title: Multisource Transfer Learning With Convolutional Neural Networks for Lung Pattern Analysis

abstract: Early diagnosis of interstitial lung diseases is crucial for their treatment, but even experienced physicians find it difficult, as their clinical manifestations are similar. In order to assist with the diagnosis, computer-aided diagnosis (CAD) systems have been developed. These commonly rely on a fixed scale classifier that scans CT images, recognizes textural lung patterns and generates a map of pathologies. In a previous study, we proposed a method for classifying lung tissue patterns using a deep convolutional neural network (CNN), with an architecture designed for the specific problem. In this study, we present an improved method for training the proposed network by transferring knowledge from the similar domain of general texture classification. Six publicly available texture databases are used to pretrain networks with the proposed architecture, which are then fine-tuned on the lung tissue data. The resulting CNNs are combined in an ensemble and their fused knowledge is compressed back to a network with the original architecture. The proposed approach resulted in an absolute increase of about 2% in the performance of the proposed CNN. The results demonstrate the potential of transfer learning in the field of medical image analysis, indicate the textural nature of the problem and show that the method used for training a network can be as important as designing its architecture.


title: How mental systems believe

abstract: Is there a difference between believing and merely understanding an idea?Descartes thought so. He considered the acceptance and rejection of an idea to be alternative outcomes of an effortful assessment process that occurs subsequent to the automatic comprehension of that idea. This article examined Spinoza's alternative suggestion that (a) the acceptance of an idea is part of the automatic comprehension of that idea and (b) the rejection of an idea occurs subsequent to, and more effortfully than, its acceptance. In this view, the mental representation of abstract ideas is quite similar to the mental representation of physical objects: People believe in the ideas they comprehend, as quickly and automatically as they believe in the objects they see. Research in social and cognitive psychology suggests that Spinoza's model may be a more accurate account of human belief than is that of Descartes.


title: Unsupervised pattern mining from symbolic temporal data

abstract: We present a unifying view of temporal concepts and data models in order to categorize existing approaches for unsupervised pattern mining from symbolic temporal data. In particular we distinguish time point-based methods and interval-based methods as well as univariate and multivariate methods. The mining paradigms and the robustness of many proposed approaches are compared to aid the selection of the appropriate method for a given problem. For time points, sequential pattern mining algorithms can be used to express equality and order of time points with gaps in multivariate data. For univariate data and limited gaps suffix tree methods are more efficient. Recently, efficient algorithms have been proposed to mine the more general concept of partial order from time points. For time interval data with precise start and end points the relations of Allen can be used to formulate patterns. The recently proposed Time Series Knowledge Representation is more robust on noisy data and offers an alternative semantic that avoids ambiguity and is more expressive. For both pattern languages efficient mining algorithms have been proposed.


title: Learning and Planning with a Semantic Model

abstract: Building deep reinforcement learning agents that can generalize and adapt to unseen environments remains a fundamental challenge for AI. This paper describes progresses on this challenge in the context of man-made environments, which are visually diverse but contain intrinsic semantic regularities. We propose a hybrid model-based and model-free approach, LEArning and Planning with Semantics (LEAPS), consisting of a multi-target sub-policy that acts on visual inputs, and a Bayesian model over semantic structures. When placed in an unseen environment, the agent plans with the semantic model to make high-level decisions, proposes the next sub-target for the sub-policy to execute, and updates the semantic model based on new observations. We perform experiments in visual navigation tasks using House3D, a 3D environment that contains diverse human-designed indoor scenes with real-world objects. LEAPS outperforms strong baselines that do not explicitly plan using the semantic content.


title: An Energy-Aware IoT Femtocloud System

abstract: Mobile and IoT devices are becoming increasingly capable computing platforms that are often underutilized. In this paper, we propose a system that leverages the idle compute cycles in a group of heterogeneous mobile and IoT devices that can be clustered to form an edge femtocloud. At the heart of this system, we formulate a task assignment and scheduling problem that strives to maximize the computational throughput of the constructed femtocloud while maintaining the energy consumption below an operator specified threshold. Due to the NP-Completeness of this scheduling problem, we design a set of heuristics to solve this problem. We implement a prototype of our system and use it to evaluate its performance. Our results demonstrate the system's ability to utilize the available compute capacity of a group of mobile and IoT devices while adhering to pre-specified energy constraints. Compared to other schedulers, our scheduler achieves up to 40% performance improvement.


title: Forecasting directional changes in the FX markets

abstract: Most of existing studies sample markets' prices as time series when developing models to predict market's trend. Directional Changes (DC) is an approach to summarize market prices other than time series. DC marks the market as downtrend or uptrend based on the magnitude of prices changes. In this paper we address the problem of forecasting trend's direction in the foreign exchange (FX) market under the DC framework. In particularly we aim to answer the question of whether the current trend will continue for a specific percentage before the trend ends. We propose one single independent variable to make the forecast. We assess the accuracy of our approach using three currency pairs in the FX market; namely EUR/CHF, GBP/CHF, and USD/JPY. The experimental results show that the accuracy of the proposed forecasting model is very good; in some cases, forecasting accuracy was over 80%. However, under particular settings the accuracy may not outperform dummy prediction. The results confirm that directional changes are predictable, and the identified independent variable is useful for forecasting under the DC framework.


title: Comparing extreme programming and Waterfall project results

abstract: Waterfall and Extreme Programming are two software project methods used for project management. Although there are a number of opinions comparing the two methods regarding how they should be applied, none have used project data to clearly conclude which one is better. In this paper, we present the results of a controlled empirical study conducted at Carnegie Mellon University in Silicon Valley to learn about the effective transition from traditional development to agile development. We conducted a comparison research against these two approaches. Multiple teams were assigned a project; some used Waterfall development, others used Extreme Programming. The purpose of this research is to look at advantages and disadvantages based upon the outcomes, generated artifacts, and metrics produced by the teams.


title: A complete frontier-based exploration method for Pose-SLAM

abstract: In this paper we propose a complete frontier-based exploration method for Pose SLAM. The goal of current frontier-based exploration methods is to eliminate all frontiers in the environment map. This approach, however, does not meet the requirements of Pose SLAM mapping: besides arriving at a frontier free map, there is the need to close the loops in the environment. In fact, loop closing is instrumental to Pose SLAM, since the major corrections to the map result from adding place revisiting constraints. The exploration method we propose is said to be complete in the sense that all loops are guaranteed to be closed. In this paper, we also describe a hybrid map that supports our exploration scheme and detail all the mechanisms required to maintain it during exploration. The validity of the approach is demonstrated on a widely used dataset.


title: Automatic Speech Recognition for Mixed Dialect Utterances by Mixing Dialect Language Models

abstract: This paper presents an automatic speech recognition (ASR) system that accepts a mixture of various kinds of dialects. The system recognizes dialect utterances on the basis of the statistical simulation of vocabulary transformation and combinations of several dialect models. Previous dialect ASR systems were based on handcrafted dictionaries for several dialects, which involved costly processes. The proposed system statistically trains transformation rules between a common language and dialects, and simulates a dialect corpus for ASR on the basis of a machine translation technique. The rules are trained with small sets of parallel corpora to make up for the lack of linguistic resources on dialects. The proposed system also accepts mixed dialect utterances that contain a variety of vocabularies. In fact, spoken language is not a single dialect but a mixed dialect that is affected by the circumstances of speakers' backgrounds (e.g., native dialects of their parents or where they live). We addressed two methods to combine several dialects appropriately for each speaker. The first was recognition with language models of mixed dialects with automatically estimated weights that maximized the recognition likelihood. This method performed the best, but calculation was very expensive because it conducted grid searches of combinations of dialect mixing proportions that maximized the recognition likelihood. The second was integration of results of recognition from each single dialect language model. The improvements with this model were slightly smaller than those with the first method. Its calculation cost was, however, inexpensive and it worked in real-time on general workstations. Both methods achieved higher recognition accuracies for all speakers than those with the single dialect models and the common language model, and we could choose a suitable model for use in ASR that took into consideration the computational costs and recognition accuracies.


title: Syntax-Tree Regular Expression Based DFA FormalConstruction

abstract: Compiler is a program whose functionality is to translate a computer program written in source language into an equivalent machine code. Compiler construction is an advanced research area because of its size and complexity. The source codes are in higher level languages which are usually complex and, consequently, increase the level of abstraction. Due to such reasons, design and construction of error free compiler is a challenge of the twenty first century. Verification of a source program does not guarantee about correctness of code generated because the bugs in compiler may lead to an incorrect target program. Therefore, verification of compiler is more important than verifying the source programs. Lexical analyzer is a main phase of compiler used for scanning input and grouping into sequence of tokens. In this paper, formal construction of deterministic finite automata (DFA) based on regular expression is presented as a part of lexical analyzer. At first, syntax tree is described based on the augmented regular expression. Then formal description of important operators, checking nullability and computing first and last positions of internal nodes of the tree is described. In next, the transition diagram is described from the follow positions and converted into deterministic finite automata by defining a relationship among syntax tree, transition diagram and DFA. Formal specification of the procedure is described using Z notation and model analysis is provided using Z/Eves toolset.


title: Haze: privacy-preserving real-time traffic statistics

abstract: We consider mobile applications that let users learn traffic conditions based on reports from other users. However, the providers of these mobile services have access to such sensitive information as timestamped locations and movements of its users. In this paper, we introduce the model and general approach of Haze, a system for traffic-update applications that supports the creation of traffic statistics from user reports while protecting the privacy of the users. We also present preliminary experiments that indicate potential for a practical deployment of Haze.


title: The evolutionary significance of polyploidy

abstract: Polyploidy occurs frequently but is usually detrimental to survival; thus, few polyploids survive in the long term. Here, evidence linking the short-term evolutionary success of polyploids to environmental upheaval is reviewed and possible longer-term evolutionary benefits of polyploidy are discussed.


