Gambit: An Autonomous Chess-Playing Robotic System


title: Bitcoin and Beyond: A Technical Survey on Decentralized Digital Currencies

abstract: Besides attracting a billion dollar economy, Bitcoin revolutionized the field of digital currencies and influenced many adjacent areas. This also induced significant scientific interest. In this survey, we unroll and structure the manyfold results and research directions. We start by introducing the Bitcoin protocol and its building blocks. From there we continue to explore the design space by discussing existing contributions and results. In the process, we deduce the fundamental structures and insights at the core of the Bitcoin protocol and its applications. As we show and discuss, many key ideas are likewise applicable in various other fields, so that their impact reaches far beyond Bitcoin itself.


title: Reducing Risky Security Behaviours: Utilising Affective Feedback to Educate Users

abstract: Despite the number of tools created to help end-users reduce risky security behaviours, users are still falling victim to online attacks. This paper proposes a browser extension utilising affective feedback to provide warnings on detection of risky behaviour. The paper provides an overview of behaviour considered to be risky, explaining potential threats users may face online. Existing tools developed to reduce risky security behaviours in end-users have been compared, discussing the success rates of various methodologies. Ongoing research is described which attempts to educate users regarding the risks and consequences of poor security behaviour by providing the appropriate feedback on the automatic recognition of risky behaviour. The paper concludes that a solution utilising a browser extension is a suitable method of monitoring potentially risky security behaviour. Ultimately, future work seeks to implement an affective feedback mechanism within the browser extension with the aim of improving security awareness.


title: Optimal Rectangle Packing: An Absolute Placement Approach

abstract: We consider the problem of finding all enclosing rectangles of minimum area that can contain a given set of rectangles without overlap. Our rectangle packer chooses the x-coordinates of all the rectangles before any of the y-coordinates. We then transform the problem into a perfect-packing problem with no empty space by adding additional rectangles. To determine the y-coordinates, we branch on the different rectangles that can be placed in each empty position. Our packer allows us to extend the known solutions for a consecutive-square benchmark from 27 to 32 squares. We also introduce three new benchmarks, avoiding properties that make a benchmark easy, such as rectangles with shared dimensions. Our third benchmark consists of rectangles of increasingly high precision. To pack them efficiently, we limit the rectangles' coordinates and the bounding box dimensions to the set of subset sums of the rectangles' dimensions. Overall, our algorithms represent the current state-of-the-art for this problem, outperforming other algorithms by orders of magnitude, depending on the benchmark.


title: Intelligent DSS for talent management: a proposed architecture using knowledge discovery approach

abstract: Decision making tasks are subject to limitation and also depends on human knowledge, experiences, judgments and preferences. In this case, Intelligent Decision System (IDS) technologies can be used to provide realistic and consistent decisions, besides to improve the effectiveness of decision making processes. Intelligent Decision Support System (IDSS) is a contributory of IDS technology to assist decision makers in high level phases of decision making by integrating human knowledge with modeling tools. Nowadays, data mining (DM) techniques is also can be used to support Knowledge Management (KM) tasks especially for knowledge discovery and knowledge engineering. DM is emerging data analysis tool and widely used in order to produce valuable knowledge for decision making as knowledge modeling task. In Human Resource (HR), managing talent is among the challenges of HR professionals which can be handle by using IDSS and data mining technologies. For that reason, in this article, we discussed the potential to uses IDSS approach for talent management using DM techniques by proposing IDSS architecture and a case study on talent classification. This study consists of three parts; the first part is to know the previous works on IDSS, talent management and DM. The second part is discussion on proposed IDSS architecture for talent management. Finally, the third part is a case study on the use of DM classification method for talent forecasting, especially for employee's job promotion.


title: MAP ZDF Segmentation and Tracking using Active Stereo Vision: Hand

abstract: A maximum a posterior probability zero disparity filter (MAP ZDF) ensures coordinated stereo fixation upon an arbitrarily moving, rotating, re-configuring hand, performing marker-less pixel-wise segmentation of the hand. Active stereo fixation permits real-time foveal hand tracking and segmentation over a large visual workspace, allowing investigation of unrestricted natural human gesturing. Hand segmentation is shown to be robust to lighting conditions, defocus, hand colour variation, foreground and background clutter including non-tracked hands, and partial or gross occlusions including those due to non-tracked hands. The system operates at approximately 27fps on a 3GHz single processor PC.


title: Extracting Valley-Ridge Lines from Point-Cloud-Based 3D Fingerprint Models

abstract: 3D fingerprinting is an emerging technology with the distinct advantage of touchless operation. More important, 3D fingerprint models contain more biometric information than traditional 2D fingerprint images. However, current approaches to fingerprint feature detection usually must transform the 3D models to a 2D space through unwrapping or other methods, which might introduce distortions. A new approach directly extracts valley-ridge features from point-cloud-based 3D fingerprint models. It first applies the moving least-squares method to fit a local paraboloid surface and represent the local point cloud area. It then computes the local surface's curvatures and curvature tensors to facilitate detection of the potential valley and ridge points. The approach projects those points to the most likely valley-ridge lines, using statistical means such as covariance analysis and cross correlation. To finally extract the valley-ridge lines, it grows the polylines that approximate the projected feature points and removes the perturbations between the sampled points. Experiments with different 3D fingerprint models demonstrate this approach's feasibility and performance.


title: Investigating perception time in the far peripheral vision for virtual and augmented reality

abstract: Far peripheral vision (beyond 60° eccentricity) is beginning to be supported in the latest virtual and augmented reality (VR and AR) headsets. This benefits the VR and AR experiences by allowing a greater amount of information to be conveyed, reducing visual clutter, and enabling subtle visual attention management. However, the visual properties of the far periphery are different from those of the central vision, because of the physiological differences between the areas on the visual cortex responsible for the respective vision types. In this paper, we investigate the perception time in the far peripheral vision, specifically the time it takes for a user to perceive a pattern at a high eccentricity. We have characterized the perception time in the far peripheral vision by conducting a user study on 40 participants in which the participants distinguish between two types of patterns displayed at several sizes and at various eccentricities in their field of view. Our results show that at higher eccentricities, participants take longer to perceive a pattern. Based on user study data, we are able to characterize the desired scaling of patterns at higher eccentricities, so that they can be perceived within a similar amount of time as in the central vision.


Compiler optimizations for the WAM


title: AI-Powered Social Bots

abstract: This paper gives an overview of impersonation bots that generate output in one, or possibly, multiple modalities. We also discuss rapidly advancing areas of machine learning and artificial intelligence that could lead to frighteningly powerful new multi-modal social bots. Our main conclusion is that most commonly known bots are one dimensional (i.e., chatterbot), and far from deceiving serious interrogators. However, using recent advances in machine learning, it is possible to unleash incredibly powerful, human-like armies of social bots, in potentially well coordinated campaigns of deception and influence.


title: Biomineralization and biomimicry of tooth enamel

abstract: Abstract: This critical review sommarizes the basics of biomineralization of tooth enamel and scrutinizes attempts to replicate this intricate biological process in vitro. Special emphasis is given to the author’s results, obtained during studies on the formation of enamel by biomimetic means. Fundamental insights found regarding the latter process are presented. Some paradigmatically accepted aspects of the mechanism of amelogenesis, that is, biomineralization of enamel, are challenged. Amelogenin, the major protein of the developing enamel matrix, is thus claimed to be a mineralization inductor, rather than an inhibitor, presumably acting as a channel between the ionic growth units in the protein matrix and the uniaxially growing crystals of apatite. The role of water and other minor constituents of enamel is questioned, as well as the biologically active morphology of amelogenin aggregates and the reliability of recombinant proteins in studying amelogenesis in vitro. Appropriate crystal growth rates, the Ostwald–Lussac law, Tomes’ process and mineralization of dentin present other aspects of amelogenesis discussed here. It is also claimed that three fundamental facets of amelogenesis ought to be coordinated in parallel for successful biomimetic replication of the given process in the laboratory: protein assembly, proteolytic digestion and crystal growth.


title: What are dynamic optimization problems?

abstract: Dynamic Optimization Problems (DOPs) have been widely studied using Evolutionary Algorithms (EAs). Yet, a clear and rigorous definition of DOPs is lacking in the Evolutionary Dynamic Optimization (EDO) community. In this paper, we propose a unified definition of DOPs based on the idea of multiple-decision-making discussed in the Reinforcement Learning (RL) community. We draw a connection between EDO and RL by arguing that both of them are studying DOPs according to our definition of DOPs. We point out that existing EDO or RL research has been mainly focused on some types of DOPs. A conceptualized benchmark problem, which is aimed at the systematic study of various DOPs, is then developed. Some interesting experimental studies on the benchmark reveal that EDO and RL methods are specialized in certain types of DOPs and more importantly new algorithms for DOPs can be developed by combining the strength of both EDO and RL methods.


title: Evaluation of header metadata extraction approaches and tools for scientific PDF documents

abstract: This paper evaluates the performance of tools for the extraction of metadata from scientific articles. Accurate metadata extraction is an important task for automating the management of digital libraries. This comparative study is a guide for developers looking to integrate the most suitable and effective metadata extraction tool into their software. We shed light on the strengths and weaknesses of seven tools in common use. In our evaluation using papers from the arXiv collection, GROBID delivered the best results, followed by Mendeley Desktop. SciPlore Xtract, PDFMeat, and SVMHeaderParse also delivered good results depending on the metadata type to be extracted.


