title: How good are detection proposals, really?

abstract: Current top performing Pascal VOC object detectors employ detection proposals to guide the search for objects thereby avoiding exhaustive sliding window search across images. Despite the popularity of detection proposals, it is unclear which trade-offs are made when using them during object detection. We provide an in depth analysis of ten object proposal methods along with four baselines regarding ground truth annotation recall (on Pascal VOC 2007 and ImageNet 2013), repeatability, and impact on DPM detector performance. Our findings show common weaknesses of existing methods, and provide insights to choose the most adequate method for different settings.


title: Information cartography: creating zoomable, large-scale maps of information

abstract: In an era of information overload, many people struggle to make sense of complex stories, such as presidential elections or economic reforms. We propose a methodology for creating structured summaries of information, which we call zoomable metro maps. Just as cartographic maps have been relied upon for centuries to help us understand our surroundings, metro maps can help us understand the information landscape. Given large collection of news documents our proposed algorithm generates a map of connections that explicitly captures story development. As different users might be interested in different levels of granularity, the maps are zoomable, with each level of zoom showing finer details and interactions. In this paper, we formalize characteristics of good zoomable maps and formulate their construction as an optimization problem. We provide efficient, scalable methods with theoretical guarantees for generating maps. Pilot user studies over real-world datasets demonstrate that our method helps users comprehend complex stories better than prior work.


title: COALA - System for Visual Representation of Cryptography Algorithms

abstract: Educational software systems have an increasingly significant presence in engineering sciences. They aim to improve students’ attitudes and knowledge acquisition typically through visual representation and simulation of complex algorithms and mechanisms or hardware systems that are often not available to the educational institutions. This paper presents a novel software system for CryptOgraphic ALgorithm visuAl representation (COALA), which was developed to support a Data Security course at the School of Electrical Engineering, University of Belgrade. The system allows users to follow the execution of several complex algorithms (DES, AES, RSA, and DiffieHellman) on real world examples in a step by step detailed view with the possibility of forward and backward navigation. Benefits of the COALA system for students are observed through the increase of the percentage of students who passed the exam and the average grade on the exams during one school year.


title: Efficient Learning of Domain-invariant Image Representations

abstract: Abstract: We present an algorithm that learns representations which explicitly compensate for domain mismatch and which can be efficiently realized as linear classifiers. Specifically, we form a linear transformation that maps features from the target (test) domain to the source (training) domain as part of training the classifier. We optimize both the transformation and classifier parameters jointly, and introduce an efficient cost function based on misclassification loss. Our method combines several features previously unavailable in a single algorithm: multi-class adaptation through representation learning, ability to map across heterogeneous feature spaces, and scalability to large datasets. We present experiments on several image datasets that demonstrate improved accuracy and computational advantages compared to previous approaches.


title: New methods to color the vertices of a graph

abstract: This paper describes efficient new heuristic methods to color the vertices of a graph which rely upon the comparison of the degrees and structure of a graph. A method is developed which is exact for bipartite graphs and is an important part of heuristic procedures to find maximal cliques in general graphs. Finally an exact method is given which performs better than the Randall-Brown algorithm and is able to color larger graphs, and the new heuristic methods, the classical methods, and the exact method are compared.


title: Towards Comprehensive User Modeling on the Social Web for Personalized Link Recommendations

abstract: User modeling for individual users on the Social Web plays a significant role and is a fundamental step for personalization as well as recommendations. Previous studies have proposed various user modeling strategies in different dimensions such as (1) interest representation, (2) interest propagation, (3) content enrichment and (4) temporal dynamics of user interests. This research mainly focuses on the first two dimensions interest representation and propagation. In addition, we also investigate the combination of these four dimensions and their synergistic effect on the quality of user modeling. Different user modeling strategies will then be evaluated in the context of personalized link recommender systems using standard evaluation methodologies such as Mean Reciprocal Rank (MRR), recall (R@N) and success (S@N) at rank N.


title: Sample Selection for Statistical Parsing

abstract: Corpus-based statistical parsing relies on using large quantities of annotated text as training examples. Building this kind of resource is expensive and labor-intensive. This work proposes to use sample selection to find helpful training examples and reduce human effort spent on annotating less informative ones. We consider several criteria for predicting whether unlabeled data might be a helpful training example. Experiments are performed across two syntactic learning tasks and within the single task of parsing across two learning models to compare the effect of different predictive criteria. We find that sample selection can significantly reduce the size of annotated training corpora and that uncertainty is a robust predictive criterion that can be easily applied to different learning models.


abstract: Government agencies such as Badan Pengkajian Kebijakan Iklim dan Mutu Industri (BPKIMI) should have a guideline framework for developing Information System and Technologies and also sufficient documentation about existing system and technologies. Therefore, comprehensive and integrated information system strategic planning was needed by government agencies such as BPKIMI. This paper will explain the BPKIMI's information system strategic planning toward Excellent Public Services using Zachman Framework.


title: Development of the SP machine

abstract: This paper describes the main things that need to be done to develop the "SP machine", based on the "SP theory of intelligence" and its realisation in the "SP computer model". The SP machine may be developed initially as a software virtual machine with high levels of parallel processing, hosted on a high-performance computer, or driven by high-parallel search processes in any of the leading search engines. An easy-to-use user interface is needed with facilities for visualisation of knowledge structures and processing. The system needs to be generalised to work with patterns in two dimensions, including the display of multiple alignments with 2D patterns. Research is needed into how the system may discover low-level features in speech and visual images. Existing strengths of the SP system in the processing of natural language may be developed towards the understanding of natural language and its production from meanings. This may be done most effectively in conjunction with the development of existing strengths of the SP system in unsupervised learning. Existing strengths of the SP system in pattern recognition may be developed for computer vision. Further work is needed on the representation of numbers and the performance of arithmetic processes in the SP system. A computer model is needed of SP-neural drawing on the existing conceptual model. When the SP machine is relatively mature, new hardware may be developed to exploit opportunities to increase the efficiency of computations. There is potential for the SP machine to be applied on relatively short timescales in such areas as information storage and retrieval, with intelligence, software engineering with or without automation or semi-automation, and information compression.


title: The impact of corporate identity on corporate social responsibility disclosure

abstract: Corporate social responsibility (CSR) is of increasing importance for the long-term success of corporations. Extending existing literature this paper explores corporate identity as important determinant for CSR disclosure. The relationship was examined based on 498 German companies that provided English language CSR reports and responded to a company survey measuring CSR-oriented corporate identity. CSR disclosure has been analyzed with an automated content analysis technique using artificial intelligence. Results indicate that value chain and future-oriented dimensions, which were more pronounced in mature CSR concepts, foster CSR disclosure, while introversive corporate identity dimensions that were strong in low level CSR concepts hinder the release of CSR information. The paper shows that a tradition of social responsibility and values results into a low perceived need for legitimacy and outwards communication. The findings support the view that that a combination of voluntary disclosure theory and legitimacy theory is necessary to explain the drivers and constraints of CSR disclosure.


title: Ontology-based news recommendation

abstract: Recommending news items is traditionally done by term-based algorithms like TF-IDF. This paper concentrates on the benefits of recommending news items using a domain ontology instead of using a term-based approach. For this purpose, we propose Athena, which is an extension to the existing Hermes framework. Athena employs a user profile to store terms or concepts found in news items browsed by the user. Based on this information, the framework uses a traditional method based on TF-IDF, and several ontology-based methods to recommend new articles to the user. The paper concludes with the evaluation of the different methods, which shows that the new ontology-based method that we propose in this paper performs better (w.r.t. accuracy, precision, and recall) than the traditional method and, with the exception of one measure (recall), also better than the other considered ontology-based approaches.


title: Temporal Model Adaptation for Person Re-Identification

abstract: Person re-identification is an open and challenging problem in computer vision. Majority of the efforts have been spent either to design the best feature representation or to learn the optimal matching metric. Most approaches have neglected the problem of adapting the selected features or the learned model over time. To address such a problem, we propose a temporal model adaptation scheme with human in the loop. We first introduce a similarity-dissimilarity learning method which can be trained in an incremental fashion by means of a stochastic alternating directions methods of multipliers optimization procedure. Then, to achieve temporal adaptation with limited human effort, we exploit a graph-based approach to present the user only the most informative probe-gallery matches that should be used to update the model. Results on three datasets have shown that our approach performs on par or even better than state-of-the-art approaches while reducing the manual pairwise labeling effort by about \(80\,\%\).


