title: Scalable Electroactive Polymer for Variable Stiffness Suspensions

abstract: We present an approach for creating a reliable variable stiffness suspension using prestretched electroactive polymer films. In comparison to other variable-stiffness devices, the design is compact, lightweight, consumes low energy, and can change its stiffness in approximately 10 ms. Attention to the main causes of failure increases the reliability of the device so that multiple layers can be combined for increased loads and stiffness. A device with 20 layers supports loads of over 15 N at minimum stiffness and maximum voltage for displacements of $\pm 7$ mm and up to 138 N at maximum stiffness with a displacement of 20 mm. Multilayer devices have demonstrated 3 million mechanical load cycles and hundreds of thousands of stiffness variation (charging and discharging) cycles, respectively.


title: An Adaptive Social Network for Information Access: Theoretical and Experimental Results

abstract: We consider a social network of software agents who assist each other in helping their users find information. Unlike in most previous approaches, our architecture is fully distributed and includes agents who preserve the privacy and autonomy of their users. These agents learn models of each other in terms of expertise (ability to produce correct domain answers) and sociability (ability to produce accurate referrals). We study our framework experimentally to study how the social network evolves. Specifically, we find that under our multi-agent learning heuristic, the quality of the network improves with interactions: the quality is maximized when both expertise and sociability are considered; pivot agents further improve the quality of the network and have a catalytic effect on its quality even if they are ultimately removed. Moreover, the quality of the network improves when clustering decreases, reflecting the intuition that you need to talk to people outside your close circle to get the best information.


title: Increasing Psychological Well-Being and Resilience by Psychotherapeutic Methods

abstract: A specific psychotherapeutic strategy for increasing psychological well-being and resilience, well-being therapy, has been developed and validated in a number of randomized controlled trials. The findings indicate that flourishing and resilience can be promoted by specific interventions leading to a positive evaluation of one's self, a sense of continued growth and development, the belief that life is purposeful and meaningful, the possession of quality relations with others, the capacity to manage effectively one's life, and a sense of self-determination. A decreased vulnerability to depression and anxiety has been demonstrated after well-being therapy in high-risk populations. There are important implications for the state/trait dichotomy in psychological well-being and for the concept of recovery in mood and anxiety disorders.


title: Trajectory tracking control of a four-wheel differentially driven mobile robot

abstract: We consider the trajectory tracking control problem for a 4-wheel differentially driven mobile robot moving on an outdoor terrain. A dynamic model is presented accounting for the effects of wheel skidding. A model-based nonlinear controller is designed, following the dynamic feedback linearization paradigm. An operational nonholonomic constraint is added at this stage, so as to obtain a predictable behavior for the instantaneous center of rotation thus preventing excessive skidding. The controller is then robustified, using conventional linear techniques, against uncertainty in the soil parameters at the ground-wheel contact. Simulation results show the good performance in tracking spline-type trajectories on a virtual terrain with varying characteristics.


title: Improving genetic programming based symbolic regression using deterministic machine learning

abstract: Symbolic regression (SR) is a well studied method in genetic programming (GP) for discovering free-form mathematical models from observed data. However, it has not been widely accepted as a standard data science tool. The reluctance is in part due to the hard to analyze random nature of GP and scalability issues. On the other hand, most popular deterministic regression algorithms were designed to generate linear models and therefore lack the flexibility of GP based SR (GP-SR). Our hypothesis is that hybridizing these two techniques will create a synergy between the GP-SR and deterministic approaches to machine learning, which might help bring the GP based techniques closer to the realm of big learning. In this paper, we show that a hybrid deterministic/GP-SR algorithm outperforms GP-SR alone and the state-of-the-art deterministic regression technique alone on a set of multivariate polynomial symbolic regression tasks as the system to be modeled becomes more multivariate.


title: Towards Understanding the Nonverbal Signatures of Engagement in Super Mario Bros

abstract: In this paper, we present an approach for predicting users’ level of engagement from nonverbal cues within a game environment. We use a data corpus collected from 28 participants (152 minutes of video recording) playing the popular platform game Super Mario Bros. The richness of the corpus allows extraction of several visual and facial expression features that were utilised as indicators of players’ affects as captured by players’ self-reports. Neuroevolution preference learning is used to construct accurate models of player experience that approximate the relationship between extracted features and reported engagement. The method is supported by a feature selection technique for choosing the relevant subset of features. Different setup settings were implemented to analyse the impact of the type of the features and the position of the extraction window on the modelling accuracy. The results obtained show that highly accurate models can be constructed (with accuracies up to 96.82%) and that players’ nonverbal behaviour towards the end of the game is the most correlated with engagement. The framework presented is part of a bigger picture where the generated models are utilised to tailor content generation to a player’s particular needs and playing characteristics.


title: NameClarifier: A Visual Analytics System for Author Name Disambiguation

abstract: In this paper, we present a novel visual analytics system called NameClarifier to interactively disambiguate author names in publications by keeping humans in the loop. Specifically, NameClarifier quantifies and visualizes the similarities between ambiguous names and those that have been confirmed in digital libraries. The similarities are calculated using three key factors, namely, co-authorships, publication venues, and temporal information. Our system estimates all possible allocations, and then provides visual cues to users to help them validate every ambiguous case. By looping users in the disambiguation process, our system can achieve more reliable results than general data mining models for highly ambiguous cases. In addition, once an ambiguous case is resolved, the result is instantly added back to our system and serves as additional cues for all the remaining unidentified names. In this way, we open up the black box in traditional disambiguation processes, and help intuitively and comprehensively explain why the corresponding classifications should hold. We conducted two use cases and an expert review to demonstrate the effectiveness of NameClarifier.


title: Explaining Unequal Returns to Social Capital among Entrepreneurs

abstract: In this paper, I challenge the dominant claim that women generate less value from using social ties because they tend to be embedded in less resource-rich networks. Although lacking access to valua...


SalGAN : Visual Saliency Prediction with Adversarial Networks


title: A study of detecting and combating cybersickness with fuzzy control for the elderly within 3D virtual stores

abstract: Abstract Elderly individuals can access online 3D virtual stores from their homes to make purchases. However, most virtual environments (VEs) often elicit physical responses to certain types of movements in the VEs. Some users exhibit symptoms that parallel those of classical motion sickness, called cybersickness, both during and after the VE experience. This study investigated the factors that contribute to cybersickness among the elderly when immersed in a 3D virtual store. The results of the first experiment show that the simulator sickness questionnaire (SSQ) scores increased significantly by the reasons of navigational rotating speed and duration of exposure. Based on these results, a warning system with fuzzy control for combating cybersickness was developed. The results of the second and third experiments show that the proposed system can efficiently determine the level of cybersickness based on the fuzzy sets analysis of operating signals from scene rotating speed and exposure duration, and subsequently combat cybersickness.


title: Strategy and architecture of a safety concept for fully automatic and autonomous driving assistance systems

abstract: As drivers back out of the driving task, when transported automatically by an intelligent car for a longer time, they are not always able to react properly, if a driver take over request occurs. This paper presents two ways, how to deal with this problem within the scope of a functional safety concept. Thereto, the difference between fully automatic and autonomous driving assistance systems is explained. Afterwards two different strategies to reach a safe state in consequence of a system boundary crossing are proposed. In the first case the fall back state is reached by a driver take over, in the second case by an automatic, active fail-safe mechanism. Subsequently the necessary components for monitoring and reaching a safe state and their embedment in a basic, functional architecture of a driving assistance system are described. In this context, special regard is paid to aspects of redundancy as well. In the end it is concluded, that the safety concept proposed here is crucial for guaranteeing enduring safety in an automatically driving car and in consequence for making automatic driving functions commercially ready for serial production.


title: Error Correcting Codes in Quantum Theory

abstract: A new type of uncertainty relation is presented, concerning the information-bearing properties of a discrete quantum system. A natural link is then revealed between basic quantum theory and the linear error correcting codes of classical information theory. A subset of the known codes is described, having properties which are important for error correction in quantum communication. It is shown that a pair of states which are, in a certain sense, “macroscopically different,” can form a superposition in which the interference phase between the two parts is measurable. This provides a highly stabilized “Schrodinger cat” state. [S0031-9007(96)00779-X]


title: Exploiting the Data Redundancy Locality to Improve the Performance of Deduplication-Based Storage Systems

abstract: The chunk-lookup disk bottleneck and the read amplification problems are two great challenges for deduplication-based storage systems and restrict the applicability of data deduplication for large-scale data volumes. Previous studies and our experimental evaluations have shown that the amount of redundant data shared among different types of applications is negligible. Based on the observations, we propose AA-Plus which effectively groups the hash index of the same application together and divides the whole hash index into different groups based on the application types. Moreover, it groups the data chunks of the same application together on the disks. The extensive trace-driven experiments conducted on our lightweight prototype implementation of AA-Plus show that compared with AA-Dedupe, AA-Plus significantly speeds up the write throughput by a factor of up to 6.9 and with an average of 3.1, and speeds up the read throughput by a factor of up to 3.3 and with an average of 1.9.


