title: Multi-view constrained clustering with an incomplete mapping between views

abstract: Multi-view learning algorithms typically assume a complete bipartite mapping between the different views in order to exchange information during the learning process. However, many applications provide only a partial mapping between the views, creating a challenge for current methods. To address this problem, we propose a multi-view algorithm based on constrained clustering that can operate with an incomplete mapping. Given a set of pairwise constraints in each view, our approach propagates these constraints using a local similarity measure to those instances that can be mapped to the other views, allowing the propagated constraints to be transferred across views via the partial mapping. It uses co-EM to iteratively estimate the propagation within each view based on the current clustering model, transfer the constraints across views, and then update the clustering model. By alternating the learning process between views, this approach produces a unified clustering model that is consistent with all views. We show that this approach significantly improves clustering performance over several other methods for transferring constraints and allows multi-view clustering to be reliably applied when given a limited mapping between the views. Our evaluation reveals that the propagated constraints have high precision with respect to the true clusters in the data, explaining their benefit to clustering performance in both single- and multi-view learning scenarios.


title: Opsin genes, cone photopigments, color vision, and color blindness

abstract: In this chapter, we introduce the molecular structure of the genes encoding the human cone photopigments and their expression in photoreceptor cells. We also consider the consequences that alterations in those genes have on the spectral sensitivity of the photopigments, the cone photoreceptor mosaic, and the perceptual worlds of the color normal and color blind individuals who possess them. Throughout, we highlight areas in which our knowledge is still incomplete.


title: Evaluating Interactive Gaming as a Language Learning Tool

abstract: We propose a methodology for evaluating second language acquisition in the context of massive multi-player online role-playing games (MMORPG). Careful examination of learning opportunities present in gaming requires us to direct our attention to the characteristics of MMORPGs and how these characteristics support learning. MMORPGs accommodate active learners who assume the role of characters they have selected, create immersive environments that promote the development of conceptual knowledge and engage players in social interaction among a community of players. Since MMORPGs support social interaction between players, MMORPGs serve as the catalyst for fostering students language proficiency as students interact in a foreign language while playing the game. For these reasons, we believe that MMORPGs embody an interesting and underutilized learning environment for second language acquisition. Based upon this premise, we introduce a game-language learning model and explore Sony Online Entertainments 3D game Ever Quest II as a pedagogical tool for students learning English as a second language.


title: DIRECT ESTIMATION OF THE SEISMIC DEMAND AND CAPACITY OF MDOF SYSTEMS THROUGH INCREMENTAL DYNAMIC ANALYSIS OF AN SDOF APPROXIMATION

abstract: Introducing a fast and accurate method to estimate the seismic demand and capacity of first-mode-dominated multi-degree-of-freedom systems in regions ranging from nearelastic to global collapse. This is made possible by exploiting the connection between the Static Pushover (SPO) and the Incremental Dynamic Analysis (IDA). While the computer-intensive IDA would require several nonlinear dynamic analyses under multiple suitably-scaled ground motion records, the simpler SPO helps approximate the multi-degree-of-freedom system with a single-degree-of-freedom oscillator whose backbone matches the structure’s SPO curve far beyond its peak. Similar methodologies exist but they usually employ oscillators with a bilinear backbone. In contrast, the empirical equations implemented in the SPO2IDA software allow the use of a complex quadrilinear backbone shape. Thus, the entire summarized IDA curves of the resulting system are effortlessly generated, enabling an engineer-user to obtain accurate estimates of seismic demands and capacities for limit-states such as immediate occupancy or global dynamic instability. Using three multistory buildings as case studies, the methodology is favorably compared to the full IDA. CE Database keywords: Nonlinear analysis; Static analysis; Dynamic analysis; Seismic response; Earthquakes; Performance evaluation; Buildings, multistory; Approximation methods.


History and operation of NAVSTAR, the global positioning system


title: Flash Lidar Sensor Using Fiber-Coupled APDs

abstract: In contrast to scanning light detection and ranging (Lidar) system, the flash Lidar has no mechanical moving errors, and its features include light weight, small volume, and energy efficiency. This paper presents the advances of the newest research on a flash Lidar sensing instrument based on fiber-coupled avalanche photodiodes (APDs), which is named GLidar featured with easily increasable pixels of Lidar sensor, effectively reducible electrical crosstalk of adjacent pixels, and cost efficiency when compared with flash Lidar with a single APD array. A complete laser emitting system, including laser emitting and receiving system, controlling systems, electro-optic transmitting system and high precision time-interval measurement system, and different kinds of power supplies has been designed and implemented. The fiber-coupled APDs are arranged in a $5\times 5$ array as a key element of the Lidar sensor. The experimental results through the multipoint distance measurement show that the greatest error among the 25 channels of the Lidar sensor is 0.08 m when the target is 13 m away, and the accuracy validation in a hallway demonstrated the accuracy of distance measurement can reach 10.8 cm.


title: Code selection through object code optimization

abstract: Cet article montre comment une optimisation de code objet minutieux a simplifiee un compilateur et la rendu facile a recibler. Les compilateurs croises emettent un code comparable aux compilateurs specifiques vides


title: Zero-bias autoencoders and the benefits of co-adapting features

abstract: Regularized training of an autoencoder typically results in hidden unit biases that take on large negative values. We show that negative biases are a natural result of using a hidden layer whose responsibility is to both represent the input data and act as a selection mechanism that ensures sparsity of the representation. We then show that negative biases impede the learning of data distributions whose intrinsic dimensionality is high. We also propose a new activation function that decouples the two roles of the hidden layer and that allows us to learn representations on data with very high intrinsic dimensionality, where standard autoencoders typically fail. Since the decoupled activation function acts like an implicit regularizer, the model can be trained by minimizing the reconstruction error of training data, without requiring any additional regularization.


title: Impacts of organizational capabilities in information security

abstract: Purpose – This research aims to examine the relationship between information security strategy and organization performance, with organizational capabilities as important factors influencing successful implementation of information security strategy and organization performance.Design/methodology/approach – Based on existing literature in strategic management and information security, a theoretical model was proposed and validated. A self‐administered survey instrument was developed to collect empirical data. Structural equation modeling was used to test hypotheses and to fit the theoretical model.Findings – Evidence suggests that organizational capabilities, encompassing the ability to develop high‐quality situational awareness of the current and future threat environment, the ability to possess appropriate means, and the ability to orchestrate the means to respond to information security threats, are positively associated with effective implementation of information security strategy, which in turn posi...


title: Particle Swarm Optimization (PSO) for the constrained portfolio optimization problem

abstract: One of the most studied problems in the financial investment expert system is the intractability of portfolios. The non-linear constrained portfolio optimization problem with multi-objective functions cannot be efficiently solved using traditionally approaches. This paper presents a meta-heuristic approach to portfolio optimization problem using Particle Swarm Optimization (PSO) technique. The model is tested on various restricted and unrestricted risky investment portfolios and a comparative study with Genetic Algorithms is implemented. The PSO model demonstrates high computational efficiency in constructing optimal risky portfolios. Preliminary results show that the approach is very promising and achieves results comparable or superior with the state of the art solvers.


Running head : HOW VIDEO GAMES BENEFIT YOUR BRAIN How Video Games Benefit Your Brain


Do we know others' visual liking?


title: Practical private set intersection protocols with linear complexity

abstract: The constantly increasing dependence on anytime-anywhere availability of data and the commensurately increasing fear of losing privacy motivate the need for privacy-preserving techniques. One interesting and common problem occurs when two parties need to privately compute an intersection of their respective sets of data. In doing so, one or both parties must obtain the intersection (if one exists), while neither should learn anything about other set elements. Although prior work has yielded a number of effective and elegant Private Set Intersection (PSI) techniques, the quest for efficiency is still underway. This paper explores some PSI variations and constructs several secure protocols that are appreciably more efficient than the state-of-the-art.


title: Systems pharmacology and genome medicine: a future perspective

abstract: Genome medicine uses genomic information in the diagnosis of disease and in prescribing treatment. This transdisciplinary field brings together knowledge on the relationships between genetics, pathophysiology and pharmacology. Systems pharmacology aims to understand the actions and adverse effects of drugs by considering targets in the context of the biological networks in which they exist. Genome medicine forms the base on which systems pharmacology can develop. Experimental and computational approaches enable systems pharmacology to obtain holistic, mechanistic information on disease networks and drug responses, and to identify new drug targets and specific drug combinations. Network analyses of interactions involved in pathophysiology and drug response across various scales of organization, from molecular to organismal, will allow the integration of the systems-level understanding of drug action with genome medicine. The interface of the two fields will enable drug discovery for personalized medicine. Here we provide a perspective on the questions and approaches that drive the development of these new interrelated fields.


title: Signing Me onto Your Accounts through Facebook and Google: A Traffic-Guided Security Study of Commercially Deployed Single-Sign-On Web Services

abstract: With the boom of software-as-a-service and social networking, web-based single sign-on (SSO) schemes are being deployed by more and more commercial websites to safeguard many web resources. Despite prior research in formal verification, little has been done to analyze the security quality of SSO schemes that are commercially deployed in the real world. Such an analysis faces unique technical challenges, including lack of access to well-documented protocols and code, and the complexity brought in by the rich browser elements (script, Flash, etc.). In this paper, we report the first "field study" on popular web SSO systems. In every studied case, we focused on the actual web traffic going through the browser, and used an algorithm to recover important semantic information and identify potential exploit opportunities. Such opportunities guided us to the discoveries of real flaws. In this study, we discovered 8 serious logic flaws in high-profile ID providers and relying party websites, such as Open ID (including Google ID and Pay Pal Access), Face book, Jan Rain, Freelancer, Farm Ville, Sears.com, etc. Every flaw allows an attacker to sign in as the victim user. We reported our findings to affected companies, and received their acknowledgements in various ways. All the reported flaws, except those discovered very recently, have been fixed. This study shows that the overall security quality of SSO deployments seems worrisome. We hope that the SSO community conducts a study similar to ours, but in a larger scale, to better understand to what extent SSO is insecurely deployed and how to respond to the situation.


