title: Systems approaches to integrated solid waste management in developing countries

abstract: Solid waste management (SWM) has become an issue of increasing global concern as urban populations continue to rise and consumption patterns change. The health and environmental implications associated with SWM are mounting in urgency, particularly in the context of developing countries. While systems analyses largely targeting well-defined, engineered systems have been used to help SWM agencies in industrialized countries since the 1960s, collection and removal dominate the SWM sector in developing countries. This review contrasts the history and current paradigms of SWM practices and policies in industrialized countries with the current challenges and complexities faced in developing country SWM. In industrialized countries, public health, environment, resource scarcity, climate change, and public awareness and participation have acted as SWM drivers towards the current paradigm of integrated SWM. However, urbanization, inequality, and economic growth; cultural and socio-economic aspects; policy, governance, and institutional issues; and international influences have complicated SWM in developing countries. This has limited the applicability of approaches that were successful along the SWM development trajectories of industrialized countries. This review demonstrates the importance of founding new SWM approaches for developing country contexts in post-normal science and complex, adaptive systems thinking.


title: Model-based predictive direct power control of brushless doubly fed reluctance generator for wind power applications

abstract: Abstract In this paper, a predictive direct power control (PDPC) method for the brushless doubly fed reluctance generator (BDFRG) is proposed. Firstly, the BDFRG active and reactive power equations are derived and then the active and reactive power variations have been predicted within a fixed sampling period. The predicted power variations are used to calculate the required voltage of the secondary winding so that the power errors at the end of the following sampling period are eliminated. Switching pulses are produced using space vector pulse width modulation (SVPWM) approach which causes to a fixed switching frequency. The BDFRG model and the proposed control method are simulated in MATLAB/Simulink software. Simulation results indicate the good performance of the control system in tracking of the active and reactive power references in both power step and speed variation conditions. In addition, fast dynamic response and lower output power ripple are other advantages of this control method.


title: Combining neural network model with seasonal time series ARIMA model

abstract: Abstract This paper proposes a hybrid forecasting model, which combines the seasonal time series ARIMA (SARIMA) and the neural network back propagation (BP) models, known as SARIMABP. This model was used to forecast two seasonal time series data of total production value for Taiwan machinery industry and the soft drink time series. The forecasting performance was compared among four models, i.e., the SARIMABP and SARIMA models and the two neural network models with differenced and deseasonalized data, respectively. Among these methods, the mean square error (MSE), the mean absolute error (MAE), and the mean absolute percentage error (MAPE) of the SARIMABP model were the lowest. The SARIMABP model was also able to forecast certain significant turning points of the test time series.


A New Derivation and Dataset for Fitts ’ Law of Human Motion


title: Network virtualization and software defined networking for cloud computing: a survey

abstract: Network virtualization is the key to the current and future success of cloud computing. In this article, we explain key reasons for virtualization and briefly explain several of the networking technologies that have been developed recently or are being developed in various standards bodies. In particular, we explain software defined networking, which is the key to network programmability. We also illustrate SDN?s applicability with our own research on OpenADN - application delivery in a multi-cloud environment.


title: An MDP Model of Vehicle-Pedestrian Interaction at an Unsignalized Intersection

abstract: Though autonomous vehicles are currently operating in several places, many important questions within the field of autonomous vehicle research remain to be addressed satisfactorily. In this paper, we examine the role of communication between pedestrians and autonomous vehicles at unsignalized intersections. The nature of interaction between pedestrians and autonomous vehicles remains mostly in the realm of speculation currently. Of course, pedestrian's reactions towards autonomous vehicles will gradually change over time owing to habituation, but it is clear that this topic requires urgent and ongoing study, not least of all because engineers require some working model for pedestrian- autonomous-vehicle communication. Our paper proposes a decision-theoretic model that expresses the interaction between a pedestrian and a vehicle. The model considers the interaction between a pedestrian and a vehicle as expressed an MDP, based on prior work conducted by psychologists examining similar experimental conditions. We describe this model and our simulation study of behavior it exhibits. The preliminary results on evaluating the behavior of the autonomous vehicle are promising and we believe it can help reduce the data needed to develop fuller models.


title: Linear Bayes Policy for Learning in Contextual-Bandits

abstract: Machine and Statistical Learning techniques are used in almost all online advertisement systems. The problem of discovering which content is more demanded (e.g. receive more clicks) can be modeled as a multi-armed bandit problem. Contextual bandits (i.e., bandits with covariates, side information or associative reinforcement learning) associate, to each specific content, several features that define the “context” in which it appears (e.g. user, web page, time, region). This problem can be studied in the stochastic/statistical setting by means of the conditional probability paradigm using the Bayes’ theorem. However, for very large contextual information and/or real-time constraints, the exact calculation of the Bayes’ rule is computationally infeasible. In this article, we present a method that is able to handle large contextual information for learning in contextual-bandits problems. This method was tested in the Challenge on Yahoo! dataset at ICML2012’s Workshop “new Challenges for Exploration & Exploitation 3”, obtaining the second place. Its basic exploration policy is deterministic in the sense that for the same input data (as a time-series) the same results are obtained. We address the deterministic exploration vs. exploitation issue, explaining the way in which the proposed method deterministically finds an effective dynamic trade-off based solely in the input-data, in contrast to other methods that use a random number generator.


title: Temporal multi-hierarchy smoothing for estimating rates of rare events

abstract: We consider the problem of estimating rates of rare events obtained through interactions among several categorical variables that are heavy-tailed and hierarchical. In our previous work, we proposed a scalable log-linear model called LMMH (Log-Linear Models for Multiple Hierarchies) that combats data sparsity at granular levels through small sample size corrections that borrow strength from rate estimates at coarser resolutions. This paper extends our previous work in two directions. First, we model excess heterogeneity by fitting local LMMH models to relatively homogeneous subsets of the data. To ensure scalable computation, these subsets are induced through a decision tree, we call this Treed-LMMH. Second, the Treed-LMMH method is coupled with temporal smoothing procedure based on a fast Kalman filter style algorithm. We show that simultaneously performing hierarchical and temporal smoothing leads to significant improvement in predictive accuracy. Our methods are illustrated on a large scale computational advertising dataset consisting of billions of observations and hundreds of millions of attribute combinations(cells).


title: The seven tools of causal inference, with reflections on machine learning

abstract: The kind of causal inference seen in natural human thought can be "algorithmitized" to help produce human-level machine intelligence.


title: Design of wireless power transfer system with Class E inverter and half-bridge Class DE rectifier at any fixed coupling coefficient

abstract: This paper proposes a wireless power transfer system with Class E inverter and half-bridge Class DE rectifier. The proposed system consists of a Class E inverter, a half-bridge Class DE rectifier, and a loosely coupling transformer. Both the inverter and the rectifier achieve zero-voltage switching (ZVS) and zero-voltage derivative switching (ZVDS) at an optimum coupling coefficient. Therefore the switching losses are reduced under high operation frequency. The proposed system was designed, built, and tested. The circuit operation was confirmed at any fixed coupling coefficient with simulation and circuit experiment. Under the condition of the optimum coupling coefficient 0.1754 at 1.000 MHz, the measured power conversion efficiency was 70.99 % with the output power 2.320 W.


title: Imaging unconscious semantic priming.

abstract: Visual words that are masked and presented so briefly that they cannot be seen may nevertheless facilitate the subsequent processing of related words, a phenomenon called masked priming. It has been debated whether masked primes can activate cognitive processes without gaining access to consciousness. Here we use a combination of behavioural and brain-imaging techniques to estimate the depth of processing of masked numerical primes. Our results indicate that masked stimuli have a measurable influence on electrical and haemodynamic measures of brain activity. When subjects engage in an overt semantic comparison task with a clearly visible target numeral, measures of covert motor activity indicate that they also unconsciously apply the task instructions to an unseen masked numeral. A stream of perceptual, semantic and motor processes can therefore occur without awareness.


title: Explanation Methods in Deep Learning: Users, Values, Concerns and Challenges

abstract: Issues regarding explainable AI involve four components: users, laws and regulations, explanations and algorithms. Together these components provide a context in which explanation methods can be evaluated regarding their adequacy. The goal of this chapter is to bridge the gap between expert users and lay users. Different kinds of users are identified and their concerns revealed, relevant statements from the General Data Protection Regulation are analyzed in the context of Deep Neural Networks (DNNs), a taxonomy for the classification of existing explanation methods is introduced, and finally, the various classes of explanation methods are analyzed to verify if user concerns are justified. Overall, it is clear that (visual) explanations can be given about various aspects of the influence of the input on the output. However, it is noted that explanation methods or interfaces for lay users are missing and we speculate which criteria these methods/interfaces should satisfy. Finally it is noted that two important concerns are difficult to address with explanation methods: the concern about bias in datasets that leads to biased DNNs, as well as the suspicion about unfair outcomes.