From Physics to Number Theory Via Noncommutative Geometry


title: There's more to magic than meets the eye

abstract: Document S1. Supplemental Experimental ProceduresxDownload (.1 MB ) Document S1. Supplemental Experimental ProceduresMovie 1 Vanishing ball trick pro-illusion conditionxDownload (.75 MB ) Movie 1 Vanishing ball trick pro-illusion conditionMovie 2 Vanishing ball trick anti-illusion conditionxDownload (.85 MB ) Movie 2 Vanishing ball trick anti-illusion condition


title: A Dual-Broadband MIMO Antenna System for GSM/UMTS/LTE and WLAN Handsets

abstract: A novel dual-broadband multiple-input-multiple-output (MIMO) antenna system is developed. The MIMO antenna system consists of two dual-broadband antenna elements, each of which comprises two opened loops: an outer loop and an inner loop. The opened outer loop acts as a half-wave dipole and is excited by electromagnetic coupling from the inner loop, leading to a broadband performance for the lower band. The opened inner loop serves as two monopoles. A combination of the two monopoles and the higher modes from the outer loop results in a broadband performance for the upper band. The bandwidths (return loss >;10 dB) achieved for the dual-broadband antenna element are 1.5-2.8 GHz (~ 60%) for the lower band and 4.7-8.5 GHz (~ 58\%) for the upper band. Two U-shaped slots are introduced to reduce the coupling between the two dual-broadband antenna elements. The isolation achieved is higher than 15 dB in the lower band and 20 dB in the upper band, leading to an envelope correlation coefficient of less than 0.01. The dual-broadband MIMO antenna system has a compact volume of 50×17×0.8 mm3, suitable for GSM/UMTS/LTE and WLAN communication handsets.


title: Compact Diplexer Using Slotline Stepped Impedance Resonator

abstract: In this letter, a compact diplexer is implemented by using slotline hairpin stepped impedance resonator (SIR) in ground plane. Two sets of slotline SIR with different dimensions are designed to resonate at different frequencies for the diplexer operation. Also, the SIR can provide a degree of freedom to improve the out-of-band rejection by properly tuning its geometrical parameter ratio. Besides, microstrip-slotline transition is used to split the signal to two channels without extra matching circuits and thereby economizes the circuit size. Finally, good agreement between measurement and simulation is achieved.


title: Architecture Optimization Based on Incremental Approach for Airplane Digital Distributed Flight Control System

abstract: Over the years, technical improvements, such as digital computer and digital communication, are drastically changing avionics architecture designs. Airplane performance and business pressures related to cost have been the main drivers to change the flight control systems (FCS) from mechanical to digital fly-by-wire designs. In this context, there exists a great motivation to change current FCS architecture to ldquomorerdquo distributed architectures, better optimized in terms of hardware redundancy for future generation aircraft. Analysis of existing FCS architectures of Airbus and Boeing airplanes, as well as future requirements (eco-efficiency), drive us to introduce a brief overview of an incremental methodology of architectural design process based on progressive requirements injection and distribution of systempsilas functionality.


title: Behavioral and physiological responses to anabolic-androgenic steroids

abstract: Anabolic-androgenic steroids (AAS) are synthetic derivatives of testosterone originally designed for therapeutic uses to provide enhanced anabolic potency with negligible androgenic effects. Although AAS continue to be used clinically today, the medical benefits of low therapeutic doses of AAS stand in sharp contrast to the potential health risks associated with the excessive doses self-administered not only by elite athletes and body builders, but by a growing number of recreational users, including adolescent boys and girls. The deleterious effects of AAS on peripheral organs and the incidence of altered behaviors in AAS abusers have been well documented in a number of excellent current reviews for clinical populations. However, a comparable synthesis of nonclinical studies has not been made. Our purpose in this review is to summarize the literature for animal models of the effects of supraphysiological doses of AAS (e.g. those that mimic human abuse regimes) on behaviors and on the neural circuitry for these behaviors. In particular, we have focused on studies in rodents that have examined how AAS alter aggression, sexual behaviors, anxiety, reward, learning, and locomotion and how AAS alter the expression and function of neurotransmitter systems and other signaling molecules that underlie these behaviors.


title: Compact Eight-Band Frequency Reconfigurable Antenna for LTE/WWAN Tablet Computer Applications

abstract: A novel eight-band LTE/WWAN frequency reconfigurable antenna for tablet computer applications is proposed in this communication. With a small dimension of 40 × 12 × 4 mm3, the proposed antenna comprises a loop feeding strip and a shorting strip in which a single-pole four-throw RF switch is embedded. The RF switch is used to change the resonant modes of lower band among four different working states, so that the antenna can provide a multiband operation of LTE700/GSM850 /900/1800/1900/UMTS2100/LTE2300/2500 with return loss better than 6 dB. Reasonably good radiating efficiency and antenna gain are also achieved for the practical tablet computer.


title: Convolutional Neural Networks for human activity recognition using mobile sensors

abstract: A variety of real-life mobile sensing applications are becoming available, especially in the life-logging, fitness tracking and health monitoring domains. These applications use mobile sensors embedded in smart phones to recognize human activities in order to get a better understanding of human behavior. While progress has been made, human activity recognition remains a challenging task. This is partly due to the broad range of human activities as well as the rich variation in how a given activity can be performed. Using features that clearly separate between activities is crucial. In this paper, we propose an approach to automatically extract discriminative features for activity recognition. Specifically, we develop a method based on Convolutional Neural Networks (CNN), which can capture local dependency and scale invariance of a signal as it has been shown in speech recognition and image recognition domains. In addition, a modified weight sharing technique, called partial weight sharing, is proposed and applied to accelerometer signals to get further improvements. The experimental results on three public datasets, Skoda (assembly line activities), Opportunity (activities in kitchen), Actitracker (jogging, walking, etc.), indicate that our novel CNN-based approach is practical and achieves higher accuracy than existing state-of-the-art methods.


title: An SDN-enhanced load-balancing technique in the cloud system

abstract: The vast majority of Web services and sites are hosted in various kinds of cloud services, and ordering some level of quality of service (QoS) in such systems requires effective load-balancing policies that choose among multiple clouds. Recently, software-defined networking (SDN) is one of the most promising solutions for load balancing in cloud data center. SDN is characterized by its two distinguished features, including decoupling the control plane from the data plane and providing programmability for network application development. By using these technologies, SDN and cloud computing can improve cloud reliability, manageability, scalability and controllability. SDN-based cloud is a new type cloud in which SDN technology is used to acquire control on network infrastructure and to provide networking-as-a-service (NaaS) in cloud computing environments. In this paper, we introduce an SDN-enhanced Inter cloud Manager (S-ICM) that allocates network flows in the cloud environment. S-ICM consists of two main parts, monitoring and decision making. For monitoring, S-ICM uses SDN control message that observes and collects data, and decision-making is based on the measured network delay of packets. Measurements are used to compare S-ICM with a round robin (RR) allocation of jobs between clouds which spreads the workload equitably, and with a honeybee foraging algorithm (HFA). We see that S-ICM is better at avoiding system saturation than HFA and RR under heavy load formula using RR job scheduler. Measurements are also used to evaluate whether a simple queueing formula can be used to predict system performance for several clouds being operated under an RR scheduling policy, and show the validity of the theoretical approximation.


title: A data mining approach for fault diagnosis: An application of anomaly detection algorithm

abstract: Rolling-element bearing failures are the most frequent problems in rotating machinery, which can be catastrophic and cause major downtime. Hence, providing advance failure warning and precise fault detection in such components are pivotal and cost-effective. The vast majority of past research has focused on signal processing and spectral analysis for fault diagnostics in rotating components. In this study, a data mining approach using a machine learning technique called anomaly detection (AD) is presented. This method employs classification techniques to discriminate between defect examples. Two features, kurtosis and Non-Gaussianity Score (NGS), are extracted to develop anomaly detection algorithms. The performance of the developed algorithms was examined through real data from a test to failure bearing. Finally, the application of anomaly detection is compared with one of the popular methods called Support Vector Machine (SVM) to investigate the sensitivity and accuracy of this approach and its ability to detect the anomalies in early stages.