title: Theory of planning behavior (TPB) and customer satisfaction in the continued use of e-service: An integrated model

abstract: This study has devoted much effort to developing an integrated model designed to predict and explain an individual's continued use of online services based on the concepts of the expectation disconfirmation model and the theory of planned behavior. Empirical data was collected from a field survey of Cyber University System (CUS) users to verify the fit of the hypothetical model. The measurement model indicates the theoretical constructs have adequate reliability and validity while the structured equation model is illustrated as having a high model fit for empirical data. Study's findings show that a customer's behavioral intention towards e-service continuance is mainly determined by customer satisfaction and additionally affected by perceived usefulness and subjective norm. Generally speaking, the integrated model can fully reflect the spirit of the expectation disconfirmation model and take advantage of planned behavior theory. After consideration of the impact of systemic features, personal characteristics, and social influence on customer behavior, the integrated model had a better explanatory advantage than other EDM-based models proposed in prior research.


title: The impact of a flipped classroom design on learning performance in higher education: Looking for the best "blend" of lectures and guiding questions with feedback

abstract: The present study examines the differential impact of studying in a Flipped Classroom (FC) setting, as compared to a Blended Learning (BL), a Traditional Learning (TL), and an E-Learning (EL) setting on learning performance, self-efficacy beliefs, intrinsic motivation, and perceived flexibility. Participants were second year undergraduate students (N=90), enrolled in the Invertebrates course in Can Tho University (Vietnam). Participants were randomly assigned to one of the four experimental conditions (TL n=22, BL n=22, FC n=23, EL n=23). Two instructional elements - (1) lectures and (2) guiding questions - were presented through two different modes (online and face-to-face). In the blended conditions (BL and FC) the mode of these elements were altered. The results show that learning performance was superior in the FC setting as compared to other learning settings TL (Cohens' d=1.58), EL (Cohens' d=1.01) and BL (Cohens' d=0.71). Students in the BL setting had a higher learning performance as compared to the EL setting. In addition, we observed that studying in a FC setting had a positive effect on self-efficacy beliefs and intrinsic motivation, but not on perceived flexibility. These findings suggest that the FC setting could be a promising way of enhancing students learning performance. A flipped classroom results in higher learning performance compared to Face-to-Face and Fully E-Learning.A blended learning design results in higher learning outcomes compared to Fully E-Learning.Blended learning has significant effect on self-efficacy and intrinsic motivation as compared to Fully E-Learning.


title: Influence of information overload on operator’s user experience of human–machine interface in LED manufacturing systems

abstract: This paper reports on an experimental study on human–machine interface in LED manufacturing systems to measure the influence of information overload on user experience. The results are based on cognitive ergonomics. The experiment used eye-tracking methods and a questionnaire to gather data. The independent variables were interface complexity and user background. Interface complexity had three levels: high interface complexity, moderate interface complexity and low interface complexity. User background had two levels: the novice group and the expert group. The dependent variables included time to first fixation, fixations before and subjective feelings. A total of 38 operators participated in the experiment, and the results showed that (1) interface complexity caused a significant difference in time to first fixation (P < 0.05) and fixations before (P < 0.05). Furthermore, the results revealed significant differences between high complexity interfaces compared to low complexity interfaces (P < 0.05). However, no significant differences were observed between moderate and low complexity interfaces or between moderate and high complexity interfaces (P > 0.05); (2) user background significantly affected the user experience; (3) within the same complexity level, expert operators’ cognitive workload was significantly lower than that of novice operators; and (4) there was no significant relationship between the interface complexity and the user’s background. The study concludes that because interface complexity has a significant effect on the time taken to locate the target button on the screen, interface design should be as simple as possible, while still providing the necessary level of functionality.


title: A working environment for digital planetary data processing and mapping using ISIS and GRASS GIS

abstract: Abstract Since the beginning of planetary exploration, mapping has been fundamental to summarize observations returned by scientific missions. Sensor-based mapping has been used to highlight specific features from the planetary surfaces by means of processing. Interpretative mapping makes use of instrumental observations to produce thematic maps that summarize observations of actual data into a specific theme. Geologic maps, for example, are thematic interpretative maps that focus on the representation of materials and processes and their relative timing. The advancements in technology of the last 30 years have allowed us to develop specialized systems where the mapping process can be made entirely in the digital domain. The spread of networked computers on a global scale allowed the rapid propagation of software and digital data such that every researcher can now access digital mapping facilities on his desktop. The efforts to maintain planetary missions data accessible to the scientific community have led to the creation of standardized digital archives that facilitate the access to different datasets by software capable of processing these data from the raw level to the map projected one. Geographic Information Systems (GIS) have been developed to optimize the storage, the analysis, and the retrieval of spatially referenced Earth based environmental geodata; since the last decade these computer programs have become popular among the planetary science community, and recent mission data start to be distributed in formats compatible with these systems. Among all the systems developed for the analysis of planetary and spatially referenced data, we have created a working environment combining two software suites that have similar characteristics in their modular design, their development history, their policy of distribution and their support system. The first, the Integrated Software for Imagers and Spectrometers (ISIS) developed by the United States Geological Survey, represents the state of the art for processing planetary remote sensing data, from the raw unprocessed state to the map projected product. The second, the Geographic Resources Analysis Support System (GRASS) is a Geographic Information System developed by an international team of developers, and one of the core projects promoted by the Open Source Geospatial Foundation (OSGeo). We have worked on enabling the combined use of these software systems throughout the set-up of a common user interface, the unification of the cartographic reference system nomenclature and the minimization of data conversion. Both software packages are distributed with free open source licenses, as well as the source code, scripts and configuration files hereafter presented. In this paper we describe our work done to merge these working environments into a common one, where the user benefits from functionalities of both systems without the need to switch or transfer data from one software suite to the other one. Thereafter we provide an example of its usage in the handling of planetary data and the crafting of a digital geologic map.


title: SAMPLE SIZE AND MODELING ACCURACY OF DECISION TREE BASED DATA MINING TOOLS

abstract: ABSTRACT Given the cost associated with modeling very large datasets and over-fitting issues of decision-tree based models, sample based models are an attractive alternative--provided that the sample based models have a predictive accuracy approximating that of models based on all available data. This paper presents results of sets of decision-tree models generated across progressive sets of sample sizes. The models were applied to two sets of actual client data using each of six prominent commercial data mining tools. The results suggest that model accuracy improves at a decreasing rate with increasing sample size. When a power curve was fitted to accuracy estimates across various sample sizes, more than 80 percent of the time accuracy within 0.5 percent of the expected terminal (accuracy of a theoretical infinite sample) was achieved by the time the sample size reached 10,000 records. Based on these results, fitting a power curve to progressive samples and using it to establish an appropriate sample size appears to be a promising mechanism to support sample based modeling for large datasets. INTRODUCTION Data mining has emerged as a practical analytical tool primarily on the basis of its ability to deal with the large volume of data available from databases and data warehouses. Rapid increases in processor speed coupled with continuing decreases in the cost of mass storage devices and other computer hardware have made it practical to collect and maintain massive databases. Data mining software is viewed as a tool that can perform undirected or semi-directed analysis and, thus, can be applied to the full length and width of very large data sets at much lower costs than analytical techniques requiring stronger human direction. While data mining tools have typically been applied to the full volume of available data, issues of cost and model over-fitting suggest that use of data mining models based on a sample of available data may be appropriate in many instances. Thus, the relationship between sample size and model accuracy is an important issue for data mining. Despite the increases in processing speeds and reductions in processing cost, applying data mining tools to analyze all available data is costly in terms of both dollars and time required to generate and implement models. In discussing differences between statistical and data mining approaches, Mannila (2000) suggests that: "The volume of the data is probably not a very important difference: the number of variables or attributes often has a much more profound impact on the applicable analysis methods. For example, data mining has tackled width problems such as what to do in situations where the number of variables is so large that looking at all pairs of variables is computationally infeasible." The above quote suggests that the benefit of data mining tools comes from their ability to deal more effectively with complex interactions among variables rather than from the ability to process massive volumes of instances. It has been noted that decision tree based data mining tools are subject to over-fitting as the size of the data set increases, Domingos (1998) and Oates and Jensen (1997). As Oates and Jensen [1998] note, "Increasing the amount of data used to build a model often results in a linear increase in model size, even when that additional complexity results in no significant increase in model accuracy." In a similar vein Musick, Catlett, and Russell (1993) suggest that "often the economically rational decision is to use only a subset of the available data." A variety of pruning algorithms have been proposed to deal with this problem, and most commercial data mining software using decision tree based algorithms incorporate the use of pruning algorithms. While pruning helps to limit the proportion by which model complexity increases as the amount of data increases, its effectiveness can only be assessed by examining the responsiveness of model complexity and model accuracy to changes in data set size. …


title: All-optical packet/circuit switching-based data center network for enhanced scalability, latency, and throughput

abstract: Applications running inside data centers are enabled through the cooperation of thousands of servers arranged in racks and interconnected together through the data center network. Current DCN architectures based on electronic devices are neither scalable to face the massive growth of DCs, nor flexible enough to efficiently and cost-effectively support highly dynamic application traffic profiles. The FP7 European Project LIGHTNESS foresees extending the capabilities of today's electrical DCNs throPugh the introduction of optical packet switching and optical circuit switching paradigms, realizing together an advanced and highly scalable DCN architecture for ultra-high-bandwidth and low-latency server-to-server interconnection. This article reviews the current DC and high-performance computing (HPC) outlooks, followed by an analysis of the main requirements for future DCs and HPC platforms. As the key contribution of the article, the LIGHTNESS DCN solution is presented, deeply elaborating on the envisioned DCN data plane technologies, as well as on the unified SDN-enabled control plane architectural solution that will empower OPS and OCS transmission technologies with superior flexibility, manageability, and customizability.