Virtual SCADA Systems for Cyber Security


title: Dynamic XML documents with distribution and replication

abstract: The advent of XML as a universal exchange format, and of Web services as a basis for distributed computing, has fostered the apparition of a new class of documents: dynamic XML documents. These are XML documents where some data is given explicitly while other parts are given only intensionally by means of embedded calls to web services that can be called to generate the required information. By the sole presence of Web services, dynamic documents already include inherently some form of distributed computation. A higher level of distribution that also allows (fragments of) dynamic documents to be distributed and/or replicated over several sites is highly desirable in today's Web architecture, and in fact is also relevant for regular (non dynamic) documents.The goal of this paper is to study new issues raised by the distribution and replication of dynamic XML data. Our study has originated in the context of the Active XML system [1, 3, 22] but the results are applicable to many other systems supporting dynamic XML data. Starting from a data model and a query language, we describe a complete framework for distributed and replicated dynamic XML documents. We provide a comprehensive cost model for query evaluation and show how it applies to user queries and service calls. Finally, we describe an algorithm that, for a given peer, chooses data and services that the peer should replicate to improve the efficiency of maintaining and querying its dynamic data.


title: Estimation of Entropy and Mutual Information

abstract: We present some new results on the nonparametric estimation of entropy and mutual information. First, we use an exact local expansion of the entropy function to prove almost sure consistency and central limit theorems for three of the most commonly used discretized information estimators. The setup is related to Grenander's method of sieves and places no assumptions on the underlying probability measure generating the data. Second, we prove a converse to these consistency theorems, demonstrating that a misapplication of the most common estimation techniques leads to an arbitrarily poor estimate of the true information, even given unlimited data. This "inconsistency" theorem leads to an analytical approximation of the bias, valid in surprisingly small sample regimes and more accurate than the usual 1/N formula of Miller and Madow over a large region of parameter space. The two most practical implications of these results are negative: (1) information estimates in a certain data regime are likely contaminated by bias, even if "bias-corrected" estimators are used, and (2) confidence intervals calculated by standard techniques drastically underestimate the error of the most common estimation methods.Finally, we note a very useful connection between the bias of entropy estimators and a certain polynomial approximation problem. By casting bias calculation problems in this approximation theory framework, we obtain the best possible generalization of known asymptotic bias results. More interesting, this framework leads to an estimator with some nice properties: the estimator comes equipped with rigorous bounds on the maximum error over all possible underlying probability distributions, and this maximum error turns out to be surprisingly small. We demonstrate the application of this new estimator on both real and simulated data.


title: Design of a High Capacity Inter-Regional Transmission Overlay for the U.S.

abstract: Interconnection wide electric transmission system has been envisioned to facilitate the growth of renewable energy, enhance reliability, improve system operating efficiency, and reduce emissions. In this paper, we propose an explicit planning approach for transmission design at the national level. It has been applied to the U.S. system to design transmission overlays for multiple future generation scenarios. Associated simulation results suggest that a national transmission overlay provides economic, environmental, and system performance benefits.


title: A New Attempt to Silhouette-Based Gait Recognition for Human Identification

abstract: Human identification at distance by analysis of gait patterns extracted from video has recently become very popular research in biometrics. This paper presents multi-projections based approach to extract gait patterns for human recognition. Binarized silhouette of a motion object is represented by 1-D signals which are the basic image features called the distance vectors. The distance vectors are differences between the bounding box and silhouette, and extracted using four projections to silhouette. Eigenspace transformation is applied to time-varying distance vectors and the statistical distance based supervised pattern classification is then performed in the lower-dimensional eigenspace for human identification. A fusion strategy developed is finally executed to produce final decision. Based on normalized correlation on the distance vectors, gait cycle estimation is also performed to extract the gait cycle. Experimental results on four databases demonstrate that the right person in top two matches 100% of the times for the cases where training and testing sets corresponds to the same walking styles, and in top three-four matches 100% of the times for training and testing sets corresponds to the different walking styles.


title: Components for parametric urban design in Grasshopper from street network to building geometry

abstract: The main contribution of our work is in combining the methods for parametric urban design of highly specialized software such as CityEngine and general-purpose parametric modeling platform such as Grasshopper. Our work facilitates and prompts the use of parametric tools by architects and planners for urban design. In this paper we present a custom grasshopper component for street network generation and block subdivision. The component was developed in C# using the RhinoCommon SDK. We used Grasshopper for the development of an urban design proposal at a teaching exercise. To meet the requirements of the urban design project, additional functionalities had to be added to the range of existing Grasshopper components. In particular, we needed components for street network generation and block subdivision. To develop the component we implemented the street expansion strategies described in (Weber et al., 2009) and the methods for block subdivision described in (Vanegas et al., 2009). Additionally, we adapted and enhanced the strategies to meet the NURBS modeling capabilities of Rhinoceros.


title: The Bibliometric Properties of Article Readership Information

abstract: Digital libraries such as the NASA Astrophysics Data System (Kurtz et al., 2005) permit the easy accumulation of a new type of bibliometric measure, the number of electronic accesses (“reads”) of individual articles. We explore various aspects of this new measure. We examine the obsolescence function as measured by actual reads and show that it can be well fit by the sum of four exponentials with very different time constants. We compare the obsolescence function as measured by readership with the obsolescence function as measured by citations. We find that the citation function is proportional to the sum of two of the components of the readership function. This proves that the normative theory of citation is true in the mean. We further examine in detail the similarities and differences among the citation rate, the readership rate, and the total citations for individual articles, and discuss some of the causes. Using the number of reads as a bibliometric measure for individuals, we introduce the read–cite diagram to provide a two-dimensional view of an individual's scientific productivity. We develop a simple model to account for an individual's reads and cites and use it to show that the position of a person in the read–cite diagram is a function of age, innate productivity, and work history. We show the age biases of both reads and cites and develop two new bibliometric measures which have substantially less age bias than citations: SumProd, a weighted sum of total citations and the readership rate, intended to show the total productivity of an individual; and Read10, the readership rate for articles published in the last 10 years, intended to show an individual's current productivity. We also discuss the effect of normalization (dividing by the number of authors on a paper) on these statistics. We apply SumProd and Read10 using new, nonparametric techniques to compare the quality of different astronomical research organizations.


title: Unravelling seams: improving mobile gesture recognition with visual feedback techniques

abstract: Gesture recognition is emerging as an engaging interaction technique in mobile scenarios, and high recognition rates promote user acceptance. Several factors influence recognition rates including the nature of the gesture set and the suitability of the gesture recognition algorithm. This work explores how seamfulness in gesture stroke visualization affects recognition rates. We present the results of a user evaluation of a gesture recognition system that shows that raw (seamful) visualization of low-delity gesture stroke data has recognition rates comparable to no feedback. Providing filtered (seamless) stroke visualization to the user, while retaining the un-filtered input data for recognition, resulted in a 34.9% improvement in gesture recognition rate over raw stroke data. The results provide insights into the broader design space of seamful design, and identifies areas where seamlessness is advantageous.


title: Current bone substitutes for implant dentistry

abstract: Abstract Purpose Alveolar ridge augmentation is essential for success in implant therapy and depends on the biological performance of bone graft materials. This literature review aims to comprehensively explain the clinically relevant capabilities and limitations of currently available bone substitutes for bone augmentation in light of biomaterial science. Study selection The biological performance of calcium phosphate-based bone substitutes was categorized according to space-making capability, biocompatibility, bioabsorption, and volume maintenance over time. Each category was reviewed based on clinical studies, preclinical animal studies, and in vitro studies. Results Currently available bone substitutes provide only osteoconduction as a scaffold but not osteoinduction. Particle size, sensitivity to enzymatic or chemical dissolution, and mechanical properties affect the space-making capability of bone substitutes. The nature of collagen fibers, particulate size, and release of calcium ions influence the biocompatibility of bone substitutes. Bioabsorption of bone substitutes is determined by water solubility (chemical composition) and acid resistance (integrity of apatite structure). Bioabsorption of remnant bone substitute material and volume maintenance of the augmented bone are inversely related. Conclusion It is necessary to improve the biocompatibility of currently available bone substitutes and to strike an appropriate balance between bioabsorption and volume maintenance to achieve ideal bone remodeling.


title: Automatic image segmentation by integrating color-edge extraction and seeded region growing

abstract: We propose a new automatic image segmentation method. Color edges in an image are first obtained automatically by combining an improved isotropic edge detector and a fast entropic thresholding technique. After the obtained color edges have provided the major geometric structures in an image, the centroids between these adjacent edge regions are taken as the initial seeds for seeded region growing (SRG). These seeds are then replaced by the centroids of the generated homogeneous image regions by incorporating the required additional pixels step by step. Moreover, the results of color-edge extraction and SRG are integrated to provide homogeneous image regions with accurate and closed boundaries. We also discuss the application of our image segmentation method to automatic face detection. Furthermore, semantic human objects are generated by a seeded region aggregation procedure which takes the detected faces as object seeds.


title: Beyond duty cycling: Wake-up radio with selective awakenings for long-lived wireless sensing systems

abstract: Emerging wake-up radio technologies have the potential to bring the performance of sensing systems and of the Internet of Things to the levels of low latency and very low energy consumption required to enable critical new applications. This paper provides a step towards this goal with a twofold contribution. We first describe the design and prototyping of a wake-up receiver (WRx) and its integration to a wireless sensor node. Our WRx features very low power consumption (< 1.3μW), high sensitivity (up to —55dBm), fast reactivity (wake-up time of 130μs), and selective addressing, a key enabler of new high performance protocols. We then present ALBA-WUR, a cross-layer solution for data gathering in sensing systems that redesigns a previous leading protocol, ALBA-R, extending it to exploit the features of our WRx. We evaluate the performance of ALBA-WUR via simulations, showing that the use of the WRx produces remarkable energy savings (up to five orders of magnitude), and achieves lifetimes that are decades longer than those obtained by ALBA-R in sensing systems with duty cycling, while keeping latencies at bay.