Change Management as Designed Adoption


title: Intrinsic motivation in a new light

abstract: Economics is solidly based on the workings of incentives. Extra rewards will increase and extra costs will decrease the frequency of a particular type of behavior. In sociology, Weber had pointed to a kind of behavior that was presumably not steered by incentives: value-rational behavior, found in religions and strong reasoned convictions. Because of an increasing rationalization of the world, Weber saw this kind of value-rational behavior slowly displaced by what he called goal-rational (zweckrational) behavior. Though often a popular topic of discussion, the displacement of value-rational by goal-rational behavior had never led to a viable research program within sociology. Instead, it was psychologists who had discovered a related issue of ‘intrinsic’ versus ‘extrinsic’ motivation. For intrinsically motivated behavior, there is no apparent reward but the behavior itself. The psychologists had been able to forge a booming program over many years. On this basis, Frey (1997) reintroduced the issue back into the social sciences which, in turn, drew attention to the work of these psychologists. Within psychology, the research by Deci (1971) and Lepper et al. (1973) was a pioneering stab at behaviorist theory by pointing to situations in which rewards decrease rather than increase the frequency of behavior. This work had spawned a thriving research paradigm (Deci and Ryan 1985)1 and a host of studies. Briefly, the findings converge to the following. Expected tangible rewards tend to reduce intrinsic motivation whereas praise and other posi-


title: NON-CONTACT MEASUREMENT OF SCULPTURED SURFACE OF ROTATION

abstract: A method for measuring the sculptured surface of rotation by using coordinate measuring machine (CMM) and rotary table is proposed. The measurement is realized during the continuous rotation of the workpiece mounted on the rotary table while the probe moves along the generatrix of the surface step by step. This method possesses lots of advantages such as simplicity of probe motion, high reliability and efficiency. Some key techniques including calibration of the effective radius of the probing system, determination of the position of axis of rotation, auto-centering of the workpiece, data processing algorithm, are discussed. Approaches for determining the coordinates on measured surface, establishing workpiece coordinate system and surface fitting are presented in detail. The method can be used with contact or non-contact probes. Some fragile ceramic and plaster parts are measured by using the system consisting of a CMM, rotary table, motorized head and non-contact laser triangulation probe. The measuring uncertainty is about 0.02 mm which meets the general requirement in most cases.


title: A closed-loop design to enhance weight stability of memristor based neural network chips

abstract: Compared with the algorithm optimizations, brain-inspired neural network chips aim to fundamentally change the computer architecture and therefore enhance the computation capability and performance in advanced data processing. In recent years, memristor technology has been investigated in developing high-speed and large-capacity neural network chips. However, it has been observed that memristance values that represent the well-trained network weights can be disturbed by electrical or thermal perturbations. It severely degrades overall system reliability and emerges as a major design challenge. In this work, we systematically analyze the impacts of low-voltage induced memristance drift upon weight disturbance after times of recall operations. A closed-loop design by introducing a real-time feedback controller is proposed to enhance the weight stability of memristor based neural network chips. By mimicking the training process, the controller adaptively compensates the memristance deviation, according to the relation of the input data and recall output. In view of tiny disturbance per access, we integrate the memristance compensation into regular recall operation to avoid the execution speed degradation. Our simulations based on the implementation of representative single-layer (two-layer) network show that the proposed closed-loop design can prolong the service time of memristor-based neural network chip by 14.85x (14.94x), without reducing computational speed. Extra circuitry of the feedback controller induces a negligible overhead about 1.16% on overall power consumption.


Multi-View Subspace Clustering via Relaxed L 1 -Norm of Tensor Multi-Rank.


title: Isoperimetric graph partitioning for image segmentation

abstract: Spectral graph partitioning provides a powerful approach to image segmentation. We introduce an alternate idea that finds partitions with a small isoperimetric constant, requiring solution to a linear system rather than an eigenvector problem. This approach produces the high quality segmentations of spectral methods, but with improved speed and stability.


title: Next Generation 5G Wireless Networks: A Comprehensive Survey

abstract: The vision of next generation 5G wireless communications lies in providing very high data rates (typically of Gbps order), extremely low latency, manifold increase in base station capacity, and significant improvement in users’ perceived quality of service (QoS), compared to current 4G LTE networks. Ever increasing proliferation of smart devices, introduction of new emerging multimedia applications, together with an exponential rise in wireless data (multimedia) demand and usage is already creating a significant burden on existing cellular networks. 5G wireless systems, with improved data rates, capacity, latency, and QoS are expected to be the panacea of most of the current cellular networks’ problems. In this survey, we make an exhaustive review of wireless evolution toward 5G networks. We first discuss the new architectural changes associated with the radio access network (RAN) design, including air interfaces, smart antennas, cloud and heterogeneous RAN. Subsequently, we make an in-depth survey of underlying novel mm-wave physical layer technologies, encompassing new channel model estimation, directional antenna design, beamforming algorithms, and massive MIMO technologies. Next, the details of MAC layer protocols and multiplexing schemes needed to efficiently support this new physical layer are discussed. We also look into the killer applications, considered as the major driving force behind 5G. In order to understand the improved user experience, we provide highlights of new QoS, QoE, and SON features associated with the 5G evolution. For alleviating the increased network energy consumption and operating expenditure, we make a detail review on energy awareness and cost efficiency. As understanding the current status of 5G implementation is important for its eventual commercialization, we also discuss relevant field trials, drive tests, and simulation experiments. Finally, we point out major existing research issues and identify possible future research directions.


title: The Impact of Traffic Information on Drivers’ Day-to-Day Route Choice Behavior

abstract: The influence of different traffic information on drivers’ day-to-day route choice behavior based on microscopic simulation is investigated. Firstly, it is assumed that drivers select routes in terms of drivers’ perceived travel time on routes. Consequently, the route choice model is developed. Then, updating the drivers’ perceived travel time on routes is modeled in three kinds of traffic information conditions respectively, which no information, releasing historical information and releasing predictive information. Finally, by setting a simple road network with two parallel paths, the drivers’ day-to-day route choice is simulated. The statistical characteristics of drivers’ behavior are computed. Considering user equilibrium as a yardstick, the effects of three kinds of traffic information are compared. The results show that the impacts of traffic information on drivers are related to the random level of driver’s route choice and reliance on the information. In addition, the road network cannot reach user equilibrium in three kinds of information. This research results can provide a useful reference for the application of traffic information system.


title: Multiphysics modeling approach for micro electro-thermo-mechanical actuator: Failure mechanisms coupled analysis

abstract: Abstract The lifetime of micro electro–thermo–mechanical actuators with complex electro–thermo–mechanical coupling mechanisms can be decreased significantly due to unexpected failure events. Even more serious is the fact that various failures are tightly coupled due to micro-size and multi-physics effects. Interrelation between performance and potential failures should be established to predict reliability of actuators and improve their design. Thus, a multiphysics modeling approach is proposed to evaluate such interactive effects of failure mechanisms on actuators, where potential failures are pre-analyzed via FMMEA (Failure Modes, Mechanisms, and Effects Analysis) tool for guiding the electro–thermo–mechanical-reliability modeling process. Peak values of temperature, thermal stresses/strains and tip deflection are estimated as indicators for various failure modes and factors (e.g. residual stresses, thermal fatigue, electrical overstress, plastic deformation and parameter variations). Compared with analytical solutions and experimental data, the obtained simulation results were found suitable for coupled performance and reliability analysis of micro actuators and assessment of their design.


title: Orbital fat preservation in lower-lid blepharoplasty.

abstract: In the last 4 years, there have been a few reports concerning unconventional management of the fat posterior to the orbital septum (intraorbital) as well as periorbital fat located superficial to the orbital septum in a plane both superficial and deep to the orbicularis oculi muscle. With reference to intraorbital fat, some reports suggest that this fat be preserved and mobilized to other areas to recontour the lower lid, while other authors suggest repositioning the fat within the bony orbit. To address these new concepts, a discussion of the theories behind these approaches is needed.


License agreements on the Internet


title: The Depression Anxiety Stress Scales (DASS): Normative data and latent structure in a large non-clinical sample

abstract: Objectives. To provide UK normative data for the Depression Anxiety and Stress Scale (DASS) and test its convergent, discriminant and construct validity. Design. Cross-sectional, correlational and confirmatory factor analysis (CFA). Methods. The DASS was administered to a non-clinical sample, broadly representative of the general adult UK population ( N = 1,771) in terms of demographic variables. Competing models of the latent structure of the DASS were derived from theoretical and empirical sources and evaluated using confirmatory factor analysis. Correlational analysis was used to determine the influence of demographic variables on DASS scores. The convergent and discriminant validity of the measure was examined through correlating the measure with two other measures of depression and anxiety (the HADS and the sAD), and a measure of positive and negative affectivity (the PANAS). Results. The best fitting model (CFI = .93) of the latent structure of the DASS consisted of three correlated factors corresponding to the depression, anxiety and stress scales with correlated error permitted between items comprising the DASS subscales. Demographic variables had only very modest influences on DASS scores. The reliability of the DASS was excellent, and the measure possessed adequate convergent and discriminant validity Conclusions. The DASS is a reliable and valid measure of the constructs it was intended to assess. The utility of this measure for UK clinicians is enhanced by the provision of large sample normative data. The Depression Anxiety Stress Scale (DASS) is a 42-item self-report measure of anxiety, depression and stress developed by Lovibond and Lovibond (1995) which is increasingly used in diverse settings. Its popularity is partly attributable to the fact


title: A Meta-learning Method Based on Temporal Difference Error

abstract: In general, meta-parameters in a reinforcement learning system, such as a learning rate and a discount rate, are empirically determined and fixed during learning. When an external environment is therefore changed, the sytem cannot adapt itself to the variation. Meanwhile, it is suggested that the biological brain might conduct reinforcement learning and adapt itself to the external environment by controlling neuromodulators corresponding to the meta-parameters. In the present paper, based on the above suggestion, a method to adjust meta-parameters using a temporal difference (TD) error is proposed. Through various computer simulations using a maze search problem and an inverted pendulum control problem, it is verified that the proposed method could appropriately adjust meta-parameters according to the variation of the external environment.