title: Safety climate in OHSAS 18001-certified organisations: antecedents and consequences of safety behaviour.

abstract: The occupational health and safety standard OHSAS 18001 has gained considerable acceptance worldwide, and firms from diverse sectors and of varying sizes have implemented it. Despite this, very few studies have analysed safety management or the safety climate in OHSAS 18001-certified organisations. The current work aims to analyse the safety climate in these organisations, identify its dimensions, and propose and test a structural equation model that will help determine the antecedents and consequences of employees' safety behaviour. For this purpose, the authors carry out an empirical study using a sample of 131 OHSAS 18001-certified organisations located in Spain. The results show that management's commitment, and particularly communication, have an effect on safety behaviour and on safety performance, employee satisfaction, and firm competitiveness. These findings are particularly important for management since they provide evidence about the factors that should be encouraged to reduce risks and improve performance in this type of organisation.


title: Work-based learning in health care organisations experienced by nursing staff: A systematic review of qualitative studies.

abstract: The aim of this review is to systematically summarise qualitative evidence about work-based learning in health care organisations as experienced by nursing staff. Work-based learning is understood as informal learning that occurs inside the work community in the interaction between employees. Studies for this review were searched for in the CINAHL, PubMed, Scopus and ABI Inform ProQuest databases for the period 2000-2015. Nine original studies met the inclusion criteria. After the critical appraisal by two researchers, all nine studies were selected for the review. The findings of the original studies were aggregated, and four statements were prepared, to be utilised in clinical work and decision-making. The statements concerned the following issues: (1) the culture of the work community; (2) the physical structures, spaces and duties of the work unit; (3) management; and (4) interpersonal relations. Understanding the nurses' experiences of work-based learning and factors behind these experiences provides an opportunity to influence the challenges of learning in the demanding context of health care organisations.


title: Automated reverse engineering of nonlinear dynamical systems

abstract: Complex nonlinear dynamics arise in many fields of science and engineering, but uncovering the underlying differential equations directly from observations poses a challenging task. The ability to symbolically model complex networked systems is key to understanding them, an open problem in many disciplines. Here we introduce for the first time a method that can automatically generate symbolic equations for a nonlinear coupled dynamical system directly from time series data. This method is applicable to any system that can be described using sets of ordinary nonlinear differential equations, and assumes that the (possibly noisy) time series of all variables are observable. Previous automated symbolic modeling approaches of coupled physical systems produced linear models or required a nonlinear model to be provided manually. The advance presented here is made possible by allowing the method to model each (possibly coupled) variable separately, intelligently perturbing and destabilizing the system to extract its less observable characteristics, and automatically simplifying the equations during modeling. We demonstrate this method on four simulated and two real systems spanning mechanics, ecology, and systems biology. Unlike numerical models, symbolic models have explanatory value, suggesting that automated “reverse engineering” approaches for model-free symbolic nonlinear system identification may play an increasing role in our ability to understand progressively more complex systems in the future.


Speaker Change Detection in Broadcast TV Using Bidirectional Long Short-Term Memory Networks


title: Decoupled Deep Neural Network for Semi-supervised Semantic Segmentation

abstract: We propose a novel deep neural network architecture for semi-supervised semantic segmentation using heterogeneous annotations. Contrary to existing approaches posing semantic segmentation as a single task of region-based classification, our algorithm decouples classification and segmentation, and learns a separate network for each task. In this architecture, labels associated with an image are identified by classification network, and binary segmentation is subsequently performed for each identified label in segmentation network. The decoupled architecture enables us to learn classification and segmentation networks separately based on the training data with image-level and pixel-wise class labels, respectively. It facilitates to reduce search space for segmentation effectively by exploiting class-specific activation maps obtained from bridging layers. Our algorithm shows outstanding performance compared to other semi-supervised approaches with much less training images with strong annotations in PASCAL VOC dataset.


title: A Data-Driven Approach to Predict Default Risk of Loan for Online Peer-to-Peer (P2P) Lending

abstract: Online Peer-to-Peer (P2P) lending has achieved explosive development recently, which could be beneficial to both sides of individual lending. In this study, a data mining (DM) approach to predict the performance of P2P loan before funded is proposed. Using data from the Lending Club, we explore the characteristics of loan and its applicant and use random forest to do the feature selection in the modeling phase. The Difference from other risk prediction models is that the prediction is classified into three or four categories, rather than just two the default and not default classes. Then we compare five DM models: two decision trees (DTs), two neural networks (NNs) and one support vector machine (SVM) and use two metrics: average percent hit rate and area of the lift cumulative curve to evaluate the prediction results. The Empirical result shows that the term of loan, annual income, the amount of loan, debt-to-income ratio, credit grade and revolving line utilization play an important role in loan defaults. And SVM, Classification and Regression Tree (CART) and Multi-layer perceptron (MPL)'s prediction performance are almost equal.


title: Nonmonotonic Reasoning, Conditional Objects and Possibility Theory

abstract: Abstract This short paper relates the conditional object-based and possibility theory-based approaches for reasoning with conditional statements pervaded with exceptions, to other methods in nonmonotonic reasoning which have been independently proposed: namely, Lehmann's preferential and rational closure entailments which obey normative postulates, the infinitesimal probability approach, and the conditional (modal) logics-based approach. All these methods are shown to be equivalent with respect to their capabilities for reasoning with conditional knowledge although they are based on different modeling frameworks. It thus provides a unified understanding of nonmonotonic consequence relations. More particularly, conditional objects, a purely qualitative counterpart to conditional probabilities, offer a very simple semantics, based on a 3-valued calculus, for the preferential entailment, while in the purely ordinal setting of possibility theory both the preferential and the rational closure entailments can be represented.


title: An incremental batch technique for community detection

abstract: In the analysis of real world networks, it is often of interest to partition nodes into groups referred to as communities, whereby each community is densely connected and different communities are sparsely connected to one another. While community detection on static networks has been extensively researched on, updating community structures efficiently and accurately on evolving networks is a relatively new research area. In this paper, we discuss the inadequacies of previous techniques as well as justify the need for a new class of techniques that can handle complex batch changes in networks. We then propose one such incremental technique. Compared to earlier work, the proposed technique is much more efficient in scenarios where a network evolves significantly while maintaining a high level of accuracy. Experiments on both artificial and real world networks validate the utility of the proposed technique.


title: CS principles goes to middle school: learning how to teach "Big Data"

abstract: Spurred by evidence that students' future studies are highly influenced during middle school, recent efforts have seen a growing emphasis on introducing computer science to middle school learners. This paper reports on the in-progress development of a new middle school curricular module for Big Data, situated as part of a new CS Principles-based middle school curriculum. Big Data is of widespread societal importance and holds increasing implications for the computer science workforce. It also has appeal as a focus for middle school computer science because of its rich interplay with other important computer science principles. This paper examines three key aspects of a Big Data unit for middle school: its alignment with emerging curricular standards; the perspectives of middle school classroom teachers in mathematics, science, and language arts; and student feedback as explored during a middle school pilot study with a small subset of the planned curriculum. The results indicate that a Big Data unit holds great promise as part of a middle school computer science curriculum.


title: Steganography and steganalysis in digital multimedia: Hype or hallelujah

abstract: In this tutorial, we introduce the basic theory behind Steganography and Steganalysis, and present some recent algorithms and developments of these fields. We show how the existing techniques used nowadays are related to Image Processing and Computer Vision, point out several trendy applications of Steganography and Steganalysis, and list a few great research opportunities just waiting to be addressed.