Queuing Algorithem Based Quality of Service ( Qos ) For Scheduling Environment Model in Wimax Network with Opnet Modeler


title: High voltage Bi-directional flyback converter for capacitive actuator

abstract: This paper presents a high voltage DC-DC converter topology for bi-directional energy transfer between a low voltage DC source and a high voltage capacitive load. The topology is a bi-directional flyback converter with variable switching frequency control during the charge mode, and constant switching frequency control during the discharge mode. The converter is capable of charging the capacitive load from 24 V DC source to 2.5 kV, and discharges it to 0 V. The flyback converter has been analyzed in detail during both charge and discharge modes, by considering all the parasitic elements in the converter, including the most dominating parameters of the high voltage transformer viz., self-capacitance and leakage inductance. The specific capacitive load for this converter is a dielectric electro active polymer (DEAP) actuator, which can be used as an effective replacement for conventional actuators in a number of applications. In this paper, the discharging energy efficiency definition is introduced. The proposed converter has been experimentally tested with the film capacitive load and the DEAP actuator, and the experimental results are shown together with the efficiency measurements.


title: A Broadband U-Slot Coupled Microstrip-to-Waveguide Transition

abstract: A novel planar broadband microstrip-to-waveguide transition is proposed in this paper. The referred waveguide can be either rectangular waveguide or ridged waveguide. The transition consists of an open-circuited microstrip quarter-wavelength resonator and a resonant U-shaped slot on the upper broadside wall of a short-circuited waveguide. A physics-based equivalent-circuit model is also developed for interpreting the working mechanism and providing a coarse model for engineering design. The broadband transition can be regarded as a stacked two-pole resonator filter. Each coupling circuit can be approximately designed separately using the group-delay information at the center frequency. In addition to its broadband attribute, the transition is compact in size, vialess, and is highly compatible with planar circuits. These good features make the new transition very attractive for the system architecture where waveguide devices need to be surface mounted on a multilayered planer circuit. Two design examples are given to demonstrate the usefulness of the transition: one is a broadband ridged-waveguide bandpass filter and the other is a surface-mountable broadband low-temperature co-fired ceramic laminated waveguide cavity filter. Both filters are with the proposed transition for interfacing with microstrip lines, showing promising potentials in practical applications.


title: Design and Implementation of Bézier Curve Trajectory Planning in DELTA Parallel Robots

abstract: This paper focuses on proposing a real-time path-smoothing method for DELTA parallel robots, which utilizes the 5-th Bezier curve to blend the adjacent linear segments. By using the proposed method, a smoothing trajectory with the continuities of second-order derivative and curvature is generated. Meanwhile, an S-shape acceleration/deceleration (ACC/DEC) process and a predictor-corrector interpolator are employed for reducing the mechanism vibration and velocity fluctuation. Therefore, smoothing transition between vertical and horizontal segments of the operation path can be guaranteed. Experiments are carried out on the self-designed DELTA parallel robot and the results verify the feasibility of the proposed real-time path-smoothing method.


title: Using OS Design Patterns to Provide Reliability and Security as-a-Service for VM-based Clouds

abstract: This paper extends the concepts behind cloud services to offer hypervisor-based reliability and security monitors for cloud virtual machines. Cloud VMs can be heterogeneous and as such guest OS parameters needed for monitoring can vary across different VMs and must be obtained in some way. Past work involves running code inside the VM, which is unacceptable for a cloud environment. We solve this problem by recognizing that there are common OS design patterns that can be used to infer monitoring parameters from the guest OS. We extract information about the cloud user's guest OS with the user's existing VM image and knowledge of OS design patterns as the only inputs to analysis. To demonstrate the range of monitoring functionality possible with this technique, we implemented four sample monitors: a guest OS process tracer, an OS hang detector, a return-to-user attack detector, and a process-based keylogger detector.


title: A Survey on Routing Protocols for Large-Scale Wireless Sensor Networks

abstract: With the advances in micro-electronics, wireless sensor devices have been made much smaller and more integrated, and large-scale wireless sensor networks (WSNs) based the cooperation among the significant amount of nodes have become a hot topic. “Large-scale” means mainly large area or high density of a network. Accordingly the routing protocols must scale well to the network scope extension and node density increases. A sensor node is normally energy-limited and cannot be recharged, and thus its energy consumption has a quite significant effect on the scalability of the protocol. To the best of our knowledge, currently the mainstream methods to solve the energy problem in large-scale WSNs are the hierarchical routing protocols. In a hierarchical routing protocol, all the nodes are divided into several groups with different assignment levels. The nodes within the high level are responsible for data aggregation and management work, and the low level nodes for sensing their surroundings and collecting information. The hierarchical routing protocols are proved to be more energy-efficient than flat ones in which all the nodes play the same role, especially in terms of the data aggregation and the flooding of the control packets. With focus on the hierarchical structure, in this paper we provide an insight into routing protocols designed specifically for large-scale WSNs. According to the different objectives, the protocols are generally classified based on different criteria such as control overhead reduction, energy consumption mitigation and energy balance. In order to gain a comprehensive understanding of each protocol, we highlight their innovative ideas, describe the underlying principles in detail and analyze their advantages and disadvantages. Moreover a comparison of each routing protocol is conducted to demonstrate the differences between the protocols in terms of message complexity, memory requirements, localization, data aggregation, clustering manner and other metrics. Finally some open issues in routing protocol design in large-scale wireless sensor networks and conclusions are proposed.


title: Sponsored Search Auctions: Recent Advances and Future Directions

abstract: Sponsored search has been proven to be a successful business model, and sponsored search auctions have become a hot research direction. There have been many exciting advances in this field, especially in recent years, while at the same time, there are also many open problems waiting for us to resolve. In this article, we provide a comprehensive review of sponsored search auctions in hopes of helping both industry practitioners and academic researchers to become familiar with this field, to know the state of the art, and to identify future research topics. Specifically, we organize the article into two parts. In the first part, we review research works on sponsored search auctions with basic settings, where fully rational advertisers without budget constraints, preknown click-through rates (CTRs) without interdependence, and exact match between queries and keywords are assumed. Under these assumptions, we first introduce the generalized second price (GSP) auction, which is the most popularly used auction mechanism in the industry. Then we give the definitions of several well-studied equilibria and review the latest results on GSP’s efficiency and revenue in these equilibria. In the second part, we introduce some advanced topics on sponsored search auctions. In these advanced topics, one or more assumptions made in the basic settings are relaxed. For example, the CTR of an ad could be unknown and dependent on other ads; keywords could be broadly matched to queries before auctions are executed; and advertisers are not necessarily fully rational, could have budget constraints, and may prefer rich bidding languages. Given that the research on these advanced topics is still immature, in each section of the second part, we provide our opinions on how to make further advances, in addition to describing what has been done by researchers in the corresponding direction.


title: Citation content analysis (cca): A framework for syntactic and semantic analysis of citation content

abstract: This study proposes a new framework for citation content analysis (CCA), for syntactic and semantic analysis of citation content that can be used to better analyze the rich sociocultural context of research behavior. This framework could be considered the next generation of citation analysis. The authors briefly review the history and features of content analysis in traditional social sciences and its previous application in library and information science (LIS). Based on critical discussion of the theoretical necessity of a new method as well as the limits of citation analysis, the nature and purposes of CCA are discussed, and potential procedures to conduct CCA, including principles to identify the reference scope, a two-dimensional (citing and cited) and two-module (syntactic and semantic) codebook, are provided and described. Future work and implications are also suggested.


title: Wide-baseline stereo matching with line segments

abstract: We present a new method for matching line segments between two uncalibrated wide-baseline images. Most current techniques for wide-baseline matching are based on viewpoint invariant regions. Those methods work well with highly textured scenes, but fail with poorly textured ones. We show that such scenes can be successfully matched using line segments. Moreover, since line segments and regions provide complementary information, their combined matching allows to deal with a broader range of scenes. We generate an initial set of line segment correspondences, and then iteratively increase their number by adding matches consistent with the topological structure of the current ones. Finally, a coplanar grouping stage allows to estimate the fundamental matrix even from line segments only.


title: Using Stochastic Spiking Neural Networks on SpiNNaker to Solve Constraint Satisfaction Problems

abstract: Constraint satisfaction problems (CSP) are at the core of numerous scientific and technological applications. However, CSPs belong to the NP-complete complexity class, for which the existence (or not) of efficient algorithms remains a major unsolved question in computational complexity theory. In the face of this fundamental difficulty heuristics and approximation methods are used to approach instances of NP (e.g. decision and hard optimisation problems). The human brain efficiently handles CSPs both in perception and behaviour using spiking neural networks (SNNs), and recent studies have demonstrated that the noise embedded within an SNN can be used as a computational resource to solve CSPs. Here, we provide a software framework for the implementation of such noisy neural solvers on the SpiNNaker massively parallel neuromorphic hardware, further demonstrating their potential to implement a stochastic search that solves instances of P and NP problems expressed as CSPs. This facilitates the exploration of new optimization strategies and the understanding of the computational abilities of SNNs. We demonstrate the basic principles of the framework by solving difficult instances of the Sudoku puzzle and of the map colour problem, and explore its application to spin glasses. The solver works as a stochastic dynamical system, which is attracted by the configuration that solves the CSP. The noise allows an optimal exploration of the space of configurations, looking for the satisfiability of all the constraints; if applied discontinuously, it can also force the system to leap to a new random configuration effectively causing a restart.


title: A framework for inverse tone mapping

abstract: In recent years many tone mapping operators (TMOs) have been presented in order to display high dynamic range images (HDRI) on typical display devices. TMOs compress the luminance range while trying to maintain contrast. The inverse of tone mapping, inverse tone mapping, expands a low dynamic range image (LDRI) into an HDRI. HDRIs contain a broader range of physical values that can be perceived by the human visual system. We propose a new framework that approximates a solution to this problem. Our framework uses importance sampling of light sources to find the areas considered to be of high luminance and subsequently applies density estimation to generate an expand map in order to extend the range in the high luminance areas using an inverse tone mapping operator. The majority of today’s media is stored in the low dynamic range. Inverse tone mapping operators (iTMOs) could thus potentially revive all of this content for use in high dynamic range display and image based lighting (IBL). Moreover, we show another application that benefits quick capture of HDRIs for use in IBL.