title: Sketching Designs Using the Five Design-Sheet Methodology

abstract: Sketching designs has been shown to be a useful way of planning and considering alternative solutions. The use of lo-fidelity prototyping, especially paper-based sketching, can save time, money and converge to better solutions more quickly. However, this design process is often viewed to be too informal. Consequently users do not know how to manage their thoughts and ideas (to first think divergently, to then finally converge on a suitable solution). We present the Five Design Sheet (FdS) methodology. The methodology enables users to create information visualization interfaces through lo-fidelity methods. Users sketch and plan their ideas, helping them express different possibilities, think through these ideas to consider their potential effectiveness as solutions to the task (sheet 1); they create three principle designs (sheets 2,3 and 4); before converging on a final realization design that can then be implemented (sheet 5). In this article, we present (i) a review of the use of sketching as a planning method for visualization and the benefits of sketching, (ii) a detailed description of the Five Design Sheet (FdS) methodology, and (iii) an evaluation of the FdS using the System Usability Scale, along with a case-study of its use in industry and experience of its use in teaching.


title: An online air pollution forecasting system using neural networks

abstract: Abstract In this work, an online air pollution forecasting system for Greater Istanbul Area is developed. The system predicts three air pollution indicator (SO 2 , PM 10 and CO) levels for the next three days (+ 1, + 2, and + 3 days) using neural networks. AirPolTool, a user-friendly website ( http://airpol.fatih.edu.tr ), publishes + 1, + 2, and + 3 days predictions of air pollutants updated twice a day. Experiments presented in this paper show that quite accurate predictions of air pollutant indicator levels are possible with a simple neural network. It is shown that further optimizations of the model can be achieved using different input parameters and different experimental setups. Firstly, + 1, + 2, and + 3 days' pollution levels are predicted independently using same training data, then + 2 and + 3 days are predicted cumulatively using previously days predicted values. Better prediction results are obtained in the cumulative method. Secondly, the size of training data base used in the model is optimized. The best modeling performance with minimum error rate is achieved using 3–15 past days in the training data set. Finally, the effect of the day of week as an input parameter is investigated. Better forecasts with higher accuracy are observed using the day of week as an input parameter.


title: Layout impact on the performance of a locally strained PMOSFET

abstract: We present a study on the layout dependence of a SiGe S/D PMOSFET technology. While 65% increase in drive current is obtained for 45nm gate length transistors with large active areas, measurements and simulations show that this improvement may be seriously degraded when transistor dimensions, such as the source-drain length (L/sub s/d/) and the device width are further scaled. TDDB and NBTI measurements show that the oxide reliability is not degraded for this technology.


title: Multi-View Clustering and Feature Learning via Structured Sparsity

abstract: Combining information from various data sources has become an important research topic in machine learning with many scientific applications. Most previous studies employ kernels or graphs to integrate different types of features, which routinely assume one weight for one type of features. However, for many problems, the importance of features in one source to an individual cluster of data can be varied, which makes the previous approaches ineffective. In this paper, we propose a novel multi-view learning model to integrate all features and learn the weight for every feature with respect to each cluster individually via new joint structured sparsity-inducing norms. The proposed multi-view learning framework allows us not only to perform clustering tasks, but also to deal with classification tasks by an extension when the labeling knowledge is available. A new efficient algorithm is derived to solve the formulated objective with rigorous theoretical proof on its convergence. We applied our new data fusion method to five broadly used multi-view data sets for both clustering and classification. In all experimental results, our method clearly outperforms other related state-of-the-art methods.


title: Vacuum gate dielectric gate-all-around nanowire for hot carrier injection and bias temperature instability free transistor

abstract: A gate-all-around (GAA) field effect transistor with vacuum gate dielectric is presented as a structure free from hot-carrier injection and bias temperature instability. A conventional GAA fabrication process is used along with selective removal of the sacrificial gate oxide as an extra process step. The lowered dielectric constant in vacuum gate dielectric can be compensated by the nature of the nanowire and physical oxide thickness reduction. As the nanowire channel is fully surrounded by empty space, reliability issues relevant to the gate dielectric can be completely cleared.


title: Modeling problem difficulty and expertise in stackoverflow

abstract: Supporting expert communities is becoming a 'must-have' capability whenever users are helping each other solve problems. Examples of these expert communities abound in the form of enthusiast communities, both inside and outside of organizations. In order to achieve success, these systems have to connect several different actors. In this paper we aim to inform the design of these Hybrid Intelligence Systems through the investigation of StackOverflow. Our focus in this paper is to develop indicators for hard problems and experts. The long-term goal of our study is to examine how complex problems are handled and dispatched across multiple experts. We outline implications for modeling these attributes and how they might inform better design in the future.


title: One Scan Connected Component Labeling Technique

abstract: This paper, presents a new component labeling algorithm which is based on scanning and labeling the objects in a single scan. The algorithm has the ability to test the four and eight connected branches of the object. This algorithm, which is fast and requires low memory allocation, can also process an image that contains large numbers of objects. The algorithm is used to scan the image from left to right and from top to bottom to find the unlabeled objects. A comparison analysis is performed with other component labeling algorithms. Our algorithm has shown an outstanding performance with respect to the processing time. A practical application with computer based mammography is also included.


title: It’s All Fun and Games until Someone Annotates: Video Games with a Purpose for Linguistic Annotation

abstract: Annotated data is prerequisite for many NLP applications. Acquiring large-scale annotated corpora is a major bottleneck, requiring significant time and resources. Recent work has proposed turning annotation into a game to increase its appeal and lower its cost; however, current games are largely text-based and closely resemble traditional annotation tasks. We propose a new linguistic annotation paradigm that produces annotations from playing graphical video games. The effectiveness of this design is demonstrated using two video games: one to create a mapping from WordNet senses to images, and a second game that performs Word Sense Disambiguation. Both games produce accurate results. The first game yields annotation quality equal to that of experts and a cost reduction of 73% over equivalent crowdsourcing; the second game provides a 16.3% improvement in accuracy over current state-of-the-art sense disambiguation games with WordNet.


title: Spoofing 2D Face Detection: Machines See People Who Aren't There

abstract: Machine learning is increasingly used to make sense of the physical world yet may suffer from adversarial manipulation. We examine the Viola-Jones 2D face detection algorithm to study whether images can be created that humans do not notice as faces yet the algorithm detects as faces. We show that it is possible to construct images that Viola-Jones recognizes as containing faces yet no human would consider a face. Moreover, we show that it is possible to construct images that fool facial detection even when they are printed and then photographed.


title: A surgical protocol for bicondylar four-quadrant tibial plateau fractures

abstract: PURPOSE ::: Bicondylar tibial plateau fractures involving four articular quadrants are severe and complex injuries, and they remain a challenging problem in orthopaedic trauma. The aim of this study was to introduce a new treatment protocol with dual-incision and multi-plate fixation in the floating supine patient position as well as to report the preliminary clinical results. ::: ::: ::: METHODS ::: From January 2006 to December 2011, 16 consecutive patients with closed bicondylar four-quadrant tibial plateau fractures (Schatzker type VI, OTA/AO 41C2/3) were treated with posteromedial inverted L-shaped and anterolateral incisions. With the posteromedial approach, three quadrants (posteromedial, anteromedial and posterolateral) can be exposed, reduced and fixed with multiple small antiglide plates and short screws in an enclosure pattern. With the anterolateral approach, after articular elevation and bone substitute grafting, a strong locking plate with long screws to the medial cortex is used to raft-buttress the reduced lateral plateau fracture, hold the entire reconstructed tibial condyles together, and contact the condyles with the tibial shaft. All patients were encouraged to exercise knee motion at an early stage. The outcome was evaluated clinically and radiologically after a minimum two-year follow-up. ::: ::: ::: RESULTS ::: The average operation time was 98 ± 26 minutes (range 70-128) and the average duration of hospitalization was 29 ± 8.6 days (range 20-41). Three cases used five plates, nine cases used four plates, and four cases used three plates. All patients were followed for a mean of 28.7 ± 6.1 months (range 26-38). Fifteen incisions healed initially, while one patient developed a medial wound dehiscence and was successfully managed by debridement. All patients achieved radiological fracture union after an average of 20.2 weeks. At the two-year follow up, the average knee range of motion (ROM) was 98° ± 13.7 (range 88-125°), with a Hospital for Special Surgery (HSS) knee score of 87.7 ± 10.3 (range 75-95), and SMFA score of 21.3 ± 8.6 (range 12-33). ::: ::: ::: CONCLUSION ::: For bicondylar four-quadrant tibial plateau fractures, the treatment protocol of multiple medial-posterior small plates combined with a lateral strong locking plate through dual incisions can provide stable fracture fixation to allow for early stage rehabilitation. Good clinical outcomes can be anticipated.


title: 4-dimensional local spatio-temporal features for human activity recognition

abstract: Recognizing human activities from common color image sequences faces many challenges, such as complex backgrounds, camera motion, and illumination changes. In this paper, we propose a new 4-dimensional (4D) local spatio-temporal feature that combines both intensity and depth information. The feature detector applies separate filters along the 3D spatial dimensions and the 1D temporal dimension to detect a feature point. The feature descriptor then computes and concatenates the intensity and depth gradients within a 4D hyper cuboid, which is centered at the detected feature point, as a feature. For recognizing human activities, Latent Dirichlet Allocation with Gibbs sampling is used as the classifier. Experiments are performed on a newly created database that contains six human activities, each with 33 samples with complex variations. Experimental results demonstrate the promising performance of the proposed features for the task of human activity recognition.