title: A novel type of compliant and underactuated robotic hand for dexterous grasping

abstract: The usefulness and versatility of a robotic end-effector depends on the diversity of grasps it can accomplish and also on the complexity of the control methods required to achieve them. We believe that soft hands are able to provide diverse and robust grasping with low control complexity. They possess many mechanical degrees of freedom and are able to implement complex deformations. At the same time, due to the inherent compliance of soft materials, only very few of these mechanical degrees have to be controlled explicitly. Soft hands therefore may combine the best of both worlds. In this paper, we present RBO Hand 2, a highly compliant, underactuated, robust, and dexterous anthropomorphic hand. The hand is inexpensive to manufacture and the morphology can easily be adapted to specific applications. To enable efficient hand design, we derive and evaluate computational models for the mechanical properties of the hand’s basic building blocks, called PneuFlex actuators. The versatility of RBO Hand 2 is evaluated by implementing the comprehensive Feix taxonomy of human grasps. The manipulator’s capabilities and limits are demonstrated using the Kapandji test and grasping experiments with a variety of objects of varying weight. Furthermore, we demonstrate that the effective dimensionality of grasp postures exceeds the dimensionality of the actuation signals, illustrating that complex grasping behavior can be achieved with relatively simple control.


title: Dynamic threshold-voltage MOSFET (DTMOS) for ultra-low voltage VLSI

abstract: In this paper, we propose a novel operation of a MOSFET that is suitable for ultra-low voltage (0.6 V and below) VLSI circuits. Experimental demonstration was carried out in a Silicon-On-Insulator (SOI) technology. In this device, the threshold voltage of the device is a function of its gate voltage, i.e., as the gate voltage increases the threshold voltage (V/sub t/) drops resulting in a much higher current drive than standard MOSFET for low-power supply voltages. On the other hand, V/sub t/ is high at V/sub gs/=0, therefore the leakage current is low. We provide extensive experimental results and two-dimensional (2-D) device and mixed-mode simulations to analyze this device and compare its performance with a standard MOSFET. These results verify excellent inverter dc characteristics down to V/sub dd/=0.2 V, and good ring oscillator performance down to 0.3 V for Dynamic Threshold-Voltage MOSFET (DTMOS).


title: 0.13/spl mu/m CMOS SOI SP6T antenna switch for multi-standard handsets

abstract: This paper presents the design of a single pole 6 throw antenna switch able to manage all the four GSM standards, i.e. 850-900-1800-1900 MHz. The switch has been integrated in a 0.13/spl mu/m CMOS SOI process with high resistivity substrate and a thick oxide (50/spl Aring/) option. The use of high resistivity substrate allows a good loss (IL)-isolation trade-off: IL is kept in the range of 0.55-0.8 dB for the RXs and at 0.7 dB for the TXs, while isolation varies from 40 dB at 900 MHz to 30 dB at 1900 MHz. Power handling capability is well compatible with GSM standards since an ICP/sub 0.1dB/ of 36 dBm has been measured and harmonics distortion is below -39 dBm for an input power of 34 dBm. Robustness to antenna mismatching condition has been successfully demonstrated up to a VSWR of 10:1. The chip size is of 1.23 mm/sup 2/ and the power consumption is below 10/spl mu/A and 0.5 mA respectively in stand by mode and during switching, under 2.5 voltage supply.


title: Recent Advances in Open Set Recognition: A Survey

abstract: In real-world recognition/classification tasks, limited by various objective factors, it is usually difficult to collect training samples to exhaust all classes when training a recognizer or classifier. A more realistic scenario is open set recognition (OSR), where incomplete knowledge of the world exists at training time, and unknown classes can be submitted to an algorithm during testing, requiring the classifiers to not only accurately classify the seen classes, but also effectively deal with unseen ones. This paper provides a comprehensive survey of existing open set recognition techniques covering various aspects ranging from related definitions, representations of models, datasets, evaluation criteria, and algorithm comparisons. Furthermore, we briefly analyze the relationships between OSR and its related tasks including zero-shot, one-shot (few-shot) recognition/learning techniques, classification with reject option, and so forth. Additionally, we also review the open world recognition which can be seen as a natural extension of OSR. Importantly, we highlight the limitations of existing approaches and point out some promising subsequent research directions in this field.


title: Multi-hierarchical Independent Correlation Filters for Visual Tracking

abstract: For visual tracking, most of the traditional correlation filters (CF) based methods suffer from the bottleneck of feature redundancy and lack of motion information. In this paper, we design a novel tracking framework, called multi-hierarchical independent correlation filters (MHIT). The framework consists of motion estimation module, hierarchical features selection, independent CF online learning, and adaptive multi-branch CF fusion. Specifically, the motion estimation module is introduced to capture motion information, which effectively alleviates the object partial occlusion in the temporal video. The multi-hierarchical deep features of CNN representing different semantic information can be fully excavated to track multi-scale objects. To better overcome the deep feature redundancy, each hierarchical features are independently fed into a single branch to implement the online learning of parameters. Finally, an adaptive weight scheme is integrated into the framework to fuse these independent multi-branch CFs for the better and more robust visual object tracking. Extensive experiments on OTB and VOT datasets show that the proposed MHIT tracker can significantly improve the tracking performance. Especially, it obtains a 20.1% relative performance gain compared to the top trackers on the VOT2017 challenge, and also achieves new state-of-the-art performance on the VOT2018 challenge.


title: Scalable energy-efficient, low-latency implementations of trained spiking Deep Belief Networks on SpiNNaker

abstract: Deep neural networks have become the state-of-the-art approach for classification in machine learning, and Deep Belief Networks (DBNs) are one of its most successful representatives. DBNs consist of many neuron-like units, which are connected only to neurons in neighboring layers. Larger DBNs have been shown to perform better, but scaling-up poses problems for conventional CPUs, which calls for efficient implementations on parallel computing architectures, in particular reducing the communication overhead. In this context we introduce a realization of a spike-based variation of previously trained DBNs on the biologically-inspired parallel SpiNNaker platform. The DBN on SpiNNaker runs in real-time and achieves a classification performance of 95% on the MNIST handwritten digit dataset, which is only 0.06% less than that of a pure software implementation. Importantly, using a neurally-inspired architecture yields additional benefits: during network run-time on this task, the platform consumes only 0.3 W with classification latencies in the order of tens of milliseconds, making it suitable for implementing such networks on a mobile platform. The results in this paper also show how the power dissipation of the SpiNNaker platform and the classification latency of a network scales with the number of neurons and layers in the network and the overall spike activity rate.


title: A hybrid feature selection method for data sets of thousands of variables

abstract: Feature selection has become the focus of research areas of applications with datasets of thousands of variables. In this study we present a hybrid feature selection (HFS) method that adopts both filter and wrapper models of feature subset selection. In the first stage of the feature selection, we use the filter model to rank the features by the mutual information (MI) between each feature and each class, and then choose k highest relevant features to the classes. In the second stage, we complete a wrapper model based feature selection algorithm, which uses Shepley value to evaluate the contribution of features to the classification task in a feature subset. Experimental results show obviously that the HFS method obtains better classification performance than solo Shepley value based or solo MI based feature selection method.


title: Image warps for artistic perspective manipulation

abstract: Painters and illustrators commonly sketch vanishing points and lines to guide the construction of perspective images. We present a tool that gives users the ability to manipulate perspective in photographs using image space controls similar to those used by artists. Our approach computes a 2D warp guided by constraints based on projective geometry. A user annotates an image by marking a number of image space constraints including planar regions of the scene, straight lines, and associated vanishing points. The user can then use the lines, vanishing points, and other point constraints as handles to control the warp. Our system optimizes the warp such that straight lines remain straight, planar regions transform according to a homography, and the entire mapping is as shape-preserving as possible. While the result of this warp is not necessarily an accurate perspective projection of the scene, it is often visually plausible. We demonstrate how this approach can be used to produce a variety of effects, such as changing the perspective composition of a scene, exploring artistic perspectives not realizable with a camera, and matching perspectives of objects from different images so that they appear consistent for compositing.


title: A Dark Side of Happiness? How, When, and Why Happiness Is Not Always Good

abstract: Happiness is generally considered a source of good outcomes. Research has highlighted the ways in which happiness facilitates the pursuit of important goals, contributes to vital social bonds, broadens people’s scope of attention, and increases well-being and psychological health. However, is happiness always a good thing? This review suggests that the pursuit and experience of happiness might sometimes lead to negative outcomes. We focus on four questions regarding this purported ‘‘dark side’’ of happiness. First, is there a wrong degree of happiness? Second, is there a wrong time for happiness? Third, are there wrong ways to pursue happiness? Fourth, are there wrong types of happiness? Cumulatively, these lines of research suggest that although happiness is often highly beneficial, it may not be beneficial at every level, in every context, for every reason, and in every variety.