title: Stability of Causal Inference

abstract: We consider the sensitivity of causal identification to small perturbations in the input. A long line of work culminating in papers by Shpitser and Pearl (2006) and Huang and Valtorta (2008) led to a complete procedure for the causal identification problem. In our main result in this paper, we show that the identification function computed by these procedures is in some cases extremely unstable numerically. Specifically, the "condition number" of causal identification can be of the order of Ω(exp(n0.49)) on an identifiable semi-Markovian model with n visible nodes. That is, in order to give an output accurate to d bits, the empirical probabilities of the observable events need to be obtained to accuracy d + Ω(n0.49) bits.


title: A compact millimeter-wave broadband stacked coupled balun

abstract: This paper presents a compact 27.6–65.5GHz broadband Marchand balun in a 65-nm 1P6M CMOS technology. It uses stacked coupled structure for millimeter-wave application. The stacked coupled structure uses different layers coupling signals, instead of a planar layer, to reduce Machand balun size The balun bandwidth is 81.2%, the amplitude imbalance is less than 0.8 dB, and the phase imbalance is less than 8°. The balun occupies a 0.12mm 2 die area.


title: Averaged Gabor Filter Features for Facial Expression Recognition

abstract: An efficient automatic facial expression recognition method is proposed. The method uses a set of characteristic features obtained by averaging the outputs from the Gabor filter bank with 5 frequencies and 8 different orientations, and then further reducing the dimensionality by the means of principal component analysis. The performance of the proposed system was compared with the full Gabor filter bank method. The classification tasks were performed using the K-Nearest neighbor (K-NN) classifier. The training and testing images were selected from the publicly available JAFFE database. The classification results show that the average Gabor filter (AGF) provides very high computational efficiency at the cost of a relatively small decrease in performance when compared to the full Gabor filter features.


title: Point group associations for radar-based vehicle self-localization

abstract: Point-shaped objects are a common landmark type for environment-based vehicle self-localization. In this paper, different ways to describe point-shaped landmarks for recognition are investigated: description by absolute position, by features or by constellation in a rigid group with other point-shaped landmarks. Points described by a point group can be associated independently of the prior vehicle pose uncertainty. An efficient algorithm for association of point groups is introduced. Evaluations on real-traffic measurement data from our radar application show the high gain in reliability for associations by point groups and therefore its value for vehicle self-localization tasks.


title: ‘Strong is the new skinny’: A content analysis of #fitspiration images on Instagram

abstract: ‘Fitspiration’ is an online trend designed to inspire viewers towards a healthier lifestyle by promoting exercise and healthy food. This study provides a content analysis of fitspiration imagery on the social networking site Instagram. A set of 600 images were coded for body type, activity, objectification and textual elements. Results showed that the majority of images of women contained only one body type: thin and toned. In addition, most images contained objectifying elements. Accordingly, while fitspiration images may be inspirational for viewers, they also contain a number of elements likely to have negative effects on the viewer’s body image.


title: A Wideband Circularly Polarized Omnidirectional Antenna Based on Excitation of Two Orthogonal Circular TE21 Modes

abstract: A novel wideband circularly polarized (CP) omnidirectional antenna operating at L-band is presented. The antenna operates based on excitation of a field distribution similar to circular waveguide TE21 mode over a wide bandwidth. It is shown that the circular TE21 mode generates a linearly polarized far-field electric field over the horizontal plane with its polarization tilt angle varying as a function of azimuth angle. It is also shown that the tilt angle varies by 90° for an azimuthal angle increase of 45°. To obtain circular polarization, two circular TE21 modes are excited such that one mode is spatially rotated by 45° with respect to the other mode. The phase difference between two modes is 90°. The modes are excited by an array of 16 monopole sectorial loop antennas to achieve a wide axial-ratio (AR) bandwidth. A right-handed CP antenna based on the proposed structure is fabricated and tested against simulation results. A feed network including Wilkinson power dividers and low-pass/high-pass phase shifters is also designed and fabricated to feed the antenna elements. The measurement results show 58% of 3-dB AR and 10-dB impedance bandwidth with the center frequency at 1.565 GHz. The total height of the antenna is 47 mm.


title: INTERSPEECH 2011 Language Recognition via Ivectors and Dimensionality Reduction

abstract: In this paper, a new language identification system is presented based on the total variability approach previously developed in the field of speaker identification. Various techniques are employed to extract the most salient features in the lower dimensional i-vector space and the system developed results in excellent performance on the 2009 LRE evaluation set without the need for any post-processing or backend techniques. Additional performance gains are observed when the system is combined with other acoustic systems.


title: Automatic web page segmentation and information extraction using conditional random fields

abstract: With the rapid development of Internet, Web pages have been more and more complex. Useful information is mixed with a lot of redundant information. In the current Web information extraction systems, manual or semi-manual methods are the majority. To improve the efficiency of information extraction, it requires us to further research the automatic method of Web information extraction. Firstly, we analyze the Web page's basic object according to the Functional-based Object Model. Then we give an automatic method to segment the Web page into semantic blocks using conditional random fields (CRFs). In order to further improve the effect of the semantic block segmentation, combining DOM structure and tree edit distance, the optimization algorithm of the semantic block is given. Finally, we give an automatic Web information extraction tool. Based on this tool, relevant experiments are carried out to evaluate the efficiency of information extraction. Compared to DOM-based Web information extraction systems, the experimental results show the increase in accuracy and recall rate.


title: Genetic Algorithm for Multi-Objective Optimization of Container Allocation in Cloud Architecture

abstract: The use of containers in cloud architectures has become widespread, owing to advantages such as limited overheads, easier and faster deployment, and higher portability. Moreover, they present a suitable architectural solution for the deployment of applications created using a microservice development pattern. Despite the large number of solutions and implementations, there remain open issues that have not been completely addressed in container automation and management. Container resource allocation influences system performance and resource consumption, and so it is a key factor for cloud providers. We propose a genetic algorithm approach, using the Non-dominated Sorting Genetic Algorithm-II (NSGA-II), to optimize container allocation and elasticity management, motivated by the good results obtained with this algorithm in other resource management optimization problems in cloud architectures. Our optimization algorithm enhances system provisioning, system performance, system failure, and network overhead. A model for cloud clusters, containers, microservices, and four optimization objectives is presented. Experimental results demonstrate that our approach is a suitable solution for addressing the problem of container allocation and elasticity, and it obtains better objective values than the container management policies implemented in Kubernetes.


title: Resilience Thinking: Integrating Resilience, Adaptability and Transformability

abstract: Resilience thinking addresses the dynamics and development of complex social-ecological systems (SES). Three aspects are central: resilience, adaptability and transformability. These aspects interrelate across multiple scales. Resilience in this context is the capacity of a SES to continually change and adapt yet remain within critical thresholds. Adaptability is part of resilience. It represents the capacity to adjust responses to changing external drivers and internal processes and thereby allow for development along the current trajectory (stability domain). Transformability is the capacity to cross thresholds into new development trajectories. Transformational change at smaller scales enables resilience at larger scales. The capacity to transform at smaller scales draws on resilience from multiple scales, making use of crises as windows of opportunity for novelty and innovation, and recombining sources of experience and knowledge to navigate social-ecological transitions. Society must seriously consider ways to foster resilience of smaller more manageable SESs that contribute to Earth System resilience and to explore options for deliberate transformation of SESs that threaten Earth System resilience.


title: Social decision-making: Insights from game theory and neuroscience

abstract: By combining the models and tasks of Game Theory with modern psychological and neuroscientific methods, the neuroeconomic approach to the study of social decision-making has the potential to extend our knowledge of brain mechanisms involved in social decisions and to advance theoretical models of how we make decisions in a rich, interactive environment. Research has already begun to illustrate how social exchange can act directly on the brain's reward system, how affective factors play an important role in bargaining and competitive games, and how the ability to assess another's intentions is related to strategic play. These findings provide a fruitful starting point for improved models of social decision-making, informed by the formal mathematical approach of economics and constrained by known neural mechanisms.


title: Gas turbine performance prognostic for condition-based maintenance

abstract: Gas turbine engines experience degradations over time that cause great concern to gas turbine users on engine reliability, availability and operating costs. Gas turbine diagnostics and prognostics is one of the key technologies to enable the move from time-scheduled maintenance to condition-based maintenance in order to improve engine reliability and availability and reduce life cycle costs. This paper describes a prognostic approach to estimate the remaining useful life of gas turbine engines before their next major overhaul based on historical health information. A combined regression techniques, including both linear and quadratic models, is proposed to predict the remaining useful life of gas turbine engines. A statistic "compatibility check" is used to determine the transition point from a linear regression to a quadratic regression. The developed prognostic approach has been applied to a model gas turbine engine similar to Rolls-Royce industrial gas turbine AVON 1535 implemented with compressor degradation over time. The analysis shows that the developed prognostic approach has a great potential to provide an estimation of engine remaining useful life before next major overhaul for gas turbine engines experiencing a typical soft degradation.