title: Two mm-Wave Vector Modulator Active Phase Shifters With Novel IQ Generator in 28 nm FDSOI CMOS

abstract: This paper presents two 4-bit (16-phase) mm-wave vector modulator phase shifters exploiting a novel in-phase and quadrature signal generator that consists of a single-input double-output cascode amplifier incorporating a lumped-element coupled-line quadrature coupler. The two circuit implementations have been designed and fabricated in a 28 nm fully depleted silicon-on-insulator CMOS. The first (PS1) achieves a higher gain and the second (PS2) has a more compact area (reduced to about 50%). Each consumes 18 mA from a 1.2 V supply. PS1 exhibits an average gain of 2.3 dB at 87.4 GHz and $B_{3\text {dB}}$ from 78.8 to 92.8 GHz; rms gain error of 1.68 dB at 87.4 GHz and $B_{3\text {dB}}$ ; rms phase error of 9.4° at 87.4 GHz and $B_{3\text {dB}}$ ; $S_{11} dB in $B_{3\text {dB}}$ ; average $P_{1\text {dB}}$ of −7 dBm; and average noise figure (NF) equal to 10.8 dB at 87 GHz. PS2 exhibits an average gain of 0.83 dB at 89.2 GHz and $B_{3\text {dB}}$ from 80.2 to 96.8 GHz; rms gain error of 1.46 dB at 89.2 GHz and $B_{3\text {dB}}$ ; rms phase error of 11.2° at 89.2 GHz and $B_{3\text {dB}}$ ; $S_{11} dB in $B_{3\text {dB}}$ ; average $P_{1\text {dB}}$ of −6 dBm; and average NF of 11.9 dB at 89 GHz.


Design of a 4-DOF Robot Manipulator with Optimized Algorithm for Inverse Kinematics


title: PedX: Benchmark Dataset for Metric 3-D Pose Estimation of Pedestrians in Complex Urban Intersections

abstract: This letter presents a novel dataset titled PedX , a large-scale multimodal collection of pedestrians at complex urban intersections. PedX consists of more than 5 000 pairs of high-resolution (12MP) stereo images and LiDAR data along with providing two-dimensional (2-D) image labels and 3-D labels of pedestrians in a global coordinate frame. Data were captured at three four-way stop intersections with heavy pedestrian–vehicle interaction. We also present a 3-D model fitting algorithm for automatic labeling harnessing constraints across different modalities and novel shape and temporal priors. All annotated 3-D pedestrians are localized into the real-world metric space, and the generated 3-D models are validated using a motion capture system configured in a controlled outdoor environment to simulate pedestrians in urban intersections. We also show that the manual 2-D image labels can be replaced by state-of-the-art automated labeling approaches, thereby facilitating automatic generation of large scale datasets.


title: Urdu Optical Character Recognition Systems: Present Contributions and Future Directions

abstract: This paper gives an across-the-board comprehensive review and survey of the most prominent studies in the field of Urdu optical character recognition (OCR). This paper introduces the OCR technology and presents a historical review of the OCR systems, providing comparisons between the English, Arabic, and Urdu systems. Detailed background and literature have also been provided for Urdu script, discussing the script’s past, OCR categories, and phases. This paper further reports all state-of-the-art studies for different phases, namely, image acquisition, pre-processing, segmentation, feature extraction, classification/recognition, and post-processing for an Urdu OCR system. In the segmentation section, the analytical and holistic approaches for Urdu text have been emphasized. In the feature extraction section, a comparison has been provided between the feature learning and feature engineering approaches. Deep learning and traditional machine learning approaches have been discussed. The Urdu numeral recognition systems have also been deliberated concisely. The research paper concludes by identifying some open problems and suggesting some future directions.


title: Parallel Separable 3D Convolution for Video and Volumetric Data Understanding

abstract: For video and volumetric data understanding, 3D convolution layers are widely used in deep learning, however, at the cost of increasing computation and training time. Recent works seek to replace the 3D convolution layer with convolution blocks, e.g. structured combinations of 2D and 1D convolution layers. In this paper, we propose a novel convolution block, Parallel Separable 3D Convolution (PmSCn), which applies m parallel streams of n 2D and one 1D convolution layers along different dimensions. We first mathematically justify the need of parallel streams (Pm) to replace a single 3D convolution layer through tensor decomposition. Then we jointly replace consecutive 3D convolution layers, common in modern network architectures, with the multiple 2D convolution layers (Cn). Lastly, we empirically show that PmSCn is applicable to different backbone architectures, such as ResNet, DenseNet, and UNet, for different applications, such as video action recognition, MRI brain segmentation, and electron microscopy segmentation. In all three applications, we replace the 3D convolution layers in state-of-the art models with PmSCn and achieve around 14% improvement in test performance and 40% reduction in model size and on average.


title: CNN+LSTM Architecture for Speech Emotion Recognition with Data Augmentation

abstract: In this work we design a neural network for recognizing emotions in speech, using the IEMOCAP dataset. Following the latest advances in audio analysis, we use an architecture involving both convolutional layers, for extracting high-level features from raw spectrograms, and recurrent ones for aggregating long-term dependencies. We examine the techniques of data augmentation with vocal track length perturbation, layer-wise optimizer adjustment, batch normalization of recurrent layers and obtain highly competitive results of 64.5% for weighted accuracy and 61.7% for unweighted accuracy on four emotions.


title: Innovative Seismic Response-Controlled System with Shear Wall and Concentrated Dampers in Lower Stories

abstract: A new structural control system using damper-installed shear-walls in lower stories with reduced stiffness is proposed for vibration control of high-rise RC buildings. That system has some design variables, i.e. height of shear-wall, degree of stiffness reduction at lower stories, and quantity of dampers. In this paper, some parametric studies on the shear-beam model with a stiff beam against two kinds of ground motion, a pulse-type sinusoidal wave and a resonant sinusoidal wave, are conducted to clarify the vibration characteristics of the proposed structural control system. It is shown that the optimal combination of design parameters depends on the input ground motion. It is also shown that it is possible to prevent from increasing the response under the one-cycle sinusoidal input resonant to the lowest mode and reduce the steady-state response under the harmonic input with the resonant fundamental period by reducing the stiffness in the lower structure and increasing the damper deformation.


title: A Value Transmission Method for Abstract Data Types

abstract: data types have proved to be a useful technique for structuring systems. In large systems it is sometimes useful to have different regions of the system use different representations for the abstract data values. A technique is described for communicating abstract values between such regions. The method was developed for use in constructing distributed systems, where the regions exist at different computers and the values are communicated over a network. The method defines a call-by-value semantics; it is also useful in nondistributed systems wherever call by value is the desired semantics. An important example of such a use is a repository, such as a file system, for storing long- lived data. Abstract data types have proved to be a useful technique for structuring systems. They permit the programmer to encapsulate details of the representation of data so that these details can be changed with minimal impact on the program as a whole. It is sometimes useful, especially in large programs, to use different implementations of a data abstraction in different regions of the program. Current languages that support data abstraction, however, limit a program to a single implementation (11, 17). The reason for this limitation is the difficulty of com- municating between the regions using different implementations. This paper describes a technique for communicating abstract values between such regions. The method defines a call-by-value semantics. The method was developed for use in distributed systems, where the regions exist at different computers and the values are communicated over the network. It is also useful in


Spiking Neural Networks : An Algorithmic Perspective ( Extended Abstract ) ∗


title: Deep learning with time-frequency representation for pulse estimation from facial videos

abstract: Accurate pulse estimation is of pivotal importance in acquiring the critical physical conditions of human subjects under test, and facial video based pulse estimation approaches recently gained attention owing to their simplicity. In this work, we have endeavored to develop a novel deep learning approach as the core part for pulse (heart rate) estimation by using a common RGB camera. Our approach consists of four steps. We first begin by detecting the face and its landmarks, and thereby locate the required facial ROI. In Step 2, we extract the sample mean sequences of the R, G, and B channels from the facial ROI, and explore three processing schemes for noise removal and signal enhancement. In Step 3, the Short-Time Fourier Transform (STFT) is employed to build the 2D Time-Frequency Representations (TFRs) of the sequences. The 2D TFR enables the formulation of the pulse estimation as an image-based classification problem, which can be solved in Step 4 by a deep Con-volutional Neural Network (CNN). Our approach is one of the pioneering works for attempting real-time pulse estimation using a deep learning framework. We have developed a pulse database, called the Pulse from Face (PFF), and used it to train the CNN. The PFF database will be made publicly available to advance related research. When compared to state-of-the-art pulse estimation approaches on the standard MAHNOB-HCI database, the proposed approach has exhibited superior performance.


