included manual and mechanical lymph drainage associated with short-strech (<50 mm Hg) compression stockings custom made using a cotton-polyester fabric. Volumetry and perimetry were performed before starting and after the treatment and the legs were photographed. Volumetric and perimetric reductions were obtained suggesting the involvement of regional cutaneous lymphostasis in this disease.


title: Image aesthetics assessment using Deep Chatterjee's machine

abstract: Image aesthetics assessment has been challenging due to its subjective nature. Inspired by the Chatterjee's visual neuroscience model, we design Deep Chatterjee's Machine (DCM) tailored for this task. DCM first learns attributes through the parallel supervised pathways, on a variety of selected feature dimensions. A high-level synthesis network is trained to associate and transform those attributes into the overall aesthetics rating. We then extend DCM to predicting the distribution of human ratings, since aesthetics ratings are often subjective. We also highlight our first-of-its-kind study of label-preserving transformations in the context of aesthetics assessment, which leads to an effective data augmentation approach. Experimental results on the AVA dataset show that DCM gains significant performance improvement, compared to other state-of-the-art models.


title: Optimising the high frequency bandwidth and immuntity to interference of Rogowski coils in measurement applications with large local dV/dt

abstract: Electrostatic interference through capacitive coupling onto a Rogowski coil causes unwanted pick-up noise on the output of the integrator. An electrostatic screen fitted to a Rogowski coil reduces this interference. This paper discusses how to maximise the high frequency (hf) bandwidth of a Rogowski transducer with a screened coil.


ANRL: Attributed Network Representation Learning via Deep Neural Networks


title: Dual-Path Convolutional Image-Text Embedding with Instance Loss

abstract: Matching images and sentences demands a fine understanding of both modalities. In this paper, we propose a new system to discriminatively embed the image and text to a shared visual-textual space. In this field, most existing works apply the ranking loss to pull the positive image / text pairs close and push the negative pairs apart from each other. However, directly deploying the ranking loss is hard for network learning, since it starts from the two heterogeneous features to build inter-modal relationship. To address this problem, we propose the instance loss which explicitly considers the intra-modal data distribution. It is based on an unsupervised assumption that each image / text group can be viewed as a class. So the network can learn the fine granularity from every image/text group. The experiment shows that the instance loss offers better weight initialization for the ranking loss, so that more discriminative embeddings can be learned. Besides, existing works usually apply the off-the-shelf features, i.e., word2vec and fixed visual feature. So in a minor contribution, this paper constructs an end-to-end dual-path convolutional network to learn the image and text representations. End-to-end learning allows the system to directly learn from the data and fully utilize the supervision. On two generic retrieval datasets (Flickr30k and MSCOCO), experiments demonstrate that our method yields competitive accuracy compared to state-of-the-art methods. Moreover, in language based person retrieval, we improve the state of the art by a large margin. The code has been made publicly available.


title: Deep Reinforcement Learning for Swarm Systems

abstract: Recently, deep reinforcement learning (RL) methods have been applied successfully to multi-agent scenarios. Typically, the observation vector for decentralized decision making is represented by a concatenation of the (local) information an agent gathers about other agents. However, concatenation scales poorly to swarm systems with a large number of homogeneous agents as it does not exploit the fundamental properties inherent to these systems: (i) the agents in the swarm are interchangeable and (ii) the exact number of agents in the swarm is irrelevant. Therefore, we propose a new state representation for deep multi-agent RL based on mean embeddings of distributions, where we treat the agents as samples and use the empirical mean embedding as input for a decentralized policy. We define different feature spaces of the mean embedding using histograms, radial basis functions and neural networks trained end-to-end. We evaluate the representation on two well-known problems from the swarm literature in a globally and locally observable setup. For the local setup we furthermore introduce simple communication protocols. Of all approaches, the mean embedding representation using neural network features enables the richest information exchange between neighboring agents, facilitating the development of complex collective strategies.


title: Synthesis and anticancer activity of thiosemicarbazones.