title: Information asset analysis: credit scoring and credit suggestion

abstract: Risk assessment is important for financial institutions, especially in loan applications. Some have already implemented their own credit-scoring mechanisms to evaluate their clients' risk and make decisions based on this indicator. In fact, the data gathered by financial institutions is a valuable source of information to create information assets, from which credit-scoring mechanisms can be developed. The purpose of this paper is to create, from information assets, a decision mechanism that is able to evaluate a client's risk. Furthermore, a suggestive algorithm is presented to better explain and give insights on how the decision mechanism values attributes.


title: A compact, 37% fractional bandwidth millimeter-wave phase shifter using a wideband lange coupler for 60-GHz and E-band systems

abstract: A wideband 57.7–84.2 GHz Phase Shifter is presented using a compact Lange coupler to generate in-phase and quadrature signal. The Lange coupler is followed by two balun transformers that provide the IQ vector modulation with differential I and Q signals. The implemented Phase Shifter demonstrates an average 6-dB insertion loss and 5-dB gain variation. The measured average rms phase and gain errors are 7 degrees and 1 dB, respectively. The phase shifter is implemented in GlobalFoundries 45-nm SOI CMOS technology using a trap-rich substrate. The chip area is 385 μm × 285 μm and the Phase Shifter consumes less than 17 mW. To the best of authors knowledge, this is the first phase shifter that covers both 60 GHz band and E-band frequencies with a fractional bandwidth of 37%.


title: Joint visual denoising and classification using deep learning

abstract: Visual restoration and recognition are traditionally addressed in pipeline fashion, i.e. denoising followed by classification. Instead, observing correlations between the two tasks, for example clearer image will lead to better categorization and vice visa, we propose a joint framework for visual restoration and recognition for handwritten images, inspired by advances in deep autoencoder and multi-modality learning. Our model is a 3-pathway deep architecture with a hidden-layer representation which is shared by multi-inputs and outputs, and each branch can be composed of a multi-layer deep model. Thus, visual restoration and classification can be unified using shared representation via non-linear mapping, and model parameters can be learnt via backpropagation. Using MNIST and USPS data corrupted with structured noise, the proposed framework performs at least 20% better in classification than separate pipelines, as well as clearer recovered images.


Paper in Business Analytics Feature Selection using LASSO


title: Domain Adaptation of Deformable Part-Based Models

abstract: The accuracy of object classifiers can significantly drop when the training data (source domain) and the application scenario (target domain) have inherent differences. Therefore, adapting the classifiers to the scenario in which they must operate is of paramount importance. We present novel domain adaptation (DA) methods for object detection. As proof of concept, we focus on adapting the state-of-the-art deformable part-based model (DPM) for pedestrian detection. We introduce an adaptive structural SVM (A-SSVM) that adapts a pre-learned classifier between different domains. By taking into account the inherent structure in feature space (e.g., the parts in a DPM), we propose a structure-aware A-SSVM (SA-SSVM). Neither A-SSVM nor SA-SSVM needs to revisit the source-domain training data to perform the adaptation. Rather, a low number of target-domain training examples (e.g., pedestrians) are used. To address the scenario where there are no target-domain annotated samples, we propose a self-adaptive DPM based on a self-paced learning (SPL) strategy and a Gaussian Process Regression (GPR). Two types of adaptation tasks are assessed: from both synthetic pedestrians and general persons (PASCAL VOC) to pedestrians imaged from an on-board camera. Results show that our proposals avoid accuracy drops as high as 15 points when comparing adapted and non-adapted detectors.


title: Will This Paper Increase Your h-index?: Scientific Impact Prediction

abstract: Scientific impact plays a central role in the evaluation of the output of scholars, departments, and institutions. A widely used measure of scientific impact is citations, with a growing body of literature focused on predicting the number of citations obtained by any given publication. The effectiveness of such predictions, however, is fundamentally limited by the power-law distribution of citations, whereby publications with few citations are extremely common and publications with many citations are relatively rare. Given this limitation, in this work we instead address a related question asked by many academic researchers in the course of writing a paper, namely: "Will this paper increase my h-index?" Using a real academic dataset with over 1.7 million authors, 2 million papers, and 8 million citation relationships from the premier online academic service ArnetMiner, we formalize a novel scientific impact prediction problem to examine several factors that can drive a paper to increase the primary author's h-index. We find that the researcher's authority on the publication topic and the venue in which the paper is published are crucial factors to the increase of the primary author's h-index, while the topic popularity and the co-authors' h-indices are of surprisingly little relevance. By leveraging relevant factors, we find a greater than 87.5% potential predictability for whether a paper will contribute to an author's h-index within five years. As a further experiment, we generate a self-prediction for this paper, estimating that there is a 76% probability that it will contribute to the h-index of the co-author with the highest current h-index in five years. We conclude that our findings on the quantification of scientific impact can help researchers to expand their influence and more effectively leverage their position of "standing on the shoulders of giants."


title: Comparison of four different categories of prosthetic feet during ramp ambulation in unilateral transtibial amputees

abstract: BACKGROUND ::: Comparative effectiveness of prosthetic feet during ramp ambulation in unilateral transtibial amputees, who function at different Medicare Functional Classification Levels, has not been published. ::: ::: ::: OBJECTIVE ::: To determine differences in symmetry in external work between four categories of prosthetic feet in K-Level-2 and K-Level-3 unilateral transtibial amputees during ramp ascent and descent. ::: ::: ::: STUDY DESIGN ::: Randomized repeated-measures trial. ::: ::: ::: METHODS ::: Ten subjects completed six testing sessions during which symmetry in external work was calculated using F-scan in-sole sensors. Between testing sessions 1 and 2, subjects received standardized functional prosthetic training. In Sessions 3-6, subjects tested four feet--solid ankle cushion heel, stationary attachment flexible endoskeleton, Talux (categories K1, K2, and K3, respectively), and Proprio-Foot (microprocessor ankle)--using a study socket and had a 10- to 14-day accommodation period with each foot. ::: ::: ::: RESULTS ::: During ramp descent, K-Level-2 subjects demonstrated higher symmetry in external work values with Talux and Proprio-Foot compared to the solid ankle cushion heel foot. K-Level-3 subjects also had higher symmetry in external work values with the Talux foot than the solid ankle cushion heel foot. Ramp ascent symmetry in external work values were not significantly different between feet. ::: ::: ::: CONCLUSIONS ::: Prosthetic foot category appears to influence symmetry in external work more during decline walking than incline walking. K-Level-2 unilateral transtibial amputees achieve greater symmetry from K3 dynamic response prosthetic feet with J-shaped ankle and microprocessor ankles while descending ramps. ::: ::: ::: CLINICAL RELEVANCE ::: The findings suggest that K-Level-2 unilateral transtibial amputees benefit from K3 dynamic response prosthetic feet with J-shaped ankle. These results support the prescription of K3 feet for K-Level-2 amputees who frequently negotiate ramps.


title: Energy-driven computing: Rethinking the design of energy harvesting systems

abstract: Energy harvesting computing has been gaining increasing traction over the past decade, fueled by technological developments and rising demand for autonomous and battery-free systems. Energy harvesting introduces numerous challenges to embedded systems but, arguably the greatest, is the required transition from an energy source that typically provides virtually unlimited power for a reasonable period of time until it becomes exhausted, to a power source that is highly unpredictable and dynamic (both spatially and temporally, and with a range spanning many orders of magnitude). The typical approach to overcome this is the addition of intermediate energy storage/buffering to smooth out the temporal dynamics of both power supply and consumption. This has the advantage that, if correctly sized, the system ‘looks like’ a battery-powered system; however, it also adds volume, mass, cost and complexity and, if not sized correctly, unreliability. In this paper, we consider energy-driven computing, where systems are designed from the outset to operate from an energy harvesting source. Such systems typically contain little or no additional energy storage (instead relying on tiny parasitic and decoupling capacitance), alleviating the aforementioned issues. Examples of energy-driven computing include transient systems (which power down when the supply disappears and efficiently continue execution when it returns) and power-neutral systems (which operate directly from the instantaneous power harvested, gracefully modulating their consumption and performance to match the supply). In this paper, we introduce a taxonomy of energy-driven computing, articulating how power-neutral, transient, and energy-driven systems present a different class of computing to conventional approaches.


title: Five styles of Customer Knowledge Management, and how smart companies use them to create value

abstract: Corporations are beginning to realize that the proverbial 'if we only knew what we know' also includes 'if we only knew what our customers know.' The authors discuss the concept of Customer Knowledge Management (CKM), which refers to the management of knowledge from customers, i.e. knowledge resident in customers. CKM is contrasted with knowledge about customers, e.g. customer characteristics and preferences prevalent in previous work on knowledge management and customer relationship management. Five styles of CKM are proposed and practically illustrated by way of corporate examples. Implications are discussed for knowledge management, the resource based view, and strategy process research.


title: A survey on reactive programming

abstract: Reactive programming has recently gained popularity as a paradigm that is well-suited for developing event-driven and interactive applications. It facilitates the development of such applications by providing abstractions to express time-varying values and automatically managing dependencies between such values. A number of approaches have been recently proposed embedded in various languages such as Haskell, Scheme, JavaScript, Java, .NET, etc. This survey describes and provides a taxonomy of existing reactive programming approaches along six axes: representation of time-varying values, evaluation model, lifting operations, multidirectionality, glitch avoidance, and support for distribution. From this taxonomy, we observe that there are still open challenges in the field of reactive programming. For instance, multidirectionality is supported only by a small number of languages, which do not automatically track dependencies between time-varying values. Similarly, glitch avoidance, which is subtle in reactive programs, cannot be ensured in distributed reactive programs using the current techniques.


title: Learning a convolutional neural network for non-uniform motion blur removal

abstract: In this paper, we address the problem of estimating and removing non-uniform motion blur from a single blurry image. We propose a deep learning approach to predicting the probabilistic distribution of motion blur at the patch level using a convolutional neural network (CNN). We further extend the candidate set of motion kernels predicted by the CNN using carefully designed image rotations. A Markov random field model is then used to infer a dense non-uniform motion blur field enforcing motion smoothness. Finally, motion blur is removed by a non-uniform deblurring model using patch-level image prior. Experimental evaluations show that our approach can effectively estimate and remove complex non-uniform motion blur that is not handled well by previous approaches.