title: A High-Quality Video Denoising Algorithm based on Reliable Motion Estimation

abstract: Although the recent advances in the sparse representations of images have achieved outstanding denosing results, removing real, structured noise in digital videos remains a challenging problem. We show the utility of reliable motion estimation to establish temporal correspondence across frames in order to achieve high-quality video denoising. In this paper, we propose an adaptive video denosing framework that integrates robust optical flow into a nonlocal means (NLM) framework with noise level estimation. The spatial regularization in optical flow is the key to ensure temporal coherence in removing structured noise. Furthermore, we introduce approximate K-nearest neighbor matching to significantly reduce the complexity of classical NLM methods. Experimental results show that our system is comparable with the state of the art in removing AWGN, and significantly outperforms the state of the art in removing real, structured noise.


title: Performance comparison between total variation (TV)-based compressed sensing and statistical iterative reconstruction algorithms

abstract: Of all available reconstruction methods, statistical iterative reconstruction algorithms appear particularly promising since they enable accurate physical noise modeling. The newly developed compressive sampling/compressed sensing (CS) algorithm has shown the potential to accurately reconstruct images from highly undersampled data. The CS algorithm can be implemented in the statistical reconstruction framework as well. In this study, we compared the performance of two standard statistical reconstruction algorithms (penalized weighted least squares and q-GGMRF) to the CS algorithm. In assessing the image quality using these iterative reconstructions, it is critical to utilize realistic background anatomy as the reconstruction results are object dependent. A cadaver head was scanned on a Varian Trilogy system at different dose levels. Several figures of merit including the relative root mean square error and a quality factor which accounts for the noise performance and the spatial resolution were introduced to objectively evaluate reconstruction performance. A comparison is presented between the three algorithms for a constant undersampling factor comparing different algorithms at several dose levels. To facilitate this comparison, the original CS method was formulated in the framework of the statistical image reconstruction algorithms. Important conclusions of the measurements from our studies are that (1) for realistic neuro-anatomy, over 100 projections are required to avoid streak artifacts in the reconstructed images even with CS reconstruction, (2) regardless of the algorithm employed, it is beneficial to distribute the total dose to more views as long as each view remains quantum noise limited and (3) the total variation-based CS method is not appropriate for very low dose levels because while it can mitigate streaking artifacts, the images exhibit patchy behavior, which is potentially harmful for medical diagnosis.


title: Folded Marchand Balun Design and Validation Using Sonnet

abstract: The Marchand balun has larger bandwidth than other types of combiners and has been used extensively since its introduction. The Marchand balun has also been extensively used in planar systems in both broadsidecoupled and edgedcoupled configu rations. This paper describes the design and development of two microstrip, folded, edgecoupled Marchand baluns for use as antiphase power combiner. The balun formfactor is thus reduced, m aking the circuit more compact and amenable for use in highdensity printed circuit boards.


title: Short Text Classification Improved by Learning Multi-Granularity Topics

abstract: Understanding the rapidly growing short text is very important. Short text is different from traditional documents in its shortness and sparsity, which hinders the application of conventional machine learning and text mining algorithms. Two major approaches have been exploited to enrich the representation of short text. One is to fetch contextual information of a short text to directly add more text; the other is to derive latent topics from existing large corpus, which are used as features to enrich the representation of short text. The latter approach is elegant and efficient in most cases. The major trend along this direction is to derive latent topics of certain granularity through well-known topic models such as latent Dirichlet allocation (LDA). However, topics of certain granularity are usually not sufficient to set up effective feature spaces. In this paper, we move forward along this direction by proposing an method to leverage topics at multiple granularity, which can model the short text more precisely. Taking short text classification as an example, we compared our proposed method with the state-of-the-art baseline over one open data set. Our method reduced the classification error by 20.25% and 16.68% respectively on two classifiers.


title: Predicting Task Execution Time on Natural User Interfaces based on Touchless Hand Gestures

abstract: Model-based evaluation has been widely used in HCI. However, current predictive models are insufficient to evaluate Natural User Interfaces based on touchless hand gestures. The purpose of this paper is to present a model based on KLM to predict performance time for doing tasks using this interface type. The required model operators were defined considering the temporal structure of hand gestures (i.e. using gesture units) and performing a systematic bibliographic review. The times for these operators were estimated by a user study consisting of various parts. Finally, the model empirical evaluation gave acceptable results (root-mean-square error = 10%, R2 = 0.936) when compared to similar models developed for other interaction styles. Thus, the proposed model should be helpful to software designers to carry out usability assessments by predicting performance time without user participation.


title: Clone Smells in Software Evolution

abstract: Although source code cloning (copy&paste programming) represents a significant threat to the maintainability of a software system, problems usually start to arise only when the system evolves. Most of the related research papers tackle the question of finding code clones in one particular version of the software only, leaving the dynamic behavior of the clones out of consideration. Eliminating these clones in large software systems often seems absolutely hopeless, as there might exist several thousands of them. Alternatively, tracking the evolution of individual clones can be used to identify those occurrences that could really cause problems in the future versions. In this paper we present an approach for mapping clones from one particular version of the software to another one, based on a similarity measure. This mapping is used to define conditions under which clones become suspicious (or "smelly") compared to their other occurrences. Accordingly, these conditions introduce the notion of dynamic clone smells. The usefulness of these smells is validated on the Mozilla Firefox internet browser, where the approach was able to find specific bugs that resulted from neglecting earlier copy&paste activities.


title: Logistics as Care and Control: An Investigation into the UNICEF Supply Division

abstract: This paper investigates emerging practices and infrastructures in global humanitarian relief to argue for logistics as an essential but often neglected component of ICTD and broader HCI work. Logistics -- the artful coordination of human and material flows -- leverages scholarship on "coordination," "articulation" and "infrastructure" to provide insight into the complex role of new IT systems (and HCI as a field) in the global circulation of goods and relations. Drawing on fieldwork with the UNICEF Supply Division, we argue that contemporary logistics operates simultaneously as a form of care and control. We demonstrate that logisticians at Supply traverse messy and dynamic information and material infrastructures, and that effective logistical work must marry and bridge these worlds. Our work extends ICTD and postcolonial computing research by casting light on the nature, experience and ambivalence of the global flows that enable and support HCI work in development and postcolonial settings.


Models for Digital Storytelling and Interactive Narratives Léonie Schäfer


title: General Framework for Evaluating Password Complexity and Strength

abstract: Although it is common for users to select bad passwords that can be easily cracked by attackers, password-based authentication remains the most widely-used method. To encourage users to select good passwords, enterprises often enforce policies. Such policies have been proven to be ineffectual in practice. Accurate assessment of a password's resistance to cracking attacks is still an unsolved problem, and our work addresses this challenge. Although the best way to determine how difficult it may be to crack a user-selected password is to check its resistance to cracking attacks employed by attackers in the wild, implementing such a strategy at an enterprise would be infeasible in practice. We first formalize the concepts of password complexity and strength with concrete definitions emphasizing their differences. Our framework captures human biases and many known techniques attackers use to recover stolen credentials in real life, such as brute-force attacks. Building on our definitions, we develop a general framework for calculating password complexity and strength that could be used in practice. Our approach is based on the key insight that an attacker's success at cracking a password must be defined by its available computational resources, time, function used to store that password, as well as the topology that bounds that attacker's search space based on that attacker's available inputs, transformations it can use to tweak and explore its inputs, and the path of exploration which can be based on the attacker's perceived probability of success. We also provide a general framework for assessing the accuracy of password complexity and strength estimators that can be used to compare other tools available in the wild.


title: Management of probabilistic data: foundations and challenges

abstract: Many applications today need to manage large data sets with uncertainties. In this paper we describe the foundations of managing data where the uncertainties are quantified as probabilities. We review the basic definitions of the probabilistic data model, present some fundamental theoretical result for query evaluation on probabilistic databases, and discuss several challenges, open problems, and research directions.


title: Efficient parallel partition-based algorithms for similarity search and join with edit distance constraints

abstract: The quantity of data in real-world applications is growing significantly while the data quality is still a big problem. Similarity search and similarity join are two important operations to address the poor data quality problem. Although many similarity search and join algorithms have been proposed, they did not utilize the abilities of modern hardware with multi-core processors. It calls for new parallel algorithms to enable multi-core processors to meet the high performance requirement of similarity search and join on big data. To this end, in this paper we propose parallel algorithms to support efficient similarity search and join with edit-distance constraints. We adopt the partition-based framework and extend it to support parallel similarity search and join on multi-core processors. We also develop two novel pruning techniques. We have implemented our algorithms and the experimental results on two real datasets show that our parallel algorithms achieve high performance and obtain good speedup.


title: Improving top-N recommendation with heterogeneous loss

abstract: Personalized top-N recommendation systems have great impact on many real world applications such as E-commerce platforms and social networks. Most existing methods produce personalized top-N recommendations by minimizing a specific uniform loss such as pairwise ranking loss or pointwise recovery loss. In this paper, we propose a novel personalized top-N recommendation approach that minimizes a combined heterogeneous loss based on linear self-recovery models. The heterogeneous loss integrates the strengths of both pairwise ranking loss and pointwise recovery loss to provide more informative recommendation predictions. We formulate the learning problem with heterogeneous loss as a constrained convex minimization problem and develop a projected stochastic gradient descent optimization algorithm to solve it. We evaluate the proposed approach on a set of personalized top-N recommendation tasks. The experimental results show the proposed approach outperforms a number of state-of-the-art methods on top-N recommendation.