Base Station ON-OFF Switching in 5G Wireless Systems: Approaches and Challenges


title: Automatic Heavy-weight Static Analysis Tools for Fin- ding Bugs in Safety-critical Embedded C/C++ Code

abstract: This paper motivates the use of automatic heavy-weight static analysis tools to find bugs in C (and C++) code for safety-critical embedded systems. By heavy-weight we mean tools that employ powerful analysis to cover all cases. The paper introduces two automatic and relatively heavy-weight tools that are currently employed in the automotive industry, and depicts their underlying techniques, advantages, and disadvantages. Since their results are often imprecise (false positives or false negatives), we advocate the use of alternative techniques such as software bounded model checking (SBMC), which can achieve bit-precise results. Finally, the tool LLBMC is described as an example of a tool implementing SBMC, which makes use of satisfiability modulo theories (SMT) decision procedures as well as the LLVM compiler framework.


title: Augmentation Techniques for Mobile Cloud Computing: A Taxonomy, Survey, and Future Directions

abstract: Despite the rapid growth of hardware capacity and popularity in mobile devices, limited resources in battery and processing capacity still lack the ability to meet increasing mobile users’ demands. Both conventional techniques and emerging approaches are brought together to fill this gap between user demand and mobile devices’ limited capabilities. Recent research has focused on enhancing the performance of mobile devices via augmentation techniques. Augmentation techniques for mobile cloud computing refer to the computing paradigms and solutions to outsource mobile device computation and storage to more powerful computing resources in order to enhance a mobile device’s computing capability and energy efficiency (e.g., code offloading). Adopting augmentation techniques in the heterogeneous and intermittent mobile cloud computing environment creates new challenges for computation management, energy efficiency, and system reliability. In this article, we aim to provide a comprehensive taxonomy and survey of the existing techniques and frameworks for mobile cloud augmentation regarding both computation and storage. Different from the existing taxonomies in this field, we focus on the techniques aspect, following the idea of realizing a complete mobile cloud computing system. The objective of this survey is to provide a guide on what available augmentation techniques can be adopted in mobile cloud computing systems as well as supporting mechanisms such as decision-making and fault tolerance policies for realizing reliable mobile cloud services. We also present a discussion on the open challenges and future research directions in this field.


title: Predicting Taxi–Passenger Demand Using Streaming Data

abstract: Informed driving is increasingly becoming a key feature for increasing the sustainability of taxi companies. The sensors that are installed in each vehicle are providing new opportunities for automatically discovering knowledge, which, in return, delivers information for real-time decision making. Intelligent transportation systems for taxi dispatching and for finding time-saving routes are already exploring these sensing data. This paper introduces a novel methodology for predicting the spatial distribution of taxi-passengers for a short-term time horizon using streaming data. First, the information was aggregated into a histogram time series. Then, three time-series forecasting techniques were combined to originate a prediction. Experimental tests were conducted using the online data that are transmitted by 441 vehicles of a fleet running in the city of Porto, Portugal. The results demonstrated that the proposed framework can provide effective insight into the spatiotemporal distribution of taxi-passenger demand for a 30-min horizon.


title: A 3-10 GHz Ultra-Wideband Bandpass Filter with 5-6GHz Rejection Band

abstract: A novel ultra-wideband (UWB) bandpass filter with 5 to 6 GHz rejection band is proposed. The multiple coupled line structure is incorporated with multiple-mode resonator (MMR) to provide wide transmission band and enhance out-of band performance. To inhibit the signals ranged from 5- to 6-GHz, four stepped-impedance open stubs are implemented on the MMR without increasing the size of the proposed filter. The design of the proposed UWB filter has two transmission bands. The first passband from 2.8 GHz to 5 GHz has less than 2 dB insertion loss and greater than 18 dB return loss. The second passband within 6 GHz and 10.6 GHz has less than 1.5 dB insertion loss and greater than 15 dB return loss. The rejection at 5.5 GHz is better than 50 dB. This filter can be integrated in UWB radio systems and efficiently enhance the interference immunity from WLAN.


title: The language-ready head: Evolutionary considerations

abstract: This article offers a succinct overview of the hypothesis that the evolution of cognition could benefit from a close examination of brain changes reflected in the shape of the neurocranium. I provide both neurological and genetic evidence in support of this hypothesis, and conclude that the study of language evolution need not be regarded as a mystery.


title: SQAK: doing more with keywords

abstract: Today's enterprise databases are large and complex, often relating hundreds of entities. Enabling ordinary users to query such databases and derive value from them has been of great interest in database research. Today, keyword search over relational databases allows users to find pieces of information without having to write complicated SQL queries. However, in order to compute even simple aggregates, a user is required to write a SQL statement and can no longer use simple keywords. This not only requires the ordinary user to learn SQL, but also to learn the schema of the complex database in detail in order to correctly construct the required query. This greatly limits the options of the user who wishes to examine a database in more depth. As a solution to this problem, we propose a framework called SQAK1 (SQL Aggregates using Keywords) that enables users to pose aggregate queries using simple keywords with little or no knowledge of the schema. SQAK provides a novel and exciting way to trade-off some of the expressive power of SQL in exchange for the ability to express a large class of aggregate queries using simple keywords. SQAK accomplishes this by taking advantage of the data in the database and the schema (tables, attributes, keys, and referential constraints). SQAK does not require any changes to the database engine and can be used with any existing database. We demonstrate using several experiments that SQAK is effective and can be an enormously powerful tool for ordinary users.


title: A new single current strategy for high-performance brushless DC motor drives

abstract: For closed-loop current control of brushless dc (BLDC) motors, instantaneous phase currents are measured using appropriate current sensors. Such sensors are often bulky, heavy, and expensive, making them appealing for elimination. Likewise, the use of two or more separated sensors can cause undesirable imbalances in the phase currents as well as pulsating torque due to mismatched in the current sensor sensitivities. To overcome these problems, a new single current strategy for high performance BLDC motor drives is proposed. It is based on estimation and regulation of phase currents, using two single sensors for dc-link voltage and current. In this method, the phase currents are reconstructed in a two-stage process including estimation and regulation. Estimation is based on dynamic motor model, while regulation relies on the inverter switches states and the measured dc-link current. Besides, to access better dynamic response characteristic of the motor speed, Particle Swarm Optimization (PSO) has been used to regulate the PID parameters of speed controller. The effectiveness of proposed system has been validated by comparative studies and simulation results.


title: Adaptive Scale Selection for Multiscale Segmentation of Satellite Images

abstract: With dramatically increasing of the spatial resolution of satellite imaging sensors, object-based image analysis (OBIA) has been gaining prominence in remote sensing applications. Multiscale image segmentation is a prerequisite step that splits an image into hierarchical homogeneous segmented objects for OBIA. However, scale selection remains a challenge in multiscale segmentation. In this study, we presented an adaptive approach for defining and estimating the optimal scale in the multiscale segmentation process. Central to our method is the combined use of image features from segmented objects and prior knowledge from historical thematic maps in a top-down segmentation procedure. Specifically, the whole image was first split into segmented objects, with the largest scale in a presupposition segmentation scale sequence. Second, based on segmented object features and prior knowledge in the local region of thematic maps, we calculated complexity values for each segmented object. Third, if the complexity values of an object were large enough, this object would be further split into multiple segmented objects with a smaller scale in the scale sequence. Then, in the similar manner, complex segmented objects were split into the simplest objects iteratively. Finally, the final segmentation result was obtained and evaluated. We have applied this method on a GF-1 multispectral satellite image and a ZY-3 multispectral satellite image to produce multiscale segmentation maps and further classification maps, compared with the state-of-the-art and the traditional mean shift algorithm. The experimental results illustrate that the proposed method is practically helpful and efficient to produce the appropriate segmented image objects with optimal scales.


title: Algorithm Aversion: People Erroneously Avoid Algorithms after Seeing Them Err

abstract: Research shows that evidence-based algorithms more accurately predict the future than do human forecasters. Yet, when forecasters are deciding whether to use a human forecaster or a statistical algorithm, they often choose the human forecaster. This phenomenon, which we call algorithm aversion, is costly, and it is important to understand its causes. We show that people are especially averse to algorithmic forecasters after seeing them perform, even when they see them outperform a human forecaster. This is because people more quickly lose confidence in algorithmic than human forecasters after seeing them make the same mistake. In five studies, participants either saw an algorithm make forecasts, a human make forecasts, both, or neither. They then decided whether to tie their incentives to the future predictions of the algorithm or the human. Participants who saw the algorithm perform were less confident in it, and less likely to choose it over an inferior human forecaster. This was true even among those who saw the algorithm outperform the human.


title: Integrated systems biology analysis of KSHV latent infection reveals viral induction and reliance on peroxisome mediated lipid metabolism

abstract: Kaposi's Sarcoma associated Herpesvirus (KSHV), an oncogenic, human gamma-herpesvirus, is the etiological agent of Kaposi's Sarcoma the most common tumor of AIDS patients world-wide. KSHV is predominantly latent in the main KS tumor cell, the spindle cell, a cell of endothelial origin. KSHV modulates numerous host cell-signaling pathways to activate endothelial cells including major metabolic pathways involved in lipid metabolism. To identify the underlying cellular mechanisms of KSHV alteration of host signaling and endothelial cell activation, we identified changes in the host proteome, phosphoproteome and transcriptome landscape following KSHV infection of endothelial cells. A Steiner forest algorithm was used to integrate the global data sets and, together with transcriptome based predicted transcription factor activity, cellular networks altered by latent KSHV were predicted. Several interesting pathways were identified, including peroxisome biogenesis. To validate the predictions, we showed that KSHV latent infection increases the number of peroxisomes per cell. Additionally, proteins involved in peroxisomal lipid metabolism of very long chain fatty acids, including ABCD3 and ACOX1, are required for the survival of latently infected cells. In summary, novel cellular pathways altered during herpesvirus latency that could not be predicted by a single systems biology platform, were identified by integrated proteomics and transcriptomics data analysis and when correlated with our metabolomics data revealed that peroxisome lipid metabolism is essential for KSHV latent infection of endothelial cells.


title: Self-Determination Theory Applied to Health Contexts: A Meta-Analysis.

abstract: Behavior change is more effective and lasting when patients are autonomously motivated. To examine this idea, we identified 184 independent data sets from studies that utilized self-determination theory (SDT; Deci & Ryan, 2000) in health care and health promotion contexts. A meta-analysis evaluated relations between the SDT-based constructs of practitioner support for patient autonomy and patients’ experience of psychological need satisfaction, as well as relations between these SDT constructs and indices of mental and physical health. Results showed the expected relations among the SDT variables, as well as positive relations of psychological need satisfaction and autonomous motivation to beneficial health outcomes. Several variables (e.g., participants’ age, study design) were tested as potential moderators when effect sizes were heterogeneous. Finally, we used path analyses of the meta-analyzed correlations to test the interrelations among the SDT variables. Results suggested that SDT is a viable conce...


