title: A Stereo and Color-based Method for Face Pose Estimation and Facial Feature Extraction

abstract: This paper describes a method to perform face pose estimation and high resolution facial feature extraction on the basis of stereoscopic color images. Unlike other approaches no light projection is required at running time. In our method face detection is based on color driven clustering of 3D points derived from stereo. A mesh model is registered with the post-processed face cluster using a variant of the iterative closest point algorithm (ICP). Pose is derived from correspondence. Then, pose and model information is used for face normalization and facial feature localization. Results show, stereo and color are powerful cues for finding the face and its pose under a wide range of poses, illuminations and expressions (PIE). Head orientation may vary in out of plane rotations up to plusmn45deg


title: On Some Aspects of Nature-Based Algorithms to Solve Multi-Objective Problems

abstract: This chapter presents an overview of various nature-based algorithms to solve multi-objective problems with the particular emphasis on Multi-Objective Evolutionary Algorithms based on Genetic Algorithm. Some of the significant hybridization and the modification of the benchmark algorithms have also been discussed as well. The complexity issues have been outlined and various test problems to show the effectiveness of such algorithms have also been summarized. At the end, a brief discussion on the software packages used to model these type of algorithms are presented.


title: Future control technologies in motor DIAGNOSIS and system WELLNESS

abstract: This paper presents electrical equipment maintenance by condition-based maintenance (CBM). In the CBM approach, equipment maintenance based on a routine schedule can be replaced with an approach based on system wellness diagnostics. This approach might rely on noninvasive monitoring of three-phase induction motors to report equipment condition and enable maintenance intervention before a failure occurs. Pump cavitation, rotational unbalance and mechanical alignment are some of the areas where a mathematical model has been developed using fast Fourier transform (FFT) analysis, enabling online diagnostics during operation. Also discusses mathematical model and field tests to validate the model and introduce a motor-wellness relay that could be used to perform online diagnostics. Alternative system communication architecture to support a wellness platform are also reviewed and discussed.


title: User Friendly Recommender Systems

abstract: Recommender systems are a recent but increasingly widely used resource. Yet most, if not all of them suffer from serious deficiencies. Recommender systems often require first time users to enter ratings for a large number of items — a tedious process that often deters users. Thus, this thesis investigated whether useful recommendations could be made without requiring users to explicitly rate items. It was shown that ratings automatically generated from implicit information about a user can be used to make useful recommendations. Most recommender systems also provide no explanations for the recommendations that they make, and give users little control over the recommendation process. Thus, when these systems make a poor recommendation, users can not understand why it was made, and are not able to easily improve their recommendations. Hence, this thesis investigated ways in which scrutability and control could be implemented in such systems. A comprehensive questionnaire was completed by 18 participants as a basis for a broader understanding of the issues mentioned above and to inform the design of a prototype; a prototype was then created and two separate evaluations performed, each with at least 9 participants. This investigation highlighted a number of key scrutability and control features that could be useful additions to existing recommender systems. The findings of this thesis can be used to improve the effectiveness, usefulness and user friendliness of existing recommender systems. These findings include: • Explanations, controls and a map based presentation are all useful additions to a recommender system. • Specific explanation types can be more useful than others for explaining particular recommendation techniques. • Specific recommendation techniques can be useful even when a user has not entered many ratings. • Ratings generated from purely implicit information about a user can be used to made useful recommendations.


title: Statistical Relational Learning for Game Theory

abstract: In this paper, we motivate the use of models and algorithms from the area of Statistical Relational Learning (SRL) as a framework for the description and the analysis of games. SRL combines the powerful formalism of first-order logic with the capability of probabilistic graphical models in handling uncertainty in data and representing dependencies between random variables: for this reason, SRL models can be effectively used to represent several categories of games, including games with partial information, graphical games and stochastic games. Inference algorithms can be used to approach the opponent modeling problem, as well as to find Nash equilibria or Pareto optimal solutions. Structure learning algorithms can be applied, in order to automatically extract probabilistic logic clauses describing the strategies of an opponent with a high-level, human-interpretable formalism. Experiments conducted using Markov logic networks, one of the most used SRL frameworks, show the potential of the approach.