title: Automated Classification of Free-text Pathology Reports for Registration of Incident Cases of Cancer

abstract: Objective: Our study aimed to construct and evaluate functions called “classifiers”, produced by supervised machine learning techniques, in order to categorize automatically pathology reports using solely their content. Methods: Patients from the Poitou-Charentes Cancer Registry having at least one pathology report and a single non-metastatic invasive neoplasm were included. A descriptor weighting function accounting for the distribution of terms among targeted classes was developed and compared to classic methods based on inverse document frequencies. The classification was performed with support vector machine (SVM) and Naive Bayes classifiers. Two levels of granularity were tested for both the topographical and the morphological axes of the ICD-O3 code. The ability to correctly attribute a precise ICD-O3 code and the ability to attribute the broad category defined by the International Agency for Research on Cancer (IARC) for the multiple primary cancer registration rules were evaluated using F1-measures. Results: 5121 pathology reports produced by 35 pathologists were selected. The best performance was achieved by our class-weighted descriptor, associated with a SVM classifier. Using this method, the pathology reports were properly classified in the IARC categories with F1-measures of 0.967 for both topography and morphology. The ICD-O3 code attribution had lower performance with a 0.715 F1-measure for topography and 0.854 for morphology. Conclusion: These results suggest that free-text pathology reports could be useful as a data source for automated systems in order to identify and notify new cases of cancer. Future work is needed to evaluate the improvement in performance obtained from the use of natural language processing, including the case of multiple tumor description and possible incorporation of other medical documents such as surgical reports.


title: Multi-view Unit Intact Space Learning

abstract: Multi-view learning is a hot research topic in different research fields. Recently, a model termed multi-view intact space learning has been proposed and drawn a large amount of attention. The model aims to find the latent intact representation of data by integrating information from different views. However, the model has two obvious shortcomings. One is that the model needs to tune two regularization parameters. The other is that the optimization algorithm is too time-consuming. Based on the unit intact space assumption, we propose an improved model, termed multi-view unit intact space learning, without introducing any prior parameters. Besides, an efficient algorithm based on proximal gradient scheme is designed to solve the model. Extensive experiments have been conducted on four real-world datasets to show the effectiveness of our method.


title: Familial partial lipodystrophy: two types of an X linked dominant syndrome, lethal in the hemizygous state.

abstract: Familial lipodystrophy (referred to in publications as the Köbberling-Dunnigan syndrome) comprises at least two clinical phenotypes which are consistent within each pedigree. In type 1 familial lipodystrophy, loss of subcutaneous fat is confined to the limbs, sparing the face and trunk. In type 2 familial lipodystrophy, the trunk is also affected with the exception of the vulva, giving an appearance of labial hypertrophy. Diabetes mellitus, hyperlipoproteinaemia, and acanthosis nigricans are present to a variable degree in some but not all patients with familial lipodystrophy, and the abnormal distribution of subcutaneous fat is the essential hallmark of the syndrome. In addition to a survey of published reports, new cases with the syndrome are described. Both types of partial lipodystrophy, occurring either as familial disease or as sporadic cases, have only been observed in female patients. Study of the pedigrees of five families with familial lipodystrophy (two Scottish and three German) suggests an X linked dominant mode of transmission, lethal in the hemizygous (XY) state. The two clinical phenotypes with their variably expressive metabolic abnormalities are consistent either with different mutants of the same allele or with two genes on adjacent loci. Other clinical phenotypes of familial lipodystrophy may exist due to further mutations of the same allele or of genes on adjacent loci. The nature of the disorder in patients with familial lipodystrophy usually escapes recognition for many years and the syndrome is almost certainly much commoner than the few families described to date suggest.


title: Comparative analysis of SVPWM and SPWM techniques for multilevel inverter

abstract: The aim of this paper is to analyze the comparative relationship between Space-Vector Pulse Width Modulation (SVPWM) and Sinusoidal Pulse Width Modulation (SPWM) for three phase multilevel inverters. Detailed comparative analysis of Total Harmonic Distortion (THD) for both modulation strategies analyzed. The converter technologies are receiving increased attention due to high power applications. This increased attention is probably due to the fact that the output waveforms are much improved over those of the three-phase carrier based sinusoidal pulse width modulation (SPWM).


Collaboration and Satisfaction About Care Decisions


title: Unsupervised Change Detection in Satellite Images Using Principal Component Analysis and $k$-Means Clustering

abstract: In this letter, we propose a novel technique for unsupervised change detection in multitemporal satellite images using principal component analysis (PCA) and k-means clustering. The difference image is partitioned into h times h nonoverlapping blocks. S, S les h2, orthonormal eigenvectors are extracted through PCA of h times h nonoverlapping block set to create an eigenvector space. Each pixel in the difference image is represented with an S-dimensional feature vector which is the projection of h times h difference image data onto the generated eigenvector space. The change detection is achieved by partitioning the feature vector space into two clusters using k-means clustering with k = 2 and then assigning each pixel to the one of the two clusters by using the minimum Euclidean distance between the pixel's feature vector and mean feature vector of clusters. Experimental results confirm the effectiveness of the proposed approach.


title: Joint Modeling of Topics, Citations, and Topical Authority in Academic Corpora

abstract: Much of scientific progress stems from previously published findings, but searching through the vast sea of scientific publications is difficult. We often rely on metrics of scholarly authority to find the prominent authors but these authority indices do not differentiate authority based on research topics. We present Latent Topical-Authority Indexing (LTAI) for jointly modeling the topics, citations, and topical authority in a corpus of academic papers. Compared to previous models, LTAI differs in two main aspects. First, it explicitly models the generative process of the citations, rather than treating the citations as given. Second, it models each author's influence on citations of a paper based on the topics of the cited papers, as well as the citing papers. We fit LTAI to four academic corpora: CORA, Arxiv Physics, PNAS, and Citeseer. We compare the performance of LTAI against various baselines, starting with the latent Dirichlet allocation, to the more advanced models including author-link topic model and dynamic author citation topic model. The results show that LTAI achieves improved accuracy over other similar models when predicting words, citations and authors of publications.


title: Motion sickness.

abstract: The number of recently published papers on motion sickness may convey the impression that motion sickness is far from being understood. The current review focusses on a concept which tends to unify the different manifestations and theories of motion sickness. The paper highlights the relations between ergonomic principles to minimise motion sickness and the concept predictions. The clinical management of sufferers from motion sickness in terms of selection, pharmacological measures and desensitisation courses is treated as well.


title: Household clustering of asymptomatic malaria infections in Xepon district, Savannakhet province, Lao PDR

abstract: Background ::: In the Lao PDR, malaria morbidity and mortality have remarkably decreased over the past decade. However, asymptomatic infections in rural villages contribute to the on-going local transmission. The primary objective of this study was to explore the characteristics of infections in a malaria-endemic district of the Lao PDR. The specific objectives were to investigate the prevalence and species of malaria parasites using molecular methods and to assess individual and household parasite levels and the characteristics associated with malaria infection.


Research Statement Compressed Sensing and Its Applications in Video Processing 1 Introduction


title: A Pacific Culture among Wild Baboons: Its Emergence and Transmission

abstract: Reports exist of transmission of culture in nonhuman primates. We examine this in a troop of savanna baboons studied since 1978. During the mid-1980s, half of the males died from tuberculosis; because of circumstances of the outbreak, it was more aggressive males who died, leaving a cohort of atypically unaggressive survivors. A decade later, these behavioral patterns persisted. Males leave their natal troops at adolescence; by the mid-1990s, no males remained who had resided in the troop a decade before. Thus, critically, the troop's unique culture was being adopted by new males joining the troop. We describe (a) features of this culture in the behavior of males, including high rates of grooming and affiliation with females and a “relaxed” dominance hierarchy; (b) physiological measures suggesting less stress among low-ranking males; (c) models explaining transmission of this culture; and (d) data testing these models, centered around treatment of transfer males by resident females.


title: Compact planar microstrip crossover for beamforming networks

abstract: The design of a fully planar microstrip crossover for beamforming networks is presented. The design starts by using a conventional half-wavelength square patch and two sets of orthogonal feeding lines. Rectangular and circular slots are introduced on the square patch in order to reduce the required area of the patch by 82%. The proposed crossover is fabricated and tested for performance confirmation. The measured data shows less than 1 dB insertion loss, more than 13 dB isolation, and around 0.1 ns deviation in the group delay across 12% fractional bandwidth. The proposed crossover is suitable for planar Butler matrix which is a key component in beamforming networks.