abstract: Abstract Twenty-six thiosemicarbazones (III-1–III-26) were synthesized via three steps starting from hydrazine hydrate and carbon disulfide. The testing of anticancer activity of these compounds in vitro against P-388, A-549, and SGC-7901 shows that compounds III-15 and III-16 possess a higher inhibitory ability for P-388 and SGC-7901. Further testing shows that the value of IC 50 of compound III-16 against SGC-7901 reaches to 0.032 μM.


title: Personalized healthcare cloud services for disease risk assessment and wellness management using social media

abstract: We propose a cloud based framework that effectively manages the health related Big-data and benefits from the ubiquity of the Internet and social media. The framework facilitates the mobile and desktop users by offering: (a) disease risk assessment service and (b) consultation service with the health experts on Twitter. The disease risk assessment is performed through a collaborative filtering based approach whereas the hubs and authorities based approach is employed to identify the health experts from Twitter. The framework is implemented as Software as a Service (SaaS) to provide the disease risk assessment and expert user interaction services. Experimental results exhibit that the proposed framework achieves high accuracy as compared to the state-of-the-art approaches in terms of disease risk assessment and expert user recommendation.


title: Impact factors of contribution area threshold in extracting drainage network for rivers in China

abstract: Determination of threshold plays an important role in extracting drainage network based on DEM and affects the topological and geometric characteristics of the digital river system,thereby affecting hillslope and fluvial process.In this paper,50 sub-basins were selected as the sample to extract drainage network based on DEM and to determine the best critical contribution area whose main spatial impact factors were analyzed.The relationships among precipitation,topography,land surface cover and threshold were investigated by using correlation analysis and multiple linear regression analysis.The results show that the critical contribution area decreases with the increasing precipitation,slope and the total area index(TAI),and the quantitative equation of the threshold and precipitation,slope,and land surface cover has been established.The equation has a good behavior in other watersheds making the determination of the threshold more objective and convenient.


title: Qualitative comparison of open-source SDN controllers

abstract: Telecommunication companies with expensive networks may become the biggest beneficiaries of SDN; however, in contrast to traditional routers, the development of SDN controllers is driven by open-source projects with involvement of the industry. Two prevalent projects in SDN development are the OpenDaylight and the ONOS controllers. These SDN controllers are advanced in their development — having gone through a number of releases — and have been described as being useful for a large number of use-cases. In this work, we compare and evaluate these controllers, in particular their northbound interfaces, by configuring them for a representative use-case, port-mirroring.


title: A GPU-Based Implementation of Position Based Dynamics for Interactive Deformable Bodies

abstract: Position Based Dynamics (PBD) is a popular approach used for animating constrained particle systems representing soft bodies, rigid bodies, and fluids. In this article, we present a massively parallel implementation of PBD for fast, interactive animation of deformable bodies. We divide the set of constraints in independent partitions using a fast, greedy coloring graph algorithm. Then, during the animation, the constraints belonging to each partition are solved in parallel on the GPU. We employ an efficient simulation pipeline using a memory layout that favors both the memory access time for computation and batching for visualization. Our experiments show that the achieved performance speed-up is several orders of magnitude faster than its serial counterpart.


title: Fast Convex Layers Algorithm for Near-Duplicate Image Detection

abstract: This paper builds on a novel, fast algorithm for generating the convex layers on grid points with linear time complexity. Convex layers are extracted from the binary image. The obtained convex hulls are characterized by the number of their vertices and used as representative image features. A computational geometric approach to near-duplicate image detection stems from these features. Similarity of feature vectors of given images is assessed by correlation coefficient. This way, all images with closely related structure and contents can be retrieved from large databases of images quickly and efficiently. The algorithm can be used in various applications such as video surveillance, image and video duplication search, or image alignment. Our approach is rather robust up to moderate signal-to-noise ratios, tolerates lossy image compression, and copes with translated, rotated and scaled image contents.


title: Approximation capabilities of multilayer feedforward networks

abstract: Abstract We show that standard multilayer feedforward networks with as few as a single hidden layer and arbitrary bounded and nonconstant activation function are universal approximators with respect to L p (μ) performance criteria, for arbitrary finite input environment measures μ, provided only that sufficiently many hidden units are available. If the activation function is continuous, bounded and nonconstant, then continuous mappings can be learned uniformly over compact input sets. We also give very general conditions ensuring that networks with sufficiently smooth activation functions are capable of arbitrarily accurate approximation to a function and its derivatives.