title: Cuanta: quantifying effects of shared on-chip resource interference for consolidated virtual machines

abstract: Workload consolidation is very attractive for cloud platforms due to several reasons including reduced infrastructure costs, lower energy consumption, and ease of management. Advances in virtualization hardware and software continue to improve resource isolation among consolidated workloads but a particular form of resource interference is yet to see a commercially widely adopted solution - the interference due to shared processor caches. Existing solutions for handling cache interference require new hardware features, extensive software changes, or reduce the achieved overall throughput. A crucial requirement for effective consolidation is to be able to predict the impact of cache interference among consolidated workloads. In this paper, we present a practical technique for predicting performance interference due to shared processor cache which works on current processor architectures and requires minimal software changes. While performance degradation can be empirically measured for a given placement of consolidated workloads, the number of possible placements grows exponentially with the number of workloads and actual measurement of degradation is thus not practical for every possible placement. Our technique predicts the degradation for any possible placement using only a linear number of measurements, and can be used to select the most efficient consolidation pattern, for required performance and resource constraints. An average prediction error of less than 4% is achieved across a wide variety of benchmark workloads, using Xen VMM on Intel Core 2 Duo and Nehalem quad-core processor platforms. We also illustrate the usefulness of our prediction technique in realizing better workload placement decisions for given performance and resource cost objectives.


title: Role of extracellular polymeric substances (EPS) production in bioaggregation: application to wastewater treatment

abstract: This paper reviews the formation, structure, and stability of bioaggregates with an emphasis on the composition and distribution of extracellular polymeric substances (EPS) and their role in bioaggregation. Bioaggregation is ubiquitous in natural environment and is of great importance in biological wastewater treatment processes. It greatly influences the flocculability, settleability, and dewaterability for flocs and sludge retention and shear resistance for biofilms. The physico-chemical and microbial structures of bioaggregates are dependent on operational conditions as well as microbial diversity and spatial distribution. The formation of bioaggregates is mediated by the physico-chemical interactions as well as the microbial interactions such as EPS production and quorum sensing. EPS are composed of a mixture of macromolecules including proteins, polysaccharides, humic-like substances, and nucleic acids, which entrap the microbial cells in a three-dimensional matrix. The composition and physico-chemical characteristics of EPS have significant influence on the maintenance of the bioaggregate structure and the process performance of the wastewater treatment. However, the mechanisms of bioaggregation are still unclear and the conclusions on the role of EPS were mostly drawn from the established correlations and hypotheses. This paper expects to provide up-to-date knowledge on bioaggregation and insights for further studies and applications.


title: Pfinder: Real-time tracking of the human body

abstract: Pfinder is a real-time system for tracking people and interpreting their behavior. It runs at 10 Hz on a standard SGI Indy computer, and has performed reliably on thousands of people in many different physical locations. The system uses a multiclass statistical model of color and shape to obtain a 2D representation of head and hands in a wide range of viewing conditions. Pfinder has been successfully used in a wide range of applications including wireless interfaces, video databases, and low-bandwidth coding.


title: TGSum: Build Tweet Guided Multi-Document Summarization Dataset

abstract: The development of summarization research has been significantly hampered by the costly acquisition of reference summaries. This paper proposes an effective way to automatically collect large scales of news-related multi-document summaries with reference to social media's reactions. We utilize two types of social labels in tweets, i.e., hashtags and hyper-links. Hashtags are used to cluster documents into different topic sets. Also, a tweet with a hyper-link often highlights certain key points of the corresponding document. We synthesize a linked document cluster to form a reference summary which can cover most key points. To this aim, we adopt the ROUGE metrics to measure the coverage ratio, and develop an Integer Linear Programming solution to discover the sentence set reaching the upper bound of ROUGE. Since we allow summary sentences to be selected from both documents and high-quality tweets, the generated reference summaries could be abstractive. Both informativeness and readability of the collected summaries are verified by manual judgment. In addition, we train a Support Vector Regression summarizer on DUC generic multi-document summarization benchmarks. With the collected data as extra training resource, the performance of the summarizer improves a lot on all the test sets. We release this dataset for further research.


title: Image enhancement via lateral inhibition: An analysis under illumination changes

abstract: Abstract Image enhancement is a fundamental issue in digital image processing. Image enhancement via lateral inhibition is a relatively new method inspired by the phenomenon that the optic nerves in illuminatingly light regions inhibit those in darkness. However, it is common that the observed images incorporate illumination noises. This potentially limits the utilization of an image enhancement method because amplifying the noises would make the situation even worse. In this study, performance of lateral inhibition to handle illumination changes is investigated through theoretical analyses and simulations on a few transportation cases. Specifically, uniform, linearly varying and stochastic illumination changes are considered. Experimental results confirm that lateral inhibition can enhance the original image without amplifying the illumination noises in the aforementioned three scenarios.


title: Classifying Questions in Question Answering System Using Finite State Machines with a Simple Learning Approach

abstract: Question Classification plays a significant part in Question Answering system. In order to obtain a classifier, we present in this paper a pragmatic approach that utilizes simple sentence structures observed and learned from the question sentence patterns, trains a set of Finite State Machines (FSM) based on keywords appearing in the sentences and uses the trained FSMs to classify various questions to their relevant classes. Although, questions can be placed using various syntactic structures and keywords, we have carefully observed that this variation is within a small finite limit and can be traced down using a limited number of FSMs and a simple semantic understanding instead of using complex semantic analysis. WordNet semantic meaning of various keywords to extend the FSMs capability to accept a wide variety of wording used in the questions. Various kinds of questions written in English language and belonging to diverse classes from the Conference and Labs of the Evaluation Forum’s Question Answering track are used for the training purpose and a separate set of questions from the same track is used for analyzing the FSMs competence to map the questions to one of the recognizable classes. With the use of learning strategies and application of simple voting functions along with training the weights for the keywords appearing in the questions, we have managed to achieve a classification accuracy as high as 94%. The system was trained by placing questions in various orders to see if the system built up from those orders have any subtle impact on the accuracy rate. The usability of this approach lies in its simplicity and yet it performs well to cope up with various sentence patterns. 1 This work was supported by national funds through FCT – Fundacao para a Ciencia e a Tecnologia, under project PEstOE/EEI/LA0021/2013


title: BER analysis of QAM with transmit diversity in Rayleigh fading channels

abstract: In this paper, we present a log-likelihood ratio (LLR) based approach to analyze the bit error rate (BER) performance of quadrature amplitude modulation (QAM) on Rayleigh fading channels without and with transmit diversity. We derive LLRs for the individual bits forming a QAM symbol both on flat fading channels without diversity as well as on channels with transmit diversity using two transmit antennas (Alamouti's scheme) and multiple receive antennas. Using the LLRs of the individual bits forming the QAM symbol, we derive expressions for the probability of error for various bits in the QAM symbol, and hence the average BER. In addition to being used in the BER analysis, the LLRs derived can be used as soft inputs to decoders for various coded QAM schemes including turbo coded QAM with transmit diversity, as in high speed downlink packet access (HSDPA) in 3G.


title: Confidentiality issues on a GPU in a virtualized environment

abstract: General-Purpose computing on Graphics Processing Units (GPGPU) combined to cloud computing is already a commercial success. However, there is little literature that investigates its security implications. Our objective is to highlight possible information leakage due to GPUs in virtualized and cloud computing environments. We provide insight into the different GPU virtualization techniques, along with their security implications. We systematically experiment and analyze the behavior of GPU global memory in the case of direct device assignment. We find that the GPU global memory is zeroed only in some configurations. In those configurations, it happens as a side effect of Error Correction Codes (ECC) and not for security reasons. As a consequence, an adversary can recover data of a previously executed GPGPU application in a variety of situations. These situations include setups where the adversary launches a virtual machine after the victim’s virtual machine using the same GPU, thus bypassing the isolation mechanisms of virtualization. Memory cleaning is not implemented by the GPU card itself and we cannot generally exclude the existence of data leakage in cloud computing environments. We finally discuss possible countermeasures for current GPU clouds users and providers.


Ultrasonic Automatic Braking System For Forward Collision Avoidance With Accelerator Pedal Disengagement Mechanism


title: Negative Correlation Ensemble Learning for Ordinal Regression

abstract: In this paper, two neural network threshold ensemble models are proposed for ordinal regression problems. For the first ensemble method, the thresholds are fixed a priori and are not modified during training. The second one considers the thresholds of each member of the ensemble as free parameters, allowing their modification during the training process. This is achieved through a reformulation of these tunable thresholds, which avoids the constraints they must fulfill for the ordinal regression problem. During training, diversity exists in different projections generated by each member is taken into account for the parameter updating. This diversity is promoted in an explicit way using a diversity-encouraging error function, extending the well-known negative correlation learning framework to the area of ordinal regression, and inheriting many of its good properties. Experimental results demonstrate that the proposed algorithms can achieve competitive generalization performance when considering four ordinal regression metrics.


title: Reference hijacking: patching, protecting and analyzing on unmodified and non-rooted android devices

abstract: Many efforts have been paid to enhance the security of Android. However, less attention has been given to how to practically adopt the enhancements on off-the-shelf devices. In particular, securing Android devices often requires modifying their write-protected underlying system component files (especially the system libraries) by flashing or rooting devices, which is unacceptable in many realistic cases. In this paper, a novel technique, called reference hijacking, is presented to address the problem. By introducing a specially designed reset procedure, a new execution environment is constructed for the target application, in which the reference to the underlying system libraries will be redirected to the security-enhanced alternatives. The technique can be applicable to both the Dalvik and Android Runtime (ART) environments and to almost all mainstream Android versions (2.x to 5.x). To demonstrate the capability of reference hijacking, we develop three prototype systems, PatchMan, ControlMan, and TaintMan, to enforce specific security enhancements, involving patching vulnerabilities, protecting inter-component communications, and performing dynamic taint analysis for the target application. These three prototypes have been successfully deployed on a number of popular Android devices from different manufacturers, without modifying the underlying system. The evaluation results show that they are effective and do not introduce noticeable overhead. They strongly support that reference hijacking can substantially improve the practicability of many security enhancement efforts for Android.