title: Overview of Algorithms for Swarm Intelligence

abstract: Swarm intelligence (SI) is based on collective behavior of selforganized systems. Typical swarm intelligence schemes include Particle Swarm Optimization (PSO), Ant Colony System (ACS), Stochastic Diffusion Search (SDS), Bacteria Foraging (BF), the Artificial Bee Colony (ABC), and so on. Besides the applications to conventional optimization problems, SI can be used in controlling robots and unmanned vehicles, predicting social behaviors, enhancing the telecommunication and computer networks, etc. Indeed, the use of swarm optimization can be applied to a variety of fields in engineering and social sciences. In this paper, we review some popular algorithms in the field of swarm intelligence for problems of optimization. The overview and experiments of PSO, ACS, and ABC are given. Enhanced versions of these are also introduced. In addition, some comparisons are made between these algorithms.


title: Sinusoidal Pulse Width Modulation Design Based DDS

abstract: FPGA chip using Cyclone series designed and implemented a new type of sinusoidal pulse width modulation (SPWM) generator. The generator used direct digital synthesis (DDS) technology with DSP Builder (FPGA chip supported the corresponding algorithm sub-circuits) discreteness as the hardware middle layer, produced triangle and sine modulated carrier wave which can be controlled in frequency, phase, amplitude. After comparator comparison, the output state was automatic overturn at the time of the waveform intersection, brought SPWM waveform to achieve the control of corresponding switching devices on-off. This method avoided compiling complex hardware program, at the same time ensured the scalability of the system, improved the development efficiency and met the requirements of embedded system, but also has on-site programmability. The simulation results show that the SPWM waveform can neatly achieve the adjustment about frequency, phase, amplitude, and have high frequency resolution.


title: Visualization of ATM Usage Patterns to Detect Counterfeit Cards Usage

abstract: People relish the flexibility of being able access their monetary assets when and where they need them. The abundance of cards able to withdraw funds from Automatic Teller Machines (ATMs) has not gone unnoticed by the cyber criminal element. Means for skimming and cloning cards exist and the market continues to grow. While the methods for obtaining access to another's funds vary greatly, there are cases in which visualization of a class of card-present fraud can combine the visualization with the powerful abductive reasoning capabilities of the human mind to help identify the threat. As part of a defense-in-depth strategy, this can contribute to the evolution and refinement of rule sets that facilitate the detection of the crime prior to the cash-out phase of the illegal operation. This paper discusses card-present fraud and provides an example of how visualization techniques, coupled with human abductive reasoning, can be used to guide the evolution of analytical tools to help protect our digital assets.


title: Ontology building by dictionary database mining

abstract: The paper examines the problem of building ontologies in automatic and semi-automatic way by means of mining a dictionary database. An overview of data mining tools and methods is presented. On this basis an extended and improved approach is proposed which involves operations for pre-processing the dictionary database, clustering and associating database entries for extracting hierarchical and nonhierarchical relations. The approach is applied on sample dictionary database in the environment of the Rapid Miner mining tool. As a result the dictionary database is complemented to thesaurus database which can be further on easily converted to reusable formal ontology.


Wake-Up-Receiver in energy efficient Wireless Sensor Networks for security applications


title: Automatic tag recommendation for metadata annotation using probabilistic topic modeling

abstract: The increase of the complexity and advancement in ecological and environmental sciences encourages scientists across the world to collect data from multiple places, times, and thematic scales to verify their hypotheses. Accumulated over time, such data not only increases in amount, but also in the diversity of the data sources spread around the world. This poses a huge challenge for scientists who have to manually search for information. To alleviate such problems, ONEMercury has recently been implemented as part of the DataONE project to serve as a portal for accessing environmental and observational data across the globe. ONEMercury harvests metadata from the data hosted by multiple repositories and makes it searchable. However, harvested metadata records sometimes are poorly annotated or lacking meaningful keywords, which could affect effective retrieval. Here, we develop algorithms for automatic annotation of metadata. We transform the problem into a tag recommendation problem with a controlled tag library, and propose two variants of an algorithm for recommending tags. Our experiments on four datasets of environmental science metadata records not only show great promises on the performance of our method, but also shed light on the different natures of the datasets.


title: Error analysis of a inverted pendulum on an inclined rail

abstract: According to inverted pendulum system control error, the method of error analysis and compensation is proposed, which establishes the structural model of inverted pendulum system, elaborates control principles of tilt inverted pendulum system, and studies error causes of inverted pendulum and inclination system error and dynamic error in the control process. But errors involving electric control system and model of inverted pendulum system are combined together to constitute composite error of tilt inverted pendulum system. The purpose of studying the relationship among composite error, motion parameters and structural parameters is to rediscover a solution to minimize composite error and to improve the accuracy and precision of control system provides theory basis. The simulation results reveal the necessity of optimal control error compensation of tilt inverted pendulum.