Real-time obstacle avoidance for non-point mobile robots


title: Calibration methods for a multi-channel multi-frequency EIT system

abstract: Multi-channel multi-frequency electrical impedance tomography (EIT) systems require a careful calibration to minimize systematic errors. We describe novel calibration methods for the recently developed KHU Mark1 EIT system. Current source calibration includes maximization of output resistance and minimization of output capacitance using multiple generalized impedance converters. Phase and gain calibrations are used for voltmeters. Phase calibration nulls out the total system phase shift in measured voltage data. Gain calibrations are performed in two steps of intra- and inter-channel calibrations. Intra-channel calibration for each voltmeter compensates frequency dependence of its voltage gain and also discrepancy between design and actual gains. Inter-channel calibration compensates channel-dependent voltage gains of all voltmeters. Using the calibration methods described in this paper, we obtained 1 MΩ minimal output impedance of the current source in the frequency range 10 Hz–500 kHz. The reciprocity error was as small as 0.05% after intra- and inter-channel voltmeter calibrations. To demonstrate effects of calibration in reconstructed images, we used a homogenous phantom from which uniform images should be produced. Reconstructed time- and frequency-difference images using uncalibrated data showed spurious anomalies. By using calibrated data, standard deviations of time- and frequency-difference images of the homogenous phantom were reduced by about 40% and 90%, respectively.


title: Enhancing BPMN 2.0 informational perspective to support interoperability for cross-organizational business processes

abstract: Business Process Modeling Notation (BPMN) is being adopted as one of the ::: industry standards for modeling cross-organizational business processes ::: (CBPs). BPMN analyzes a business process as a set of interrelated activities, ::: focusing primarily on the functional perspective of the process. However, for ::: successful CBP modeling, an informational perspective is important. Although ::: BPMN 2.0 supports information flow design, existing representations of ::: data/information elements are not sufficient to support CBP modeling ::: requirements. In this light, the paper proposes an approach for formal ::: modeling and specification of information requirements used and generated in ::: the CBPs. A UML View Profile is introduced to specify information ::: requirements as views over the common reference ontology. A BPMN 2.0 ::: extension is introduced to connect the defined views and the corresponding ::: process activities. Ultimately, the proposed information requirements ::: specification enables generation of the message instance and its ::: transformation at the implementation level.


title: Towards an unanimous international regulatory body for responsible use of Artificial Intelligence [UIRB-AI]

abstract: Artificial Intelligence (AI), is once again in the phase of drastic advancements. Unarguably, the technology itself can revolutionize the way we live our everyday life. But the exponential growth of technology poses a daunting task for policy researchers and law makers in making amendments to the existing norms. In addition, not everyone in the society is studying the potential socio-economic intricacies and cultural drifts that AI can bring about. It is prudence to reflect from our historical past to propel the development of technology in the right direction. To benefit the society of the present and future, I scientifically explore the societal impact of AI. While there are many public and private partnerships working on similar aspects, here I describe the necessity for an Unanimous International Regulatory Body for all applications of AI (UIRB-AI). I also discuss the benefits and drawbacks of such an organization. To combat any drawbacks in the formation of an UIRB-AI, both idealistic and pragmatic perspectives are discussed alternatively. The paper further advances the discussion by proposing novel policies on how such organization should be structured and how it can bring about a win-win situation for everyone in the society.


title: Stimulating a blink: reduction of eye fatigue with visual stimulus

abstract: Computers make incredible amounts of information available at our fingertips. As computers become integral parts of our lives, we spend more time staring at computer monitor than ever before, sometimes with negative effects. One major concern is the increasing number of people suffering from Computer Vision Syndrome (CVS). CVS is caused by extensive use of computers, and its symptoms include eye fatigue, frequent headaches, dry eyes, and blurred vision. It is possible to partially alleviate CVS if we can remind users to blink more often. We present a prototype system that uses a camera to monitor a user's blink rate, and when the user has not blinked in a while, the system triggers a blink stimulus. We investigated four different types of eye-blink stimulus: screen blurring, screen flashing, border flashing, and pop-up notifications. Users also rated each stimulus type in terms of effectiveness, intrusiveness, and satisfaction. Results from our user studies show that our stimuli are effective in increasing user blink rate with screen blurring being the best.


title: A Convolutional Neural Network Approach to Brain Tumor Segmentation

abstract: We consider the problem of fully automatic brain focal pathology segmentation, in MR images containing low and high grade gliomas and ischemic stroke lesion. We propose a Convolutional Neural Network (CNN) approach which is amongst the top performing methods while also being extremely computationally efficient, a balance that existing methods have struggled to achieve. Our CNN is trained directly on the image modalities and thus learns a feature representation directly from the data. We propose a cascaded architecture with two pathways: one which focuses on small details in gliomas and one on the larger context. We also propose a two-phase patch-wise training procedure allowing us to train models in a few hours. Fully exploiting the convolutional nature of our model also allows us to segment a complete brain image in 25 s to 3 min. Experimental results on BRain Tumor Segmentation challenges (BRATS’13, BRATS’15) and Ischemic Stroke Lesion Segmentation challenge (ISLES’15) reveal that our approach is among the most accurate in the literature, while also being computationally very efficient.


title: Optimistic crash consistency

abstract: We introduce optimistic crash consistency, a new approach to crash consistency in journaling file systems. Using an array of novel techniques, we demonstrate how to build an optimistic commit protocol that correctly recovers from crashes and delivers high performance. We implement this optimistic approach within a Linux ext4 variant which we call OptFS. We introduce two new file-system primitives, osync() and dsync(), that decouple ordering of writes from their durability. We show through experiments that OptFS improves performance for many workloads, sometimes by an order of magnitude; we confirm its correctness through a series of robustness tests, showing it recovers to a consistent state after crashes. Finally, we show that osync() and dsync() are useful in atomic file system and database update scenarios, both improving performance and meeting application-level consistency demands.


title: DropIn: Making reservoir computing neural networks robust to missing inputs by dropout

abstract: The paper presents a novel, principled approach to train recurrent neural networks from the Reservoir Computing family that are robust to missing part of the input features at prediction time. By building on the ensembling properties of Dropout regularization, we propose a methodology, named DropIn, which efficiently trains a neural model as a committee machine of subnetworks, each capable of predicting with a subset of the original input features. We discuss the application of the DropIn methodology in the context of Reservoir Computing models and targeting applications characterized by input sources that are unreliable or prone to be disconnected, such as in pervasive wireless sensor networks and ambient intelligence. We provide an experimental assessment using real-world data from such application domains, showing how the Dropin methodology allows to maintain predictive performances comparable to those of a model without missing features, even when 20\%-50\% of the inputs are not available.


title: Affordance-based Active Belief: Recognition using visual and manual actions

abstract: This paper presents an active, model-based recognition system. It applies information theoretic measures in a belief-driven planning framework to recognize objects using the history of visual and manual interactions and to select the most informative actions. A generalization of the aspect graph is used to construct forward models of objects that account for visual transitions. We use populations of these models to define the belief state of the recognition problem. This paper focuses on the impact of the belief-space and object model representations on recognition efficiency and performance. A benchmarking system is introduced to execute controlled experiments in a challenging mobile manipulation domain. It offers a large population of objects that remain ambiguous from single sensor geometry or from visual or manual actions alone. Results are presented for recognition performance on this dataset using locomotive, pushing, and lifting controllers as the basis for active information gathering on single objects. An information theoretic approach that is greedy over the expected information gain is used to select informative actions, and its performance is compared to a sequence of random actions.


title: An Assumption Analysis of CMM From an Organizational Culture Perspective

abstract: The capability maturity model (CMM) approach to software process improvement is the most dominant paradigm of organizational change that software organizations implement. While some organizations have achieved various levels of success with the CMM, the vast majority have failed. In this paper, we investigate the assumptions about organizational culture embedded in the CMM models and we discuss their implications for software process improvement (SPI) initiatives. In this paper, we utilize the well-known competing values model to surface and analyze the assumptions underlying the CMM. Our analysis reveals contradictory sets of assumptions about organizational culture in the CMM approach. We believe that an understanding of these contradictions can help researchers address some of the difficulties that have been observed in implementing and institutionalizing SPI programs in organizations. Further, this research can help to open up a much-needed line of research that would examine the organization theory assumptions that underpin CMM. This type of research is important if CMM is to evolve as an effective organizational change paradigm for software organizations.


title: Robust Face Recognition with Deeply Normalized Depth Images

abstract: Depth information has been proven useful for face recognition. However, existing depth-image-based face recognition methods still suffer from noisy depth values and varying poses and expressions. In this paper, we propose a novel method for normalizing facial depth images to frontal pose and neutral expression and extracting robust features from the normalized depth images. The method is implemented via two deep convolutional neural networks (DCNN), normalization network (\(Net_{N}\)) and feature extraction network (\(Net_{F}\)). Given a facial depth image, \(Net_{N}\) first converts it to an HHA image, from which the 3D face is reconstructed via a DCNN. \(Net_{N}\) then generates a pose-and-expression normalized (PEN) depth image from the reconstructed 3D face. The PEN depth image is finally passed to \(Net_{F}\), which extracts a robust feature representation via another DCNN for face recognition. Our preliminary evaluation results demonstrate the superiority of the proposed method in recognizing faces of arbitrary poses and expressions with depth images.