title: A Millimeter-Wave (40–45 GHz) 16-Element Phased-Array Transmitter in 0.18-$\mu$ m SiGe BiCMOS Technology

abstract: This paper demonstrates a 16-element phased-array transmitter in a standard 0.18-mum SiGe BiCMOS technology for Q-band satellite applications. The transmitter array is based on the all-RF architecture with 4-bit RF phase shifters and a corporate-feed network. A 1:2 active divider and two 1:8 passive tee-junction dividers constitute the corporate-feed network, and three-dimensional shielded transmission-lines are used for the passive divider to minimize area. All signals are processed differentially inside the chip except for the input and output interfaces. The phased-array transmitter results in a 12.5 dB of average power gain per channel at 42.5 GHz with a 3-dB gain bandwidth of 39.9-45.6 GHz. The RMS gain variation is < 1.3 dB and the RMS phase variation is < for all 4-bit phase states at 35-50 GHz. The measured input and output return losses are < -10 dB at 36.6-50 GHz, and <-10 dB at 37.6-50 GHz, respectively. The measured peak-to-peak group delay variation is plusmn 20 ps at 40-45 GHz. The output P-1dB is -5plusmn1.5 dBm and the maximum saturated output power is - 2.5plusmn1.5 dBm per channel at 42.5 GHz. The transmitter shows <1.8 dB of RMS gain mismatch and < 7deg of RMS phase mismatch between the 16 different channels over all phase states. A - 30 dB worst-case port-to-port coupling is measured between adjacent channels at 30-50 GHz, and the measured RMS gain and phase disturbances due to the inter-channel coupling are < 0.15 dB and < 1deg, respectively, at 35-50 GHz. All measurements are obtained without any on-chip calibration. The chip consumes 720 mA from a 5 V supply voltage and the chip size is 2.6times3.2 mm2.


title: Practical Bayesian Optimization for Model Fitting with Bayesian Adaptive Direct Search

abstract: Computational models in fields such as computational neuroscience are often evaluated via stochastic simulation or numerical approximation. Fitting these models implies a difficult optimization problem over complex, possibly noisy parameter landscapes. Bayesian optimization (BO) has been successfully applied to solving expensive black-box problems in engineering and machine learning. Here we explore whether BO can be applied as a general tool for model fitting. First, we present a novel BO algorithm, Bayesian adaptive direct search (BADS), that achieves competitive performance with an affordable computational overhead for the running time of typical models. We then perform an extensive benchmark of BADS vs. many common and state-of-the-art nonconvex, derivative-free optimizers on a set of model-fitting problems with real data and models from six studies in behavioral, cognitive, and computational neuroscience. With default settings, BADS consistently finds comparable or better solutions than other methods, showing great promise for BO, and BADS in particular, as a general model-fitting tool.


title: AMB Vibration Control for Structural Resonance of Double-Gimbal Control Moment Gyro With High-Speed Magnetically Suspended Rotor

abstract: This paper explores a robust μ-synthesis control scheme for structural resonance vibration suppression of high-speed rotor systems supported by active magnetic bearings (AMBs) in the magnetically suspended double-gimbal control moment gyro (MSDGCMG). The derivation of a nominal linearized model about an operating point was presented. Sine sweep test was conducted on each component of AMB control system to obtain parameter variations and high-frequency unmodeled dynamics, including the structural resonance modes. A fictitious uncertainty block was introduced to represent the performance requirements for the augmented system. Finally, D-K iteration procedure was employed to solve the robust μ-controller. Rotor run-up experiments on the originally developed MSDGCMG prototype show that the designed μ-controller has a good performance for vibration rejection of structural resonance mode with the excitation of coupling torques. Further investigations indicate that the proposed method can also ensure the robust stability and performance of high-speed rotor system subject to the reaction of a large gyro torque.


title: Toward a computational theory of conscious processing

abstract: The study of the mechanisms of conscious processing has become a productive area of cognitive neuroscience. Here we review some of the recent behavioral and neuroscience data, with the specific goal of constraining present and future theories of the computations underlying conscious processing. Experimental findings imply that most of the brain’s computations can be performed in a non-conscious mode, but that conscious perception is characterized by an amplification, global propagation and integration of brain signals. A comparison of these data with major theoretical proposals suggests that firstly, conscious access must be carefully distinguished from selective attention; secondly, conscious perception may be likened to a non-linear decision that ‘ignites’ a network of distributed areas; thirdly, information which is selected for conscious perception gains access to additional computations, including temporary maintenance, global sharing, and flexible routing; and finally, measures of the complexity, long-distance correlation and integration of brain signals provide reliable indices of conscious processing, clinically relevant to patients recovering from coma.


title: Find me if you can: improving geographical prediction with social and spatial proximity

abstract: Geography and social relationships are inextricably intertwined; the people we interact with on a daily basis almost always live near us. As people spend more time online, data regarding these two dimensions -- geography and social relationships -- are becoming increasingly precise, allowing us to build reliable models to describe their interaction. These models have important implications in the design of location-based services, security intrusion detection, and social media supporting local communities. Using user-supplied address data and the network of associations between members of the Facebook social network, we can directly observe and measure the relationship between geography and friendship. Using these measurements, we introduce an algorithm that predicts the location of an individual from a sparse set of located users with performance that exceeds IP-based geolocation. This algorithm is efficient and scalable, and could be run on a network containing hundreds of millions of users.


title: ViDeX: A Platform for Personalizing Educational Videos

abstract: As video-based learning is increasingly used in all sectors of education, there is a need for video players that support active viewing practices. We introduce a video player that allows students to mark up video with highlights, tags, and notes in order to personalize their video-based learning experience.


title: Vehicle-to-Grid Reactive Power Operation Using Plug-In Electric Vehicle Bidirectional Offboard Charger

abstract: The number of offboard fast charging stations is increasing as plug-in electric vehicles (PEVs) are more widespread in the world. Additional features on the operation of chargers will result in more benefits for investors, utility companies, and PEV owners. This paper investigates reactive power support operation using offboard PEV charging stations while charging a PEV battery. The topology consists of a three-phase ac-dc boost rectifier that is capable of operating in all four quadrants. The operation modes that are of interest are power-factor-corrected charging operation, and charging and capacitive/inductive reactive power operation. This paper also presents a control system for the PQ command following of a bidirectional offboard charger. The controller only receives the charging power command from a user and the reactive power command (when needed) from a utility, and it adjusts the line current and the battery charging current correspondingly. The vehicle's battery is not affected during the reactive power operation. A simulation study is developed utilizing PSIM, and the control system is experimentally tested using a 12.5-kVA charging station design.


title: SmartSkin: an infrastructure for freehand manipulation on interactive surfaces

abstract: This paper introduces a new sensor architecture for making interactive surfaces that are sensitive to human hand and finger gestures. This sensor recognizes multiple hand positions and shapes and calculates the distance between the hand and the surface by using capacitive sensing and a mesh-shaped antenna. In contrast to camera-based gesture recognition systems, all sensing elements can be integrated within the surface, and this method does not suffer from lighting and occlusion problems. This paper describes the sensor architecture, as well as two working prototype systems: a table-size system and a tablet-size system. It also describes several interaction techniques that would be difficult to perform without using this architecture


title: Optimising Results from Minimal Access Cranial Suspension Lifting (MACS-Lift)

abstract: Between November 1999 and February 2005, 450 minimal access cranial suspension (MACS) lifts were performed. Starting with the idea of suspension for sagging soft tissues using permanent purse-string sutures, a new comprehensive approach to facial rejuvenation was developed in which the vertical vector appeared to be essential. The neck is corrected by extended submental liposuction and strong vertical traction on the lateral part of the platysma by means of a first vertical purse-string suture. The volume of the jowls and the cheeks is repositioned in a cranial direction with a second, slightly oblique purse-string suture. The descent of the midface is corrected by suspending the malar fat pad in a nearly vertical direction. In 23 cases (5.1%), the result in the neck was unsatisfactory, and additional work had to be done secondarily, or in later cases, primarily. The problem that appeared was unsatisfactory correction of platysmal bands (resolved with an additional anterior cervicoplasty) or vertical skin folds that appeared in the infralobular region (corrected with an additional posterior cervicoplasty). This article describes two ancillary procedures that, although not frequently necessary, can optimise the result of MACS lifting.


title: Autonomous decision-making: a data mining approach

abstract: The researchers and practitioners of today create models, algorithms, functions, and other constructs defined in abstract spaces. The research of the future will likely be data driven. Symbolic and numeric data that are becoming available in large volumes will define the need for new data analysis techniques and tools. Data mining is an emerging area of computational intelligence that offers new theories, techniques, and tools for analysis of large data sets. In this paper, a novel approach for autonomous decision-making is developed based on the rough set theory of data mining. The approach has been tested on a medical data set for patients with lung abnormalities referred to as solitary pulmonary nodules (SPNs). The two independent algorithms developed in this paper either generate an accurate diagnosis or make no decision. The methodology discussed in the paper depart from the developments in data mining as well as current medical literature, thus creating a variable approach for autonomous decision-making.


title: $L_{1}$ -Norm Low-Rank Matrix Decomposition by Neural Networks and Mollifiers

abstract: The L1-norm cost function of the low-rank approximation of the matrix with missing entries is not smooth, and also cannot be transformed into a standard linear or quadratic programming problem, and thus, the optimization of this cost function is still not well solved. To tackle this problem, first, a mollifier is used to smooth the cost function. High closeness of the smoothed function to the original one can be obtained by tuning the parameters contained in the mollifier. Next, a recurrent neural network is proposed to optimize the mollified function, which will converge to a local minimum. In addition, to boost the speed of the system, the mollifying process is implemented by a filtering procedure. The influence of two mollifier parameters is theoretically analyzed and experimentally confirmed, showing that one of the parameters is critical to computational efficiency and accuracy, while the other not. A large number of experiments on synthetic data show that the proposed method is competitive to the state-of-the-art methods. In particular, the experiments on large matrices and a real application in the structure from motion indicate that the memory requirement of the proposed algorithm is mild, making it suitable for real applications that often involve large-scale matrix decomposition.