title: Knowledge Graph Completion by Context-Aware Convolutional Learning with Multi-Hop Neighborhoods

abstract: The main focus of relational learning for knowledge graph completion (KGC) lies in exploiting rich contextual information for facts. Many state-of-the-art models incorporate fact sequences, entity types, and even textual information. Unfortunately, most of them do not fully take advantage of rich structural information in a KG, i.e., connectivity patterns around each entity. In this paper, we propose a context-aware convolutional learning (CACL) model which jointly learns from entities and their multi-hop neighborhoods. Since we directly utilize the connectivity patterns contained in each multi-hop neighborhood, the structural role similarity among entities can be better captured, resulting in more informative entity and relation embeddings. Specifically, CACL collects entities and relations from the multi-hop neighborhood as contextual information according to their relative importance and uniquely maps them to a linear vector space. Our convolutional architecture leverages a deep learning technique to represent each entity along with its linearly mapped contextual information. Thus, we can elaborately extract the features of key connectivity patterns from the context and incorporate them into a score function which evaluates the validity of facts. Experimental results on the newest datasets show that CACL outperforms existing approaches by successfully enriching embeddings with neighborhood information.


title: Abstract Image encryption using chaotic logistic map

abstract: Abstract In recent years, the chaos based cryptographic algorithms have suggested some new and efficient ways to develop secure image encryption techniques. In this communication, we propose a new approach for image encryption based on chaotic logistic maps in order to meet the requirements of the secure image transfer. In the proposed image encryption scheme, an external secret key of 80-bit and two chaotic logistic maps are employed. The initial conditions for the both logistic maps are derived using the external secret key by providing different weightage to all its bits. Further, in the proposed encryption process, eight different types of operations are used to encrypt the pixels of an image and which one of them will be used for a particular pixel is decided by the outcome of the logistic map. To make the cipher more robust against any attack, the secret key is modified after encrypting each block of sixteen pixels of the image. The results of several experimental, statistical analysis and key sensitivity tests show that the proposed image encryption scheme provides an efficient and secure way for real-time image encryption and transmission.


title: DesnowNet: Context-Aware Deep Network for Snow Removal

abstract: Existing learning-based atmospheric particle-removal approaches such as those used for rainy and hazy images are designed with strong assumptions regarding spatial frequency, trajectory, and translucency. However, the removal of snow particles is more complicated because they possess additional attributes of particle size and shape, and these attributes may vary within a single image. Currently, hand-crafted features are still the mainstream for snow removal, making significant generalization difficult to achieve. In response, we have designed a multistage network named DesnowNet to in turn deal with the removal of translucent and opaque snow particles. We also differentiate snow attributes of translucency and chromatic aberration for accurate estimation. Moreover, our approach individually estimates residual complements of the snow-free images to recover details obscured by opaque snow. Additionally, a multi-scale design is utilized throughout the entire network to model the diversity of snow. As demonstrated in the qualitative and quantitative experiments, our approach outperforms state-of-the-art learning-based atmospheric phenomena removal methods and one semantic segmentation baseline on the proposed Snow100K dataset. The results indicate our network would benefit applications involving computer vision and graphics.


title: Can maturity models support cyber security?

abstract: We are living in a cyber space with an unprecedented rapid expansion of the space and its elements. All interactive information is processed and exchanged via this space. Clearly a well-built cyber security is vital to ensure the security of the cyber space. However the definitions and scopes of both cyber space and cyber security are still not well-defined and this makes it difficult to establish sound security models and mechanisms for protecting this space. Out of existing models, maturity models offer a manageable approach for assessing the security level of a system or organization. The paper first provides a review of various definitions of cyber space and cyber security in order to ascertain a common understanding of the space and its security. The paper investigates existing security maturity models, focusing on their defining characteristics and identifying their strengths and weaknesses. Finally, the paper discusses and suggests measures for a sound and applicable cyber security model.


title: A Generalized Representer Theorem

abstract: Wahba's classical representer theorem states that the solutions of certain risk minimization problems involving an empirical risk term and a quadratic regularizer can be written as expansions in terms of the training examples. We generalize the theorem to a larger class of regularizers and empirical risk terms, and give a self-contained proof utilizing the feature space associated with a kernel. The result shows that a wide range of problems have optimal solutions that live in the finite dimensional span of the training examples mapped into feature space, thus enabling us to carry out kernel algorithms independent of the (potentially infinite) dimensionality of the feature space.


title: Community Detection based on Distance Dynamics

abstract: In this paper, we introduce a new community detection algorithm, called Attractor, which automatically spots communities in a network by examining the changes of "distances" among nodes (i.e. distance dynamics). The fundamental idea is to envision the target network as an adaptive dynamical system, where each node interacts with its neighbors. The interaction will change the distances among nodes, while the distances will affect the interactions. Such interplay eventually leads to a steady distribution of distances, where the nodes sharing the same community move together and the nodes in different communities keep far away from each other. Building upon the distance dynamics, Attractor has several remarkable advantages: (a) It provides an intuitive way to analyze the community structure of a network, and more importantly, faithfully captures the natural communities (with high quality). (b) Attractor allows detecting communities on large-scale networks due to its low time complexity (O(|E|)). (c) Attractor is capable of discovering communities of arbitrary size, and thus small-size communities or anomalies, usually existing in real-world networks, can be well pinpointed. Extensive experiments show that our algorithm allows the effective and efficient community detection and has good performance compared to state-of-the-art algorithms.


title: Towards a general theory of driver behaviour

abstract: Taylor (Taylor, D.H., 1964. Drivers' galvanic skin response and the risk of accident. Ergonomics 7, 439-451) argued that drivers attempt to maintain a constant level of anxiety when driving which Wilde (Wilde, G.J.S., 1982. The theory of risk homeostasis: implications for safety and health. Risk Anal. 2, 209-225) interpreted to be coupled to subjective estimates of the probability of collision. This theoretical paper argues that what drivers attempt to maintain is a level of task difficulty. N ¨¨¨ anen and Summala (N¨ a¨ atR., Summala, H., 1976. Road User Behaviour and Traffic Accidents. North Holland/Elsevier, Amsterdam, New York) similarly rejected the concept of statistical risk as a determinant of driver behaviour, but in so doing fell back on the learning process to generate a largely automatised selection of appropriate safety margins. However it is argued here that driver behaviour cannot be acquired and executed principally in such S-R terms. The concept of task difficulty is elaborated within the framework of the task-capability interface (TCI) model, which describes the dynamic interaction between the determinants of task demand and driver capability. It is this interaction which produces different levels of task difficulty. Implications of the model are discussed regarding variation in performance, resource allocation, hierarchical decision-making and the interdependence of demand and capability. Task difficulty homeostasis is proposed as a key sub-goal in driving and speed choice is argued to be the primary solution to the problem of keeping task difficulty within selected boundaries. The relationship between task difficulty and mental workload and calibration is clarified. Evidence is cited in support of the TCI model, which clearly distinguishes task difficulty from estimates of statistical risk. However, contrary to expectation, ratings of perceived risk depart from ratings of statistical risk but track difficu lty ratings almost perfectly. It now appears that feelings of risk may inform driver decision making, as Taylor originally suggested, but not in terms of risk of collision, but rather in terms of task difficulty. Finally risk homeostasis is presented as a special case of task difficulty homeostasis. © 2005 Elsevier Ltd. All rights reserved.


title: Mechanisms of stress in the brain

abstract: The brain is the central organ involved in perceiving and adapting to social and physical stressors via multiple interacting mediators, from the cell surface to the cytoskeleton to epigenetic regulation and nongenomic mechanisms. A key result of stress is structural remodeling of neural architecture, which may be a sign of successful adaptation, whereas persistence of these changes when stress ends indicates failed resilience. Excitatory amino acids and glucocorticoids have key roles in these processes, along with a growing list of extra- and intracellular mediators that includes endocannabinoids and brain-derived neurotrophic factor (BDNF). The result is a continually changing pattern of gene expression mediated by epigenetic mechanisms involving histone modifications and CpG methylation and hydroxymethylation as well as by the activity of retrotransposons that may alter genomic stability. Elucidation of the underlying mechanisms of plasticity and vulnerability of the brain provides a basis for understanding the efficacy of interventions for anxiety and depressive disorders as well as age-related cognitive decline.


title: A study of the difficulties of novice programmers

abstract: Programming is related to several fields of technology, and many university students are studying the basics of it. Unfortunately, they often face difficulties already on the basic courses. This work studies the difficulties in learning programming in order to support developing learning materials for basic programming courses. The difficulties have to be recognized to be able to aid learning and teaching in an effective way.An international survey of opinions was organized for more than 500 students and teachers. This paper analyses the results of the survey. The survey provides information of the difficulties experienced and perceived when learning and teaching programming. The survey results also provide basis for recommendations for developing learning materials and approaches.