title: 16.3 A 330mW 14b 6.8GS/s dual-mode RF DAC in 16nm FinFET achieving −70.8dBc ACPR in a 20MHz channel at 5.2GHz

abstract: Direct-RF synthesis has gained increasing attention in recent years [1] [2] as it simplifies the transmitter system by eliminating the intermediate frequency stage. It also offers the opportunity to address the extensive range of cellular bands with the same architecture and building blocks. Direct synthesis of carriers in the 5 to 6GHz unlicenced bands remains a challenge for RF-DACs operating in the 1st Nyquist band, as sampling rates in excess of 12GS/s are required. A more power efficient way to synthesize directly these frequencies is to use wideband mixing-DACs, which increase the output power in the 2nd and 3rd Nyquist bands [3]. In [3] the mixing is done using the quad-switch configuration, which doubles the number of switches and drivers, directly impacting the overall DAC width. In [4] the mixer is inserted in-line between the current cell switch and the output cascode, which requires additional headroom in the output stage. Both implementations impact the overall performance and power of the DAC even when the mixing operation is not used.


title: Toward Drowsiness Detection Using Non-hair-Bearing EEG-Based Brain-Computer Interfaces

abstract: Drowsy driving is one of the major causes that lead to fatal accidents worldwide. For the past two decades, many studies have explored the feasibility and practicality of drowsiness detection using electroencephalogram (EEG)-based brain-computer interface (BCI) systems. However, on the pathway of transitioning laboratory-oriented BCI into real-world environments, one chief challenge is to obtain high-quality EEG with convenience and long-term wearing comfort. Recently, acquiring EEG from non-hair-bearing (NHB) scalp areas has been proposed as an alternative solution to avoid many of the technical limitations resulted from the interference of hair between electrodes and the skin. Furthermore, our pilot study has shown that informative drowsiness-related EEG features are accessible from the NHB areas. This study extends the previous work to quantitatively evaluate the performance of drowsiness detection using cross-session validation with widely studied machine-learning classifiers. The offline results showed no significant difference between the accuracy of drowsiness detection using the NHB EEG and the whole-scalp EEG across all subjects ( ). The findings of this study demonstrate the efficacy and practicality of the NHB EEG for drowsiness detection and could catalyze explorations and developments of many other real-world BCI applications.


title: Opinion Extraction, Summarization and Tracking

abstract: Humans like to express their opinions and are eager to know others’ opinions. Automatically mining and organizing opinions from heterogeneous information sources are very useful for individuals, organizations and even governments. Opinion extraction, opinion summarization and opinion tracking are three important techniques for understanding opinions. Opinion extraction mines opinions at word, sentence and document levels from articles. Opinion summarization summarizes opinions of articles by telling sentiment polarities, degree and the correlated events. In this paper, both news and web blog articles are investigated. TREC, NTCIR and articles collected from web blogs serve as the information sources for opinion extraction. Documents related to the issue of animal cloning are selected as the experimental materials. Algorithms for opinion extraction at word, sentence and document level are proposed. The issue of relevant sentence selection is discussed, and then topical and opinionated information are summarized. Opinion summarizations are visualized by representative sentences. Text-based summaries in different languages, and from different sources, are compared. Finally, an opinionated curve showing supportive and nonsupportive degree along the timeline is illustrated by an opinion tracking system.


title: Design and Evaluation of an RPL-based Multi-Sink Routing Protocol for Low-Power and Lossy Networks

abstract: Due to the energy and resource constraints of the embedded Wireless Sensor Networking (WSN) devices that form Low-Power and Lossy Networks (LLNs), efficient support for mobile data sinks is essential. Further, such support should be fully compatible with the broad class of networking mechanisms now defined for LLN support, such as the routing protocol RPL. This paper describes the Hierarchical network of Observable devices with Itinerant Sinks Transporting data (HOIST) protocol. HOIST is designed to service LLNs using an RPL-friendly three-tier hybrid cluster-tree architecture to support sensor nodes deployed over geographically segregated areas. The mobile aspects of HOIST allow for collection from such remote fields without the need for long-distance wireless transfers, which would result in a higher level of energy drain. HOIST supports both real-time data capture and collected data retrieval. We have implemented and field-tested HOIST in a working physical system, as well as in simulation, with results indicating viability for both sparse and dense deployments of nodes under multiple, geographically constrained, uncontrolled mobility models.


title: Impact of Anesthesia and Euthanasia on Metabolomics of Mammalian Tissues: Studies in a C57BL/6J Mouse Model

abstract: A critical application of metabolomics is the evaluation of tissues, which are often the primary sites of metabolic dysregulation in disease. Laboratory rodents have been widely used for metabolomics studies involving tissues due to their facile handing, genetic manipulability and similarity to most aspects of human metabolism. However, the necessary step of administration of anesthesia in preparation for tissue sampling is not often given careful consideration, in spite of its potential for causing alterations in the metabolome. We examined, for the first time using untargeted and targeted metabolomics, the effect of several commonly used methods of anesthesia and euthanasia for collection of skeletal muscle, liver, heart, adipose and serum of C57BL/6J mice. The data revealed dramatic, tissue-specific impacts of tissue collection strategy. Among many differences observed, post-euthanasia samples showed elevated levels of glucose 6-phosphate and other glycolytic intermediates in skeletal muscle. In heart and liver, multiple nucleotide and purine degradation metabolites accumulated in tissues of euthanized compared to anesthetized animals. Adipose tissue was comparatively less affected by collection strategy, although accumulation of lactate and succinate in euthanized animals was observed in all tissues. Among methods of tissue collection performed pre-euthanasia, ketamine showed more variability compared to isoflurane and pentobarbital. Isoflurane induced elevated liver aspartate but allowed more rapid initiation of tissue collection. Based on these findings, we present a more optimal collection strategy mammalian tissues and recommend that rodent tissues intended for metabolomics studies be collected under anesthesia rather than post-euthanasia.


title: Reinforcement learning in Portfolio Management and its interpretation

abstract: Since all machine learning methods commonly in use today are viewed ::: as black boxes, the goal of this paper is to make one of these meth- ::: ods transparent in the context of Portfolio management. I interpret ::: the strategies implied by reinforcement learning (RL) and relate them ::: to the strategies implied by academic portfolio advice with the help of ::: their classical portfolio (CP) management models. Because RL is ac- ::: tually approximate dynamic programming (DP), it is perfectly suited ::: for the volatile DP environment of portfolio management compared to ::: other machine learning methods in use. In terms of performance, this ::: RL method is able to: 1) achieve the same average terminal wealth of ::: 1.33, which is an increase of 33% in portfolio value over ve years, as ::: the CP model with a low risk-aversion 2) diminish the standard devia- ::: tion of the terminal wealth by 30% from 0.35 to 0.25 3) have a lower ::: turnover than the same CP model by three percent. This can mostly ::: be explained by the conservative investing of the reinforcement learning ::: method overall.


title: A coordinate-transform based FFBP algorithm for high-resolution spotlight SAR imaging

abstract: This paper proposes a coordinate-transform (CT) implementation for the fast factorized backprojection (FFBP) algorithm (CT-FFBP) to process high-resolution spotlight synthetic aperture radar (SAR) data. Unlike the FFBP utilizing two-dimensional image-domain interpolation for sub-aperture fusion, CT-FFBP finishes the image-projection using CT with the accommodation of chirp-z transform and circular shifting. Without interpolation, CT-FFBP yields enhanced efficiency over the interpolation based FFBP, besides maintaining high precision simultaneously. Both simulation and real-data experiments verifies the efficiency and precision superiorities of the CT-FFBP.


OSI-930: A Novel Selective Inhibitor of Kit and Kinase Insert Domain Receptor Tyrosine Kinases with Antitumor Activity in Mouse Xenograft Models


title: A new methodology based on q-entropy for breast lesion classification in 3-D ultrasound images.

abstract: Classification of breast lesions is clinically most relevant for breast radiologists and pathologists for early breast cancer detection. This task is not easy due to poor ultrasound resolution and large amount of patient data size. This paper proposes a five step novel and automatic methodology for breast lesion classification in 3-D ultrasound images. The first three steps yield an accurate segmentation of the breast lesions based on the combination of (a) novel non-extensive entropy, (b) morphologic cleaning and (c) accurate region and boundary extraction in level set framework. Segmented lesions then undergo five feature extractions consisting of: area, circularity, protuberance, homogeneity, and acoustic shadow. These breast lesion features are then input to a support vector machine (SVM)-based classifier that classifies the breast lesions between malignant and benign types. SVM utilizes B-spline as a kernel in its framework. Using a data base of 250 breast ultrasound images (100 benign and 150 malignant) and utilizing the cross-validation protocol, we demonstrate system's accuracy, sensitivity, specificity, positive predictive value and negative predictive value as: 95%, 97%, 94%, 92% and 98% respectively in terms of ROC curves and Az areas, better in performance than the current literature offers.