Towards Improving Data Quality


title: Astrolabe: A robust and scalable technology for distributed system monitoring, management, and data mining

abstract: Scalable management and self-organizational capabilities areemerging as central requirements for a generation of large-scale,highly dynamic, distributed applications. We have developed anentirely new distributed information management system calledAstrolabe. Astrolabe collects large-scale system state, permittingrapid updates and providing on-the-fly attribute aggregation. Thislatter capability permits an application to locate a resource, andalso offers a scalable way to track system state as it evolves overtime. The combination of features makes it possible to solve a widevariety of management and self-configuration problems. This paperdescribes the design of the system with a focus upon itsscalability. After describing the Astrolabe service, we presentexamples of the use of Astrolabe for locating resources,publish-subscribe, and distributed synchronization in largesystems. Astrolabe is implemented using a peer-to-peer protocol,and uses a restricted form of mobile code based on the SQL querylanguage for aggregation. This protocol gives rise to a novelconsistency model. Astrolabe addresses several securityconsiderations using a built-in PKI. The scalability of the systemis evaluated using both simulation and experiments; these confirmthat Astrolabe could scale to thousands and perhaps millions ofnodes, with information propagation delays in the tens of seconds.


title: Related Pins at Pinterest: The Evolution of a Real-World Recommender System

abstract: Related Pins is the Web-scale recommender system that powers over 40% of user engagement on Pinterest. This paper is a longitudinal study of three years of its development, exploring the evolution of the system and its components from prototypes to present state. Each component was originally built with many constraints on engineering effort and computational resources, so we prioritized the simplest and highest-leverage solutions. We show how organic growth led to a complex system and how we managed this complexity. Many challenges arose while building this system, such as avoiding feedback loops, evaluating performance, activating content, and eliminating legacy heuristics. Finally, we offer suggestions for tackling these challenges when engineering Web-scale recommender systems.


title: Hierarchical Organization of Human Cortical Networks in Health and Schizophrenia

abstract: The complex organization of connectivity in the human brain is incompletely understood. Recently, topological measures based on graph theory have provided a new approach to quantify large-scale cortical networks. These methods have been applied to anatomical connectivity data on nonhuman species, and cortical networks have been shown to have small-world topology, associated with high local and global efficiency of information transfer. Anatomical networks derived from cortical thickness measurements have shown the same organizational properties of the healthy human brain, consistent with similar results reported in functional networks derived from resting state functional magnetic resonance imaging (MRI) and magnetoencephalographic data. Here we show, using anatomical networks derived from analysis of inter-regional covariation of gray matter volume in MRI data on 259 healthy volunteers, that classical divisions of cortex (multimodal, unimodal, and transmodal) have some distinct topological attributes. Although all cortical divisions shared nonrandom properties of small-worldness and efficient wiring (short mean Euclidean distance between connected regions), the multimodal network had a hierarchical organization, dominated by frontal hubs with low clustering, whereas the transmodal network was assortative. Moreover, in a sample of 203 people with schizophrenia, multimodal network organization was abnormal, as indicated by reduced hierarchy, the loss of frontal and the emergence of nonfrontal hubs, and increased connection distance. We propose that the topological differences between divisions of normal cortex may represent the outcome of different growth processes for multimodal and transmodal networks and that neurodevelopmental abnormalities in schizophrenia specifically impact multimodal cortical organization.


title: Automated Diagnosis for UMTS Networks Using Bayesian Network Approach

abstract: This paper presents an automated diagnosis in troubleshooting (TS) for Universal Mobile Telecommunications System (UMTS) networks using a Bayesian network (BN) approach. An automated diagnosis model is first described using the Naive Bayesian Classifier. To increase the performance of the diagnosis model, the entropy minimization discretization (EMD) method is incorporated into the model to select optimal segments for the discretization of the input symptoms. In the first phase, the diagnosis model is constructed using a dynamic simulator. The simulator TS platform allows generation of a large amount of data required to study the relations between faults and symptoms. In the second phase, the diagnosis model is adapted to a real UMTS network using counters and key performance indicators (KPIs) recovered from an Operations and Maintenance Center (OMC). Results for the automated diagnosis using both network simulator and real UMTS network measurements illustrate the efficiency of the proposed TS approach and its importance to mobile network operators.


title: Clinical narrative classification using discriminant word embeddings with ELM

abstract: Clinical texts are inherently complex due to the medical domain expertise required for content comprehension. In addition, the unstructured nature of these narratives poses a challenge for automatically extracting information. In natural language processing, the use of word embeddings are an effective approach to generate word representations (vectors) in a low dimensional space. In this paper we use a log-linear model (a type of neural language model) and Linear Discriminant Analysis with a kernel-based Extreme Learning Machine (ELM) to map the clinical texts to the medical code. Experimental results on clinical texts indicate improvement with ELM in comparison to SVM and neural network approaches.


title: Body talk: crowdshaping realistic 3D avatars with words

abstract: Realistic, metrically accurate, 3D human avatars are useful for games, shopping, virtual reality, and health applications. Such avatars are not in wide use because solutions for creating them from high-end scanners, low-cost range cameras, and tailoring measurements all have limitations. Here we propose a simple solution and show that it is surprisingly accurate. We use crowdsourcing to generate attribute ratings of 3D body shapes corresponding to standard linguistic descriptions of 3D shape. We then learn a linear function relating these ratings to 3D human shape parameters. Given an image of a new body, we again turn to the crowd for ratings of the body shape. The collection of linguistic ratings of a photograph provides remarkably strong constraints on the metric 3D shape. We call the process crowdshaping and show that our Body Talk system produces shapes that are perceptually indistinguishable from bodies created from high-resolution scans and that the metric accuracy is sufficient for many tasks. This makes body "scanning" practical without a scanner, opening up new applications including database search, visualization, and extracting avatars from books.


title: Analysis of precision grip force for uGRIPP (underactuated gripper for power and precision grasp)

abstract: This study analyzed the precision grip force for the underactuated gripper for power and precision grasp (uGRIPP). uGRIPP is a robot hand developed for dual arm manipulation. It can grasp objects through power and precision grips. For a more detailed analysis of the performance on the grip force and control of the grip force, a relationship was derived between the grip and actuation forces by using an analytical method based on the principle of virtual power. Finally, theoretical values of the grip force were calculated according to the analysis, and the actual grip force was measured experimentally. The experimental results show the validity of the analysis.


title: Reinventing Radiology: Big Data and the Future of Medical Imaging

abstract: Purpose: Today, data surrounding most of our lives are collected and stored. Data scientists are beginning to explore applications that could harness this information and make sense of it. Materials and Methods: In this review, the topic of Big Data is explored, and applications in modern health care are considered. Results: Big Data is a concept that has evolved from the modern trend of “scientism.” One of the primary goals of data scientists is to develop ways to discover new knowledge from the vast quantities of increasingly available information. Conclusions: Current and future opportunities and challenges with respect to radiology are provided with emphasis on cardiothoracic imaging.


title: Robot gains social intelligence through multimodal deep reinforcement learning

abstract: For robots to coexist with humans in a social world like ours, it is crucial that they possess human-like social interaction skills. Programming a robot to possess such skills is a challenging task. In this paper, we propose a Multimodal Deep Q-Network (MDQN) to enable a robot to learn human-like interaction skills through a trial and error method. This paper aims to develop a robot that gathers data during its interaction with a human, and learns human interaction behavior from the high dimensional sensory information using end-to-end reinforcement learning. This paper demonstrates that the robot was able to learn basic interaction skills successfully, after 14 days of interacting with people.


title: 13.5-mW, 5-GHz WLAN, CMOS frequency synthesizer using a true single phase clock divider

abstract: An integrated 5 GHz frequency synthesizer consuming only 5.4 mA from a 2.5 V supply is demonstrated in 0.25 /spl mu/m CMOS technology. The divider within the synthesizer employs the True Single Phase Clock logic. The output frequency spans from 5.14 to 5.70 GHz, with steps of 20 MHz. The reference spurs are -70 dBc and the phase noise is lower than -116 dBc/Hz at 1 MHz offset over the whole tuning range. The synthesizer is suitable for the HiperLAN II and the IEEE 802.11a standards.