title: SMASheD: Sniffing and Manipulating Android Sensor Data for Offensive Purposes

abstract: The current Android sensor security model either allows only restrictive read access to sensitive sensors (e.g., an app can only read its own touch data) or requires special install-time permissions (e.g., to read microphone, camera, or GPS). Moreover, Android does not allow write access to any of the sensors. Sensing-based security and non-security applications, therefore, crucially rely upon the sanity of the Android sensor security model. In this paper, we show that such a model can be effectively circumvented. Specifically, we build SMASheD, a legitimate framework under the current Android ecosystem that can be used to stealthily sniff as well as manipulate many of the Android's restricted sensors (even touch input). SMASheD exploits the Android debug bridge functionality and enables a malicious app with only the INTERNET permission to read, and write to, multiple different sensor data files at will. SMASheD is the first framework, to the best of our knowledge, that can sniff and manipulate protected sensors on unrooted Android devices, without user awareness, without constant device-PC connection and without the need to infect the PC. The primary contributions of this paper are twofold. First, we design and develop the SMASheD framework, and evaluate its effectiveness on multiple Android devices, including phones, watches, and glasses. Second, as an offensive implication of the SMASheD framework, we introduce a wide array of potentially devastating attacks. Our attacks against the touch sensor range from accurately logging the touchscreen input (TouchLogger) to injecting touch events for accessing restricted sensors and resources, installing and granting special permissions to other malicious apps, accessing user accounts, and authenticating on behalf of the user-essentially almost doing whatever the device user can do (secretively). Our attacks against various physical sensors (motion, position, and environmental) can subvert the functionality provided by numerous existing sensing-based security and non-security applications, including those used for (continuous) authentication, authorization, safety, and elderly care.


title: Retractions in the scientific literature : do authors deliberately commit research fraud ?

abstract: Background Papers retracted for fraud (data fabrication or data falsification) may represent a deliberate effort to deceive, a motivation fundamentally different from papers retracted for error. It is hypothesised that fraudulent authors target journals with a high impact factor (IF), have other fraudulent publications, diffuse responsibility across many co-authors, delay retracting fraudulent papers and publish from countries with a weak research infrastructure. Methods All 788 English language research papers retracted from the PubMed database between 2000 and 2010 were evaluated. Data pertinent to each retracted paper were abstracted from the paper and the reasons for retraction were derived from the retraction notice and dichotomised as fraud or error. Data for each retracted article were entered in an Excel spreadsheet for analysis. Results Journal IF was higher for fraudulent papers (p 2 =8.71; p Conclusions This study reports evidence consistent with the ‘deliberate fraud’ hypothesis. The results suggest that papers retracted because of data fabrication or falsification represent a calculated effort to deceive. It is inferred that such behaviour is neither naive, feckless nor inadvertent.


title: An importance-performance analysis of hospital information system attributes: A nurses' perspective

abstract: Abstract Purpose Health workers have numerous concerns about hospital IS (HIS) usage. Addressing these concerns requires understanding the system attributes most important to their satisfaction and productivity. Following a recent HIS implementation, our objective was to identify priorities for managerial intervention based on user evaluations of the performance of the HIS attributes as well as the relative importance of these attributes to user satisfaction and productivity outcomes. Procedures We collected data along a set of attributes representing system quality, data quality, information quality, and service quality from 154 nurse users. Their quantitative responses were analysed using the partial least squares approach followed by an importance-performance analysis. Qualitative responses were analysed using thematic analysis to triangulate and supplement the quantitative findings. Main findings Two system quality attributes (responsiveness and ease of learning), one information quality attribute (detail), one service quality attribute (sufficient support), and three data quality attributes (records complete, accurate and never missing) were identified as high priorities for intervention. Conclusions Our application of importance-performance analysis is unique in HIS evaluation and we have illustrated its utility for identifying those system attributes for which underperformance is not acceptable to users and therefore should be high priorities for intervention.


title: Buragga” Comparison between Conventional and Fuzzy Logic PID Controllers for Controlling DC

abstract: Fuzzy logic and proportional-integral-derivative (PID) controllers are compared for use in direct current (DC) motors positioning system. A simulation study of the PID position controller for the armature-controlled with fixed field and fieldcontrolled with fixed armature current DC motors is performed. Fuzzy rules and the inferencing mechanism of the fuzzy logic controller (FLC) are evaluated by using conventional rule-lookup tables that encode the control knowledge in a rules form. The performance assessment of the studied position controllers is based on transient response and error integral criteria. The results obtained from the FLC are not only superior in the rise time, speed fluctuations, and percent overshoot but also much better in the controller output signal structure, which is much remarkable in terms of the hardware implementation.


title: Planar high-frequency contactless inductive position sensor

abstract: This paper presents a low cost, precise and reliable inductive absolute position measurement system. It is suitable for rough industrial environments, offers a high inherent resolution (0.1 % to 0.01 % of antenna length), can measure target position over a wide measurement range and can potentially measure multiple target locations. The position resolution is improved by adding two additional finer pitched receive channels. The sensor works on principles similar to contactless resolvers. It consists of a rectangular antenna PCB and a passive LC resonance target. A mathematical model and the equivalent circuit of this kind of sensor is explained in detail. Such sensors suffer from transmitter to receiver coil capacitive crosstalk, which results in a phase sensitive offset. This crosstalk will be analyzed by a mathematical model and will be verified by measurements. Moreover, the mechanical transducer arrangement, the measurement setup and measured results will be presented.


title: A Universal Catalyst for First-Order Optimization

abstract: We introduce a generic scheme for accelerating first-order optimization methods in the sense of Nesterov, which builds upon a new analysis of the accelerated proximal point algorithm. Our approach consists of minimizing a convex objective by approximately solving a sequence of well-chosen auxiliary problems, leading to faster convergence. This strategy applies to a large class of algorithms, including gradient descent, block coordinate descent, SAG, SAGA, SDCA, SVRG, Finito/MISO, and their proximal variants. For all of these methods, we provide acceleration and explicit support for non-strongly convex objectives. In addition to theoretical speed-up, we also show that acceleration is useful in practice, especially for ill-conditioned problems where we measure significant improvements.


title: Illumination Invariant Face Recognition: A Survey

abstract: The illumination variation problem is one of the well-known problems in face recognition in uncontrolled environment. In this paper an extensive and up-to-date survey of the existing techniques to address this problem is presented. This survey covers the passive techniques that attempt to solve the illumination problem by studying the visible light images in which face appearance has been altered by varying illumination, as well as the active techniques that aim to obtain images of face modalities invariant to environmental illumination.


title: What you read matters: The role of fiction genre in predicting interpersonal sensitivity.

abstract: Previous studies have found a positive relationship between exposure to fiction and interpersonal sensitivity. However, it is unclear whether exposure to different genres of fiction may be differentially related to these outcomes for readers. The current study investigated the role of four fiction genres (i.e., Domestic Fiction, Romance, Science-Fiction/Fantasy, and Suspense/Thriller) in the relationship between fiction and interpersonal sensitivity, controlling for other individual differences. Participants completed a survey that included a lifetime print-exposure measure along with an interpersonal sensitivity task. Some, but not all, fiction genres were related to higher scores on our measure of interpersonal sensitivity. Furthermore, after controlling for personality, gender, age, English fluency, and exposure to nonfiction, only the Romance and Suspense/Thriller genres remained significant predictors of interpersonal sensitivity. The findings of this study demonstrate that in discussing the influence of fiction print-exposure on readers it is important to consider the genre of the literature being consumed.


title: Urban informatics

abstract: The increasing ubiquity of digital technology, internet services and social media in our everyday lives allows for a seamless transitioning between the visible and the invisible infrastructure of cities: road systems, building complexes, information and communication technology, and people networks create a buzzing environment that is alive and exciting. Driven by curiosity, initiative and interdisciplinary exchange, the Urban Informatics Research Lab at Queensland University of Technology (QUT), Brisbane, Australia, is an emerging cluster of people interested in research and development at the intersection of people, place and technology with a focus on cities, locative media and mobile technology. This paper seeks to define, for the first time, what we mean by 'urban informatics' and outline its significance as a field of study today. It describes the relevant background and trends in each of the areas of people, place and technology, and highlights the relevance of urban informatics to the concerns and evolving challenges of CSCW. We then position our work in academia juxtaposed with related research concentrations and labels, followed by a discussion of disciplinary influences. The paper concludes with an expose of the three current research themes of the lab around augmented urban spaces, urban narratives, and environmental sustainability in order to illustrate specific cases and methods, and to draw out distinctions that our affiliation with the Creative Industries Faculty affords.


title: Implementation of the IEEE 802.11p/1609.4 DSRC/WAVE in NS-2

abstract: Due to the high costs of deploying and testing VANETs, simulations are required for development and evaluation of new protocols at any layer of WAVE protocol stack. Although there are powerful tools to simulate VANETs and especially the IEEE 802.11p/1609.4 DSRC/WAVE protocols, however, they are not free or not publically available. NS-2 Network Simulator is a free and widely accepted simulator used by researchers to simulate DSRC/WAVE. To the best of our knowledge, current version of NS-2 and developed simulation tools based on NS-2 do not appropriately support for the multichannel operation of the IEEE 802.11p/1609.4. In this paper, a new developed simulation tool based on NS-2 is introduced that provides a more realistic implementation of DSRC multiple channels and IEEE 1609.4 multichannel operation. The developed simulation tool has been tested through some simulation experiments.


title: Scan Matching for Graph SLAM in Indoor Dynamic Scenarios

abstract: SLAM (Simultaneous Localization And Mapping) plays an essential and important role for mobile robotic autonomous navigation. SLAM in dynamic environ- ments with moving objects is a challenging problem. We focus on scan matching for Graph-SLAM in indoor dynamic scenarios. Scan matching algorithm is pro- posed and implemented, which consists of the follow- ing phases: first, conditioned Hough Transform based segmentation is performed to extract and group line features; second, occupancy-analysis based moving ob- jects detection is done to detect and discard the seg- ments corresponding to the moving objects; third, lin- ear regression based line feature matching is executed to estimate the roto-translation parameters. Simulations to estimate roto-translation and the entire trajectory of the robot effectively verified the robustness of this al- gorithm in a dynamic scenario. The proposed algorithm is based on the line features of the indoor environment. It is robust to disturbances from moving objects in the dynamic scenario, and is especially suitable for the case when large rotational displacement is present.