title: The application of digital image processing in grading of begonia pot plants

abstract: Digital image processing (DIP) is a potential tool for measuring and classifying pot plants in various growth stages in an automatic, objective and consistent way, with a high capacity and low labour input. In this study, features of begonia cuttings which could be relevant for grading were analysed. Images of unrooted and rooted Begonia elatior [B. hiemalis] cuttings were acquired and analysed with DIP. The various parts of the cuttings were identified and measured using knowledge based image processing. These measurements were shown to be consistent and to be well correlated with the features measured in conventional ways. Experts graded the rooted cuttings into 3 classes: small, medium and large. The effect of grading unrooted cuttings and growing them with similar-sized cuttings was still apparent 4 weeks later - the rooted cuttings in graded units were more uniform than those in random units. Two models were constructed to determine the quality of the rooted cuttings based on DIP measurements, one based on multiple linear regression and one based on a neural network. Both were able to grade at least 75% of the rooted cuttings in the same class as the expert. The neural network-based model performed slightly (5%) better, especially for the classification of small and large plants. The lack of objective quality criteria is a major obstacle for the development of grading models for pot plants.


title: Wheelchair control using speech recognition

abstract: In this paper a speech controlled wheelchair for physically disabled person is developed which can be used for different languages. A speech recognition system using Mel Frequency Cepstral Coefficients (MFCC) was developed in the laptop with an interactive and user friendly GUI and the normal wheelchair was converted to an electric wheelchair by applying a gear mechanism to the wheels with DC motor attached to the gear. An Arduino Uno board is used to acquire the control signal from MATLAB and give it to relay driver circuit which intern results in the motion of wheelchair in desired direction. The speech inputs such as forward, back, left, right and stop acquired from the user and then the motion of the wheelchair made according to the respective command. The performance of MFCC in presence of noise and for different languages was studied to know the reliability of the algorithm in different condition.


title: Pattern of antenna within radome: Simulation vs. measurements

abstract: We performed a comparison between the radiation pattern of a simulated antenna and its actual field measurement in the presence of a radome. The simulated main beam properties fit identically within the accuracy of the measurements. The side lobes levels are determined in the region of ±30° to within the accuracy of <3dB in intensity.


A Deep Reinforcement Learning Based Multimodal Coaching Model (DCM) for Slot Filling in Spoken Language Understanding(SLU)


title: An architecture of optimised SIFT feature detection for an FPGA implementation of an image matcher

abstract: This paper has proposed an architecture of optimised SIFT (Scale Invariant Feature Transform) feature detection for an FPGA implementation of an image matcher. In order for SIFT based image matcher to be implemented on an FPGA efficiently, in terms of speed and hardware resource usage, the original SIFT algorithm has been significantly optimised in the following aspects: 1) Upsampling has been replaced with downsampling to save the interpolation operation. 2) Only four scales with two octaves are needed for our image matcher with moderate degradation of matching performance. 3) The total dimension of the feature descriptor has been reduced to 72 from 128 of the original SIFT, which leads to significantly simplify the image matching operation. With the optimisation above, the proposed FPGA implementation is able to detect the features of a typical image of 640×480 pixels within 31 milliseconds. Therefore, compared with the existing SIFT FPGA implementation, which requires 33 milliseconds for an image of 320×240 pixels, a significant improvement has been achieved for our proposed architecture.


title: Locally Adaptive Metric Nearest Neighbor Classification

abstract: Nearest-neighbor classification assumes locally constant class conditional probabilities. This assumption becomes invalid in high dimensions with finite samples due to the curse of dimensionality. Severe bias can be introduced under these conditions when using the nearest-neighbor rule. We propose a locally adaptive nearest-neighbor classification method to try to minimize bias. We use a chi-squared distance analysis to compute a flexible metric for producing neighborhoods that are highly adaptive to query locations. Neighborhoods are elongated along less relevant feature dimensions and constricted along most influential ones. As a result, the class conditional probabilities are smoother in the modified neighborhoods, whereby better classification performance can be achieved. The efficacy of our method is validated and compared against other techniques using both simulated and real-world data.


title: Spectrum and Energy-Efficient Beamspace MIMO-NOMA for Millimeter-Wave Communications Using Lens Antenna Array

abstract: The recent concept of beamspace multiple input multiple output (MIMO) can significantly reduce the number of required radio-frequency (RF) chains in millimeter-wave (mmWave) massive MIMO systems without obvious performance loss. However, the fundamental limit of existing beamspace MIMO is that, the number of supported users cannot be larger than the number of RF chains at the same time-frequency resources. To break this fundamental limit, in this paper we propose a new spectrum and energy efficient mmWave transmission scheme that integrates the concept of non-orthogonal multiple access (NOMA) with beamspace MIMO, i.e., beamspace MIMO-NOMA. By using NOMA in beamspace MIMO systems, the number of supported users can be larger than the number of RF chains at the same time-frequency resources. Particularly, the achievable sum rate of the proposed beamspace MIMO-NOMA in a typical mmWave channel model is analyzed, which shows an obvious performance gain compared with the existing beamspace MIMO. Then, a precoding scheme based on the principle of zero-forcing (ZF) is designed to reduce the inter-beam interferences in the beamspace MIMO-NOMA system. Furthermore, to maximize the achievable sum rate, a dynamic power allocation is proposed by solving the joint power optimization problem, which not only includes the intra-beam power optimization, but also considers the inter-beam power optimization. Finally, an iterative optimization algorithm with low complexity is developed to realize the dynamic power allocation. Simulation results show that the proposed beamspace MIMO-NOMA can achieve higher spectrum and energy efficiency compared with existing beamspace MIMO.


title: Automated Correction for Syntax Errors in Programming Assignments using Recurrent Neural Networks

abstract: We present a method for automatically generating repair feedback for syntax errors for introductory programming problems. Syntax errors constitute one of the largest classes of errors (34%) in our dataset of student submissions obtained from a MOOC course on edX. The previous techniques for generating automated feed- back on programming assignments have focused on functional correctness and style considerations of student programs. These techniques analyze the program AST of the program and then perform some dynamic and symbolic analyses to compute repair feedback. Unfortunately, it is not possible to generate ASTs for student pro- grams with syntax errors and therefore the previous feedback techniques are not applicable in repairing syntax errors. ::: We present a technique for providing feedback on syntax errors that uses Recurrent neural networks (RNNs) to model syntactically valid token sequences. Our approach is inspired from the recent work on learning language models from Big Code (large code corpus). For a given programming assignment, we first learn an RNN to model all valid token sequences using the set of syntactically correct student submissions. Then, for a student submission with syntax errors, we query the learnt RNN model with the prefix to- ken sequence to predict token sequences that can fix the error by either replacing or inserting the predicted token sequence at the error location. We evaluate our technique on over 14, 000 student submissions with syntax errors. Our technique can completely re- pair 31.69% (4501/14203) of submissions with syntax errors and in addition partially correct 6.39% (908/14203) of the submissions.


title: Cuckoo Search: Recent Advances and Applications

abstract: Cuckoo search (CS) is a relatively new algorithm, developed by Yang and Deb in 2009, and the same has been found to be efficient in solving global optimization problems. In this paper, we review the fundamental ideas of cuckoo search and the latest developments as well as its applications. We analyze the algorithm and gain insight into its search mechanisms and find out why it is efficient. We also discuss the essence of algorithms and its link to self-organizing systems, and finally, we propose some important topics for further research.


title: Multi-drive feature association for automated map generation using low-cost sensor data

abstract: In this paper, we present an approach targeting the automated road map generation for autonomously driving vehicles using low-cost GPS sensor data in a multi-drive setup. Multiple drives with deployed commodity smartphone and stereo camera system are recorded as input data. To overcome the high position uncertainties of the GPS sensor, the GPS trajectory is fused with ego-motion estimates of the vehicle computed by visual odometry. Landmarks are extracted from the recorded imagery data and fused over all recorded drives. The resulting road map consists of a simple, parametric representation of globally referenced lane markings with low storage impact. The challenging aspect in this work is the feature association between multiple drives. Different characteristics of dashed center lines are exploited for this purpose to handle the low precision of the sensor data. The resulting association information is the building block for graph-based SLAM to optimize vehicle poses and landmarks simultaneously. The approach is finally evaluated on real world data comparing the low-precision sensor data with high-precision sensor data as ground-truth.


title: Design of a new Dual Rotor Radial Flux BLDC motor with Halbach array magnets for an electric vehicle

abstract: This paper presents a novel topology of dual rotor radial flux-BLDC motor with Halbach array magnets as an efficient solution for drives. Energy efficient motors are the need of day for electric vehicles, industrial and consumer products. Especially this concept is arrived to cater for the requirements of electrical vehicle. Due to advent of high permeability, high saturation magnetic materials, high energy product permanent magnets, BLDC motors are suitable candidate for electric vehicle propulsion. Traditionally, outer rotor BLDC motor is a wide choice which is being used in commercially available electric vehicles. New topologies of BLDC motors are being proposed by research activities which aim at improvement of torque density of motors. Dual rotor brushless DC machine (DR-BLDC) is one of the consequences of such research and claimed to have very high torque density and power density. In this paper, a new topology of Dual Rotor Halbach (DR-HB) BLDC motor is proposed which is aimed to increase the torque density of motor. The complete design of DR-HB BLDC motor is carried out and compared with DR-BLDC of the same specifications and volume. The obtained performance characteristics of the proposed design are very much encouraging in terms of torque density.