title: Californium: Scalable cloud services for the Internet of Things with CoAP

abstract: The Internet of Things (IoT) is expected to interconnect a myriad of devices. Emerging networking and backend support technology not only has to anticipate this dramatic increase in connected nodes, but also a change in traffic patterns. Instead of bulk data such as file sharing or multimedia streaming, IoT devices will primarily exchange real-time sensory and control data in small but numerous messages. Often cloud services will handle these data from a huge number of devices, and hence need to be extremely scalable to support conceivable large-scale IoT applications. To this end, we present a system architecture for IoT cloud services based on the Constrained Application Protocol (CoAP), which is primarily designed for systems of tiny, low-cost, resource-constrained IoT devices. Along with our system architecture, we systematically evaluate the performance of the new Web protocol in cloud environments. Our Californium (Cf) CoAP framework shows 33 to 64 times higher throughput than high-performance HTTP Web servers, which are the state of the art for classic cloud services. The results substantiate that the low overhead of CoAP does not only enable Web technology for lowcost IoT devices, but also significantly improves backend service scalability for vast numbers of connected devices.


title: Heuristics for High-Utility Local Process Model Mining

abstract: Local Process Models (LPMs) describe structured fragments of process behavior occurring in the context of less structured business processes. In contrast to traditional support-based LPM discovery, which aims to generate a collection of process models that describe highly frequent behavior, High-Utility Local Process Model (HU-LPM) discovery aims to generate a collection of process models that provide useful business insights by specifying a utility function. Mining LPMs is a computationally expensive task, because of the large search space of LPMs. In supportbased LPM mining, the search space is constrained by making use of the property that support is anti-monotonic. We show that in general, we cannot assume a provided utility function to be anti-monotonic, therefore, the search space of HU-LPMs cannot be reduced without loss. We propose four heuristic methods to speed up the mining of HU-LPMs while still being able to discover useful HU-LPMs. We demonstrate their applicability on three real-life data sets.


title: Deep Gaussian Processes for Regression using Approximate Expectation Propagation

abstract: Deep Gaussian processes (DGPs) are multilayer hierarchical generalisations of Gaussian processes (GPs) and are formally equivalent to neural networks with multiple, infinitely wide hidden layers. DGPs are nonparametric probabilistic models and as such are arguably more flexible, have a greater capacity to generalise, and provide better calibrated uncertainty estimates than alternative deep models. This paper develops a new approximate Bayesian learning scheme that enables DGPs to be applied to a range of medium to large scale regression problems for the first time. The new method uses an approximate Expectation Propagation procedure and a novel and efficient extension of the probabilistic backpropagation algorithm for learning. We evaluate the new method for non-linear regression on eleven real-world datasets, showing that it always outperforms GP regression and is almost always better than state-of-the-art deterministic and sampling-based approximate inference methods for Bayesian neural networks. As a by-product, this work provides a comprehensive analysis of six approximate Bayesian methods for training neural networks.


title: Temperature-Dependent Thermal Properties of Supported MoS2 Monolayers

abstract: Thermal properties can substantially affect the operation of various electronics and optoelectronics devices based on two-dimensional materials. In this work, we describe our investigation of temperature-dependent thermal conductivity and interfacial thermal conductance of molybdenum disulfide monolayers supported on SiO2/Si substrates, using Raman spectroscopy. We observed that the calculated thermal conductivity (κ) and interfacial thermal conductance (g) decreased with increasing temperature from 62.2 W m–1 K–1 and 1.94 MW m–2 K–1 at 300 K to 7.45 W m–1 K–1 and 1.25 MW m–2 K–1 at 450 K, respectively.


title: Music Discovery with Social Networks

abstract: Current music recommender systems rely on techniques like collaborative ltering on user-provided information in order to generate relevant recommendations based upon users’ music collections or listening habits. In this paper, we examine whether better recommendations can be obtained by taking into account the music preferences of the user’s social contacts. We assume that music is naturally diused through the social network of its listeners, and that we can propagate automatic recommendations in the same way through the network. In order to test this statement, we developed a music recommender application called Starnet on a Social Networking Service. It generated recommendations based either on positive ratings of friends (social recommendations), positive ratings of others in the network (nonsocial recommendations), or not based on ratings (random recommendations). The user responses to each type of recommendation indicate that social recommendations are better than non-social recommendations, which are in turn better than random recommendations. Likewise, the discovery of novel and relevant music is more likely via social recommendations than non-social. Social shue recommendations enable people to discover music through a serendipitous process powered by human relationships and tastes, exploiting the user’s social network to share cultural experiences.


title: A 0.5-V 4.8GHz CMOS LC VCO with wide tuning range

abstract: A 4.8GHz CMOS LC VCO with 37.5% tuning range in 0.5-V supply is presented. The VCO achieves low power consumption by optimum selection of inductance in the L-C tank. To increase the tuning range, a three-bit switching capacitor array is used for the digital switched tuning. A voltage-boosted technique is used to generate a high voltage for the switches in the digital tuning scheme. Designed in 0.13μm CMOS technology, the proposed VCO achieves a phase noise of −113.8dBc/Hz at 1MHz offset and consumes 2.4mA core current at 0.5 voltage supply.


title: Recognising Human Emotions from Body Movement and Gesture Dynamics

abstract: We present an approach for the recognition of acted emotional states based on the analysis of body movement and gesture expressivity. According to research showing that distinct emotions are often associated with different qualities of body movement, we use non- propositional movement qualities (e.g. amplitude, speed and fluidity of movement) to infer emotions, rather than trying to recognise different gesture shapes expressing specific emotions. We propose a method for the analysis of emotional behaviour based on both direct classification of time series and a model that provides indicators describing the dynamics of expressive motion cues. Finally we show and interpret the recognition rates for both proposals using different classification algorithms.


title: Fireworks algorithm for optimization

abstract: Inspired by observing fireworks explosion, a novel swarm intelligence algorithm, called Fireworks Algorithm (FA), is proposed for global optimization of complex functions In the proposed FA, two types of explosion (search) processes are employed, and the mechanisms for keeping diversity of sparks are also well designed In order to demonstrate the validation of the FA, a number of experiments were conducted on nine benchmark test functions to compare the FA with two variants of particle swarm optimization (PSO) algorithms, namely Standard PSO and Clonal PSO It turns out from the results that the proposed FA clearly outperforms the two variants of the PSOs in both convergence speed and global solution accuracy.


title: Deep Recurrent Regression for Facial Landmark Detection

abstract: We propose a novel end-to-end deep architecture for face landmark detection, based on a deep convolutional and deconvolutional network followed by carefully designed recurrent network structures. The pipeline of this architecture consists of three parts. Through the first part, we encode an input face image to resolution-preserved deconvolutional feature maps via a deep network with stacked convolutional and deconvolutional layers. Then, in the second part, we estimate the initial coordinates of the facial key points by an additional convolutional layer on top of these deconvolutional feature maps. In the last part, by using the deconvolutional feature maps and the initial facial key points as input, we refine the coordinates of the facial key points by a recurrent network that consists of multiple long short-term memory components. Extensive evaluations on several benchmark data sets show that the proposed deep architecture has superior performance against the state-of-the-art methods.


title: Simple Analytical Model of On Operation of Amorphous In–Ga–Zn–O Thin-Film Transistors

abstract: The on operation of an amorphous In-Ga-Zn-O thin-film transistor (a-IGZO TFT) was studied employing a gated-four-probe (GFP) structure TFT to build a simple analytical model based on bias-dependent field-effect mobility (μFE). The electrical characteristics of the a-IGZO GFP TFT revealed that the contact resistances were negligible compared with the channel resistance. The bias-dependent μFE was derived from the transfer characteristics at low drain voltages (VD) , and approximately represented by a power function of the bias voltage, like that for hydrogenated amorphous Si (a-Si:H) TFTs. The mobility model reproduced both the current-voltage characteristics and the potential distribution in the channel, including the high VD region. Si TFTs exhibit non-negligible variation of drain current (ID) in the saturation region and their models require extra parameters to describe it. In contrast, the a-IGZO TFTs exhibit flat ID-VD characteristics in the saturation region, and their model does not require an extra parameter. Due to these features, the model of the a-IGZO TFTs is simpler than those of a-Si:H TFTs. The temperature dependence of the TFT characteristics indicated that the bias dependence of μFE cannot be explained just by the exponential subgap traps. The dependence should be understood by introducing the carrier-density dependent mobility of a-IGZO films.


title: Pixel-Oriented Visualization of Change in Social Networks

abstract: We propose a new approach to visualize social networks. Most common network visualizations rely on graph drawing. While without doubt useful, graphs suffer from limitations like cluttering and important patterns may not be realized especially when networks change over time. Our approach adapts pixel-oriented visualization techniques to social networks as an addition to traditional graph visualizations. The visualization is exemplified using social networks based on corporate wikis.


title: The effects of occupational therapy with sensory integration emphasis on preschool-age children with autism.

abstract: OBJECTIVE ::: Using single-subject research design, the effects of an occupational therapy intervention emphasizing sensory integration with five preschool children with autism were examined. ::: ::: ::: METHOD ::: In the AB design, nonengagement, mastery play, and interaction were measured, using videotape clips of each child's free play in the preschool. Following a 3-week baseline, an occupational therapist provided one-on-one sessions and consultation to teachers for 10 weeks. ::: ::: ::: RESULTS ::: When baseline and intervention phases were compared, four children demonstrated decreased frequency of nonengaged behavior, and three demonstrated increased frequency of mastery (goal-directed) play. Improvements in frequency of interaction were minimal. ::: ::: ::: CONCLUSION ::: The results support descriptions in the literature regarding the behavioral changes that children with autism can make when participating in intervention using a sensory integration approach.


title: The Design of Small UAV Autopilot Hardware System Based on DSP

abstract: Now in many countries, the small UAV autopilot has been developed, and some products have already been on the market, but few of these products choose DSP as their processors. This article is proposed to design a small UAV autopilot hardware system which based on DSP. The entire process of the design of the autopilot hardware will be described in detail and the software debugging in brief.