title: SparkBench: a comprehensive benchmarking suite for in memory data analytic platform Spark

abstract: Spark has been increasingly adopted by industries in recent years for big data analysis by providing a fault tolerant, scalable and easy-to-use in memory abstraction. Moreover, the community has been actively developing a rich ecosystem around Spark, making it even more attractive. However, there is not yet a Spark specify benchmark existing in the literature to guide the development and cluster deployment of Spark to better fit resource demands of user applications. In this paper, we present S park B ench , a Spark specific benchmarking suite, which includes a comprehensive set of applications. S park B ench covers four main categories of applications, including machine learning, graph computation, SQL query and streaming applications. We also characterize the resource consumption, data flow and timing information of each application and evaluate the performance impact of a key configuration parameter to guide the design and optimization of Spark data analytic platform.


title: Intrinsic images in the wild

abstract: Intrinsic image decomposition separates an image into a reflectance layer and a shading layer. Automatic intrinsic image decomposition remains a significant challenge, particularly for real-world scenes. Advances on this longstanding problem have been spurred by public datasets of ground truth data, such as the MIT Intrinsic Images dataset. However, the difficulty of acquiring ground truth data has meant that such datasets cover a small range of materials and objects. In contrast, real-world scenes contain a rich range of shapes and materials, lit by complex illumination. In this paper we introduce Intrinsic Images in the Wild, a large-scale, public dataset for evaluating intrinsic image decompositions of indoor scenes. We create this benchmark through millions of crowdsourced annotations of relative comparisons of material properties at pairs of points in each scene. Crowdsourcing enables a scalable approach to acquiring a large database, and uses the ability of humans to judge material comparisons, despite variations in illumination. Given our database, we develop a dense CRF-based intrinsic image algorithm for images in the wild that outperforms a range of state-of-the-art intrinsic image algorithms. Intrinsic image decomposition remains a challenging problem; we release our code and database publicly to support future research on this problem, available online at http://intrinsic.cs.cornell.edu/.


title: Social Rewards and Social Networks in the Human Brain

abstract: The rapid development of social media and social networking sites in human society within the past decade has brought about an increased focus on the value of social relationships and being connected with others. Research suggests that we pursue socially valued or rewarding outcomes—approval, acceptance, reciprocity—as a means toward learning about others and fulfilling social needs of forming meaningful relationships. Focusing largely on recent advances in the human neuroimaging literature, we review findings highlighting the neural circuitry and processes that underlie pursuit of valued rewarding outcomes across non-social and social domains. We additionally discuss emerging human neuroimaging evidence supporting the idea that social rewards provide a gateway to establishing relationships and forming social networks. Characterizing the link between social network, brain, and behavior can potentially identify contributing factors to maladaptive influences on decision making within social situations.


title: A lightweight docking station for a hovering AUV

abstract: The concept of underwater docking stations has long been proposed to support the long term deployment of AUVs, but the number of successful solutions is still very disappointing. Hovering type AUVs can navigate arbitrarily slow, simplifying the docking maneuver and the requirements for the receiving structure. This paper describes a docking system that was developed to extend the mission duration of the MARES AUV, a man portable hovering type AUV. Given the wide range of operational scenarios and configurations of this AUV, one of the design requirements was to have a simple modular structure, that could easily be reconfigured to support different vehicle configurations, deployment scenarios and docking maneuvers. The paper provides details of the mechanical aspects, the onboard electronic subsystems, and the general operational procedure, as well as preliminary data from the first trials.


title: Coding scheme for 3D vertical flash memory

abstract: Recently introduced 3D vertical flash memory is expected to be a disruptive technology since it overcomes scaling challenges of conventional 2D planar flash memory by stacking up cells in the vertical direction. However, 3D vertical flash memory suffers from a new problem known as fast detrapping, which is a rapid charge loss problem. In this paper, we propose a scheme to compensate the effect of fast detrapping by intentional inter-cell interference (ICI). In order to properly control the intentional ICI, our scheme relies on a coding technique that incorporates the side information of fast detrapping during the encoding stage. This technique is closely connected to the well-known problem of coding in a memory with defective cells. Numerical results show that the proposed scheme can effectively address the problem of fast detrapping.


title: Intelligent tutors as teachers' aides: exploring teacher needs for real-time analytics in blended classrooms

abstract: Intelligent tutoring systems (ITSs) are commonly designed to enhance student learning. However, they are not typically designed to meet the needs of teachers who use them in their classrooms. ITSs generate a wealth of analytics about student learning and behavior, opening a rich design space for real-time teacher support tools such as dashboards. Whereas real-time dashboards for teachers have become popular with many learning technologies, we are not aware of projects that have designed dashboards for ITSs based on a broad investigation of teachers' needs. We conducted design interviews with ten middle school math teachers to explore their needs for on-the-spot support during blended class sessions, as a first step in a user-centered design process of a real-time dashboard. Based on multi-methods analyses of this interview data, we identify several opportunities for ITSs to better support teachers' needs, noting that the analytics commonly generated by existing teacher support tools do not strongly align with the analytics teachers expect to be most useful. We highlight key tensions and tradeoffs in the design of such real-time supports for teachers, as revealed by "Speed Dating" possible futures with teachers. This paper has implications for our ongoing co-design of a real-time dashboard for ITSs, as well as broader implications for the design of ITSs that can effectively collaborate with teachers in classroom settings.


title: Consensus rank orderings of molecular fingerprints illustrate the ‘most genuine’ similarities between marketed drugs and small endogenous human metabolites, but highlight exogenous natural products as the most important ‘natural’ drug transporter substrates

abstract: We compare several molecular fingerprint encodings for marketed, small molecule drugs, and assess how their rank order varies with the fingerprint in terms of the Tanimoto similarity to the most similar endogenous human metabolite as taken from Recon2. For the great majority of drugs, the rank order varies very greatly depending on the encoding used, and also somewhat when the Tanimoto similarity (TS) is replaced by the Tversky similarity. However, for a subset of such drugs, amounting to some 10% of the set and a Tanimoto similarity of ~0.8 or greater, the similarity coefficient is relatively robust to the encoding used. This leads to a metric that, while arbitrary, suggests that a Tanimoto similarity of 0.75-0.8 or greater genuinely does imply a considerable structural similarity of two molecules in the drug-endogenite space. Although comparatively few (<10% of) marketed drugs are, in this sense, robustly similar to an endogenite, there is often at least one encoding with which they are genuinely similar (e.g. TS > 0.75). This is referred to as the Take Your Pick Improved Cheminformatic Analytical Likeness or TYPICAL encoding, and on this basis some 66% of drugs are within a TS of 0.75 to an endogenite. We next explicitly recognise that natural evolution will have selected for the ability to transport dietary substances, including plant, animal and microbial ‘secondary’ metabolites, that are of benefit to the host. These should also be explored in terms of their closeness to marketed drugs. We thus compared the TS of marketed drugs with the contents of various databases of natural products. When this is done, we find that some 80% of marketed drugs are within a TS of 0.7 to a natural product, even using just the MACCS encoding. For patterned and TYPICAL encodings, 80% and 98% of drugs are within a TS of 0.8 to (an endogenite or) an exogenous natural product. This implies strongly that it is these exogeneous (dietary and medicinal) natural products that are more to be seen as the ‘natural’ substrates of drug transporters (as is recognised, for instance, for the solute carrier SLC22A4 and ergothioneine). This novel analysis casts an entirely different light on the kinds of natural molecules that are to be seen as most like marketed drugs, and hence potential transporter substrates, and further suggests that a renewed exploitation of natural products as drug scaffolds would be amply rewarded.


title: Dynamic rough clustering and its applications

abstract: Dynamic data mining has gained increasing attention in the last decade. It addresses changing data structures which can be observed in many real-life applications, e.g. buying behavior of customers. As opposed to classical, i.e. static data mining where the challenge is to discover pattern inherent in given data sets, in dynamic data mining the challenge is to understand - and in some cases even predict - how such pattern will change over time. Since changes in general lead to uncertainty, the appropriate approaches for uncertainty modeling are needed in order to capture, model, and predict the respective phenomena considered in dynamic environments. As a consequence, the combination of dynamic data mining and soft computing is a very promising research area. The proposed algorithm consists of a dynamic clustering cycle when the data set will be refreshed from time to time. Within this cycle criteria check if the newly arrived data have structurally changed in comparison to the data already analyzed. If yes, appropriate actions are triggered, in particular an update of the initial settings of the cluster algorithm. As we will show, rough clustering offers strong tools to detect such changing data structures. To evaluate the proposed dynamic rough clustering algorithm it has been applied to synthetic as well as to real-world data sets where it provides new insights regarding the underlying dynamic phenomena.