title: Loving‐Kindness Meditation for Posttraumatic Stress Disorder: A Pilot Study

abstract: Loving-kindness meditation is a practice designed to enhance feelings of kindness and compassion for self and others. Loving-kindness meditation involves repetition of phrases of positive intention for self and others. We undertook an open pilot trial of loving-kindness meditation for veterans with posttraumatic stress disorder (PTSD). Measures of PTSD, depression, self-compassion, and mindfulness were obtained at baseline, after a 12-week loving-kindness meditation course, and 3 months later. Effect sizes were calculated from baseline to each follow-up point, and self-compassion was assessed as a mediator. Attendance was high; 74% attended 9–12 classes. Self-compassion increased with large effect sizes and mindfulness increased with medium to large effect sizes. A large effect size was found for PTSD symptoms at 3-month follow-up (d = −0.89), and a medium effect size was found for depression at 3-month follow-up (d = −0.49). There was evidence of mediation of reductions in PTSD symptoms and depression by enhanced self-compassion. Overall, loving-kindness meditation appeared safe and acceptable and was associated with reduced symptoms of PTSD and depression. Additional study of loving-kindness meditation for PTSD is warranted to determine whether the changes seen are due to the loving-kindness meditation intervention versus other influences, including concurrent receipt of other treatments.


title: Simulation of urban terrain models using VBS2, TerraTools and FZK Viewer

abstract: Virtual representation of urban terrain and simulation in real-world scenarios are becoming increasingly important in military and civil areas, for example, for operations research, mission rehearsal or debriefing. This work demonstrates the possibility of automatized import of semantic models, which result from our urban terrain reconstruction process, based on simple video data taken from UAV flights, into various simulation systems. We provide the justification for our choice of three simulation development tools- Virtual Battlespace, TerraTools and FZK Viewer — and we describe the main advantages and draw-backs for automatic data exchange into these systems.


title: Content-adaptive focus configuration for near-eye multi-focal displays

abstract: Near-eye multi-focal (light field) displays are known to be more advantageous than conventional stereoscopic 3D displays, because they can alleviate the vergence-accommodation conflict. While stereoscopic 3D displays are restricted to projecting images on a single focal plane, near-eye light field displays can form multiple focal planes and volumetrically render 3D data. Most existing near-eye light field displays use simplistic, uniformly spaced focal plane configuration (in dioptric space). In this paper, we present a novel technique that optimizes the focal plane configuration based on characteristics of the content to be rendered. We show that this technique can significantly improve the perceived visual quality of content visualized on such displays.


title: Design of concentric ring slot on bow-tie antenna with defected ground structure

abstract: In this paper, bow-tie antenna with defected ground structure (DGS) has presented. The radiating element is bow-tie antenna, two concentric ring slots are presented on it for producing the circular polarization (CP). Rectangular patch is loaded to bow-tie antenna for enhance the gain. On the ground plane four equal bow-tie slots are considered for enhancing the impedance bandwidth upto 10.3%. It is operated at 14.5 GHz frequency. The gain is 5.9 dB. The good agreement between the simulated and measured values is observed. These results are very useful for airborne and radar applications.


Open-Domain Audio-Visual Speech Recognition: A Deep Learning Approach.


title: Virtualization Technology: Cross-VM Cache Side Channel Attacks make it Vulnerable

abstract: Cloud computing provides an effective business model for the deployment of IT infrastructure, platform, and software services. Often, facilities are outsourced to cloud providers and this offers the service consumer virtualization technologies without the added cost burden of development. However, virtualization introduces serious threats to service delivery such as Denial of Service (DoS) attacks, Cross-VM Cache Side Channel attacks, Hypervisor Escape and Hyper-jacking. One of the most sophisticated forms of attack is the cross-VM cache side channel attack that exploits shared cache memory between VMs. A cache side channel attack results in side channel data leakage, such as cryptographic keys. Various techniques used by the attackers to launch cache side channel attack are presented, as is a critical analysis of countermeasures against cache side channel attacks.


title: Cross-Language Authorship Attribution

abstract: This paper presents a novel task of cross-language authorship attribution (CLAA), an extension of authorship attribution task to multilingual settings: given data labelled with authors in language X , the objective is to determine the author of a document written in language Y , where X 6= Y . We propose a number of cross-language stylometric features for the task of CLAA, such as those based on sentiment and emotional markers. We also explore an approach based on machine translation (MT) with both lexical and cross-language features. We experimentally show that MT could be used as a starting point to CLAA, since it allows good attribution accuracy to be achieved. The cross-language features provide acceptable accuracy while using jointly with MT, though do not outperform lexical


title: MINOS: Multimodal Indoor Simulator for Navigation in Complex Environments

abstract: We present MINOS, a simulator designed to support the development of multisensory models for goal-directed navigation in complex indoor environments. The simulator leverages large datasets of complex 3D environments and supports flexible configuration of multimodal sensor suites. We use MINOS to benchmark deep-learning-based navigation methods, to analyze the influence of environmental complexity on navigation performance, and to carry out a controlled study of multimodality in sensorimotor learning. The experiments show that current deep reinforcement learning approaches fail in large realistic environments. The experiments also indicate that multimodality is beneficial in learning to navigate cluttered scenes. MINOS is released open-source to the research community at this http URL . A video that shows MINOS can be found at this https URL


title: Impact of Office Design on Employees' Productivity: A Case study of Banking Organizations of Abbottabad, Pakistan

abstract: Increased personal control and comfort needs of employees triggered the concern among organizations to provide them with an environment and office design, which fulfills the employees' needs and helps to boost their productivity. The main objective of this study is to find out the relationship between office design and productivity. For this purpose, 31 bank branches of 13 banks were contacted and studied. The findings of this study show that office design is very vital in terms of increasing employees' productivity. Comfortable and ergonomic office design motivates the employees and increases their performance substantially. Hughes (2007) surveyed 2000 employees pertain to various organizations and industries in multiple levels. The reported results of these survey showed that nine out of ten believed that a workspace quality affects the attitude of employees and increases their productivity. Employees in different organizations have different office designs. Every office has unique furniture and spatial arrangements, lighting and heating arrangements and different levels of noise. The purpose of this study is to analyze the impact of the office design factors on employees' productivity. The literature reveals that good office design has a positive affect on employees' productivity and the same assumption is being tested in this study for the banking sector of Pakistan. This study will try to find out the effects of office design on employees' productivity. The area chosen is the banking sector of Abbottabad District, Pakistan. The study will be based on primary data collected through a structured questionnaire (Appendix 1). 2. Significance of the Study The impact and significance of office design on employees' productivity is addressed in this study. Human resource professionals in the organizations are well aware of the importance of this issue. In the context of Pakistan, this is a relatively new topic. Very few researchers addressed in the context of human resource management. Very few researches can be found in the field of Human Resource Management, this huge gap needs to be filled by new research scholars. In Pakistan, workplace environment and its related issues are significantly neglected. It is evident that there is less importance to office design, incentives and assisting facilities and also it is not available to the employees. The situation is that they cannot even complain about them. These circumstances are affecting the performance of the employees greatly, in the form of delay in work completion, frustration, effect on personal growth etc. This study will try to find out the effects of office design in terms of furniture, noise, lighting, temperature and spatial arrangement on employees' productivity.


title: Alcohol based tissue fixation as an alternative for formaldehyde: influence on immunohistochemistry.

abstract: Background Formaldehyde is commonly used in histopathology to fix tissue. Not only are the carcinogenic properties of this solution a hazard to the people in the workplace, it is also a major burden on the environment, and it crosslinks molecular groups that hinder immunohistochemistry. Aims The influence of two new alcohol based non-crosslinking fixatives on immunohistochemical staining properties was tested on various tissues. Methods Fresh tissue samples were cut into three equal pieces, which were then fixed in the alcohol based fixatives Boonfix or RCL2, or in neutral buffered formaldehyde (NBF) as control. After fixation, tissue was routinely processed to paraffin sections. Deparaffinised slides were blocked for endogenous peroxidase and subsequently submitted to the usual NBF based immunohistochemical protocols for 85 different common antibodies either not requiring antigen retrieval (AR), or AR in citrate buffer, EDTA or pepsin. Results NBF fixed tissues provided significantly better immunostaining results (84% good staining) than RCL2 (66% good) and Boonfix (60%). The lesser performance of RCL2 and Boonfix was especially caused by pepsin AR which caused significant tissue damage. Omission of pepsin AR resulted in better immunostaining for these antibodies for RCL2 fixed tissues which were overall no longer significantly worse than NBF fixed tissue. Conclusion Tissues fixed in non-crosslinking alcohol based fixatives can successfully be immunohistochemically stained for most antibodies following the usual NBF based protocols. Omission of pepsin pretreatment seems to be important to retain proper morphology of immunostained tissues preserved in alcohol based fixatives. Therefore, when switching to less toxic and non-carcinogenic alcohol-based fixatives like RCL2, no major changes in the daily routine of immunohistochemistry are anticipated.


title: Achieving efficient and privacy-preserving truth discovery in crowd sensing systems

abstract: Abstract Benefiting from the development of network and mobile communication technologies, crowd sensing systems have emerged as new technology to sense and collect data via mobile devices. However, aggregated results of the collected data may not be accurate since information provided by devices may not be reliable. To tackle this problem, approaches based on truth discovery have been proposed to improve accuracy of data aggregation operations in crowd sensing systems. Nevertheless, it should be noted that most existing truth discovery proposals failed to consider user's data privacy. In this paper, we propose an Efficient and Privacy-preserving Truth Discovery (EPTD) scheme in crowd sensing systems. Specifically, we utilize the additive homomorphic privacy-preserving data aggregation and super-increasing sequence techniques to achieve both high performance and strong privacy protection. Security analysis indicates that the EPTD can achieve confidentiality of observed values and privacy protection of users' weights. Furthermore, extensive experiments demonstrate that the EPTD is better than existing proposals in terms of communication and computation overhead.