title: NBA (network balancing act): a high-performance packet processing framework for heterogeneous processors

abstract: We present the NBA framework, which extends the architecture of the Click modular router to exploit modern hardware, adapts to different hardware configurations, and reaches close to their maximum performance without manual optimization. NBA takes advantages of existing performance-excavating solutions such as batch processing, NUMA-aware memory management, and receive-side scaling with multi-queue network cards. Its abstraction resembles Click but also hides the details of architecture-specific optimization, batch processing that handles the path diversity of individual packets, CPU/GPU load balancing, and complex hardware resource mappings due to multi-core CPUs and multi-queue network cards. We have implemented four sample applications: an IPv4 and an IPv6 router, an IPsec encryption gateway, and an intrusion detection system (IDS) with Aho-Corasik and regular expression matching. The IPv4/IPv6 router performance reaches the line rate on a commodity 80 Gbps machine, and the performances of the IPsec gateway and the IDS reaches above 30 Gbps. We also show that our adaptive CPU/GPU load balancer reaches near-optimal throughput in various combinations of sample applications and traffic conditions.


title: Multiple Range-Restricted Bidirectional Gated Recurrent Units with Attention for Relation Classification

abstract: Most of neural approaches to relation classification have focused on finding short patterns that represent the semantic relation using Convolutional Neural Networks (CNNs) and those approaches have generally achieved better performances than using Recurrent Neural Networks (RNNs). In a similar intuition to the CNN models, we propose a novel RNN-based model that strongly focuses on only important parts of a sentence using multiple range-restricted bidirectional layers and attention for relation classification. Experimental results on the SemEval-2010 relation classification task show that our model is comparable to the state-of-the-art CNN-based and RNN-based models that use additional linguistic information.


title: Effects of temperature and salinity on the life cycle of Neobenedenia sp. (Monogenea: Capsalidae) infecting farmed barramundi (Lates calcarifer)

abstract: Effective parasite management can be achieved through strategically timed treatments that break the life cycle. We examined the effects of temperature (2 °C increments from 22 to 34 °C) and salinity (0, 11, 22, 35, 40‰) on the life cycle (embryonation period, hatching success, oncomiracidia (larvae) longevity, infection success, and time to sexual maturity) of Neobenedenia sp. (Monogenea: Capsalidae), a harmful ectoparasite of farmed marine fishes. Experiments were conducted in controlled conditions in the laboratory. The life cycle was faster in warm, high saline conditions compared to cooler conditions (10-13 days between 26-32 °C, 40‰; 15-16 days between 22-24 °C at 40‰). Warm seawater and high saline conditions (24-32 °C, 35-40‰) improved egg hatching success, reduced time to sexual maturity, and resulted in parasites reaching sexual maturity at a larger size (at 30-32 °C) compared to cooler conditions (22 °C). In contrast, cool, hypersaline conditions (22 °C, 40‰) increased oncomiracidia longevity and infection success. Linear and quantile regression models were used to construct an interactive, online parasite management interface to enable strategic treatment of parasites in aquaculture corresponding to observed temperature and salinity variation on farms in the tropics. It was recommended that farmers treat their stock more frequently during summer (27-31 °C) when parasites can complete their life cycle more quickly. Nevertheless, farmers should be aware of the potential for increased Neobenedenia sp. infections during winter months (21-26 °C) due to increased infection success.


title: The DSM Diagnostic Criteria for Gender Identity Disorder in Children

abstract: In this article, I review the diagnostic criteria for Gender Identity Disorder (GID) in children as they were formulated in the DSM-III, DSM-III-R, and DSM-IV. The article focuses on the cumulative evidence for diagnostic reliability and validity. It does not address the broader conceptual discussion regarding GID as “disorder,” as this issue is addressed in a companion article by Meyer-Bahlburg (2009). This article addresses criticisms of the GID criteria for children which, in my view, can be addressed by extant empirical data. Based in part on reanalysis of data, I conclude that the persistent desire to be of the other gender should, in contrast to DSM-IV, be a necessary symptom for the diagnosis. If anything, this would result in a tightening of the diagnostic criteria and may result in a better separation of children with GID from children who display marked gender variance, but without the desire to be of the other gender.