title: A Penalty-Function Approach for Pruning Feedforward Neural Networks

abstract: This article proposes the use of a penalty function for pruning feedforward neural network by weight elimination. The penalty function proposed consists of two terms. The first term is to discourage the use of unnecessary connections, and the second term is to prevent the weights of the connections from taking excessively large values. Simple criteria for eliminating weights from the network are also given. The effectiveness of this penalty function is tested on three well-known problems: the contiguity problem, the parity problems, and the monks problems. The resulting pruned networks obtained for many of these problems have fewer connections than previously reported in the literature.


title: Monocular-SLAM–based navigation for autonomous micro helicopters in GPS-denied environments

abstract: Autonomous micro aerial vehicles (MAVs) will soon play a major role in tasks such as search and rescue, environment monitoring, surveillance, and inspection. They allow us to easily access environments to which no humans or other vehicles can get access. This reduces the risk for both the people and the environment. For the above applications, it is, however, a requirement that the vehicle is able to navigate without using GPS, or without relying on a preexisting map, or without specific assumptions about the environment. This will allow operations in unstructured, unknown, and GPS-denied environments. We present a novel solution for the task of autonomous navigation of a micro helicopter through a completely unknown environment by using solely a single camera and inertial sensors onboard. Many existing solutions suffer from the problem of drift in the xy plane or from the dependency on a clean GPS signal. The novelty in the here-presented approach is to use a monocular simultaneous localization and mapping (SLAM) framework to stabilize the vehicle in six degrees of freedom. This way, we overcome the problem of both the drift and the GPS dependency. The pose estimated by the visual SLAM algorithm is used in a linear optimal controller that allows us to perform all basic maneuvers such as hovering, set point and trajectory following, vertical takeoff, and landing. All calculations including SLAM and controller are running in real time and online while the helicopter is flying. No offline processing or preprocessing is done. We show real experiments that demonstrate that the vehicle can fly autonomously in an unknown and unstructured environment. To the best of our knowledge, the here-presented work describes the first aerial vehicle that uses onboard monocular vision as a main sensor to navigate through an unknown GPS-denied environment and independently of any external artificial aids. © 2011 Wiley Periodicals, Inc. © 2011 Wiley Periodicals, Inc.


title: Correlated Percolation, Fractal Structures, and Scale-Invariant Distribution of Clusters in Natural Images

abstract: Natural images are scale invariant with structures at all length scales.We formulated a geometric view of scale invariance in natural images using percolation theory, which describes the behavior of connected clusters on graphs.We map images to the percolation model by defining clusters on a binary representation for images. We show that critical percolating structures emerge in natural images and study their scaling properties by identifying fractal dimensions and exponents for the scale-invariant distributions of clusters. This formulation leads to a method for identifying clusters in images from underlying structures as a starting point for image segmentation.


title: The Study of a Dual-Mode Ring Oscillator

abstract: An analytical investigation of a dual-mode ring oscillator is presented. The ring oscillator is designed using a CMOS 0.18-μm technology with differential eight-stage delay cells employing auxiliary input devices. With a proper startup control, the oscillator operates in two different modes covering two different frequency bands. A nonlinear model, along with the linearization method, is used to obtain the transient and steady-state behaviors of the dual-mode ring oscillator. The analytical derivations are verified through HSPICE simulation. The oscillator operates at the frequency bands from 2-5 GHz and from 0.1-2 GHz, respectively.


title: Using multiple hypotheses to improve depth-maps for multi-view stereo

abstract: We propose an algorithm to improve the quality of depth-maps used for Multi-View Stereo (MVS). Many existing MVS techniques make use of a two stage approach which estimates depth-maps from neighbouring images and then merges them to extract a final surface. Often the depth-maps used for the merging stage will contain outliers due to errors in the matching process. Traditional systems exploit redundancy in the image sequence (the surface is seen in many views), in order to make the final surface estimate robust to these outliers. In the case of sparse data sets there is often insufficient redundancy and thus performance degrades as the number of images decreases. In order to improve performance in these circumstances it is necessary to remove the outliers from the depth-maps. We identify the two main sources of outliers in a top performing algorithm: (1) spurious matches due to repeated texture and (2) matching failure due to occlusion, distortion and lack of texture. We propose two contributions to tackle these failure modes. Firstly, we store multiple depth hypotheses and use a spatial consistency constraint to extract the true depth. Secondly, we allow the algorithm to return an unknownstate when the a true depth estimate cannot be found. By combining these in a discrete label MRF optimisation we are able to obtain high accuracy depth-maps with low numbers of outliers. We evaluate our algorithm in a multi-view stereo framework and find it to confer state-of-the-art performance with the leading techniques, in particular on the standard evaluation sparse data sets.


title: How to Engage Users through Gamification: The Prevalent Effects of Playing and Mastering over Competing

abstract: Over recent years, gamification has been a frequent strategy to increase user engagement. Gamification of systems is usually associated with incorporating mechanisms for attributing points and badges to guide users' behaviors. However, since the dawn of the digital game industry in the 1980's, Malone's work has shown that the desire to play and master a game are important motivations to engage users. This paper aims to analyze the most engaging factors for gamers in the current context of technology. Using a sample of 717 users whose game preferences were classified into eight categories, representing different emphasis on playing, mastering, and competing, results show that competing is the least important factor to motivate engagement. As a consequence, we question the relevance of some of the most used gamification strategies like attributing points, badges, and reputation to participants. Additionally, we offer some suggestions for development of gamification of systems.


title: Finding latent code errors via machine learning over program executions

abstract: This paper proposes a technique for identifying program properties that indicate errors. The technique generates machine learning models of program properties known to result from errors, and applies these models to program properties of user-written code to classify and rank properties that may lead the user to errors. Given a set of properties produced by the program analysis, the technique selects a subset of properties that are most likely to reveal an error. An implementation, the fault invariant classifier, demonstrates the efficacy of the technique. The implementation uses dynamic invariant detection to generate program properties. It uses support vector machine and decision tree learning tools to classify those properties. In our experimental evaluation, the technique increases the relevance (the concentration of fault-revealing properties) by a factor of 50 on average for the C programs, and 4.8 for the Java programs. Preliminary experience suggests that most of the fault-revealing properties do lead a programmer to an error.


title: © DIGITAL VISION, LTD. Feature Complex Networks: Small-World, Scale-Free and Beyond

abstract: In the past few years, the discovery of small-world and scale-free properties of many natural and artificial complex networks has stimulated a great deal of interest in studying the underlying organizing principles of various complex networks, which has led to dramatic advances in this emerging and active field of research. The present article reviews some basic concepts, important progress, and significant results in the current studies of various complex networks, with emphasis on the relationship between the topology and the dynamics of such complex networks. Some fundamental properties and typical complex network models are described; and, as an example, epidemic dynamics are analyzed and discussed in some detail. Finally, the important issue of robustness versus fragility of dynamical synchronization in complex networks is introduced and discussed.


title: A Survey on Density Based Clustering Algorithms for Mining Large Spatial Databases

abstract: Density based clustering algorithm is one of the primary methods for clustering in data mining. The clusters which are formed based on the density are easy to understand and it does not limit itself to the shapes of clusters. This paper gives a detailed survey of the existing density based algorithms namely DBSCAN, VDBSCAN, DVBSCAN, ST-DBSCAN and DBCLASD based on the essential parameters needed for a good clustering algorithm. We analyse the algorithms in terms of the parameters essential for creating meaningful clusters.


title: An evolutionary algorithm for the one-dimensional cutting stock problem

abstract: This paper deals with the one-dimensional integer cutting stock problem, which consists of cutting a set of available objects in stock in order to produce ordered smaller items in such a way as to minimize the waste of material. The case in which there are various types of objects available in stock in limited quantities is studied. A new heuristic method based on the evolutionary algorithm concept is proposed to solve the problem. This heuristic is empirically analyzed by solving randomly generated instances and the results are compared with other methods from the literature.


title: K-NN classifier for data confidentiality in cloud computing

abstract: Securing the data in cloud is still a challenging issue. In cloud, many techniques are used to secure the data. Data encryption is a data security technique which is widely used for data security. Deciding data security approach for the data without understanding the security needs of the data is not a technically valid approach. Before applying any security on data in cloud, it is best to know the security needs of the data. What data need security and what data do not need security. In this paper, we propose a data classification approach based on data confidentiality. K-NN data classification technique is modulated in the cloud virtual environment. The aim to use K-NN is to classify the data based on their security needs. The data is classified into two classes, sensitive and non-sensitive (public) data. After data classification we got that what data need security and what data does not need security. The RSA algorithm is used to encrypt the sensitive data to keep it secure. We used CloudSim simulator to find the results of proposed approach in cloud. The proposed approach will easily decide the security needs of the data. After the data classification, it is easy to select an appropriate security for data according to the need of data. The results show that this approach is appropriate as compared to store data in cloud without understanding the security requirements of data.


title: An IOT by information retrieval approach: Smart lights controlled using WiFi

abstract: In recent years, the rapid development of Internet of Technology (IOT) makes the intelligent home come true as people expect. The intelligent home system creates the more comfortable, safer, humane and intelligent living environment. It can resolve the problems facing by the people who have busy schedules and get a very less amount of time to spend at home which is increasing rapidly around the world. For the solution of this problem, user can depend on the automated machines and gadgets like smart phones. These smart gadgets are using cloud computing which sends and receives signal o the cloud. The data that is of our use can be fetched by matching some key values using the concept of information retrieval. The key objective of this paper is to create a full-fledged application which could let user to operate the lights of their house from any remote location. The user have a list of options to select which light is to be on and when. The only requirement is to have working wifi at home to which the lights are connected. The is developed in Lua Language by using the Esplorer Integrated Development Environment (IDE). We have also used the micro controller chip ESP 8266 to build our board.


title: Monocular SLAM as a Graph of Coalesced Observations

abstract: We present a monocular SLAM system that avoids inconsistency by coalescing observations into independent local coordinate frames, building a graph of the local frames, and optimizing the resulting graph. We choose coordinates that minimize the nonlinearity of the updates in the nodes, and suggest a heuristic measure of such nonlinearity, using it to guide our traversal of the graph. The system operates in real-time on sequences with several hundreds of landmarks while performing global graph optimization, yielding accurate and nearly consistent estimation relative to offline bundle adjustment, and considerably better consistency than EKF SLAM and FastSLAM.