title: Analysis of the Frequency Offset Effect on Zadoff–Chu Sequence Timing Performance

abstract: Zadoff-Chu (ZC) sequences have been used as synchronization sequences in modern wireless communication systems, replacing the conventional pseudorandom noise sequences due to their perfect autocorrelation properties. We first study the problem of ambiguity between a timing offset and a frequency offset, which arises when a ZC sequence is used as a synchronization signal. We then show how a frequency offset can impair the timing property of a ZC sequence, causing irreducible timing errors. An analytical framework, particularly the timing spectrum, is developed, which fully characterizes a ZC sequence's timing properties and its fundamental limitations as a time synchronization sequence in the presence of a frequency offset between the transmitter and the receiver. This analytical framework provides a powerful analytical tool for timing signal design and performance analysis of ZC sequences.


title: Global-residual and Local-boundary Refinement Networks for Rectifying Scene Parsing Predictions

abstract: Most of the existing scene parsing methods suffer from the serious problems of both inconsistent parsing results and object boundary shift. To tackle these issues, we first propose a Global-residual Refinement Network (GRN) through exploiting global contextual information to predict the parsing residuals and iteratively smoothen the inconsistent parsing labels. Furthermore, we propose a Localboundary Refinement Network (LRN) to learn the position-adaptive propagation coefficients so that local contextual information from neighbors can be optimally captured for refining object boundaries. Finally, we cascade the proposed two refinement networks after a fully residual convolutional neural network within a uniform framework. Extensive experiments on ADE20K and Cityscapes datasets well demonstrate the effectiveness of the two refinement methods for refining scene parsing predictions.


title: Building a Critical Limb Ischemia Program

abstract: The need to develop highly qualified, multidisciplinary critical limb ischemia (CLI) programs has gained significant momentum. Due to the systemic nature of the disease, patients with CLI are inherently medically complex and often present with multiple comorbidities. Successful care for these patients depends on community screening, early referral, accurate diagnosis, risk stratification, risk factor modification, invasive and non-invasive treatment strategies, and appropriate surveillance. Patients with CLI are often treated by multiple subspecialists—often lacking a unified team which could lead to inefficiencies and redundancy. Establishing an effective critical limb ischemia program relies on the joint efforts of multiple physician specialists, nurses, technicians, wound care specialists, researchers, and administrators who together form an integrated network that is easily accessible to the patient. This article will focus on the various modalities needed to build an advanced critical limb ischemia program, while addressing the challenges facing the medical community in caring for this population.


title: Recursive Hetero-Associative Memories for Translation

abstract: This paper presents a modification of Pollack's RAAM (Recursive Auto-Associative Memory), called a Recursive Hetero-Associative Memory (RHAM), and shows that it is capable of learning simple translation tasks, by building a state-Space representation of each input string and unfolding it to obtain the corresponding output string. RHAM-based translators are computationally more powerful and easier to train than their corresponding double-RAAM counterparts in the literature.


title: Multimodal Prediction of Affective Dimensions and Depression in Human-Computer Interactions

abstract: Depression is one of the most common mood disorders. Technology has the potential to assist in screening and treating people with depression by robustly modeling and tracking the complex behavioral cues associated with the disorder (e.g., speech, language, facial expressions, head movement, body language). Similarly, robust affect recognition is another challenge which stands to benefit from modeling such cues. The Audio/Visual Emotion Challenge (AVEC) aims toward understanding the two phenomena and modeling their correlation with observable cues across several modalities. In this paper, we use multimodal signal processing methodologies to address the two problems using data from human-computer interactions. We develop separate systems for predicting depression levels and affective dimensions, experimenting with several methods for combining the multimodal information. The proposed depression prediction system uses a feature selection approach based on audio, visual, and linguistic cues to predict depression scores for each session. Similarly, we use multiple systems trained on audio and visual cues to predict the affective dimensions in continuous-time. Our affect recognition system accounts for context during the frame-wise inference and performs a linear fusion of outcomes from the audio-visual systems. For both problems, our proposed systems outperform the video-feature based baseline systems. As part of this work, we analyze the role played by each modality in predicting the target variable and provide analytical insights.


title: 57–64 GHz seven-pole bandpass filter Substrate Integrated Waveguide (SIW) in LTCC

abstract: This paper presents the design and characterization of a Substrate Integrated Waveguide (SIW) filter, with a large bandwidth of 7 GHz, designed to cover the unlicensed frequency band, 57–64 GHz. The filter, a seven-pole Chebyshev is integrated in Low-Temperature Co-fired Ceramic (LTCC). It is characterized by a measured bandwidth of 11.8% around a central frequency of 59.1 GHz. It presents a low passband insertion loss of 2.4 dB and high stop band insertion loss in the lower and higher band, respectively 50 dB and 30 dB. The filter has a compact size of 9450 × 2100 × 200 µm3.


title: Algorithms for Deterministic Incremental Dependency Parsing

abstract: Parsing algorithms that process the input from left to right and construct a single derivation have often been considered inadequate for natural language parsing because of the massive ambiguity typically found in natural language grammars. Nevertheless, it has been shown that such algorithms, combined with treebank-induced classifiers, can be used to build highly accurate disambiguating parsers, in particular for dependency-based syntactic representations. In this article, we first present a general framework for describing and analyzing algorithms for deterministic incremental dependency parsing, formalized as transition systems. We then describe and analyze two families of such algorithms: stack-based and list-based algorithms. In the former family, which is restricted to projective dependency structures, we describe an arc-eager and an arc-standard variant; in the latter family, we present a projective and a non-projective variant. For each of the four algorithms, we give proofs of correctness and complexity. In addition, we perform an experimental evaluation of all algorithms in combination with SVM classifiers for predicting the next parsing action, using data from thirteen languages. We show that all four algorithms give competitive accuracy, although the non-projective list-based algorithm generally outperforms the projective algorithms for languages with a non-negligible proportion of non-projective constructions. However, the projective algorithms often produce comparable results when combined with the technique known as pseudo-projective parsing. The linear time complexity of the stack-based algorithms gives them an advantage with respect to efficiency both in learning and in parsing, but the projective list-based algorithm turns out to be equally efficient in practice. Moreover, when the projective algorithms are used to implement pseudo-projective parsing, they sometimes become less efficient in parsing (but not in learning) than the non-projective list-based algorithm. Although most of the algorithms have been partially described in the literature before, this is the first comprehensive analysis and evaluation of the algorithms within a unified framework.


title: Design and implementation of smart movable road divider using IOT

abstract: Road Divider is generically used for dividing the Road for ongoing and incoming traffic. This helps keeping the flow of traffic. Generally, there is equal number of lanes for both ongoing and incoming traffic. For example, in any city, there is industrial area or shopping area where the traffic generally flows in one direction in the morning or evening. The other side of Road divider is mostly either empty or under-utilized. This is true for peak morning and evening hours. This results in loss of time for the car owners, traffic jams as well as underutilization of available resources. Our idea is to formulate a mechanism of automated movable road divider that can shift lanes, so that we can have more number of lanes in the direction of the rush. The cumulative impact of the time and fuel that can be saved by adding even one extra lane to the direction of the rush will be significant. With the smart application proposed below, we will also eliminate the dependency on manual intervention and manual traffic coordination so that we can have a smarter traffic all over the city. An Automated movable road divider can provide a solution to the above-mentioned problem effectively. This is possible through IOT. IOT refers to Internet of Things where the actual digitalization comes into picture. Here sensors play a major role. We can achieve this using Arduino board. The sensors placed on the dividers sense the flow of traffic whether flow of traffic is smooth or not? If the flow is smooth on either side then there is nothing to worry but the lane which is having more traffic, the divider is moved to a certain distance to the smoother lane in order to smoothen the busy lane.


title: Deep State Space Models for Time Series Forecasting

abstract: We present a novel approach to probabilistic time series forecasting that combines state space models with deep learning. By parametrizing a per-time-series linear state space model with a jointly-learned recurrent neural network, our method retains desired properties of state space models such as data efficiency and interpretability, while making use of the ability to learn complex patterns from raw data offered by deep learning approaches. Our method scales gracefully from regimes where little training data is available to regimes where data from millions of time series can be leveraged to learn accurate models. We provide qualitative as well as quantitative results with the proposed method, showing that it compares favorably to the state-of-the-art.


title: Diverse Image Synthesis From Semantic Layouts via Conditional IMLE

abstract: Most existing methods for conditional image synthesis are only able to generate a single plausible image for any given input, or at best a fixed number of plausible images. In this paper, we focus on the problem of generating images from semantic segmentation maps and present a simple new method that can generate an arbitrary number of images with diverse appearance for the same semantic layout. Unlike most existing approaches which adopt the GAN framework, our method is based on the recently introduced Implicit Maximum Likelihood Estimation (IMLE) framework. Compared to the leading approach, our method is able to generate more diverse images while producing fewer artifacts despite using the same architecture. The learned latent space also has sensible structure despite the lack of supervision that encourages such behaviour.


title: Buffer utilization based scheduling of maintenance activities by a shifting priority approach - a simulation study

abstract: Machine breakdowns and improper maintenance management cause production systems to function inefficiently. Particularly, breakdowns cause rippling effects on other machines in terms of starved and blocked states. Effective planning of maintenance can lead to improved production system efficiency. This paper aims at improving system throughput through prioritization of maintenance work orders by continuously monitoring buffer levels. This paper proposes and tests a new approach to determine the machine priorities for dynamic scheduling of maintenance work orders by identifying buffer utilization. The approach is exemplified in an industrial use-case. The results have shown to increase throughput in comparison to a first-come-first-served approach for executing maintenance work orders. This new approach relies on simple data collection and analysis, which makes it a viable option for industries to implement with minimal effort. The results can suggest that systems view for maintenance prioritization can be a powerful decision support tool for maintenance planning.