title: iSAX: disk-aware mining and indexing of massive time series datasets

abstract: Current research in indexing and mining time series data has produced many interesting algorithms and representations. However, the algorithms and the size of data considered have generally not been representative of the increasingly massive datasets encountered in science, engineering, and business domains. In this work, we introduce a novel multi-resolution symbolic representation which can be used to index datasets which are several orders of magnitude larger than anything else considered in the literature. To demonstrate the utility of this representation, we constructed a simple tree-based index structure which facilitates fast exact search and orders of magnitude faster, approximate search. For example, with a database of one-hundred million time series, the approximate search can retrieve high quality nearest neighbors in slightly over a second, whereas a sequential scan would take tens of minutes. Our experimental evaluation demonstrates that our representation allows index performance to scale well with increasing dataset sizes. Additionally, we provide analysis concerning parameter sensitivity, approximate search effectiveness, and lower bound comparisons between time series representations in a bit constrained environment. We further show how to exploit the combination of both exact and approximate search as sub-routines in data mining algorithms, allowing for the exact mining of truly massive real world datasets, containing tens of millions of time series.


title: Fingerphoto spoofing in mobile devices: A preliminary study

abstract: Biometric-based authentication for smart handheld devices promises to provide a reliable and alternate security mechanism compared to traditional methods such as pins, patterns, and passwords. Although fingerprints are a viable source for authentication, they generally require installation of an additional hardware such as optical and swipe sensors on mobile devices, and are only available in expensive, high-end smartphones. Alternatively, fingerphoto images captured using the smartphone camera for authentication is one of the promising biometric approaches. However, using fingerphotos for authentication brings along a major challenge of fingerphoto spoofing. This research is aimed at understanding the effect of spoofing on fingerphotos. There are three major contributions of this research: (i) create a large spoofed fingerphoto database and make it publicly available for research, (ii) to establish the effect of print attack and photo attack in fingerphoto spoofing, and (iii) understand the performance of existing spoofing detection algorithms on fingerphoto spoofing.


title: Grounding Visual Explanations

abstract: Existing visual explanation generating agents learn to fluently justify a class prediction. However, they may mention visual attributes which reflect a strong class prior, although the evidence may not actually be in the image. This is particularly concerning as ultimately such agents fail in building trust with human users. To overcome this limitation, we propose a phrase-critic model to refine generated candidate explanations augmented with flipped phrases which we use as negative examples while training. At inference time, our phrase-critic model takes an image and a candidate explanation as input and outputs a score indicating how well the candidate explanation is grounded in the image. Our explainable AI agent is capable of providing counter arguments for an alternative prediction, i.e. counterfactuals, along with explanations that justify the correct classification decisions. Our model improves the textual explanation quality of fine-grained classification decisions on the CUB dataset by mentioning phrases that are grounded in the image. Moreover, on the FOIL tasks, our agent detects when there is a mistake in the sentence, grounds the incorrect phrase and corrects it significantly better than other models.


title: Post-surgical wound management of pilonidal cysts with a haemoglobin spray: a case series

abstract: Painful acute cysts in the natal cleft or lower back, known as pilonidal sinus disease, are a severe burden to many younger patients. Although surgical intervention is the preferred first line treatment, postsurgical wound healing disturbances are frequently reported due to infection or other complications. Different treatment options of pilonidal cysts have been discussed in the literature, however, no standardised guideline for the postsurgical wound treatment is available. After surgery, a common recommended treatment to patients is rinsing the wound with clean water and dressing with a sterile compress. We present a case series of seven patients with wounds healing by secondary intention after surgical intervention of a pilonidal cyst. The average age of the patients was 40 years old. Of the seven patients, three had developed a wound healing disturbance, one wound had started to develop a fibrin coating and three were in a good condition. The applied wound care regimens comprised appropriate mechanical or autolytic debridement, rinsing with an antimicrobial solution, haemoglobin application, and primary and secondary dressings. In all seven cases a complete wound closure was achieved within an average of 76 days with six out of seven wounds achieving wound closure within 23-98 days. Aesthetic appearance was deemed excellent in five out of seven cases excellent and acceptable in one. Treatment of one case with a sustained healing disturbance did result in wound closure but with a poor aesthetic outcome and an extensive cicatrisation of the new tissue. Based on these results we recommend that to avoid healing disturbances of wounds healing by secondary intention after surgical pilonidal cyst intervention, an adequate wound care regime comprising appropriate wound debridement, rinsing, topically applied haemoglobin and adequate wound dressing is recommendable as early as possible after surgery.


title: Online power system state estimation using alternating direction method of multipliers

abstract: The convex relaxation approaches for power system state estimation (PSSE) offer robust alternatives to the conventional PSSE algorithms, by avoiding local optima and providing guaranteed convergence, critical especially when the states deviate significantly from the nominal conditions. On the other hand, the associated semidefinite programming problem may be computationally demanding. In this work, a variable splitting technique called alternating direction method of multipliers is employed to reduce the complexity, and also efficiently accommodate a regularizer promoting desired low-rank matrix solutions. Both static and online formulations are developed. Numerical tests verify the efficacy of the proposed techniques.


title: Automatic Text Generation by Learning from Literary Structures

abstract: Are mind and machine capable of solving the same tasks? Creativity is one of the arguments that some philosophers and psychologists use as a proof of what computers cannot achieve; however, these arguments might be based on a misconception of what both intelligence and creativity mean. This book provides arguments supporting that creativity, as storytelling, can be emulated through computer programs. The assumption of creativity presents a major problem: Complexity. Even if we consider creativity just as a product of novel ways of achieving a goal, the number of combinations found when dealing with the ‘real world’ is astronomically huge. We can recall The Library of Babel (Borges, 1944), a library that contains any possible book that could be written in the history of humanity. This metaphor reveals the combinatory problem that emerges if a brute force algorithm is designed to generate texts. According to our hypothesis, our proposal is a heuristic that uses simple syntactic and semantic properties found in a text corpus in order to generate novel and coherent fiction texts based on what has been already written.


title: Light mixture estimation for spatially varying white balance

abstract: White balance is a crucial step in the photographic pipeline. It ensures the proper rendition of images by eliminating color casts due to differing illuminants. Digital cameras and editing programs provide white balance tools that assume a single type of light per image, such as daylight. However, many photos are taken under mixed lighting. We propose a white balance technique for scenes with two light types that are specified by the user. This covers many typical situations involving indoor/outdoor or flash/ambient light mixtures. Since we work from a single image, the problem is highly underconstrained. Our method recovers a set of dominant material colors which allows us to estimate the local intensity mixture of the two light types. Using this mixture, we can neutralize the light colors and render visually pleasing images. Our method can also be used to achieve post-exposure relighting effects.


title: The Tridimensional Personality Questionnaire: U.S. Normative Data:

abstract: The Tridimensional Personality Questionnaire is a self-report personality inventory measuring three major personality dimensions: Novelty Seeking, Harm Avoidance, and Reward Dependence. Normative data, based on a U.S. national probability sample of 1,019 adults, are presented and the psychometric properties of the questionnaire are discussed.


title: Side-channel leakage aware instruction scheduling

abstract: Speed-optimized side-channel protected software implementations of block ciphers are important for the security of embedded IoT devices based on general-purpose microcontrollers. The recent work of Schwabe et al. published at SAC 2016 introduced a bit-sliced implementation of AES and a first-order Boolean-masked version of it, targeting ARM Cortex-M CPU cores. The authors claim to be secure against timing as well as first-order power and electromagnetic side-channel attacks. However, the author's security claims are not taking the actual leakage characteristics of the underlying CPU architecture into account, hence making the scheme potentially vulnerable to first-order attacks in practice. In this work we show indeed that such a masking scheme can be attacked very easily by first-order electromagnetic side-channel attacks. In order to fix the issue and provide practical first-order security, we provide a strategy to schedule program instructions in way that the specific leakage of the CPU does not impair the side-channel countermeasure.


title: Universal maximum power point tracking in wind-solar hybrid system for battery storage application

abstract: This paper proposes a universal control algorithm to harvest maximum possible power from wind-solar hybrid energy system for battery storage application. It consists of main and auxiliary battery storage system which has higher energy yield as compared to conventional energy storage system. The universal control strategy improves the performance of the hybrid system for large variations in wind velocity and solar radiation conditions with reduced complexity of the controller. Thus the system achieves higher reliability and better energy yield, especially in poor environmental conditions. The algorithm also maintains constant voltage at the DC bus. A separate boost and buck converters are used for the maximum power point tracking (MPPT) and voltage regulation using the universal controller. Operation of the complete proposed system under different climatic conditions is verified through the PSCAD/EMTDC simulation studies.


title: Inductive teaching and learning methods: Definitions, comparisons, and research bases

abstract: Traditional engineering instruction is deductive, beginning with theories and progressing to the applications of those theories. Alternative teaching approaches are more inductive. Topics are introduced by presenting specific observations, case studies or problems, and theories are taught or the students are helped to discover them only after the need to know them has been established. This study reviews several of the most commonly used inductive teaching methods, including inquiry learning, problem-based learning, project-based learning, case-based teaching, discovery learning, and just-in-time teaching. The paper defines each method, highlights commonalities and specific differences, and reviews research on the effectiveness of the methods. While the strength of the evidence varies from one method to another, inductive methods are consistently found to be at least equal to, and in general more effective than, traditional deductive methods for achieving a broad range of learning outcomes.