Privacy leakage vs. protection measures: the growing disconnect


title: Rasterizing and antialiasing vector line art in the pixel art style

abstract: Pixel artists rasterize vector shapes by hand to minimize artifacts at low resolutions and emphasize the aesthetics of visible pixels. We describe Superpixelator, an algorithm that automates this process by rasterizing vector line art at a low resolution pixel art style. Our technique successfully eliminates most rasterization artifacts and draws smoother curves. To draw shapes more effectively, we use optimization techniques to preserve shape properties such as symmetry, aspect ratio, and sharp angles. Our algorithm also supports "manual antialiasing," the style of antialiasing used in pixel art. Professional pixel artists report that Superpixelator's results are as good, or better, than hand-rasterized drawings by artists.


title: A High Resolution EEG Study of Dynamic Brain Activity during Video Game Play

abstract: A high resolution EEG study was conducted on healthy human subjects during video game play. Throughout the game playing experiment short time segments of spontaneous activity were recorded. Spectral analysis was performed on these segments for the theta-wave (4-8 Hz) and alpha-wave (8-13 Hz) bands to investigate the modulatory effects of long-lasting game play and the dynamic changes of spectral contribution in range of alpha- and theta-wave. The present results revealed that a) the frontal midline theta-wave activity increased over time relative to the eye open resting condition and b) the parietal alpha-wave activity initially decreased relative to the resting condition, then followed by a slow increase. These experimental results indicate the high resolution EEG provides a useful quantitative analysis tool for studying dynamic brain activity.


title: Stagnation-Point Pressure Distribution and Wall Shear Stress: Numerical Simulation and Similarity Solution

abstract: Two-dimensional stagnation-point flow in three different aspect ratios is studied by solving the full Navier-Stokes equations with the computational fluid dynamics (CFD) software of STAR-CD and results are compared with those obtained from the similarity solution. Pressure variation in the directions normal and parallel to the wall, as well as wall shear stress along the wall are investigated. Results show that pressure profiles are in good agreement for both numerical simulation and similarity solution at high aspect ratio in the stagnation-point region. Discrepancy of results in pressure profiles increases as aspect ratio decreases and in region which is away from the stagnation-point flow. It is found that wall shear stress is proportional to the distance from the stagnation-point along the wall as expected by similarity solution.


title: Impact of heterogeneity and collaborative conflict management style on the performance of synchronous global virtual teams

abstract: Virtual teams cut across organizational cultures, national cultures, and functional areas, thereby increasing group heterogeneity, which may result in increased conflict among team members and less effective performance of the team. Our study explored the relationships that might exist among the heterogeneity of the virtual teams, their collaborative conflict management style, and their performance outcomes. The paper reports the findings of a laboratory experiment in which homogeneous and heterogeneous virtual teams, consisting of subjects from the USA and India, worked independently on a decision task involving the adoption of a computer use fee by an online university. Team members, used a web-based group decision support system (GDSS) that allowed them the opportunity to discuss task options, critique suggestions, and vote on the result. The data analyses suggested that collaborative conflict management style positively impacted satisfaction with the decision making process, perceived decision quality, and perceived participation of the virtual teams. There was weak evidence that links a group's heterogeneity to its collaborative conflict management styles.


title: The critical hitch angle for jackknife avoidance during slow backing up of vehicle–trailer systems

abstract: We set out to answer the question: At what hitch angle does it become impossible for a vehicle and trailer to continue to backing up without getting into a jackknife? Jackknifing during backing up of trailers occurs when the hitch angle increases to a point such that the vehicle and trailer fold together about the hitch point like a jackknife. If the backward motion is continued, the jackknife effect progressively worsens, until the vehicle and trailer are in physical contact with each other. Jackknifing can result in traffic disruptions and wasted time, and can potentially cause damage or personal injury. Our goal is to analytically determine the ‘critical hitch angle’ (θcr), the hitch angle threshold beyond which a continued reverse motion causes an inescapable jackknifing. In this paper, we provide a formal definition of θcr for slow backing up of vehicle–trailer systems on a level solid surface, beyond which the vehicle must stop backing up and revert to forward motion in order to escape from jackknif...


title: CREWS-SAVRE: Scenarios for Acquiring and Validating Requirements

abstract: This paper reports research into semi-automatic generation of scenarios for validating software-intensive system requirements. The research was undertaken as part of the ESPRIT IV 21903 ‘CREWS’ long-term research project. The paper presents the underlying theoretical models of domain knowledge, computational mechanisms and user-driven dialogues needed for scenario generation. It describes how CREWS draws on theoretical results from the ESPRIT III 6353 ‘NATURE’ basic research action, that is object system models which are abstractions of the fundamental features of different categories of problem domain. CREWS uses these models to generate normal course scenarios, then draws on theoretical and empirical research from cognitive science, human-computer interaction, collaborative systems and software engineering to generate alternative courses for these scenarios. The paper describes a computational mechanism for deriving use cases from object system models, simple rules to link actions in a use case, taxonomies of classes of exceptions which give rise to alternative courses in scenarios, and a computational mechanism for generation of multiple scenarios from a use case specification.


title: Prediction With Gaussian Processes: From Linear Regression To Linear Prediction And Beyond

abstract: The main aim of this paper is to provide a tutorial on regression with Gaussian processes. We start from Bayesian linear regression, and show how by a change of viewpoint one can see this method as a Gaussian process predictor based on priors over functions, rather than on priors over parameters. This leads in to a more general discussion of Gaussian processes in section 4. Section 5 deals with further issues, including hierarchical modelling and the setting of the parameters that control the Gaussian process, the covariance functions for neural network models and the use of Gaussian processes in classification problems.


title: Automatic rock recognition from drilling performance data

abstract: Automated rock recognition is a key step for building a fully autonomous mine. When characterizing rock types from drill performance data, the main challenge is that there is not an obvious one-to-one correspondence between the two. In this paper, a hybrid rock recognition approach is proposed which combines Gaussian Process (GP) regression with clustering. Drill performance data is also known as Measurement While Drilling (MWD) data and a rock hardness measure - Adjusted Penetration Rate (APR) is extracted using the raw data in discrete drill holes. GP regression is then applied to create a more dense APR distribution, followed by clustering which produces discrete class labels. No initial labelling is needed. Comparisons are made with alternative measures of rock hardness from MWD data as well as state-of-the-art GP classification. Experimental results from an actual mine site show the effectiveness of our proposed approach.


title: FOSHU: faster on-shelf high utility itemset mining -- with or without negative unit profit

abstract: High utility itemset (HUI) mining is a popular data mining task, which consists of discovering sets of items generating high profit in a transaction database. Recently, several efficient algorithms have been proposed for this task. But, most of them do not consider the on-shelf time periods of items, which thus lead to a bias toward items having more shelf time. Moreover, most algorithms cannot handle databases containing items with a negative unit profit, although this case is very common in real transaction databases. In this paper, we address both of these challenges by proposing a novel efficient algorithm named FOSHU (Faster On-Shelf High Utility itemset miner) to mine HUIs while considering on-shelf time periods of items, and items having positive and/or negative unit profit. An extensive experimental study with real-life datasets shows that the proposed algorithm can be up more than 1000 times faster and use up to 10 times less memory than the state-of-the-art algorithm TS-HOUN for this task. Moreover, experiments show that the proposed algorithm performs well on dense database and databases containing many time periods.