title: Let's play!: mobile health games for adults

abstract: Researchers have designed a variety of systems that promote wellness. However, little work has been done to examine how casual mobile games can help adults learn how to live healthfully. To explore this design space, we created OrderUP!, a game in which players learn how to make healthier meal choices. Through our field study, we found that playing OrderUP! helped participants engage in four processes of change identified by a well-established health behavior theory, the Transtheoretical Model: they improved their understanding of how to eat healthfully and engaged in nutrition-related analytical thinking, reevaluated the healthiness of their real life habits, formed helping relationships by discussing nutrition with others and started replacing unhealthy meals with more nutritious foods. Our research shows the promise of using casual mobile games to encourage adults to live healthier lifestyles.


title: Foreground Segmentation with Tree-Structured Sparse RPCA

abstract: Background subtraction is a fundamental video analysis technique that consists of creation of a background model that allows distinguishing foreground pixels. We present a new method in which the image sequence is assumed to be made up of the sum of a low-rank background matrix and a dynamic tree-structured sparse matrix. The decomposition task is then solved using our approximated Robust Principal Component Analysis (ARPCA) method which is an extension to the RPCA that can handle camera motion and noise. Our model dynamically estimates the support of the foreground regions via a superpixel generation step, so that spatial coherence can be imposed on these regions. Unlike conventional smoothness constraints such as MRF, our method is able to obtain crisp and meaningful foreground regions, and in general, handles large dynamic background motion better. To reduce the dimensionality and the curse of scale that is persistent in the RPCA-based methods, we model the background via Column Subset Selection Problem, that reduces the order of complexity and hence decreases computation time. Comprehensive evaluation on four benchmark datasets demonstrate the effectiveness of our method in outperforming state-of-the-art alternatives.


title: Affective state recognition from EEG with deep belief networks

abstract: With the ultimate intent of improving the quality of life, identification of human's affective states on the collected electroencephalogram (EEG) has attracted lots of attention recently. In this domain, the existing methods usually use only a few labeled samples to classify affective states consisting of over thousands of features. Therefore, important information may not be well utilized and performance is lowered due to the randomness caused by the small sample problem. However, this issue has rarely been discussed in the previous studies. Besides, many EEG channels are irrelevant to the specific learning tasks, which introduce lots of noise to the systems and further lower the performance in the recognition of affective states. To address these two challenges, in this paper, we propose a novel Deep Belief Networks (DBN) based model for affective state recognition from EEG signals. Specifically, signals from each EEG channel are firstly processed with a DBN for effectively extracting critical information from the over thousands of features. The extracted low dimensional characteristics are then utilized in the learning to avoid the small sample problem. For the noisy channel problem, a novel stimulus-response model is proposed. The optimal channel set is obtained according to the response rate of each channel. Finally, a supervised Restricted Boltzmann Machine (RBM) is applied on the combined low dimensional characteristics from the optimal EEG channels. To evaluate the performance of the proposed Supervised DBN based Affective State Recognition (SDA) model, we implement it on the Deap Dataset and compare it with five baselines. Extensive experimental results show that the proposed algorithm can successfully handle the aforementioned two challenges and significantly outperform the baselines by 11.5% to 24.4%, which validates the effectiveness of the proposed algorithm in the task of affective state recognition.


title: Comparison of Lexicon based and Naïve Bayes Classifier in Sentiment Analysis

abstract: In the recent years Sentiment analysis (SA) has gained momentum by the increase of social networking sites. Sentiment analysis has been an important topic for data mining, social media for classifying reviews and thereby rating the entities such as products, movies etc. This paper represents a comparative study of sentiment classification of lexicon based approach and naive bayes classifier of machine learning in sentiment analysis.


title: A 10-Bit Column-Driver IC With Parasitic-Insensitive Iterative Charge-Sharing Based Capacitor-String Interpolation for Mobile Active-Matrix LCDs

abstract: This paper presents a 10-bit column driver IC for active-matrix LCDs, with a proposed iterative charge-sharing based (ICSB) capacitor-string that interpolates two output voltages from a resistor-string DAC. Iterative mode change between a capacitive voltage division mode and a charge sharing mode in the ICSB capacitor-string interpolation suppresses the effect of mismatches between capacitors and that of parasitic capacitances; thus, a highly linear capacitor sub-DAC is realized. In addition, the area-sharing layout technique, which stacks the interpolation capacitor-string on top of the R-DAC area, reduces the driver channel size and extends the bit resolution of the gamma-corrected nonlinear main R-DAC. Consequently, the proposed ICSB capacitor-string interpolation scheme provides highly uniform channel performance by passively dividing the coarse voltages from the global resistor-string DAC with high area efficiency, and more effective bit resolution for nonlinear gamma correction. The prototype column driver IC was implemented using a 0.11-μm CMOS process. The area occupation of the DAC and buffer amplifier per channel is only 188 × 15 μm2, and the static power consumption is 0.9 μA/channel with no additional static power dissipation for the interpolation. The measured maximum DNL and INL are 0.25 LSB and 0.43 LSB, respectively. The measured maximum inter-channel DVO is 5.6 mV. The proposed chip achieves state-of-the-art performance in terms of chip size and channel-to-channel uniformity.


Late-Onset Inflammatory Response to Hyaluronic Acid Dermal Fillers


Autistic Traits Are Not a Strong Predictor of Binocular Rivalry Dynamics


title: Planning to teach with digital tools : Introducing the interactive whiteboard to pre-service secondary mathematics teachers

abstract: Teaching is a complex endeavour that requires teachers to meld knowledge about the nature of learners, pedagogical strategies and discipline content. In recent years an increasing variety of educational technologies are finding their way into the school classroom, including the widespread acceptance of interactive whiteboards (IWBs). The emerging literature on IWB use is mixed, with no clear verdict on the merits of this technology in relation to teaching or student learning outcomes. However, as this equipment is fast becoming standard in a growing number of classrooms, it is important for pre-service teachers to be familiar with its features, potential difficulties and to have had experience in developing lesson activities that utilise the technology. This study examines the lesson activities developed by a group (n=13) of final year undergraduate secondary mathematics pre-service teachers. The analysis is guided by the Technological Pedagogical Content Knowledge (TPCK) framework of Mishra and Koehler (2006). The study reveals that the pre-service teachers were able to plan effectively to integrate IWB features within their mathematical lessons and demonstrated developing TPCK as a result. They found that the primary benefits of the technology related to its potential to engage students with varied visual representations and virtual manipulatives which can aid conceptual understanding.


title: Beyond global sociosexual orientations: A more differentiated look at sociosexuality and its effects on courtship and romantic relationships.

abstract: Sociosexuality is usually assessed as the overall orientation toward uncommitted sex, although this global approach may mask unique contributions of different components. In a large online study (N 2,708) and a detailed behavioral assessment of 283 young adults (both singles and couples) with a 1-year follow-up, the authors established 3 theoretically meaningful components of sociosexuality: past behavioral experiences, the attitude toward uncommitted sex, and sociosexual desire (all measured by a revised version of the Sociosexual Orientation Inventory). Discriminant validity was shown with regard to (a) their factorial structure, (b) sex differences, (c) many established correlates of sociosexuality, and (d) the prediction of observed flirting behavior when meeting an attractive opposite-sex stranger, even down to the level of objectively coded behaviors, as well as (e) the self-reported number of sexual partners and (f) changes in romantic relationship status over the following year. Within couples, the 3 components also showed distinct degrees of assortative mating and distinct effects on the romantic partner. Implications for the evolutionary psychology of mating tactics are discussed.


Control of Two Wheeled Inverted Pendulum Using Silding Mode Technique


Child care practices anteceding three patterns of preschool behavior.


title: Similarity between Euclidean and cosine angle distance for nearest neighbor queries

abstract: Understanding the relationship among different distance measures is helpful in choosing a proper one for a particular application. In this paper, we compare two commonly used distance measures in vector models, namely, Euclidean distance (EUD) and cosine angle distance (CAD), for nearest neighbor (NN) queries in high dimensional data spaces. Using theoretical analysis and experimental results, we show that the retrieval results based on EUD are similar to those based on CAD when dimension is high. We have applied CAD for content based image retrieval (CBIR). Retrieval results show that CAD works no worse than EUD, which is a commonly used distance measure for CBIR, while providing other advantages, such as naturally normalized distance.


title: Multitask Learning

abstract: Multitask Learning is an approach to inductive transfer that improves generalization by using the domain information contained in the training signals of related tasks as an inductive bias. It does this by learning tasks in parallel while using a shared representation; what is learned for each task can help other tasks be learned better. This paper reviews prior work on MTL, presents new evidence that MTL in backprop nets discovers task relatedness without the need of supervisory signals, and presents new results for MTL with k-nearest neighbor and kernel regression. In this paper we demonstrate multitask learning in three domains. We explain how multitask learning works, and show that there are many opportunities for multitask learning in real domains. We present an algorithm and results for multitask learning with case-based methods like k-nearest neighbor and kernel regression, and sketch an algorithm for multitask learning in decision trees. Because multitask learning works, can be applied to many different kinds of domains, and can be used with different learning algorithms, we conjecture there will be many opportunities for its use on real-world problems.