Similarity-based Approaches to Learning from Demonstration


title: Researcher degrees of freedom in phonetic research

abstract: The results of published research critically depend on methodological decisions that have been made during data analysis. These so-called ‘researcher degrees of freedom’ (Simmons, Nelson, & Simonsohn, 2011) can affect the results and the conclusions researchers draw from it. It is argued that phonetic research faces a large number of researcher degrees of freedom due to its scientific object—speech—being inherently multidimensional and exhibiting complex interactions between multiple covariates. A Type-I error simulation is presented that demonstrates the severe inflation of false positives when exploring researcher degrees of freedom. It is argued that combined with common cognitive fallacies, exploitation of researcher degrees of freedom introduces strong bias and poses a serious challenge to quantitative phonetics as an empirical science. This paper discusses potential remedies for this problem including adjusting the threshold for significance; drawing a clear line between confirmatory and exploratory analyses via preregistration; open, honest, and transparent practices in communicating data analytical decisions; and direct replications.


title: Toward a Unified Color Space for Perception-Based Image Processing

abstract: Image processing methods that utilize characteristics of the human visual system require color spaces with certain properties to operate effectively. After analyzing different types of perception-based image processing problems, we present a list of properties that a unified color space should have. Due to contradictory perceptual phenomena and geometric issues, a color space cannot incorporate all these properties. We therefore identify the most important properties and focus on creating opponent color spaces without cross contamination between color attributes (i.e., lightness, chroma, and hue) and with maximum perceptual uniformity induced by color-difference formulas. Color lookup tables define simple transformations from an initial color space to the new spaces. We calculate such tables using multigrid optimization considering the Hung and Berns data of constant perceived hue and the CMC, CIE94, and CIEDE2000 color-difference formulas. The resulting color spaces exhibit low cross contamination between color attributes and are only slightly less perceptually uniform than spaces optimized exclusively for perceptual uniformity. We compare the CIEDE2000-based space with commonly used color spaces in two examples of perception-based image processing. In both cases, standard methods show improved results if the new space is used. All color-space transformations and examples are provided as MATLAB codes on our website.


title: A Query Focused Multi Document Automatic Summarization

abstract: The present paper describes the development of a query focused multi-document automatic summarization. A graph is constructed, where the nodes are sentences of the documents and edge scores reflect the correlation measure between the nodes. The system clusters similar texts having related topical features from the graph using edge scores. Next, query dependent weights for each sentence are added to the edge score of the sentence and accumulated with the corresponding cluster score. Top ranked sentence of each cluster is identified and compressed using a dependency parser. The compressed sentences are included in the output summary. The inter-document cluster is revisited in order until the length of the summary is less than the maximum limit. The summarizer has been tested on the standard TAC 2008 test data sets of the Update Summarization Track. Evaluation of the summarizer yielded accuracy scores of 0.10317 (ROUGE-2) and 0.13998 (ROUGE–SU-4).


title: Multi-Element Topochemical-Molten Salt Synthesis of One-Dimensional Piezoelectric Perovskite

abstract: Summary One-dimensional perovskites are an interesting material for energy and optoelectronic applications. However, exploring the full wealth of architectures these materials could allow, through multi-element doping of A-sites and B-sites, is still a challenge. Here, we report a high-yield synthetic strategy for 1D perovskites via a two-step method based on a multi-element topochemical-molten salt method. Typically, a high yield of 1D multicomponent perovskite niobates (Li0.06Na0.47K0.47)(Nb0.94Sb0.06)O3 (LNKNS2) is rapidly achieved from as-synthesized 1D K2(Nb0.94Sb0.06)8O21 with multi-element B-sites. In this process, 1D K2(Nb0.94Sb0.06)8O21 has been first achieved, and the proportion of the ions in A-sites is affected by the radius and molar ratio of ions. The z axis direction of K2(Nb0.94Sb0.06)8O21 rod is transformed into the x axis direction of LNKNS2 rod. Furthermore, the output voltage of the 1D niobates-based flexible piezoelectric device (FPD) was nearly 600% compared with that of the isotropic niobates-based FPD. This work also allows convenient fabrication of other 1D multicomponent perovskites.


title: Action Recognition with Improved Trajectories

abstract: Recently dense trajectories were shown to be an efficient video representation for action recognition and achieved state-of-the-art results on a variety of datasets. This paper improves their performance by taking into account camera motion to correct them. To estimate camera motion, we match feature points between frames using SURF descriptors and dense optical flow, which are shown to be complementary. These matches are, then, used to robustly estimate a homography with RANSAC. Human motion is in general different from camera motion and generates inconsistent matches. To improve the estimation, a human detector is employed to remove these matches. Given the estimated camera motion, we remove trajectories consistent with it. We also use this estimation to cancel out camera motion from the optical flow. This significantly improves motion-based descriptors, such as HOF and MBH. Experimental results on four challenging action datasets (i.e., Hollywood2, HMDB51, Olympic Sports and UCF50) significantly outperform the current state of the art.


title: Applying autonomous sensor systems in logistics—Combining sensor networks, RFIDs and software agents

abstract: New sensor, communication and software technologies are used to broaden the facilities of tracing and tracing systems for food transports. An embedded assessing unit detects from sensor data collected by a wireless network potential risks for the freight quality. The estimation of the current maturing state of agricultural products will be supported by measurements of the gaseous hormone ethylene as an indicator for the ripening processes. A miniaturized high-resolution gas chromatography is under construction. The system autonomously configures itself to a product specific supervision task based on data scanned by an RFID reader during freight loading. Mobile software agents accompany the freight along the supply chain. They pre-process the vast sensor data and submit only substantial changes to the freight owner.


title: Student classification for academic performance prediction using neuro fuzzy in a conventional classroom

abstract: Conventional classroom is still the main learning method applied in undergraduate program of Electrical Engineering and Information Technology Department, Gadjah Mada University. There are several problems in this method, such as large amount of students and limited number of meetings making difficult to understand each student. Student classification is a way to solve the problem by mapping the condition of each student based on certain parameters. Many methods have been applied to classify students that are based on IF-THEN rules and pattern recognition. However, many studies were done on intelligent tutoring systems and e-learning systems, not in a conventional classroom. Moreover, there are no researches that measure basic values by considering intelligence and non-intelligence performances. In this work, a student classification model was developed by applying neuro fuzzy concept; a combination of fuzzy's IF-THEN rules and neural network's ability to learn, so this method has the ability to learn from the generated rules to produce the best classification model. The model can be used to predict students' academic performance. Data were processed using ANFIS Editor-Matlab Fuzzy Logic. The results showed that combination of three parameter values -interest, talent, and motivation- is the best model for students classification, which has training RMSE value 0.12301 and testing average RMSE value 0.25611.


title: Power Factor Correction in Bridgeless-Luo Converter-Fed BLDC Motor Drive

abstract: This paper presents a power factor correction (PFC)-based bridgeless Luo (BL-Luo) converter-fed brushless dc (BLDC) motor drive. A single voltage sensor is used for the speed control of the BLDC motor and PFC at ac mains. The voltage follower control is used for a BL-Luo converter operating in discontinuous inductor current mode. The speed of the BLDC motor is controlled by an approach of variable dc-link voltage, which allows a low-frequency switching of the voltage source inverter for the electronic commutation of the BLDC motor, thus offering reduced switching losses. The proposed BLDC motor drive is designed to operate over a wide range of speed control with an improved power quality at ac mains. The power quality indices thus obtained are under the recommended limits of IEC 61000-3-2. The performance of the proposed drive is validated with test results obtained on a developed prototype of the drive.


title: Multi-objective whale optimization

abstract: This paper deals with designing a multi-objective algorithm based on recently proposed Whale Optimization Algorithm (WOA), termed as MOWOA. The original WOA algorithm is popular among the researchers due to the encircling moments of agents (Humpback whales) in the search space which provides proper balance among the exploration and exploitation, faster convergence and lessor number of parameters. The proposed multi-objective version posses all the above benefits of the original algorithm, in addition it reveals accurate convergence to the true Pareto fronts and maintain effective diversity among the solutions. The performance is demonstrated on six unconstrained bi-objective functions of IEEE CEC 2009. The obtained results are compared with that achieved by multi-objective Grey Wolf Optimization (MOGWO), multi-objective Particle Swarm Optimization (MOPSO), multi-objective Evolutionary Algorithm based on Decomposition(MOEA/D).


title: Epidemiology and Clinical Presentation in Esophageal Cancer

abstract: The esophagus begins at the level of the cricopharyngeus and traverses the length of the neck to pass through the mediastinum. It then pierces the right crus of the diaphragm and after a short abdominal component joins the stomach at the cardia. For descriptive purposes the esophagus is referred to as cervical ( 6 cm), thoracic ( 25 cm), and abdominal ( 4 cm). Surgeons often refer to the esophagus in divisions of one-third, upper, middle, and distal, as this better relates to the operative options in esophagectomy. Although not as common as cancer of other sites such as prostate, breast, and colorectum, esophageal cancer has a high lethality rate, the incidence being close to the cancer-specific mortality. Thus, in the USA in 2006, esophageal carcinoma was the 15th commonest cancer, with an estimated 14 550 cases, but it had the 8th highest mortality rate, with an estimated 13 770 deaths [1]. Esophageal cancer is remarkable for its marked variation by geographical region, ethnicity, and gender. There is a greater than tenfold difference in incidence rates between countries with a low incidence, for example, the United States, and those with a high incidence such as high-risk areas in Iran and China [2]. More than 90% of esophageal cancers are either squamous cell carcinomas (SCCs) or adenocarcinomas, with other tumor types such as melanomas, stromal tumors, lymphomas, or neuroendocrine cancers occurring only rarely in the esophagus. Most esophageal cancers occur in the lower and middle thirds, the cervical esophagus being an uncommon site of disease. Although the presentation of SCC and adenocarcinoma of the esophagus in the patient are similar, the epidemiology, etiologoy, tumor biology, treatment strategies, and outcomes are quite different, and they are really two different diseases that occur in the same organ [3,4] (Table 1.1).


title: Comparative Experiments On Disambiguating Word Senses: An Illustration Of The Role Of Bias In Machine Learning

abstract: This paper describes an experimental comparison of seven different learning algorithms on the problem of learning to disambiguate the meaning of a word from context. The algorithms tested include statistical, neural-network, decision-tree, rule-based, and case-based classification techniques. The specific problem tested involves disambiguating six senses of the word ``line'' using the words in the current and proceeding sentence as context. The statistical and neural-network methods perform the best on this particular problem and we discuss a potential reason for this observed difference. We also discuss the role of bias in machine learning and its importance in explaining performance differences observed on specific problems.