Sustainable Ecological Aquaculture Systems: The Need for a New Social Contract for Aquaculture Development


title: Using String-Kernels For Learning Semantic Parsers

abstract: We present a new approach for mapping natural language sentences to their formal meaning representations using string-kernel-based classifiers. Our system learns these classifiers for every production in the formal language grammar. Meaning representations for novel natural language sentences are obtained by finding the most probable semantic parse using these string classifiers. Our experiments on two real-world data sets show that this approach compares favorably to other existing systems and is particularly robust to noise.


title: Income inequality in today’s China

abstract: Abstract Using multiple data sources, we establish that China's income inequality since 2005 has reached very high levels, with the Gini coefficient in the range of 0.53–0.55. Analyzing comparable survey data collected in 2010 in China and the United States, we examine social determinants that help explain China’s high income inequality. Our results indicate that a substantial part of China’s high income inequality is due to regional disparities and the rural-urban gap. The contributions of these two structural forces are particularly strong in China, but they play a negligible role in generating the overall income inequality in the United States, where individual-level and family-level income determinants, such as family structure and race/ethnicity, play a much larger role.


title: A tutorial on branch and cut algorithms for the maximum stable set problem

abstract: This tutorial provides an overview of various characteristics of effective branch and cut type algorithms for the maximum stable set problem. We discuss several facet-defining inequalities for the stable set polytope along with their separation routines. In particular, we review implementation tweaks for the separation routines and reference empirical studies, illustrating the performance of these cutting planes for benchmark graphs. In addition to the polyhedral study, we present basic preprocessing, discuss heuristic methods particularly suited within a branch and cut framework, and examine a branching rule.


title: The Generalized PatchMatch Correspondence Algorithm

abstract: PatchMatch is a fast algorithm for computing dense approximate nearest neighbor correspondences between patches of two image regions [1]. This paper generalizes PatchMatch in three ways: (1) to find k nearest neighbors, as opposed to just one, (2) to search across scales and rotations, in addition to just translations, and (3) to match using arbitrary descriptors and distances, not just sum-of-squared-differences on patch colors. In addition, we offer new search and parallelization strategies that further accelerate the method, and we show performance improvements over standard kd-tree techniques across a variety of inputs. In contrast to many previous matching algorithms, which for efficiency reasons have restricted matching to sparse interest points, or spatially proximate matches, our algorithm can efficiently find global, dense matches, even while matching across all scales and rotations. This is especially useful for computer vision applications, where our algorithm can be used as an efficient general-purpose component. We explore a variety of vision applications: denoising, finding forgeries by detecting cloned regions, symmetry detection, and object detection.


title: Analyzing topic evolution in bioinformatics: investigation of dynamics of the field with conference data in DBLP

abstract: In this paper we analyze topic evolution over time within bioinformatics to uncover the underlying dynamics of that field, focusing on the recent developments in the 2000s. We select 33 bioinformatics related conferences indexed in DBLP from 2000 to 2011. The major reason for choosing DBLP as the data source instead of PubMed is that DBLP retains most bioinformatics related conferences, and to study dynamics of the field, conference papers are more suitable than journal papers. We divide a period of a dozen years into four periods: period 1 (2000---2002), period 2 (2003---2005), period 3 (2006---2008) and period 4 (2009---2011). To conduct topic evolution analysis, we employ three major procedures, and for each procedure, we develop the following novel technique: the Markov Random Field-based topic clustering, automatic cluster labeling, and topic similarity based on Within-Period Cluster Similarity and Between-Period Cluster Similarity. The experimental results show that there are distinct topic transition patterns between different time periods. From period 1 to period 3, new topics seem to have emerged and expanded, whereas from period 3 to period 4, topics are merged and display more rigorous interaction with each other. This trend is confirmed by the collaboration pattern over time.


title: Compact Low-Loss Tunable $X$-Band Bandstop Filter With Miniature RF-MEMS Switches

abstract: This paper presents a compact low-loss tunable X-band bandstop filter implemented on a quartz substrate using both miniature RF microelectromechanical systems (RF-MEMS) capacitive switches and GaAs varactors. The two-pole filter is based on capacitively loaded folded-λ/2 resonators that are coupled to a microstrip line, and the filter analysis includes the effects of nonadjacent inter-resonator coupling. The RF-MEMS filter tunes from 11.34 to 8.92 GHz with a - 20-dB rejection bandwidth of 1.18%-3.51% and a filter quality factor of 60-135. The GaAs varactor loaded filter tunes from 9.56 to 8.66 GHz with a - 20-dB bandwidth of 1.65%-2% and a filter quality factor of 55-90. Nonlinear measurements at the filter null with Δf = 1 MHz show that the RF-MEMS loaded filter results in > 25-dBm higher third-order intermodulation intercept point and P-1 dB compared with the varactor loaded filter. Both filters show high rejection levels ( > 24 dB) and low passband insertion loss ( <; 0.8 dB) from dc to the first spurious response at 19.5 GHz. The filter topology can be extended to higher order designs with an even number of poles.


title: Information retrieval from social networks: A survey

abstract: Social networks are becoming an integral part of people's life to share and disseminate information instantly. Users share their views on a particular topic or events and receive feedback from their followers, friends and colleagues in form of comments, like, dislike, upvote, downvote etc. Since the user has a very limited control on previously shared information and has limited storage space in his/her account, the management of users information is a very tedious task. In this paper, we discuss different types of social networking services and user's information shared on these services. We categorize the content based information into two categories namely textual content based information and visual content based information. Then we discuss the major efforts made for retrieval of these information from different social networks. We also outline a procedure for content based information retrieval from multiple social networks.


hdbscan: Hierarchical density based clustering


title: A Survey of Topology-based Methods in Visualization

abstract: This paper presents the state of the art in the area of topology-based visualization. It describes the process and results of an extensive annotation for generating a definition and terminology for the field. The terminology enabled a typology for topological models which is used to organize research results and the state of the art. Our report discusses relations among topological models and for each model describes research results for the computation, simplification, visualization, and application. The paper identifies themes common to subfields, current frontiers, and unexplored territory in this research area.


title: Generating realistic scaled complex networks

abstract: Research on generative models is a central project in the emerging field of network science, and it studies how statistical patterns found in real networks could be generated by formal rules. Output from these generative models is then the basis for designing and evaluating computational methods on networks, and for verification and simulation studies. During the last two decades, a variety of models has been proposed with an ultimate goal of achieving comprehensive realism for the generated networks. In this study, we (a) introduce a new generator, termed ReCoN; (b) explore how ReCoN and some existing models can be fitted to an original network to produce a structurally similar replica, (c) use ReCoN to produce networks much larger than the original exemplar, and finally (d) discuss open problems and promising research directions. In a comparative experimental study, we find that ReCoN is often superior to many other state-of-the-art network generation methods. We argue that ReCoN is a scalable and effective tool for modeling a given network while preserving important properties at both micro- and macroscopic scales, and for scaling the exemplar data by orders of magnitude in size.


title: Wideband Circularly Polarized Antenna Realizing Omnidirectional Radiation in the Wider Azimuth Planes

abstract: A novel circularly polarized (CP) antenna realizing omnidirectional radiation in the wider azimuth planes is proposed in this letter. By introducing curved branches at both the patch and ground plane and making an angle of α between them, the amplitude and phase characteristic of the antenna are improved. Meanwhile, the omnidirectional CP field is obtained in the wider azimuth planes. The measured results show that the proposed antenna has a −10-dB impedance bandwidth of 19.6% and a 3-dB axial-ratio bandwidth of 27.4%. Moreover, the omnidirectional CP fields can be obtained in the azimuth planes of $\theta \,= \,50^\circ{-}\text{130}^\circ $ .


title: Model-Based and Model-Free Mechanisms of Human Motor Learning

abstract: Motor learning can be framed theoretically as a problem of optimizing a movement policy in a potentially uncertain or changing environment. This is precisely the general problem studied in the field of reinforcement learning. Reinforcement learning theory proposes two distinct approaches to solving this general problem: Model-based approaches first identify the dynamics of the task or environment then use this knowledge to compute the optimal movement policy. Model-free approaches, by contrast, directly identify successful policies through a process of trial and error. Here, we review existing literature on motor control in the light of this distinction. Motor learning research in the last decade has been dominated by studies that elicit learning through adaptation paradigms and find the results to be consistent with a model-based framework. Studying the behavior of patients in such adaptation paradigms has implicated the cerebellum as prime candidate for the neural substrate of the internal models that sub serve model-based control. A growing body of experimental results, however, demonstrates that not all of motor learning in conventional paradigms can be explained within model-based frameworks, but can be understood in terms of an additional component of learning driven by model-free reinforcement of successful actions. We conclude that the brain maintains distinct model-based and model-free learning systems, with distinct neural substrates, which act in competitive balance to direct behavior.


title: Phonetic learning as a pathway to language: new data and native language magnet theory expanded (NLM-e)

abstract: Infants’ speech perception skills show a dual change towards the end of the first year of life. Not only does non-native speech perception decline, as often shown, but native language speech perception skills show improvement, reflecting a facilitative effect of experience with native language. The mechanism underlying change at this point in development, and the relationship between the change in native and non-native speech perception, is of theoretical interest. As shown in new data presented here, at the cusp of this developmental change, infants’ native and non-native phonetic perception skills predict later language ability, but in opposite directions. Better native language skill at 7.5 months of age predicts faster language advancement, whereas better non-native language skill predicts slower advancement. We suggest that native language phonetic performance is indicative of neural commitment to the native language, while non-native phonetic performance reveals uncommitted neural circuitry. This paper has three goals: (i) to review existing models of phonetic perception development, (ii) to present new event-related potential data showing that native and nonnative phonetic perception at 7.5 months of age predicts language growth over the next 2 years, and (iii) to describe a revised version of our previous model, the native language magnet model, expanded (NLM-e). NLM-e incorporates five new principles. Specific testable predictions for future research programmes are described.


title: Kalman filtering over a packet dropping network: A probabilistic approach

abstract: We consider the problem of state estimation of a discrete time process over a packet dropping network. Previous pioneering work on Kalman filtering with intermittent observations is concerned with the asymptotic behavior of E[Pk], i.e., the expected value of the error covariance, for a given packet arrival rate. We consider a different performance metric, Pr[Pk les M], i.e., the probability that Pk is bounded by a given M, and we derive lower and upper bounds on Pr[Pk les M]. We are also able to recover the results in the literature when using Pr[Pk les M] as a metric for scalar systems. Examples are provided to illustrate the theory developed in the paper.