linalg: Matrix Computations in Apache Spark


title: Indexing the Sphere with the Hierarchical Triangular Mesh

abstract: We describe a method to subdivide the surface of a sphere into spherical triangles of similar, but not identical, shapes and sizes. The Hierarchical Triangular Mesh (HTM) is a quad-tree that is particularly good at supporting searches at different resolutions, from arc seconds to hemispheres. The subdivision scheme is universal, providing the basis for addressing and for fast lookups. The HTM provides the basis for an efficient geospatial indexing scheme in relational databases where the data have an inherent location on either the celestial sphere or the Earth. The HTM index is superior to cartographical methods using coordinates with singularities at the poles. We also describe a way to specify surface regions that efficiently represent spherical query areas. This article presents the algorithms used to identify the HTM triangles covering such regions.


How does orthographic learning happen?


A 0 . 5 V 3 . 6 ppm / oC 2 . 2 pW 2-Transistor Voltage Reference


title: The Ability of CMIP5 Models to Simulate North Atlantic Extratropical Cyclones

abstract: AbstractThe ability of the climate models participating in phase 5 of the Coupled Model Intercomparison Project (CMIP5) to simulate North Atlantic extratropical cyclones in winter [December–February (DJF)] and summer [June–August (JJA)] is investigated in detail. Cyclones are identified as maxima in T42 vorticity at 850 hPa and their propagation is tracked using an objective feature-tracking algorithm. By comparing the historical CMIP5 simulations (1976–2005) and the ECMWF Interim Re-Analysis (ERA-Interim; 1979–2008), the authors find that systematic biases affect the number and intensity of North Atlantic cyclones in CMIP5 models. In DJF, the North Atlantic storm track tends to be either too zonal or displaced southward, thus leading to too few and weak cyclones over the Norwegian Sea and too many cyclones in central Europe. In JJA, the position of the North Atlantic storm track is generally well captured but some CMIP5 models underestimate the total number of cyclones. The dynamical intensity of cyclone...


title: People detection and tracking from fish-eye image based on probabilistic appearance model

abstract: This paper presents a method for automated people detection and tracking by using fisheye lens camera. In our method, human is modeled as probabilistic features of body silhouette and head-shoulder contour. These features are extracted from the human images taken at various distance and orientation with respect to the camera, and form the training data set. A probabilistic appearance model is built by means of kernel ridge regression (KRR) and human detection is formulated as maximum a posteriori (MAP) estimation using this model. Finally, people tracking is achieved by the combination of Kalman filter and nearest neighbor standard filter (NNSF). Experiments are conducted on indoor space where a fisheye lens camera is installed on the ceiling of crossing hallway. The feasibility and accuracy of our method is discussed through the experimental results.


title: Design of an Ultra-low Voltage 9T SRAM With Equalized Bitline Leakage and CAM-Assisted Energy Efficiency Improvement

abstract: This paper presents a 9T multi-threshold (MTCMOS) SRAM macro with equalized bitline leakage and a content-addressable-memory-assisted (CAM-assisted) write performance boosting technique for energy efficiency improvement. A 3T-based read port is proposed to equalize read bitline (RBL) leakage and to improve RBL sensing margin by eliminating data-dependence on bitline leakage current. A miniature CAM-assisted circuit is integrated to conceal the slow data development with HVT devices after data flipping in write operation and therefore enhance the write performance for energy efficiency. A 16 kb SRAM test chip is fabricated in 65 nm CMOS technology. The operating voltage of the test chip is scalable from 1.2 V down to 0.26 V with the read access time from 6 ns to 0.85 $\mu {\rm s}$ . Minimum energy of 2.07 pJ is achieved at 0.4 V with 40.3% improvement compared to the SRAM without the aid of the CAM. Energy efficiency is enhanced by 29.4% between 0.38 V $\sim$ 0.6 V by the proposed CAM-assisted circuit.