title: Isogeometric analysis of nonlinear Euler–Bernoulli beam vibrations

abstract: In this paper we analyze the vibrations of nonlinear structures by means of the novel approach of isogeometric finite elements. The fundamental idea of isogeometric finite elements is to apply the same functions, namely B-Splines and NURBS (Non-Uniform Rational B-Splines), for describing the geometry and for representing the numerical solution. In case of linear vibrational analysis, this approach has already been shown to possess substantial advantages over classical finite elements, and we extend it here to a nonlinear framework based on the harmonic balance principle. As application, the straight nonlinear Euler–Bernoulli beam is used, and overall, it is demonstrated that isogeometric finite elements with B-Splines in combination with the harmonic balance method are a powerful means for the analysis of nonlinear structural vibrations. In particular, the smoother k-method provides higher accuracy than the p-method for isogeometric nonlinear vibration analysis.


title: Creating artificial neural networks that generalize

abstract: Abstract We develop a technique to test the hypothesis that multilayered, feed-forward networks with few units on the first hidden layer generalize better than networks with many units in the first layer. Large networks are trained to perform a classification task and the redundant units are removed (“pruning”) to produce the smallest network capable of performing the task. A technique for inserting layers where pruning has introduced linear inseparability is also described. Two tests of ability to generalize are used—the ability to classify training inputs corrupted by noise and the ability to classify new patterns from each class. The hypothesis is found to be false for networks trained with noisy inputs. Pruning to the minimum number of units in the first layer produces networks which correctly classify the training set but generalize poorly compared with larger networks.


title: Soft and hard tissue assessment of immediate implant placement: a case series

abstract: OBJECTIVES ::: The aim of this prospective study was to evaluate clinically and radiographically the success and esthetic result of immediate implant placement at the time of extraction. ::: ::: ::: MATERIAL AND METHODS ::: Twelve patients with 14 titanium screw-shaped implants (13-16 mm length and 4.3 or 5 mm diameters) were placed in the extraction sockets. Defects after implant placement were recorded, and then filled up with deproteinized bovine bone mineral, bioabsorbable collagen membrane, and absorbable pins. The defect was again re-evaluated at second-stage surgery. Clinical and radiographic parameters of the peri-implant conditions were assessed at the moment of prosthesis placement and at 1-year follow-up. ::: ::: ::: RESULTS ::: The cumulative implant survival and success rate was 100% after a 1-year observation period. Analysis of the esthetic result showed that the mean pink esthetic score (PES) was 11.1 (SD 1.35) at 1-year follow-up. At 1 year, 64.3% papillae had a score of 2 and the remaining 35.7% score 3 according to the Jemt (1997) papillary index. Optimal value of width of the keratinized mucosa was recorded in 13 (92.9%) implant cases in both periods of follow-up. At 1-year follow-up, the linear distance between implant-shoulder to the bone peaks remains stable with a mean of 2.62+/-0.2 mm at the mesial and 2.9+/-0.58 mm at the distal aspect. ::: ::: ::: CONCLUSION ::: Careful evaluation of potential extraction sites before immediate implant installation promotes optimal implant esthetics.


title: Inter-AS traffic patterns and their implications

abstract: This paper reports on a study of traffic patterns among autonomous systems (ASes), based on traces taken at various points in the Internet. The traces display a highly nonuniform distribution of traffic on flows between pairs of hosts, networks, and ASes. Aggregation along coarser granularities, such as networks or ASes, accentuates this nonuniform distribution. In one typical trace, for example, the top 9% of flows between ASes accounts for 86.7% of the packets and 90.7% of the bytes transmitted. A highly nonuniform traffic pattern suggests that routers need to maintain only limited QoS flow state. The paper discusses the implications of this phenomenon on different proposed QoS mechanisms.


title: Genetics of Hypertrophic Cardiomyopathy After 20 Years Clinical Perspectives

abstract: Hypertrophic cardiomyopathy (HCM) is the most common familial heart disease with vast genetic heterogeneity, demonstrated over the past 20 years. Mutations in 11 or more genes encoding proteins of the cardiac sarcomere (>1,400 variants) are responsible for (or associated with) HCM. Explosive progress achieved in understanding the rapidly evolving science underlying HCM genomics has resulted in fee-for-service testing, making genetic information widely available. The power of HCM mutational analysis, albeit a more limited role than initially envisioned, lies most prominently in screening family members at risk for developing disease and excluding unaffected relatives, which is information not achievable otherwise. Genetic testing also allows expansion of the broad HCM disease spectrum and diagnosis of HCM phenocopies with different natural history and treatment options, but is not a reliable strategy for predicting prognosis. Interfacing a heterogeneous disease such as HCM with the vast genetic variability of the human genome, and high frequency of novel mutations, has created unforeseen difficulties in translating complex science (and language) into the clinical arena. Indeed, proband diagnostic testing is often expressed on a probabilistic scale, which is frequently incompatible with clinical decision making. Major challenges rest with making reliable distinctions between pathogenic mutations and benign variants, and those judged to be of uncertain significance. Genotyping in HCM can be a powerful tool for family screening and diagnosis. However, wider adoption and future success of genetic testing in the practicing cardiovascular community depends on a standardized approach to mutation interpretation, and bridging the communication gap between basic scientists and clinicians.


title: Pharmacy Education in India: Strategies for a Better Future

abstract: In this world of specialization and globalization the pharmacy education in India is suffering from serious backdrops and flaws. There is an urgent need to initiate an academic exercise aimed at attaining revamping of curriculum, keeping in pace with current and emerging trends in the field of pharmacy. Unfortunately all these years, enough emphasis was not laid on strengthening the components of Community Pharmacy, Hospital and Clinical pharmacy, while designing curriculum at diploma and degree levels of teaching. The curriculum followed by almost all universities in India are no were up to the world standards and students are still getting the 20-30 yrs older compounding practical exposure in labs during the graduation level. The article emphasises the concept of innovation ecosystems and quality management. Application of TQM to the educational system improves the present situation. The counseling system which serves to be the gateway of the students for entry into the profession should be brought under the scanner. Introducing specializations at the graduation level will result in professional expertise and excellence. Education is a customer focused industry and every student should be capable of evaluating themselves for continuously improving their quality and professionalism. Teacher focused mastery learning should give away to student focused smart learning. An educational institution should provide the student with a stress-free atmosphere for learning and developing his intellectual capabilities. Every college should have a counseling centre to address the problems of students in their academic and personal life. An emphasis on the concept of quality teacher is included. Revival of the pharmacy education in India is the need of the hour which in turn will pave the way for the up gradation of the pharmacy profession in the country. Key word s: Education,innovation ecosystems,pharmacy,total quality management.


title: Simulator evaluation of a prototype device to reduce medication errors in anaesthesia.

abstract: Summary ::: We undertook a randomised control led trial to evaluate the effect of a prototype device which attaches to the intravenous drug administration port, and allows injection of intravenous drugs only after the user scans the barcode on the syringe label. This requires two steps: first, that the correct drug label is generated; and second, that the syringe-with-label is scanned before administration. Ten anaesthetists, who were unaware of the primary outcome being measured, administered general anaesthesia for two simulated standardised cases each without and with our prototype (control and intervention, respectively). The primary outcome measured was compliance with a safe drug administration procedure (defined as a two-step procedure where, step one is scanning a drug ampoule to print a label for a syringe and step two is scanning of the labelled syringe before administering it intravenously). A total of 182 intravenous drug administrations occurred in the study (91 in each group). We found that the use of our prototype increased safe drug administration behaviour in experienced anaesthetists; 33 (36.3% [95% CI 26–47%]) vs. 91 (100% [95% CI 96–100%]) in the control and intervention groups, respectively (p = 0.0001).


title: Block Sparse Compressed Sensing of Electroencephalogram (EEG) Signals by Exploiting Linear and Non-Linear Dependencies

abstract: This paper proposes a compressive sensing (CS) method for multi-channel electroencephalogram (EEG) signals in Wireless Body Area Network (WBAN) applications, where the battery life of sensors is limited. For the single EEG channel case, known as the single measurement vector (SMV) problem, the Block Sparse Bayesian Learning-BO (BSBL-BO) method has been shown to yield good results. This method exploits the block sparsity and the intra-correlation (i.e., the linear dependency) within the measurement vector of a single channel. For the multichannel case, known as the multi-measurement vector (MMV) problem, the Spatio-Temporal Sparse Bayesian Learning (STSBL-EM) method has been proposed. This method learns the joint correlation structure in the multichannel signals by whitening the model in the temporal and the spatial domains. Our proposed method represents the multi-channels signal data as a vector that is constructed in a specific way, so that it has a better block sparsity structure than the conventional representation obtained by stacking the measurement vectors of the different channels. To reconstruct the multichannel EEG signals, we modify the parameters of the BSBL-BO algorithm, so that it can exploit not only the linear but also the non-linear dependency structures in a vector. The modified BSBL-BO is then applied on the vector with the better sparsity structure. The proposed method is shown to significantly outperform existing SMV and also MMV methods. It also shows significant lower compression errors even at high compression ratios such as 10:1 on three different datasets.