Building a Question-Answering Chatbot using Forum Data in the Semantic Space


title: Evaluation of different substrates for inkjet printing of rasagiline mesylate.

abstract: The main goal of the present study was to evaluate applicability of the different model substrates, namely orodispersible films (ODFs), porous copy paper sheets, and water impermeable transparency films (TFs) in preparation of the inkjet-printed drug-delivery systems. Rasagiline mesylate (RM) was used as a low-dose active pharmaceutical ingredient (API). Flexible doses of the drug in a single unit were obtained by printing several subsequent layers on top of the already printed ones, using an off-the-shelf consumer thermal inkjet (TIJ) printer. The produced drug-delivery systems were subjected to microscopic and chemical analysis together with solid-state characterization and content uniformity studies. The results revealed that RM recrystallized on the surface of ODFs and TFs, and the printed crystals were arranged in lines. No drug crystals were detected after printing on the surface of the copy paper due to absorption of the ink into the matrix of the substrate. The best linear correlation between the dose of the drug and the number of the printing layers was obtained for the porous copy paper. The other two substrates showed poor linearity and unacceptable standard deviations of the printed drug substance due to limited absorption of the API ink into the carrier. The shear stress between the substrate, the print head, and the paper feeding rollers caused smearing of the drug that had been surface-deposited during the earlier printing cycles. In conclusion, this study indicates that the edible substrates with absorption properties similar to copy paper are favorable for successful preparation of drug-delivery systems by TIJ printers.


title: Active Cells: A Computing Model for Rapid Construction of On-Chip Multi-core Systems

abstract: We present a novel computing model that allows to conveniently construct multi-core systems with different computer architectures, ranging from homogeneous many-core architectures to networks of heterogeneous general purpose processor cores or signal processing engines. A hardware library implemented on Field Programmable Gate Arrays (FPGAs) and a compiler provide a platform for prototyping and constructing distributed systems on a chip. A number of case studies have been carried out to prove the concept conveyed by the computing model.


title: A New Wideband Circularly Polarized Stair-Shaped Dielectric Resonator Antenna

abstract: A circularly polarized stair-shaped dielectric resonator antenna (DRA) with a wideband circular polarization (CP) radiation is introduced for wireless local area network (WLAN) applications. This antenna is implemented by using two rectangular dielectric resonators (DRs), which are joined together to form a stair-shaped dielectric resonator antenna (DRA). In this structure, the excitation of multiple orthogonal modes leads to a wideband CP bandwidth. Measured results show that the proposed antenna has an axial ratio (AR) bandwidth of 22% (5.2–6.5 GHz), an impedance bandwidth of 37% (4.6–6.7 GHz), and a peak gain of 5.7 dB. In addition, the antenna radiation efficiency of more than 90% is achieved.


title: The Case for Energy-Proportional Computing

abstract: Energy-proportional designs would enable large energy savings in servers, potentially doubling their efficiency in real-life use. Achieving energy proportionality will require significant improvements in the energy usage profile of every system component, particularly the memory and disk subsystems.


title: Deep neural networks for automatic detection of screams and shouted speech in subway trains

abstract: Deep Neural Networks (DNNs) have recently become a popular technique for regression and classification problems. Their capacity to learn high-order correlations between input and output data proves to be very powerful for automatic speech recognition. In this paper we investigate the use of DNNs for automatic scream and shouted speech detection, within the framework of surveillance systems in public transportation. We recorded a database of sounds occurring in subway trains in real conditions of exploitation and used DNNs to classify the sounds into screams, shouts and other categories. We report encouraging results, given the difficulty of the task, especially when a high level of surrounding noise is present.


title: Guidelines for the performance of fusion procedures for degenerative disease of the lumbar spine. Part 7: intractable low-back pain without stenosis or spondylolisthesis

abstract: Standards. Lumbar fusion is recommended as a treatment for carefully selected patients with disabling lowback pain due to oneor two-level degenerative disease without stenosis or spondylolisthesis. Guidelines. There is insufficient evidence available to support a treatment guideline. Options. An intensive course of physical therapy and cognitive therapy is recommended as a treatment option for patients with low-back pain in whom conventional medical management has failed.


title: The Taste for Privacy: An Analysis of College Student Privacy Settings in an Online Social Network

abstract: The rapid growth of contemporary social network sites (SNSs) has coincided with an increasing concern over personal privacy. College students and adolescents routinely provide personal information on profiles that can be viewed by large numbers of unknown people and potentially used in harmful ways. SNSs like Facebook and MySpace allow users to control the privacy level of their profile, thus limiting access to this information. In this paper, we take the preference for privacy itself as our unit of analysis, and analyze the factors that are predictive of a student having a private versus public profile. Drawing upon a new social network dataset based on Facebook, we argue that privacy behavior is an upshot of both social influences and personal incentives. Students are more likely to have a private profile if their friends and roommates have them; women are more likely to have private profiles than are men; and having a private profile is associated with a higher level of online activity. Finally, students who have private versus public profiles are characterized by au nique set ofcultural preferences—of which the ‘‘taste for privacy’’ may be only as mall but integral part.


title: WaveFunctionCollapse is constraint solving in the wild

abstract: Maxim Gumin's WaveFunctionCollapse (WFC) algorithm is an example-driven image generation algorithm emerging from the craft practice of procedural content generation. In WFC, new images are generated in the style of given examples by ensuring every local window of the output occurs somewhere in the input. Operationally, WFC implements a non-backtracking, greedy search method. This paper examines WFC as an instance of constraint solving methods. We trace WFC's explosive influence on the technical artist community, explain its operation in terms of ideas from the constraint solving literature, and probe its strengths by means of a surrogate implementation using answer set programming.


title: The Viable System Model: Its Provenance, Development, Methodology and Pathology

abstract: It took the author 30 years to develop the Viable System Model, which sets out to explain how systems are viable-that is, capable of independent existence. He wanted to elucidate the laws of viability in order to facilitate the management task, and did so in a stream of papers and two (of his seven) books. Much misunderstanding about the V.S.M. and its use seems to exist; especially its methodological foundations have been largely forgotten, while its major results have hardly been noted. This paper reflects on the history, nature and present status of the V.S.M., without seeking once again to expound the model in detail or to demonstrate its validity. It does, however, provide a synopsis, present the methodology and confront some highly contentious issues about both the managerial and scientific paradigms.


title: Effects of Four Computer-Mediated Communications Channels on Trust Development

abstract: When virtual teams need to establish trust at a distance, it is advantageous for them to use rich media to communicate. We studied the emergence of trust in a social dilemma game in four different communication situations: face-to-face, video, audio, and text chat. All three of the richer conditions were significant improvements over text chat. Video and audio conferencing groups were nearly as good as face-to-face, but both did show some evidence of what we term delayed trust (slower progress toward full cooperation) and fragile trust (vulnerability to opportunistic behavior)


title: Image saliency by isocentric curvedness and color

abstract: In this paper we propose a novel computational method to infer visual saliency in images. The method is based on the idea that salient objects should have local characteristics that are different than the rest of the scene, being edges, color or shape. By using a novel operator, these characteristics are combined to infer global information. The obtained information is used as a weighting for the output of a segmentation algorithm so that the salient object in the scene can easily be distinguished from the background. The proposed approach is fast and it does not require any learning. The experimentation shows that the system can enhance interesting objects in images and it is able to correctly locate the same object annotated by humans with an F-measure of 85.61% when the object size is known, and 79.19% when the object size is unknown, improving the state of the art performance on a public dataset.


title: Casalendar: a temporal interface for automated homes

abstract: Smart homes with advanced building technologies can react to sensor triggers in a variety of preconfigured ways. These rules are usually only visible within designated configuration interfaces. For this reason inhabitants who are not actively involved in the configuration process can be taken by surprise by the effects of such rules, such as for example the unexpected automated actions of lights or shades. To provide these inhabitants with better means to understand their home, as well as to increase their motivation to actively engage with its configuration, we propose Casalendar, a visualization that integrates the status of smart home technologies into the familiar interface of a calendar. We present our design and initial findings about the application of a temporal metaphor in smart home interfaces.


title: Measuring and moderating opinion polarization in social networks

abstract: The polarization of society over controversial social issues has been the subject of study in social sciences for decades (Isenberg in J Personal Soc Psychol 50(6):1141–1151, 1986, Sunstein in J Polit Philos 10(2):175–195, 2002). The widespread usage of online social networks and social media, and the tendency of people to connect and interact with like-minded individuals has only intensified the phenomenon of polarization (Bakshy et al. in Science 348(6239):1130–1132, 2015). In this paper, we consider the problem of measuring and reducing polarization of opinions in a social network. Using a standard opinion formation model (Friedkin and Johnsen in J Math Soc 15(3–4):193–206, 1990), we define the polarization index, which, given a network and the opinions of the individuals in the network, it quantifies the polarization observed in the network. Our measure captures the tendency of opinions to concentrate in network communities, creating echo-chambers. Given this numeric measure of polarization, we then consider the problem of reducing polarization in the network by convincing individuals (e.g., through education, exposure to diverse viewpoints, or incentives) to adopt a more neutral stand towards controversial issues. We formally define the ModerateInternal and ModerateExpressed problems, and we prove that both our problems are NP-hard. By exploiting the linear-algebraic characteristics of the opinion formation model we design polynomial-time algorithms for both problems. Our experiments with real-world datasets demonstrate the validity of our metric, and the efficiency and the effectiveness of our algorithms in practice.