title: Applied machine vision of plants: a review with implications for field deployment in automated farming operations

abstract: Automated visual assessment of plant condition, specifically foliage wilting, reflectance and growth parameters, using machine vision has potential use as input for real-time variable-rate irrigation and fertigation systems in precision agriculture. This paper reviews the research literature for both outdoor and indoor applications of machine vision of plants, which reveals that different environments necessitate varying levels of complexity in both apparatus and nature of plant measurement which can be achieved. Deployment of systems to the field environment in precision agriculture applications presents the challenge of overcoming image variation caused by the diurnal and seasonal variation of sunlight. From the literature reviewed, it is argued that augmenting a monocular RGB vision system with additional sensing techniques potentially reduces image analysis complexity while enhancing system robustness to environmental variables. Therefore, machine vision systems with a foundation in optical and lighting design may potentially expedite the transition from laboratory and research prototype to robust field tool.


title: Resonant drive: sense and high voltage electrostatic drive using single MEMS capacitor and low voltage electronics

abstract: This paper describes the resonant drive technique that eliminates the need for high voltage integrated circuits (IC) in MEMS drive electronics. Resonant drive also offers a versatile solution for position sense and generates the sense signal using a single drive capacitor. Resonant drive uses the fact that the voltage across the capacitor of a series LC tank can be much higher than the input signal driving the tank. The amplification is maximum at resonance and equal to the quality factor (Q). The tank circuit consists of an external inductor and the MEMS drive capacitor. Since the input signal is amplified through the tank, low voltage electronics can fully implement the drive function. As the actuator gap closes, resonance frequency of the tank decreases. This frequency shift is used for measuring the position. Resonant drive has been successfully demonstrated using torsional MEMS mirrors that require 100V for 20 deg-optical rotation. The LC tank (Q of 15) was formed by using a 390/spl mu/H inductor. All drive and sense circuits were built using only 10V CMOS parts.


title: FPGA implementation of high speed XTS-AES for data storage devices

abstract: This paper presents a novel architecture of XTS-AES mode for data storage devices. An enhanced fully pipelined and area efficient XTS-AES mode design using one AES core is proposed. We propose a design of XTS module to handle the data blocks to be encrypted using a single AES core. Considering previous work in XTS, few designs have been published that use a single AES core, and few efforts have been targeted toward their optimization. This paper describes hardware implementation of XTS-AES design with a throughput of 19.56 Gbps and a maximum achievable frequency of 153.84 MHz. This design is written in Verilog HDL and verified on Altera Cyclone II FPGA.


title: On the evidential reasoning algorithm for multiple attribute decision analysis under uncertainty

abstract: In multiple attribute decision analysis (MADA), one often needs to deal with both numerical data and qualitative information with uncertainty. It is essential to properly represent and use uncertain information to conduct rational decision analysis. Based on a multilevel evaluation framework, an evidential reasoning (ER) approach has been developed for supporting such decision analysis, the kernel of which is an ER algorithm developed on the basis of the framework and the evidence combination rule of the Dempster-Shafer (D-S) theory. The approach has been applied to engineering design selection, organizational self-assessment, safety and risk assessment, and supplier assessment. In this paper, the fundamental features of the ER approach are investigated. New schemes for weight normalization and basic probability assignments are proposed. The original ER approach is further developed to enhance the process of aggregating attributes with uncertainty. Utility intervals are proposed to describe the impact of ignorance on decision analysis. Several properties of the new ER approach are explored, which lay the theoretical foundation of the ER approach. A numerical example of a motorcycle evaluation problem is examined using the ER approach. Computation steps and analysis results are provided in order to demonstrate its implementation process.


title: Comparison of Mother, Father, and Teacher Reports of ADHD Core Symptoms in a Sample of Child Psychiatric Outpatients

abstract: Objective: To explore the significance of adding father ratings to mother and teacher ratings in the assessment of ADHD symptoms in children. Method: The ADHD Rating Scale–IV, the Child Behavior Checklist, and the Teacher Report Form were filled out by all three informants for a sample of 48 clinically referred children (79% boys) aged 6 to 15 (M = 10.1) years. Results: Correspondence between father and teacher reports on ADHD-specific symptoms (intraclass correlation coefficient [ICC] = .38) exceeded that between mothers and teachers (ICC = .23). Fathers rated their children as having fewer problems than did mothers and teachers on Total scale scores and the Inattention subscale of the ADHD Rating Scale–IV. Mother ratings were more sensitive to an ADHD diagnosis, whereas father ratings better predicted an ADHD diagnosis requiring the two-setting criterion. Conclusion: The choice of parent informant and informant combination had a considerable impact on parent–teacher concordance and estimates of ADHD sym...


title: Timely cloud gaming

abstract: This work introduces a new model for cloud gaming systems aimed at optimizing the timeliness of video frames based on an age of information (AoI) metric. Mobile clients submit actions through an access network to a game server. The game server generates video frames at a constant frame rate. At the mobile device, the display of these frames represent game status updates. We develop a Markov model to characterize the frame delivery process in low-latency edge cloud gaming systems. Based on this model, we derive a simple formula for the average status age of a tightly synchronized low-latency mobile gaming system in which the inter-frame period is a significant contributor to the system latency. We validate the model by ns-3 simulation of a low-latency edge cloud gaming system. Our evaluation scenarios included single-player games as well as multi-player games in which the game processing was conducted by a combination of a centralized game server and edge cloud renderers.


title: Evaluating Causal Models by Comparing Interventional Distributions

abstract: The predominant method for evaluating the quality of causal models is to measure the graphical accuracy of the learned model structure. We present an alternative method for evaluating causal models that directly measures the accuracy of estimated interventional distributions. We contrast such distributional measures with structural measures, such as structural Hamming distance and structural intervention distance, showing that structural measures often correspond poorly to the accuracy of estimated interventional distributions. We use a number of real and synthetic datasets to illustrate various scenarios in which structural measures provide misleading results with respect to algorithm selection and parameter tuning, and we recommend that distributional measures become the new standard for evaluating causal models.


title: Differentially Private Releasing via Deep Generative Model (Technical Report)

abstract: Privacy-preserving releasing of complex data (e.g., image, text, audio) represents a long-standing challenge for the data mining research community. Due to rich semantics of the data and lack of a priori knowledge about the analysis task, excessive sanitization is often necessary to ensure privacy, leading to significant loss of the data utility. In this paper, we present dp-GAN, a general private releasing framework for semantic-rich data. Instead of sanitizing and then releasing the data, the data curator publishes a deep generative model which is trained using the original data in a differentially private manner; with the generative model, the analyst is able to produce an unlimited amount of synthetic data for arbitrary analysis tasks. In contrast of alternative solutions, dp-GAN highlights a set of key features: (i) it provides theoretical privacy guarantee via enforcing the differential privacy principle; (ii) it retains desirable utility in the released model, enabling a variety of otherwise impossible analyses; and (iii) most importantly, it achieves practical training scalability and stability by employing multi-fold optimization strategies. Through extensive empirical evaluation on benchmark datasets and analyses, we validate the efficacy of dp-GAN.


title: Analyzing Uncertainty in Neural Machine Translation

abstract: Machine translation is a popular test bed for research in neural sequence-to-sequence models but despite much recent research, there is still a lack of understanding of these models. Practitioners report performance degradation with large beams, the under-estimation of rare words and a lack of diversity in the final translations. Our study relates some of these issues to the inherent uncertainty of the task, due to the existence of multiple valid translations for a single source sentence, and to the extrinsic uncertainty caused by noisy training data. We propose tools and metrics to assess how uncertainty in the data is captured by the model distribution and how it affects search strategies that generate translations. Our results show that search works remarkably well but that the models tend to spread too much probability mass over the hypothesis space. Next, we propose tools to assess model calibration and show how to easily fix some shortcomings of current models. We release both code and multiple human reference translations for two popular benchmarks.


title: Strategies for mitigating an influenza pandemic

abstract: Pandemic flu: talking tacticsNumerical models of the epidemiology of a potential flu pandemic show there is no single magic bullet which can control the outbreak, but that a combination of approaches could reduce transmission and save many lives. Border restrictions are unlikely to have much effect and travel restrictions within one country would make very little difference to the spread of a pandemic within that country. The models predict that a pandemic in the United Kingdom would peak within two to three months of the first case, and be over within 4 months. It also shows that vaccines need to be available within two months of the start of a pandemic to have a big effect in reducing infection rates. That means that vaccines would need to be stockpiled in advance to be effective.AbstractDevelopment of strategies for mitigating the severity of a new influenza pandemic is now a top global public health priority. Influenza prevention and containment strategies can be considered under the broad categories of antiviral, vaccine and non-pharmaceutical (case isolation, household quarantine, school or workplace closure, restrictions on travel) measures1. Mathematical models are powerful tools for exploring this complex landscape of intervention strategies and quantifying the potential costs and benefits of different options2,3,4,5. Here we use a large-scale epidemic simulation6 to examine intervention options should initial containment6,7 of a novel influenza outbreak fail, using Great Britain and the United States as examples. We find that border restrictions and/or internal travel restrictions are unlikely to delay spread by more than 2–3 weeks unless more than 99% effective. School closure during the peak of a pandemic can reduce peak attack rates by up to 40%, but has little impact on overall attack rates, whereas case isolation or household quarantine could have a significant impact, if feasible. Treatment of clinical cases can reduce transmission, but only if antivirals are given within a day of symptoms starting. Given enough drugs for 50% of the population, household-based prophylaxis coupled with reactive school closure could reduce clinical attack rates by 40–50%. More widespread prophylaxis would be even more logistically challenging but might reduce attack rates by over 75%. Vaccine stockpiled in advance of a pandemic could significantly reduce attack rates even if of low efficacy. Estimates of policy effectiveness will change if the characteristics of a future pandemic strain differ substantially from those seen in past pandemics.