title: fMRI Brain-Computer Interface: A Tool for Neuroscientific Research and Treatment

abstract: Brain-computer interfaces based on functional magnetic resonance imaging (fMRI-BCI) allow volitional control of anatomically specific regions of the brain. Technological advancement in higher field MRI scanners, fast data acquisition sequences, preprocessing algorithms, and robust statistical analysis are anticipated to make fMRI-BCI more widely available and applicable. This noninvasive technique could potentially complement the traditional neuroscientific experimental methods by varying the activity of the neural substrates of a region of interest as an independent variable to study its effects on behavior. If the neurobiological basis of a disorder (e.g., chronic pain, motor diseases, psychopathy, social phobia, depression) is known in terms of abnormal activity in certain regions of the brain, fMRI-BCI can be targeted to modify activity in those regions with high specificity for treatment. In this paper, we review recent results of the application of fMRI-BCI to neuroscientific research and psychophysiological treatment.


title: Direct Intrinsics: Learning Albedo-Shading Decomposition by Convolutional Regression

abstract: We introduce a new approach to intrinsic image decomposition, the task of decomposing a single image into albedo and shading components. Our strategy, which we term direct intrinsics, is to learn a convolutional neural network (CNN) that directly predicts output albedo and shading channels from an input RGB image patch. Direct intrinsics is a departure from classical techniques for intrinsic image decomposition, which typically rely on physically-motivated priors and graph-based inference algorithms. The large-scale synthetic ground-truth of the MPI Sintel dataset plays the key role in training direct intrinsics. We demonstrate results on both the synthetic images of Sintel and the real images of the classic MIT intrinsic image dataset. On Sintel, direct intrinsics, using only RGB input, outperforms all prior work, including methods that rely on RGB+Depth input. Direct intrinsics also generalizes across modalities, our Sintel-trained CNN produces quite reasonable decompositions on the real images of the MIT dataset. Our results indicate that the marriage of CNNs with synthetic training data may be a powerful new technique for tackling classic problems in computer vision.


title: Uncovering Latent Style Factors for Expressive Speech Synthesis

abstract: Prosodic modeling is a core problem in speech synthesis. The key challenge is producing desirable prosody from textual input containing only phonetic information. In this preliminary study, we introduce the concept of "style tokens" in Tacotron, a recently proposed end-to-end neural speech synthesis model. Using style tokens, we aim to extract independent prosodic styles from training data. We show that without annotation data or an explicit supervision signal, our approach can automatically learn a variety of prosodic variations in a purely data-driven way. Importantly, each style token corresponds to a fixed style factor regardless of the given text sequence. As a result, we can control the prosodic style of synthetic speech in a somewhat predictable and globally consistent way.


title: Social Influence by Requesting

abstract: Asking people to predict whether they will perform a target action often increases the probability of their performing that action. This article reviews published and unpublished research evidence for this “self-prophecy” phenomenon and reports 2 new experiments. The studies reviewed demonstrate that the self-prophecy effect occurs in a variety of situations and that it is a moderate-size effect. The new experiments introduce a 1 -session procedure that is considerably more efficient in testing theory than the 2-session procedure of previous experiments. In the prior studies, as in the present self-prophecy studies, participants appear to reduce a discrepancy between their principles and their behavior, made salient by prediction, through changing the behavior. Toward the ends of encouraging future investigation and developing theoretical understanding of the effect, the article concludes with discussion of related programs of research that may provide theoretical explanations for the effect.


title: Educational Leaders' Points of View toward the Role of School Curricula in Promoting Security Education in Jordan

abstract: [Abstract] This study aims at examining the educational leaders' points of view toward the role of school curricula in promoting security education in Jordan. The impact of gender, qualification, experience, job title, and participation in security education courses has been explored. An analytical descriptive model has been adopted with a questionnaire including 36 items and distributed to a sample of 71 educational leaders in Amman. Having conducted the proper statistical procedures, the study has found that the questionnaire's items have represented certain roles of school curricula in promoting security education from the subject educational leaders' perspective. There have been also significant differences among those leaders' estimates of the role of curricula due to gender in favor of males; job title (i.e., curricula member or school supervisor) in favor of curricula member; and participation in security education courses in favor of those having participated in more than three courses. However, no differences of statistical significance have been found among the educational leaders' estimates due to either qualification or experience. [Keywords] School curricula; security education; educational leaders; education in Jordan; gender roles in education. Introduction School is an institution which is concerned with serving the community by promoting security awareness and combating extremism. It is, according to many (e.g. Cox, 1999), a micro-community in which students are trained, whether overt or covert, to adapt throughout school curriculum itself and to adap to local work, accepting responsibility and enjoying discipline and uprightness, and that is what we mean herein by security education. Promoting security education at schools requires concerned departments letting students learn critical and creative dialogues. Students should see things more profoundly and take the truth from several perspectives, which enables them, in turn, to avoid any extremist thoughts of violence and corruption (Assali, 2007). The more ethical values a school curriculum provides the students with, the more secure and stable the given community will be (Saxe, 1992). Some countries, such as Finland and Australia, witnessed less crime rates, after they had included security-related materials into their school curricula (Taleb, 1997; Soufan, 2002). The United States could also overcome school violence and then minimize its emotional effects on students by means of the constant connection between schools and security offices in various security and social issues (Wahbani, 2005). Applying security education has several joint merits as this connects our educational programs with the community's needs and developing values of belongingness among our students (Berner & Dittus, 2001). The little interest paid to critical thinking and constructive dialogue and the inadequate concern with security education in schools are among the most central factors leading to extremism. Security education can promote factors, which assist the new generations to attain independent thought and enjoy critical thinking. Educational policies and programs, particularly school curriculum, should help enhance security awareness and combat extremism (Buwalon, 2005). Extremism is best combated today by the educational preventive trend, which particularly aims at building self-immunity and attempting to eliminate all factors causing unpleasant human behavior. That culture of security awareness should not be separate but be an integral part of school curricula (Qahtani, 1998; Dynneson, 1992). It is viewed, therefore, that both security education and national education have to be directly formulated as an integral part of all school curricula. Nationalism is not only a material to be taught, it also presents a desired conduct by all those concerned with schooling, which will make national loyalty, at the end, observed and lived. …


title: A Survey on Demand Response Programs in Smart Grids: Pricing Methods and Optimization Algorithms

abstract: The smart grid concept continues to evolve and various methods have been developed to enhance the energy efficiency of the electricity infrastructure. Demand Response (DR) is considered as the most cost-effective and reliable solution for the smoothing of the demand curve, when the system is under stress. DR refers to a procedure that is applied to motivate changes in the customers' power consumption habits, in response to incentives regarding the electricity prices. In this paper, we provide a comprehensive review of various DR schemes and programs, based on the motivations offered to the consumers to participate in the program. We classify the proposed DR schemes according to their control mechanism, to the motivations offered to reduce the power consumption and to the DR decision variable. We also present various optimization models for the optimal control of the DR strategies that have been proposed so far. These models are also categorized, based on the target of the optimization procedure. The key aspects that should be considered in the optimization problem are the system's constraints and the computational complexity of the applied optimization algorithm.


title: Predicting iPhone Sales from iPhone Tweets

abstract: Recent research in the field of computational social science have shown how data resulting from the widespread adoption and use of social media channels such as twitter can be used to predict outcomes such as movie revenues, election winners, localized moods, and epidemic outbreaks. Underlying assumptions for this research stream on predictive analytics are that social media actions such as tweeting, liking, commenting and rating are proxies for user/consumer's attention to a particular object/product and that the shared digital artefact that is persistent can create social influence. In this paper, we demonstrate how social media data from twitter can be used to predict the sales of iPhones. Based on a conceptual model of social data consisting of social graph (actors, actions, activities, and artefacts) and social text (topics, keywords, pronouns, and sentiments), we develop and evaluate a linear regression model that transforms iPhone tweets into a prediction of the quarterly iPhone sales with an average error close to the established prediction models from investment banks. This strong correlation between iPhone tweets and iPhone sales becomes marginally stronger after incorporating sentiments of tweets. We discuss the findings and conclude with implications for predictive analytics with big social data.


title: Research, part of a Special Feature on Ecological Restoration, Ecosystem Services, and Land Use Creating restoration landscapes: partnerships in large-scale conservation in the UK

abstract: It is increasingly recognized that ecological restoration demands conservation action beyond the borders of existing protected areas. This requires the coordination of land uses and management over a larger area, usually with a range of partners, which presents novel institutional challenges for conservation planners. Interviews were undertaken with managers of a purposive sample of large-scale conservation areas in the UK. Interviews were open-ended and analyzed using standard qualitative methods. Results show a wide variety of organizations are involved in large-scale conservation projects, and that partnerships take time to create and demand resilience in the face of different organizational practices, staff turnover, and short-term funding. Successful partnerships with local communities depend on the establishment of trust and the availability of external funds to support conservation land uses. We conclude that there is no single institutional model for large-scale conservation: success depends on finding institutional strategies that secure long-term conservation outcomes, and ensure that conservation gains are not reversed when funding runs out, private owners change priorities, or land changes hands.


title: Exploiting Surroundedness for Saliency Detection: A Boolean Map Approach

abstract: We demonstrate the usefulness of surroundedness for eye fixation prediction by proposing a Boolean Map based Saliency model (BMS). In our formulation, an image is characterized by a set of binary images, which are generated by randomly thresholding the image's feature maps in a whitened feature space. Based on a Gestalt principle of figure-ground segregation, BMS computes a saliency map by discovering surrounded regions via topological analysis of Boolean maps. Furthermore, we draw a connection between BMS and the Minimum Barrier Distance to provide insight into why and how BMS can properly captures the surroundedness cue via Boolean maps. The strength of BMS is verified by its simplicity, efficiency and superior performance compared with 10 state-of-the-art methods on seven eye tracking benchmark datasets.