title: SPEC CPU2000: measuring CPU performance in the New Millennium

abstract: As computers and software have become more powerful, it seems almost human nature to want the biggest and fastest toy you can afford. But how do you know if your toy is tops? Even if your application never does any I/O, it's not just the speed of the CPU that dictates performance. Cache, main memory, and compilers also play a role. Software applications also have differing performance requirements. So whom do you trust to provide this information? The Standard Performance Evaluation Corporation (SPEC) is a nonprofit consortium whose members include hardware vendors, software vendors, universities, customers, and consultants. SPEC's mission is to develop technically credible and objective component- and system-level benchmarks for multiple operating systems and environments, including high-performance numeric computing, Web servers, and graphical subsystems. On 30 June 2000, SPEC retired the CPU95 benchmark suite. Its replacement is CPU2000, a new CPU benchmark suite with 19 applications that have never before been in a SPEC CPU suite. The article discusses how SPEC developed this benchmark suite and what the benchmarks do.


title: Human neuronal changes in brain edema and increased intracranial pressure

abstract: Functional and molecular changes associated with pathophysiological conditions are relatively easily detected based on tissue samples collected from patients. Population specific cellular responses to disease might remain undiscovered in samples taken from organs formed by a multitude of cell types. This is particularly apparent in the human cerebral cortex composed of a yet undefined number of neuron types with a potentially different involvement in disease processes. We combined cellular electrophysiology, anatomy and single cell digital PCR in human neurons identified in situ for the first time to assess mRNA expression and corresponding functional changes in response to edema and increased intracranial pressure. In single pyramidal cells, mRNA copy numbers of AQP1, AQP3, HMOX1, KCNN4, SCN3B and SOD2 increased, while CACNA1B, CRH decreased in edema. In addition, single pyramidal cells increased the copy number of AQP1, HTR5A and KCNS1 mRNAs in response to increased intracranial pressure. In contrast to pyramidal cells, AQP1, HMOX1and KCNN4 remained unchanged in single cell digital PCR performed on fast spiking cells in edema. Corroborating single cell digital PCR results, pharmacological and immunohistochemical results also suggested the presence of KCNN4 encoding the α-subunit of KCa3.1 channels in edema on pyramidal cells, but not on interneurons. We measured the frequency of spontaneous EPSPs on pyramidal cells in both pathophysiological conditions and on fast spiking interneurons in edema and found a significant decrease in each case, which was accompanied by an increase in input resistances on both cell types and by a drop in dendritic spine density on pyramidal cells consistent with a loss of excitatory synapses. Our results identify anatomical and/or physiological changes in human pyramidal and fast spiking cells in edema and increased intracranial pressure revealing cell type specific quantitative changes in gene expression. Some of the edema/increased intracranial pressure modulated and single human pyramidal cell verified gene products identified here might be considered as novel pharmacological targets in cell type specific neuroprotection.


title: Using an information retrieval system to retrieve source code samples

abstract: Software developers often face steep learning curves in using a new framework, library, or new versions of frameworks for developing their piece of software. In large organizations, developers learn and explore use of frameworks, rarely realizing, several peers may have already explored the same. A tool that helps locate samples of code, demonstrating use of frameworks or libraries would provide benefits of reuse, improved code quality and faster development. This paper describes an approach for locating common samples of source code from a repository by providing extensions to an information retrieval system. The approach improves the existing approaches in two ways. First, it provides the scalability of an information retrieval system, supporting search over thousands of source code files of an organization. Second, it provides more specific search on source code by preprocessing source code files and understanding elements of the code as opposed to considering code as plain text.


title: Elastic Nodes for the Internet of Things: A Middleware-Based Approach

abstract: The Internet of Things has created a need for embedded devices that, despite being battery powered, can perform complex algorithms when needed. To fulfil this need we present the Elastic Node, a hardware platform that combines embedded field programmable gate arrays (FPGAs) with conventional microcontroller units (MCUs). These nodes can adapt their processing resources when their requirements change to temporarily allow more local calculations. We propose a minimalistic middleware approach to simplify development for an Elastic Node and present our first results based on a prototype implementation that uses off-the-shelf hardware.


title: Design of a CMOS System-on-Chip for Passive, Near-Field Ultrasonic Energy Harvesting and Back-Telemetry

abstract: Many packaging and structural materials are made of conductive materials such as metal or carbon-fiber composites, which limits the use of embedded radio frequency-based telemetry systems for sensing. In this paper, we present the design of a complete passive ultrasonic energy harvesting and back-telemetry system that exploits near-field acoustic coupling to wirelessly transfer energy and data across conductive barriers. The use of near-field operation makes the telemetry robust to multipath reflections that occur at barrier discontinuities and robust to crosstalk when multiple sensors are simultaneously interrogated. Underlying the proposed architecture is a system-on-chip (SoC) that integrates different ultrasonic energy harvesting and telemetry modules. The operation of the system has been verified using SoC prototypes fabricated in a 0.5- $\mu \text{m}$ CMOS process which have been integrated with a piezoelectric transducer attached to an aerospace-grade aluminum substrate. Measured results show that the proposed near-field ultrasonic telemetry system can effectively operate across a 2-mm-thick metallic barrier at a frequency of 13.56 MHz with the SoC consuming 22.3 $\mu \text{W}$ of power.


title: Exact information propagation through fully-connected feed forward neural networks

abstract: Neural network ensembles at initialisation give rise to the trainability and training speed of neural networks and thus support parameter choices at initialisation. These insights rely so far on mean field approximations that assume infinite layer width and study average squared signals. Thus, information about the full output distribution gets lost. Therefore, we derive the output distribution exactly (without mean field assumptions), for fully-connected networks with Gaussian weights and biases. The layer-wise transition of the signal distribution is guided by a linear integral operator, whose kernel has a closed form solution in case of rectified linear units for nonlinear activations. This enables us to analyze some of its spectral properties, for instance, the shape of the stationary distribution for different parameter choices and the dynamics of signal propagation.


title: Task-Relevant Object Discovery and Categorization for Playing First-person Shooter Games

abstract: We consider the problem of learning to play first-person shooter (FPS) video games using raw screen images as observations and keyboard inputs as actions. The high-dimensionality of the observations in this type of applications leads to prohibitive needs of training data for model-free methods, such as the deep Q-network (DQN), and its recurrent variant DRQN. Thus, recent works focused on learning low-dimensional representations that may reduce the need for data. This paper presents a new and efficient method for learning such representations. Salient segments of consecutive frames are detected from their optical flow, and clustered based on their feature descriptors. The clusters typically correspond to different discovered categories of objects. Segments detected in new frames are then classified based on their nearest clusters. Because only a few categories are relevant to a given task, the importance of a category is defined as the correlation between its occurrence and the agent's performance. The result is encoded as a vector indicating objects that are in the frame and their locations, and used as a side input to DRQN. Experiments on the game Doom provide a good evidence for the benefit of this approach.


title: Cloaked websites: propaganda, cyber-racism and epistemology in the digital era

abstract: This article analyzes cloaked websites, which are sites published by individuals or groups who conceal authorship in order to disguise deliberately a hidden political agenda. Drawing on the insights of critical theory and the Frankfurt School, this article examines the way in which cloaked websites conceal a variety of political agendas from a range of perspectives. Of particular interest here are cloaked white supremacist sites that disguise cyber-racism. The use of cloaked websites to further political ends raises important questions about knowledge production and epistemology in the digital era. These cloaked sites emerge within a social and political context in which it is increasingly difficult to parse fact from propaganda, and this is a particularly pernicious feature when it comes to the cyber-racism of cloaked white supremacist sites. The article concludes by calling for the importance of critical, situated political thinking in the evaluation of cloaked websites.


title: Automatic Scoring of an Analytical Response-To-Text Assessment

abstract: In analytical writing in response to text, students read a complex text and adopt an analytic stance in their writing about it. To evaluate this type of writing at scale, an automated approach for Response to Text Assessment RTA is needed. With the long-term goal of producing informative feedback for students and teachers, we design a new set of interpretable features that operationalize the Evidence rubric of RTA. When evaluated on a corpus of essays written by students in grades 4-6, our results show that our features outperform baselines based on well-performing features from other types of essay assessments.


title: SCHLECHT, OMMER: CONTOUR-BASED OBJECT DETECTION 1 Contour-based Object Detection

abstract: The arrival of appearance-based image features has dramatically influenced the field of visual object recognition. Previous work has shown, however, that contour curvature and junctions are important for shape representation and detection. We investigate a local representation of contours for object detection that complements appearance-based information, such as texture. We present a non-parametric representation of contours, curvature, and junctions which enables their accurate localization. We combine contour and appearance information into a general, voting-based detection algorithm. Besides detecting objects, we demonstrate that this approach reveals the most relevant contours and junctions supporting each object hypothesis. The experiments confirm that our contourbased representation compliments appearance information and the performance of baseline voting methods is significantly improved.


title: A Speech Dereverberation Method Using Adaptive Sparse Dictionary Learning

abstract: We present a monaural blind dereverberation method based on sparse coding of deconvolved version of reverberated speech signal in a dictionary which is learned by joint dictionary learning method, consisting of the concatenation of a clean speech and a non-negative matrix factor deconvolution result of the reverberated copy. The environment specific dictionary is originally learned off-line on a training corpus for different locations, while adaptive dictionary learning continues on-line for any other surroundings. Our approach uses both non-negative blind deconvolution and sparse coding, and achieves some improvements on objective voice quality testing’s like perceptual evaluation of speech quality.