title: Boolean Searchable Symmetric Encryption with Worst-Case Sub-linear Complexity

abstract: Recent work on searchable symmetric encryption (SSE) has focused on increasing its expressiveness. A notable example is the OXT construction (Cash et al., CRYPTO ’13) which is the first SSE scheme to support conjunctive keyword queries with sub-linear search complexity. While OXT efficiently supports disjunctive and boolean queries that can be expressed in searchable normal form, it can only handle arbitrary disjunctive and boolean queries in linear time. This motivates the problem of designing expressive SSE schemes with worst-case sub-linear search; that is, schemes that remain highly efficient for any keyword query.


title: Fine-grained Categorization for 3D Scene Understanding

abstract: Fine-grained categorization of object classes is receiving increased attention, since it promises to automate classification tasks that are difficult even for humans, such as the distinction between different animal species. In this paper, we consider fine-grained categorization for a different reason: following the intuition that fine-grained categories encode metric information, we aim to generate metric constraints from fine-grained category predictions, for the benefit of 3D scene-understanding. To that end, we propose two novel methods for fine-grained classification, both based on part information, as well as a new fine-grained category data set of car types. We demonstrate superior performance of our methods to state-of-the-art classifiers, and show first promising results for estimating the depth of objects from fine-grained category predictions from a monocular camera.


title: A 300W complete GaN solid state power amplifier for positioning system satellite payloads

abstract: An L-Band high power and efficient solid state power amplifier (SSPA) designed for the European satellite navigation system (i.e., Galileo) is presented. The developed SSPA, based on European Gallium Nitride (GaN) technology, comprises all the circuits required to interface the module with the satellite bus (i.e., a Power Supply Unit, PSU), and to control its functionalities by remote telecomand and telemetry (i.e., an Electronic Power Conditioner unit, EPC). The Radio frequency Unit (RFU) together with the PSU and EPC are accommodated in a single box with limited volume and mass. In continuous wave operating mode, the SSPA delivers an output power higher than 300W at less than 3 dB of gain compression in the whole E1-Band (i.e., center frequency ƒ0 = 1.575GHz). Moreover, the demonstrated gain and power added efficiency, including the power dissipated by the PSU and EPC, are higher than 65 dB and 44 %, respectively.


title: Map LineUps: Effects of spatial structure on graphical inference

abstract: Fundamental to the effective use of visualization as an analytic and descriptive tool is the assurance that presenting data visually provides the capability of making inferences from what we see. This paper explores two related approaches to quantifying the confidence we may have in making visual inferences from mapped geospatial data. We adapt Wickham et al. 's ‘Visual Line-up’ method as a direct analogy with Null Hypothesis Significance Testing (NHST) and propose a new approach for generating more credible spatial null hypotheses. Rather than using as a spatial null hypothesis the unrealistic assumption of complete spatial randomness, we propose spatially autocorrelated simulations as alternative nulls. We conduct a set of crowdsourced experiments (n=361) to determine the just noticeable difference (JND) between pairs of choropleth maps of geographic units controlling for spatial autocorrelation (Moran's I statistic) and geometric configuration (variance in spatial unit area). Results indicate that people's abilities to perceive differences in spatial autocorrelation vary with baseline autocorrelation structure and the geometric configuration of geographic units. These results allow us, for the first time, to construct a visual equivalent of statistical power for geospatial data. Our JND results add to those provided in recent years by Klippel et al. (2011), Harrison et al. (2014) and Kay & Heer (2015) for correlation visualization. Importantly, they provide an empirical basis for an improved construction of visual line-ups for maps and the development of theory to inform geospatial tests of graphical inference.


title: Improving Image-Sentence Embeddings Using Large Weakly Annotated Photo Collections

abstract: This paper studies the problem of associating images with descriptive sentences by embedding them in a common latent space. We are interested in learning such embeddings from hundreds of thousands or millions of examples. Unfortunately, it is prohibitively expensive to fully annotate this many training images with ground-truth sentences. Instead, we ask whether we can learn better image-sentence embeddings by augmenting small fully annotated training sets with millions of images that have weak and noisy annotations (titles, tags, or descriptions). After investigating several state-of-the-art scalable embedding methods, we introduce a new algorithm called Stacked Auxiliary Embedding that can successfully transfer knowledge from millions of weakly annotated images to improve the accuracy of retrieval-based image description.


Types and Annotations for CIDOC CRM Properties


title: Text-to-Image-to-Text Translation using Cycle Consistent Adversarial Networks

abstract: Text-to-Image translation has been an active area of research in the recent past. The ability for a network to learn the meaning of a sentence and generate an accurate image that depicts the sentence shows ability of the model to think more like humans. Popular methods on text to image translation make use of Generative Adversarial Networks (GANs) to generate high quality images based on text input, but the generated images don't always reflect the meaning of the sentence given to the model as input. We address this issue by using a captioning network to caption on generated images and exploit the distance between ground truth captions and generated captions to improve the network further. We show extensive comparisons between our method and existing methods.


title: ANALYSIS OF TECHNIQUES FOR BUSINESS

abstract: This paper is about identifying and analyzing techniques that can be used in a business process improvement (BPI) situation. To determine suitable techniques, the term BPI is defined and criteria are derived to analyze the usability of the selected techniques. Over 300 techniques from various improvement methods were reduced to those techniques which can be applied to help improve business processes and, in particular, to support the act of improvement. Identifying these techniques is helpful, because in most cases the act of improvement is a black box that lacks guidelines or structured procedures. In our research we developed an evaluation scheme to analyze the usability of BPI techniques and give hints about how to select a suitable technique for a certain improvement situation.


title: Analysis of DNN approaches to speaker identification

abstract: This work studies the usage of the Deep Neural Network (DNN) Bottleneck (BN) features together with the traditional MFCC features in the task of i-vector-based speaker recognition. We decouple the sufficient statistics extraction by using separate GMM models for frame alignment, and for statistics normalization and we analyze the usage of BN and MFCC features (and their concatenation) in the two stages. We also show the effect of using full-covariance GMM models, and, as a contrast, we compare the result to the recent DNN-alignment approach. On the NIST SRE2010, telephone condition, we show 60% relative gain over the traditional MFCC baseline for EER (and similar for the NIST DCF metrics), resulting in 0.94% EER.


title: Pupal parasitoids (Hymenoptera: Pteromalidae) of muscoid filth flies in Israel.

abstract: Pupal parasitoids of muscoid flies were collected monthly on three farms in southern Israel in preparation for an IPM programme for the control of filth-breeding flies. 50% and 25.5% of viable puparia collected during 1985 and 1986/87, respectively, were parasitized. Three species of Spalangia Latreille and two species of Muscidifurax Girault & Saunders (Hymenoptera: Pteromalidae) accounted for 82.0% and 94.2% of parasitoids recovered in the two seasons. Variation in the seasonal abundance and distribution of the leading parasite species are discussed in connection with their conservation and possible use for augmentative releases within IPM projects.


title: Blockchain Mining Games

abstract: We study the strategic considerations of miners participating in the bitcoin's protocol. We formulate and study the stochastic game that underlies these strategic considerations. The miners collectively build a tree of blocks, and they are paid when they create a node (mine a block) which will end up in the path of the tree that is adopted by all. Since the miners can hide newly mined nodes, they play a game with incomplete information. Here we consider two simplified forms of this game in which the miners have complete information. In the simplest game the miners release every mined block immediately, but are strategic on which blocks to mine. In the second more complicated game, when a block is mined it is announced immediately, but it may not be released so that other miners cannot continue mining from it. A miner not only decides which blocks to mine, but also when to release blocks to other miners. In both games, we show that when the computational power of each miner is relatively small, their best response matches the expected behavior of the bitcoin designer. However, when the computational power of a miner is large, he deviates from the expected behavior, and other Nash equilibria arise.


title: A path analysis model for explaining unsafe behavior in workplaces: the effect of perceived work pressure.

abstract: BACKGROUND ::: Unsafe behavior is closely related to occupational accidents. Work pressure is one the main factors affecting employees' behavior. The aim of the present study was to provide a path analysis model for explaining how work pressure affects safety behavior. ::: ::: ::: METHODS ::: Using a self-administered questionnaire, six variables supposed to affect safety employees' behavior were measured. The path analysis model was constructed based on several hypotheses. The goodness of fit of the model was assessed using both absolute and comparative fit indices. ::: ::: ::: RESULTS ::: Work pressure was determined not to influence safety behavior directly. However, it negatively influenced other variables. Group attitude and personal attitude toward safety were the main factors mediating the effect of work pressure on safety behavior. Among the variables investigated in the present study, group attitude, personal attitude and work pressure had the strongest effects on safety behavior. ::: ::: ::: CONCLUSION ::: Managers should consider that in order to improve employees' safety behavior, work pressure should be reduced to a reasonable level, and concurrently a supportive environment, which ensures a positive group attitude toward safety, should be provided. Replication of the study is recommended.