title: A Framework for Quality Assurance in Crowdsourcing

abstract: The emergence of online paid micro-crowdsourcing platforms, such as Amazon Mechanical Turk (AMT), allows on-demand and at scale distribution of tasks to human workers around the world. In such settings, online workers come and complete small tasks posted by a company, working for as long or as little as they wish. Such temporary employer-employee relationships give rise to adverse selection, moral hazard, and many other challenges. How can we ensure that the submitted work is accurate, especially when the verification cost is comparable to the cost of performing the task? How can we estimate the exhibited quality of the workers? What pricing strategies should be used to induce the effort of workers with varying ability levels? We develop a comprehensive framework for managing the quality in such micro-crowdsourcing settings: First, we describe an algorithm for estimating the error rates of the participating workers, and show how to separate systematic worker biases from unrecoverable errors and generate an unbiased “worker quality” measurement. Next, we present a selective repeated-labeling algorithm that acquires labels in a way so that quality requirements can be met at minimum cost. Then, we propose a quality-adjusted pricing scheme that adjusts the payment level according to the contributed value by each worker. We test our compensation scheme in a principal-agent setting in which workers respond to incentives by varying their effort. Our simulation results demonstrate that the proposed pricing scheme is able to induce workers to exert higher levels of effort and yield larger profits for employers compared to the commonly adopted uniform pricing schemes. We also describe strategies that build on our quality control and pricing framework, to tackle ∗jwang@ust.hk †panos@stern.nyu.edu ‡fprovost@stern.nyu.edu


title: Intelligent Torque Vectoring Approach for Electric Vehicles with Per-Wheel Motors

abstract: Transport electrification is currently a priority for authorities, manufacturers, and research centers around the world. The development of electric vehicles and the improvement of their functionalities are key elements in this strategy. As a result, there is a need for further research in emission reduction, efficiency improvement, or dynamic handling approaches. In order to achieve these objectives, the development of suitable Advanced Driver-Assistance Systems (ADAS) is required. Although traditional control techniques have been widely used for ADAS implementation, the complexity of electric multimotor powertrains makes intelligent control approaches appropriate for these cases. In this work, a novel intelligent Torque Vectoring (TV) system, composed of a neuro-fuzzy vertical tire forces estimator and a fuzzy yaw moment controller, is proposed, which allows enhancing the dynamic behaviour of electric multimotor vehicles. The proposed approach is compared with traditional strategies using the high fidelity vehicle dynamics simulator Dynacar. Results show that the proposed intelligent Torque Vectoring system is able to increase the efficiency of the vehicle by 10%, thanks to the optimal torque distribution and the use of a neuro-fuzzy vertical tire forces estimator which provides 3 times more accurate estimations than analytical approaches.


title: Backplay:"Man muss immer umkehren"

abstract: A long-standing problem in model-free reinforcement learning (RL) is that it requires a large number of trials to learn a good policy, especially in environments with sparse rewards. We explore a method to increase the sample efficiency of RL when we have access to demonstrations. Our approach, Backplay, uses a single demonstration to construct a curriculum for a given task. Rather than starting each training episode in the environment's fixed initial state, we start the agent near the end of the demonstration and move the starting point backwards during the course of training until we reach the initial state. We perform experiments in a competitive four-player game (Pommerman) and a path-finding maze game. We find that Backplay provides significant gains in sample complexity with a stark advantage in sparse reward settings. In some cases, it reached success rates greater than 50 and generalized to unseen initial conditions, while standard RL did not yield any improvement.


title: Switched-capacitor regulator with digital feedback

abstract: The wireless sensor node makes use of energy harvesters in order to power its modules. However, by nature of these nodes being placed in diverse locations and fluctuating conditions, their energy harvesters obtain unregulated DC voltage. Regulators undertake this problem by appropriately handling the voltage being harvested to power the node. Most regulators today use analog control methods which are inefficient at delivering power, consume a large amount of area, are more vulnerable to noise, and are less dynamic and adaptive. The 65-nm based switched capacitor regulator with digital control feedback presented in this paper addresses these problems by providing tight voltage regulation with an output accuracy of 9.5mV, peak efficiency of 89.02%, and voltage ripple as small as 2.9mV. The digital control used in the system consumes only 8.777μW of power and is able to perform more complex arithmetic to adapt to the regulator's operating conditions.


title: Collision Avatar (CA): Adding collision objects for human body in augmented reality using Kinect

abstract: The purpose of this study is the improvement of real and virtual world interaction in real-time by uses of Kinect. We propose adding geometric shapes as a collision object for human body parts in augmented reality with real-time animations. In this work the concept of collision Avatar (CA) is presented. We will describe different methods which creates geometric shapes, scale them according to body part sizes. Also we compare several types of real-time animations using different methods.


title: Aiding Pronoun Translation with Co-Reference Resolution

abstract: We propose a method to improve the translation of pronouns by resolving their co-reference to prior mentions. We report results using two different co-reference resolution methods and point to remaining challenges.


title: Socialism and the Blockchain

abstract: Bitcoin (BTC) is often cited as Libertarian. However, the technology underpinning Bitcoin, blockchain, has properties that make it ideally suited to Socialist paradigms. Current literature supports the Libertarian viewpoint by focusing on the ability of Bitcoin to bypass central authority and provide anonymity; rarely is there an examination of blockchain technology's capacity for decentralised transparency and auditability in support of a Socialist model. This paper conducts a review of the blockchain, Libertarianism, and Socialist philosophies. It then explores Socialist models of public ownership and looks at the unique cooperative properties of blockchain that make the technology ideal for supporting Socialist societies. In summary, this paper argues that blockchain technologies are not just a Libertarian tool, they also enhance Socialist forms of governance.


Online Learning and Stochastic Approximations


title: Hardware-Based Malware Detection Using Low-Level Architectural Features

abstract: Security exploits and ensuant malware pose an increasing challenge to computing systems as the variety and complexity of attacks continue to increase. In response, software-based malware detection tools have grown in complexity, thus making it computationally difficult to use them to protect systems in real-time. Therefore, software detectors are applied selectively and at a low frequency, creating opportunities for malware to remain undetected. In this paper, we propose Malware-Aware Processors (MAP)—processors augmented with a hardware-based online malware detector to serve as the first line of defense to differentiate malware from legitimate programs. The output of this detector helps the system prioritize how to apply more expensive software-based solutions. The always-on nature of MAP detector helps protect against intermittently operating malware. We explore the use of different features for classification and study both logistic regression and neural networks. We show that the detectors can achieve excellent performance, with little hardware overhead. We integrate the MAP implementation with an open-source x86-compatible core, synthesizing the resulting design to run on an FPGA.


title: Biomimetic Eye-Neck Coordination

abstract: We describe a method for coordinating eye and neck motions in the control of a humanoid robotic head. Based on the characteristics of human oculomotor behavior, we formulate the target tracking problem in a state-space control framework and show that suitable controller gains can be either manually tuned with optimal control techniques or learned from bio-mechanical data recorded from newborns subjects. The basic controller relies on eye-neck proprioceptive feedback. In biological systems, vestibular signals and target prediction compensate for external motions and allow target tracking with low lag. We provide ways to integrate inertial and prediction signals in the basic control architecture, whenever these are available. We demonstrate the ability of the method in replicating the behavior of subjects with different ages and show results obtained through a real-time implementation in a humanoid platform.


title: Collaborative filtering via co-factorization of individuals and groups

abstract: Matrix factorization is one of the most successful collaborative filtering methods for recommender systems. Traditionally, matrix factorization only uses the observed user-item feedback information, which makes predictions on cold users/items difficult. In many applications, user/item content information are also available and they have been successfully used in content-based methods. In recent years, there are attempts to incorporate content information into matrix factorization. In particular, the Factorization Machine (FM) is one of the most notable examples. However, FM is a general factorization model that models interactions between all features into a latent feature space. In this paper, we propose a novel combination of tree-based feature group learning and matrix co-factorization that extends FM to recommender systems. Experimental results on a number of benchmark data sets show that the proposed algorithm outperforms state-of-the-art methods, particularly for predictions on cold users and cold items.


title: Invited - Cross-layer approximations for neuromorphic computing: from devices to circuits and systems

abstract: Neuromorphic algorithms are being increasingly deployed across the entire computing spectrum from data centers to mobile and wearable devices to solve problems involving recognition, analytics, search and inference. For example, large-scale artificial neural networks (popularly called deep learning) now represent the state-of-the art in a wide and ever-increasing range of video/image/audio/text recognition problems. However, the growth in data sets and network complexities have led to deep learning becoming one of the most challenging workloads across the computing spectrum. We posit that approximate computing can play a key role in the quest for energy-efficient neuromorphic systems. We show how the principles of approximate computing can be applied to the design of neuromorphic systems at various layers of the computing stack. At the algorithm level, we present techniques to significantly scale down the computational requirements of a neural network with minimal impact on its accuracy. At the circuit level, we show how approximate logic and memory can be used to implement neurons and synapses in an energy-efficient manner, while still meeting accuracy requirements. A fundamental limitation to the efficiency of neuromorphic computing in traditional implementations (software and custom hardware alike) is the mismatch between neuromorphic algorithms and the underlying computing models such as von Neumann architecture and Boolean logic. To overcome this limitation, we describe how emerging spintronic devices can offer highly efficient, approximate realization of the building blocks of neuromorphic computing systems.