title: Intelligent phishing detection and protection scheme for online transactions

abstract: Phishing is an instance of social engineering techniques used to deceive users into giving their sensitive information using an illegitimate website that looks and feels exactly like the target organization website. Most phishing detection approaches utilizes Uniform Resource Locator (URL) blacklists or phishing website features combined with machine learning techniques to combat phishing. Despite the existing approaches that utilize URL blacklists, they cannot generalize well with new phishing attacks due to human weakness in verifying blacklists, while the existing feature-based methods suffer high false positive rates and insufficient phishing features. As a result, this leads to an inadequacy in the online transactions. To solve this problem robustly, the proposed study introduces new inputs (Legitimate site rules, User-behavior profile, PhishTank, User-specific sites, Pop-Ups from emails) which were not considered previously in a single protection platform. The idea is to utilize a Neuro-Fuzzy Scheme with 5 inputs to detect phishing sites with high accuracy in real-time. In this study, 2-Fold cross-validation is applied for training and testing the proposed model. A total of 288 features with 5 inputs were used and has so far achieved the best performance as compared to all previously reported results in the field.


title: A Fast Index for Semistructured Data

abstract: Queries navigate semistructured data via path expressions, and can be accelerated using an index. Our solution encodes paths as strings, and inserts those strings into a special index that is highly optimized for long and complex keys. We describe the Index Fabric, an indexing structure that provides the efficiency and flexibility we need. We discuss how "raw paths" are used to optimize ad hoc queries over semistructured data, and how "refined paths" optimize specific access paths. Although we can use knowledge about the queries and structure of the data to create refined paths, no such knowledge is needed for raw paths. A performance study shows that our techniques, when implemented on top of a commercial relational database system, outperform the more traditional approach of using the commercial system’s indexing mechanisms to query the XML.


title: Marketing campaign evaluation in targeted display advertising

abstract: In this paper, we develop an experimental analysis to estimate the causal effect of online marketing campaigns as a whole, and not just the media ad design. We analyze the causal effects on user conversion probability. We run experiments based on A/B testing to perform this evaluation. We also estimate the causal effect of the media ad design given this randomization approach. We discuss the framework of a marketing campaign in the context of targeted display advertising, and incorporate the main elements of this framework in the evaluation. We consider budget constraints, the auction process, and the targeting engine in the analysis and the experimental set up. For the effects of this evaluation, we assume the targeting engine to be a black box that incorporates the impression delivery policy, the budget constraints, and the bidding process. Our method to disaggregate the campaign causal analysis is inspired on randomized experiments with imperfect compliance and the intention-to-treat (ITT) analysis. In this framework, individuals assigned randomly to the study group might refuse to take the treatment. For estimation, we present a Bayesian approach and provide credible intervals for the causal estimates. We analyze the effects of 2 independent campaigns for different products from the Advertising.com ad network for 20M+ users each campaign.


title: Binary convolutional neural network on RRAM

abstract: Recent progress in the machine learning field makes low bit-level Convolutional Neural Networks (CNNs), even CNNs with binary weights and binary neurons, achieve satisfying recognition accuracy on ImageNet dataset. Binary CNNs (BCNNs) make it possible for introducing low bit-level RRAM devices and low bit-level ADC/DAC interfaces in RRAM-based Computing System (RCS) design, which leads to faster read-and-write operations and better energy efficiency than before. However, some design challenges still exist: (1) how to make matrix splitting when one crossbar is not large enough to hold all parameters of one layer; (2) how to design the pipeline to accelerate the whole CNN forward process. In this paper, an RRAM crossbar-based accelerator is proposed for BCNN forward process. Moreover, the special design for BCNN is well discussed, especially the matrix splitting problem and the pipeline implementation. In our experiment, BCNNs on RRAM show much smaller accuracy loss than multi-bit CNNs for LeNet on MNIST when considering device variation. For AlexNet on ImageNet, the RRAM-based BCNN accelerator saves 58.2% energy consumption and 56.8% area compared with multi-bit CNN structure.


title: Learning filter banks within a deep neural network framework

abstract: Mel-filter banks are commonly used in speech recognition, as they are motivated from theory related to speech production and perception. While features derived from mel-filter banks are quite popular, we argue that this filter bank is not really an appropriate choice as it is not learned for the objective at hand, i.e. speech recognition. In this paper, we explore replacing the filter bank with a filter bank layer that is learned jointly with the rest of a deep neural network. Thus, the filter bank is learned to minimize cross-entropy, which is more closely tied to the speech recognition objective. On a 50-hour English Broadcast News task, we show that we can achieve a 5% relative improvement in word error rate (WER) using the filter bank learning approach, compared to having a fixed set of filters.


Say the Right Thing Right: Ethics Issues in Natural Language Generation Systems


title: Hand tracking and gesture recognition system for human-computer interaction using low-cost hardware

abstract: Human-Computer Interaction (HCI) exists ubiquitously in our daily lives. It is usually achieved by using a physical controller such as a mouse, keyboard or touch screen. It hinders Natural User Interface (NUI) as there is a strong barrier between the user and computer. There are various hand tracking systems available on the market, but they are complex and expensive. In this paper, we present the design and development of a robust marker-less hand/finger tracking and gesture recognition system using low-cost hardware. We propose a simple but efficient method that allows robust and fast hand tracking despite complex background and motion blur. Our system is able to translate the detected hands or gestures into different functional inputs and interfaces with other applications via several methods. It enables intuitive HCI and interactive motion gaming. We also developed sample applications that can utilize the inputs from the hand tracking system. Our results show that an intuitive HCI and motion gaming system can be achieved with minimum hardware requirements.


title: Does self-citation pay?

abstract: Self-citations — those where authors cite their own works — account for a significant portion of all citations. These self-references may result from the cumulative nature of individual research, the need for personal gratification, or the value of self-citation as a rhetorical and tactical tool in the struggle for visibility and scientific authority. In this article we examine the incentives that underlie self-citation by studying how authors’ references to their own works affect the citations they receive from others. We report the results of a macro study of more than half a million citations to articles by Norwegian scientists that appeared in the Science Citation Index. We show that the more one cites oneself the more one is cited by other scholars. Controlling for numerous sources of variation in cumulative citations from others, our models suggest that each additional self-citation increases the number of citations from others by about one after one year, and by about three after five years. Moreover, there is no significant penalty for the most frequent self-citers — the effect of self-citation remains positive even for very high rates of self-citation. These results carry important policy implications for the use of citations to evaluate performance and distribute resources in science and they represent new information on the role and impact of self-citations in scientific communication.


title: Defining grief play in MMORPGs: player and developer perceptions

abstract: In current literature, grief play in Massively Multi-player Online Role-Playing Games (MMORPGs) refers to play styles where a player intentionally disrupts the gaming experience of other players. In our study, we have discovered that player experiences may be disrupted by others unintentionally, and under certain circumstances, some will believe they have been griefed. This paper explores the meaning of grief play, and suggests that some forms of unintentional grief play be called greed play. The paper suggests that greed play be treated as griefing, but a more subtle form. It also investigates the different types of griefing and establishes a taxonomy of terms in grief play.


title: Body image 2.0: Associations between social grooming on Facebook and body image concerns

abstract: This study examined the association between social media use and body image.Time spent on Facebook was not related to body image concerns.Social grooming behaviors were correlated with the drive for thinness.Appearance comparison mediated this relationship. A cross-sectional survey (N=186) was conducted to examine the relationships between young women's and men's social media use and body image attitudes-the drive for thinness and the drive for muscularity. Prior research has found relationships between exposure to traditional mass media and body image concerns. Drawing on social comparison theory, this study attempted to determine if similar relationships could be found for social media use. Results of this study indicated that simple exposure (time spent on social media) was not related to body image concerns. Online social grooming behaviors, such as viewing and commenting on peer's profiles, however, were significantly correlated with the drive for thinness for both female and male participants. Hierarchical regression analyses indicated that appearance comparison mediated the relationship between social grooming behaviors and the drive for thinness.


title: Twitter Sentiment Analysis: How To Hedge Your Bets In The Stock Markets

abstract: Emerging interest of trading companies and hedge funds in mining social web has created new avenues for intelligent systems that make use of public opinion in driving investment decisions. It is well accepted that at high frequency trading, investors are tracking memes rising up in microblogging forums to count for the public behavior as an important feature while making short term investment decisions. We investigate the complex relationship between tweet board literature (like bullishness, volume, agreement etc) with the financial market instruments (like volatility, trading volume and stock prices). We have analyzed Twitter sentiments for more than 4 million tweets between June 2010 and July 2011 for DJIA, NASDAQ-100 and 11 other big cap technological stocks. Our results show high correlation (upto 0.88 for returns) between stock prices and twitter sentiments. Further, using Granger’s Causality Analysis, we have validated that the movement of stock prices and indices are greatly affected in the short term by Twitter discussions. Finally, we have implemented Expert Model Mining System (EMMS) to demonstrate that our forecasted returns give a high value of R-square (0.952) with low Maximum Absolute Percentage Error (MaxAPE) of 1.76 % for Dow Jones Industrial Average (DJIA). We introduce and validate performance of market monitoring elements derived from public mood that can be exploited to retain a portfolio within limited risk state during typical market conditions.