title: Mucus mediated protection against acute colitis in adiponectin deficient mice

abstract: Background ::: Acute ulcerative colitis is an inflammation-driven condition of the bowel. It hampers the general homeostasis of gut, resulting in decreased mucus production and epithelial cell renewal. Adiponectin (APN), an adipocytokine, is secreted by the adipose tissue and has been debated both as a pro-inflammatory or anti-inflammatory protein depending on the disease condition and microenvironment. The present study delineates the role of APN depletion in mucus modulation in a model of acute colitis.


title: Design and Implementation of a P300-Based Brain-Computer Interface for Controlling an Internet Browser

abstract: An electroencephalographic (EEG) brain-computer interface (BCI) internet browser was designed and evaluated with 10 healthy volunteers and three individuals with advanced amyotrophic lateral sclerosis (ALS), all of whom were given tasks to execute on the internet using the browser. Participants with ALS achieved an average accuracy of 73% and a subsequent information transfer rate (ITR) of 8.6 bits/min and healthy participants with no prior BCI experience over 90% accuracy and an ITR of 14.4 bits/min. We define additional criteria for unrestricted internet access for evaluation of the presented and future internet browsers, and we provide a review of the existing browsers in the literature. The P300-based browser provides unrestricted access and enables free web surfing for individuals with paralysis.


title: Framing program comprehension as fault localization

abstract: Program comprehension is a time-consuming task performed during the process of reusing, reengineering, and enhancing existing systems. There are tools to assist comprehension by means of dynamic analysis, but most cannot identify the topology and the interactions of certain functionality in need of change. We propose an approach, coined Spectrum-based Feature Comprehension (SFC), that borrows techniques from software-fault localization that were proven to be effective even when debugging large applications. SFC analyses the program by exploiting run-time information from test case executions to identify the components that are important for a given feature, helping software engineers to understand how a program is structured and each of the functionality's dependencies are. We present a toolset, coined PANGOLIN, that implements SFC and displays its report to the user using an intuitive visualization. A user study presented demonstrating PANGOLIN's efficiency in locating components that should be inspected when changing a certain functionality. Participants using SFC spent a median of 50 min locating the feature with greater accuracy, whereas participants using coverage tools took 60 min. Finally, we also detail the Participatory Feature Detection approach, where user interactions with the system are captured; removing the hindrance of requiring pre-existing automated tests. Copyright © 2016 John Wiley & Sons, Ltd.


A GNU Radio-based IEEE 802 . 15 . 4 Testbed


title: TCAD for gate stack optimization in pGaN Gate HEMT devices

abstract: pGaN Gate HEMTs are promising normally-OFF transistors for which the use of magnesium (Mg) doping allows the modulation of the threshold voltage (Vth) but at the cost of an ON-state resistance (Ron) degradation. In this study, we propose rigorous TCAD simulations that describe the Mg doping impact on both electrical parameters (Vth and Ron) in agreement with experimental data. We emphasize the importance of TCAD as an additional tool to optimize this Vth-Ron process-window


title: Effects of unattended speech on performance and subjective distraction: The role of acoustic design in open-plan offices

abstract: Abstract Unattended background speech is a known source of cognitive and subjective distraction in open-plan offices. This study investigated whether the deleterious effects of background speech can be affected by room acoustic design that decreases speech intelligibility, as measured by the Speech Transmission Index (STI). The experiment was conducted in an open-plan office laboratory (84 m2) in which four acoustic conditions were physically built. Three conditions contained background speech. A quiet condition was included for comparison. The speech conditions differed in terms of the degree of absorption, screen height, desk isolation, and the level of masking sound. The speech sounds simulated an environment where phone conversations are heard from different locations varying in distance. Ninety-eight volunteers were tested. The presence of background speech had detrimental effects on the subjective perceptions of noise effects and on cognitive performance in short-term memory and working memory tasks. These effects were not attenuated nor amplified within a three-hour working period. The reduction of the STI by room acoustic means decreased subjective disturbance, whereas the effects on cognitive performance were somewhat smaller than expected. The effects of room acoustic design on subjective distraction were stronger among noise-sensitive subjects, suggesting that they benefited more from acoustic improvements than non-sensitive subjects. The results imply that reducing the STI is beneficial for performance and acoustic satisfaction especially regarding speech coming from more distant desks. However, acoustic design does not sufficiently decrease the distraction caused by speech from adjacent desks.