title: Deep neural networks for acoustic emotion recognition: Raising the benchmarks

abstract: Deep Neural Networks (DNNs) denote multilayer artificial neural networks with more than one hidden layer and millions of free parameters. We propose a Generalized Discriminant Analysis (GerDA) based on DNNs to learn discriminative features of low dimension optimized with respect to a fast classification from a large set of acoustic features for emotion recognition. On nine frequently used emotional speech corpora, we compare the performance of GerDA features and their subsequent linear classification with previously reported benchmarks obtained using the same set of acoustic features classified by Support Vector Machines (SVMs). Our results impressively show that low-dimensional GerDA features capture hidden information from the acoustic features leading to a significantly raised unweighted average recall and considerably raised weighted average recall.


title: Investigation on Capacitor Voltage Regulation in Cascaded H-Bridge Multilevel Converters With Fundamental Frequency Switching

abstract: Multilevel power electronic converters have gained popularity in high-power applications due to their lower switch voltage stress and modularity. Cascaded H-bridge converters are a promising breed of multilevel converters which generally require several separate dc voltage sources. By utilizing the redundant switching states, it is possible to replace the separate dc voltage sources with capacitors and keep only the one with the highest voltage level. Redundancy in the charge and discharge modes of the capacitors is assumed to be adequate for their voltage regulation. However, the effects of the output current of the converter as well as the time duration of the redundant switching states have been neglected. In this paper, the impacts of the connected load to the cascaded H-bridge converter as well as the switching angles on the voltage regulation of the capacitors are studied. This paper proves that voltage regulation is only attainable in a much limited operating conditions that it was originally reported. In addition, based on the analysis of the converter, a simplified formula is found which can be used to find those modulation indices that regulate the voltage of the capacitor. This formula can be used in harmonic minimization problems while capacitor voltage regulation is ensured. Simulation and laboratory results are provided to confirm the analysis.


title: Awareness Supporting Technologies used in Collaborative Systems: A Systematic Literature Review

abstract: Since the establishment of Computer Supported Collaborative Work as a research area, computer advances have change the paradigm of how technology is applied to improve the performance in collaborative scenarios. Notifications are an important part of this improvement. Technological systems have been applied in order to provide collaborators with the sufficient awareness to keep a task going. In this paper we present the protocol and results of a Systematic Literature Review that delves in the application of new technologies to provide awareness in collaborative systems. Moreover, we classify the collaborative systems found in literature using two traditional taxonomies for CSCW in order to understand which notification mechanisms are used to support which systems. Our review covers the last 10 years and classifies system prototypes based on the context in which they are applied, the notification and information gathering mechanism used, and the assessment performed. With over 400 papers reviewed, 83 that met the review requirements were included. The review results show that traditional interfaces and mobile devices are still the most common notification mechanisms. However, ubiquitous devices and non-traditional interfaces have also been used.


title: SRI-Sarnoff AURORA System at TRECVID 2012 Multimedia Event Detection and Recounting

abstract: In Multimedia Event Detection 2013 evaluation, SRI Aurora team participated in EK100, EK10, and EK0 tasks with full system evaluation. We submitted 15 runs for both pre-specified events (PS-Events) and ad-hoc events (AH-Events). The majority of them achieved satisfactory results. In particular, thanks to the well-designed concept features, our EK10 system works consistently much better for both PSEvents and AH-Events. By creating the concept language model from the web source, we build our EK0 system to perform event detection without training examples. This system achieved promising results on PS-Events. In MER task, we developed an approach to provide a breakdown of the evidences of why the MED decision has been made by exploring the SVM-based event detector. Furthermore, we designed evidence specific verification and detection to reduce uncertainty and improve key evidence discovery.


title: Colorimetric Method for Determination of Amino Acids on Thin Layer and Filter Paper Chromatography Using a Modified Ninhydrin Reagent

abstract: A simple and rapid colorimetric method for the determination of some amino acids on thin layer chromatography (TLC) and Whatman filter paper chromatography is described. The studied compounds are cysteine, proline, methionine, tryptophan glutmine, glycine, thyrosine, threonine, leucine and lysine. The method is based on the formation of color from the reaction of stannous chloride (SnCl2) and ninhydrin reagents. The procedure has been successfully applied to the assay of the standard amino acid preparation after TLC separation, and the results of the studied compounds were clear and favorable, comparing to the official methods. However, the improved TLC is much more efficient for the detection of amino acid. The main advantage of this method is that the detection of amino acids can be preformed visually.


title: Crimeware-as-a-service - A survey of commoditized crimeware in the underground market

abstract: Crimeware-as-a-service (CaaS) has become a prominent component of the underground economy. CaaS provides a new dimension to cyber crime by making it more organized, automated, and accessible to criminals with limited technical skills. This paper dissects CaaS and explains the essence of the underground economy that has grown around it. The paper also describes the various crimeware services that are provided in the underground market.


title: Ensemble deep learning for regression and time series forecasting

abstract: In this paper, for the first time, an ensemble of deep learning belief networks (DBN) is proposed for regression and time series forecasting. Another novel contribution is to aggregate the outputs from various DBNs by a support vector regression (SVR) model. We show the advantage of the proposed method on three electricity load demand datasets, one artificial time series dataset and three regression datasets over other benchmark methods.


title: Hierarchical classification of Gene Ontology-based protein functions with neural networks

abstract: Hierarchical Multi-label Classification (HMC) is a classification task where classes are organized in a hierarchical taxonomy, and instances can be simultaneously classified in more than one class. This paper investigates the HMC problem of classifying proteins in functions organized according to the Gene Ontology hierarchical taxonomy. This is a complex task, since the Gene Ontology hierarchy is organized as a Directed Acyclic Graph with thousands of classes hierarchically represented. We propose a neural network-based method to incorporate label-dependency during learning. The experimental results show that the proposed method achieves competitive results when compared to the state-of-the-art methods from the literature.


title: Multidimensional Model of Racial Identity: A Reconceptualization of African American Racial Identity

abstract: Research on African American racial identity has utilized 2 distinct approaches. The mainstream approach has focused on universal properties associated with ethnic and racial identities. In contrast, the underground approach has focused on documenting the qualitative meaning of being African American, with an emphasis on the unique cultural and historical experiences of African Americans. The Multidimensional Model of Racial Identity (MMRI) represents a synthesis of the strengths of these two approaches. The underlying assumptions associated with the model are explored. The model proposes 4 dimensions of African American racial identity: salience, centrality, regard, and ideology. A description of these dimensions is provided along with a discussion of how they interact to influence behavior at the level of the event. We argue that the MMRI has the potential to make contributions to traditional research objectives of both approaches, as well as to provide the impetus to explore new questions.


title: New dimensions in endodontic imaging: part 2. Cone beam computed tomography

abstract: Patel S. New dimensions in endodontic imaging: Part 2. Cone beam computed tomography. International Endodontic Journal, 42, 463–475, 2009. Cone beam computed tomography (CBCT) has been specifically designed to produce undistorted threedimensional information of the maxillofacial skeleton, including the teeth and their surrounding tissues with a significantly lower effective radiation dose compared with conventional computed tomography (CT). Periapical disease may be detected sooner using CBCT compared with periapical views and the true size, extent, nature and position of periapical and resorptive lesions can be assessed. Root fractures, root canal anatomy and the nature of the alveolar bone topography around teeth may be assessed. The aim of this paper is to review current literature on the applications and limitations of CBCT in the management of endodontic problems.


title: Indoor Localization of Passive UHF RFID Tags Based on Phase-of-Arrival Evaluation

abstract: This paper introduces a two-dimensional localization system for passive UHF RFID transponders based on phase-of-arrival evaluation of the backscattered tag signal. A multiple-channel system, where alternately one path is configured as a transmitter and the remaining paths work as receivers, is used to permit the position estimation. To improve localization accuracy and reduce disturbances caused by multipath propagation, a frequency-stepped continuous-waveform approach is employed. To achieve proof of concept, a local position measurement system demonstrator comprising an RFID reader, passive EPC Gen 2 RFID tags, several transceivers, baseband hardware, and signal processing was used for measurements. The results show excellent localization accuracy, even in an indoor environment.


title: Deep Learning for Predicting Asset Returns

abstract: Deep learning searches for nonlinear factors for predicting asset returns. Predictability is achieved via multiple layers of composite factors as opposed to additive ones. Viewed in this way, asset pricing studies can be revisited using multi-layer deep learners, such as rectified linear units (ReLU) or long-short-term-memory (LSTM) for time-series effects. State-of-the-art algorithms including stochastic gradient descent (SGD), TensorFlow and dropout design provide imple- mentation and efficient factor exploration. To illustrate our methodology, we revisit the equity market risk premium dataset of Welch and Goyal (2008). We find the existence of nonlinear factors which explain predictability of returns, in particular at the extremes of the characteristic space. Finally, we conclude with directions for future research.


title: Microplastics in sediments: A review of techniques, occurrence and effects

abstract: Microplastics are omnipresent in the marine environment and sediments are hypothesized to be major sinks of these plastics. Here, over 100 articles spanning the last 50 year are reviewed with following objectives: (i) to evaluate current microplastic extraction techniques, (ii) to discuss the occurrence and worldwide distribution of microplastics in sediments, and (iii) to make a comprehensive assessment of the possible adverse effects of this type of pollution to marine organisms. Based on this review we propose future research needs and conclude that there is a clear need for a standardized techniques, unified reporting units and more realistic effect assessments.