title: Aggravated fragmentation: A case study of SME behaviour in two emerging heritage tourism regions

abstract: This paper investigates the behaviour of small and medium sized enterprises (SMEs) within the heritage tourism supply chain (HTSC), in two emerging heritage regions. SMEs are conceptualised as implementers, working within the constraints of government level tourism structures and the heritage tourism supply chain. The research employs a case study approach, focusing on two emerging regions in Northern Ireland. In-depth interviews were carried out with small business owners and community associations operating within the regions. The research identifies SME dissatisfaction with the supply chain and the processes in place for the delivery of the tourism product. To overcome the perceived inadequacies of the heritage tourism supply chain SMEs engage in entrepreneurial behaviour by attempting to deliver specific products and services to meet the need of tourists. The challenge for tourism organisations is how they can integrate the entrepreneurial, innovative activities of SMEs into the heritage tourism system.


title: Using Discriminant Eigenfeatures for Image Retrieval

abstract: This paper describes the automatic selection of features from an image training set using the theories of multidimensional discriminant analysis and the associated optimal linear projection. We demonstrate the effectiveness of these most discriminating features for view-based class retrieval from a large database of widely varying real-world objects presented as "well-framed" views, and compare it with that of the principal component analysis.


title: IIoTEED: An Enhanced, Trusted Execution Environment for Industrial IoT Edge Devices

abstract: With the advent of the Internet of Things (IoT), security has emerged as a major design goal for smart connected devices. This explosion in connectivity created a larger attack surface area. Software-based approaches have been applied for security purposes; however, these methods must be extended with security-oriented technologies that promote hardware as the root of trust. The ARM TrustZone can enable trusted execution environments (TEEs), but existing solutions disregard real-time needs. Here, the authors demonstrate why TrustZone is becoming a reference technology for securing IoT edge devices, and how enhanced TEEs can help meet industrial IoT applications real-time requirements.


title: Malicious PDF detection using metadata and structural features

abstract: Owed to their versatile functionality and widespread adoption, PDF documents have become a popular avenue for user exploitation ranging from large-scale phishing attacks to targeted attacks. In this paper, we present a framework for robust detection of malicious documents through machine learning. Our approach is based on features extracted from document metadata and structure. Using real-world datasets, we demonstrate the the adequacy of these document properties for malware detection and the durability of these features across new malware variants. Our analysis shows that the Random Forests classification method, an ensemble classifier that randomly selects features for each individual classification tree, yields the best detection rates, even on previously unseen malware. Indeed, using multiple datasets containing an aggregate of over 5,000 unique malicious documents and over 100,000 benign ones, our classification rates remain well above 99% while maintaining low false positives of 0.2% or less for different classification parameters and experimental scenarios. Moreover, the classifier has the ability to detect documents crafted for targeted attacks and separate them from broadly distributed malicious PDF documents. Remarkably, we also discovered that by artificially reducing the influence of the top features in the classifier, we can still achieve a high rate of detection in an adversarial setting where the attacker is aware of both the top features utilized in the classifier and our normality model. Thus, the classifier is resilient against mimicry attacks even with knowledge of the document features, classification method, and training set.


title: Curriculum Learning for Facial Expression Recognition

abstract: Over the past few years, there has been an increased interest in machine understanding and recognition of affective states based on facial expressions. While great progress has been made, there are still a lot of challenges facing automatic emotion recognition, namely: generalizability of models across datasets, accounting for individual differences, and recognition of subtle expressions. While deep learning techniques enabled a large amount of progress in many areas of computer vision, this progress has not yet been fully translated to emotion recognition. Our work attempts to partly address that by presenting a novel learning technique for deep learning methods that leads to better generalization for emotion recognition from facial expressions.


title: Dynemo : A Video Database of Natural Facial Expressions of Emotions

abstract: DynEmo is a database available to the scientific community (https://DynEmo.liglab.fr/). It contains dynamic and natural emotional facial expressions (EFEs) displaying subjective affective states rated by both the expresser and observers. Methodological and contextual information is provided for each expression. This multimodal corpus meets psychological, ethical, and technical criteria. It is quite large, containing two sets of 233 and 125 recordings of EFE of ordinary Caucasian people (ages 25 to 65, 182 females and 176 males) filmed in natural but standardized conditions. In the Set 1, EFE recordings are associated with the affective state of the expresser (self-reported after the emotion inducing task, using dimensional, action readiness, and emotional labels items). In the Set 2, EFE recordings are both associated with the affective state of the expresser and with the time line (continuous annotations) of observers' ratings of the emotions displayed throughout the recording. The time line allows any researcher interested in analysing non-verbal human behavior to segment the expressions into emotions.


title: Array antenna integrated fan-out wafer level packaging (InFO-WLP) for millimeter wave system applications

abstract: Array antenna integrated with RF chip using InFO-WLP technology is proposed for millimeter wave system applications. Aperture-coupled patch antenna is designed on the fan-out molding compound (MC). The performance of single-element antenna is evaluated first and proved to have 5 dBi of gain. Meanwhile, the interconnect from chip to antenna feeding line is demonstrated to only have 0.7 dB loss, which can save 19 % PA output power compared with that of flip-chip package. Finally, the system performance of 4 × 4 antenna array integrated with RF chip on the InFO structure shows 14.7 dBi of array gain in a small form factor of 10 × 10 × 0.5 mm3.


title: Robot transparency, trust and utility

abstract: As robot reasoning becomes more complex, debugging becomes increasingly hard based solely on observable behaviour, even for robot designers and technical specialists. Similarly, non-specialist users have difficulty creating useful mental models of robot reasoning from observations of robot behaviour. The EPSRC Principles of Robotics mandate that our artefacts should be transparent, but what does this mean in practice, and how does transparency affect both trust and utility? We investigate this relationship in the literature and find it to be complex, particularly in nonindustrial environments where, depending on the application and purpose of the robot, transparency may have a wider range of effects on trust and utility. We outline our programme of research to support our assertion that it is nevertheless possible to create transparent agents that are emotionally engaging despite having a transparent machine nature.


Extraction from Natural Scene using PCA


title: Audio/Visual independent components

abstract: This paper presents a methodology for extracting meaningful audio/visual features from video streams. We propose a statistical method that does not distinguish between the auditory and visual data, but one that operates on a fused data set. By doing so we discover audio/visual features that correspond to events depicted in the stream. Using these features, we can obtain a segmentation of the input video stream by separating independent auditory and visual events. ICA 2003


title: Generating Electricity While Walking with Loads

abstract: We have developed the suspended-load backpack, which converts mechanical energy from the vertical movement of carried loads (weighing 20 to 38 kilograms) to electricity during normal walking [generating up to 7.4 watts, or a 300-fold increase over previous shoe devices (20 milliwatts)]. Unexpectedly, little extra metabolic energy (as compared to that expended carrying a rigid backpack) is required during electricity generation. This is probably due to a compensatory change in gait or loading regime, which reduces the metabolic power required for walking. This electricity generation can help give field scientists, explorers, and disaster-relief workers freedom from the heavy weight of replacement batteries and thereby extend their ability to operate in remote areas.


title: TRAVELLING WAVE MECHANISM AND NOVEL ANALYSIS OF THE PLANAR ARCHIMEDEAN SPIRAL ANTENNA IN FREE SPACE

abstract: While Archimedean spiral antennas were invented a half-century ago, only self- complementary impedance can be evaluated directly from the Babinet's principle. This paper examines the efiects of metal width and arm spacing on printed spiral's input impedance. A model is proposed based on examination by decomposition of planar spiral. A closed-form expression for the input impedance of Archimedean spiral antenna is obtained by evaluating the proposed model with conformal mapping techniques. Full-wave numerical simulations, Babinet's principle, and a fabricated antenna demonstrate the accuracy of the proposed model. The expression in this work can be used to flnd the impedance of a variety of spiral complementary structures analytically. The examination and discussion on the efiects of other parameters and features in addition to the spiral itself are also provided through numerical simulation.


title: Active Learning for Cross-Lingual Sentiment Classification

abstract: Cross-lingual sentiment classification aims to predict the sentiment orientation of a text in a language (named as the target language) with the help of the resources from another language (named as the source language). However, current cross-lingual performance is normally far away from satisfaction due to the huge difference in linguistic expression and social culture. In this paper, we suggest to perform active learning for cross-lingual sentiment classification, where only a small scale of samples are actively selected and manually annotated to achieve reasonable performance in a short time for the target language. The challenge therein is that there are normally much more labeled samples in the source language than those in the target language. This makes the small amount of labeled samples from the target language flooded in the aboundance of labeled samples from the source language, which largely reduces their impact on cross-lingual sentiment classification. To address this issue, we propose a data quality controlling approach in the source language to select high-quality samples from the source language. Specifically, we propose two kinds of data quality measurements, intra- and extra-quality measurements, from the certainty and similarity perspectives. Empirical studies verify the appropriateness of our active learning approach to cross-lingual sentiment classification.