title: 5G-ICN: Delivering ICN Services over 5G Using Network Slicing

abstract: The challenging requirements of 5G, from both the applications and architecture perspectives, motivate the need to explore the feasibility of delivering services over new network architectures. As 5G proposes application-centric network slicing, which enables the use of new data planes realizable over a programmable compute, storage, and transport infrastructure, we consider information- centric networking as a candidate network architecture to realize 5G objectives. This can coexist with end-to-end IP services that are offered today. To this effect, we first propose a 5G-ICN architecture and compare its benefits (i.e., innovative services offered by leveraging ICN features) to current 3GPP-based mobile architectures. We then introduce a general application-driven framework that emphasizes the flexibility afforded by network functions virtualization and software defined networking over which 5G-ICN can be realized. We specifically focus on the issue of how mobility as a service (MaaS) can be realized as a 5G-ICN slice, and give an in-depth overview on resource provisioning and inter-dependencies and coordination among functional 5G-ICN slices to meet the MaaS objectives. The article tries to show the flexibility of delivering services over ICN where virtualization of control and data plane can be used by applications to meet complex service logic execution while creating value to its end users.


title: A review on switched capacitor converters with high power density for OLED lamp driving

abstract: This paper presents a review of high-power-density non-resonant and resonant switched-capacitor (SC) converters topologies for driving organic-lighting-emitting-diode (OLEDs) lamps. These converters are light, small and cheaper in comparison with the linear power supplies and conventional DC-DC converters owing to the avoidance of large magnetic components. Resonant SC (RSC) converters show high efficiency and low EMI noise because they employ a small resonant inductor, which is connected in series with a switched capacitor in order to resonate at a frequency higher than switching frequency, thus allowing them to attain zero-current-switching (ZCS). The bidirectional step-up RSC converter has been identified as one of the best candidates to be used as an OLED driver. Simulation and experimental results from a laboratory prototype designed to supply a 60V 25W OLED lamp from a dc 30 V voltage source are presented.


title: Learning Visual Storylines with Skipping Recurrent Neural Networks

abstract: What does a typical visit to Paris look like? Do people first take photos of the Louvre and then the Eiffel Tower? Can we visually model a temporal event like “Paris Vacation” using current frameworks? In this paper, we explore how we can automatically learn the temporal aspects, or storylines of visual concepts from web data. Previous attempts focus on consecutive image-to-image transitions and are unsuccessful at recovering the long-term underlying story. Our novel Skipping Recurrent Neural Network (S-RNN) model does not attempt to predict each and every data point in the sequence, like classic RNNs. Rather, S-RNN uses a framework that skips through the images in the photo stream to explore the space of all ordered subsets of the albums via an efficient sampling procedure. This approach reduces the negative impact of strong short-term correlations, and recovers the latent story more accurately. We show how our learned storylines can be used to analyze, predict, and summarize photo albums from Flickr. Our experimental results provide strong qualitative and quantitative evidence that S-RNN is significantly better than other candidate methods such as LSTMs on learning long-term correlations and recovering latent storylines. Moreover, we show how storylines can help machines better understand and summarize photo streams by inferring a brief personalized story of each individual album.


title: DEMONSTRATION OF REDUCED AIRPORT CONGESTION THROUGH PUSHBACK RATE CONTROL

abstract: Airport surface congestion results in significant increases in taxi times, fuel burn and emissions at major airports. This paper describes the field tests of a congestion control strategy at Boston Logan International Airport. The approach determines a suggested rate to meter pushbacks from the gate, in order to prevent the airport surface from entering congested states and to reduce the time that flights spend with engines on while taxiing to the runway. The field trials demonstrated that significant benefits were achievable through such a strat- egy: during eight four-hour tests conducted during August and September 2010, fuel use was reduced by an estimated 12,000- 15,000 kg (3,900-4,900 US gallons), while aircraft gate pushback times were increased by an average of only 4.3 minutes for the 247 flights that were held at the gate.


title: Dynamic Model of the Octopus Arm. II. Control of Reaching Movements

abstract: The dynamic model of the octopus arm described in the first paper of this 2-part series was used here to investigate the neural strategies used for controlling the reaching movements of the octopus arm. These are stereotypical extension movements used to reach toward an object. In the dynamic model, sending a simple propagating neural activation signal to contract all muscles along the arm produced an arm extension with kinematic properties similar to those of natural movements. Control of only 2 parameters fully specified the extension movement: the amplitude of the activation signal (leading to the generation of muscle force) and the activation traveling time (the time the activation wave takes to travel along the arm). We found that the same kinematics could be achieved by applying activation signals with different activation amplitudes all exceeding some minimal level. This suggests that the octopus arm could use minimal amplitudes of activation to generate the minimal muscle forces required for the production of the desired kinematics. Larger-amplitude signals would generate larger forces that increase the arm’s stability against perturbations without changing the kinematic characteristics. The robustness of this phenomenon was demonstrated by examining activation signals with either a constant or a bell-shaped velocity profile. Our modeling suggests that the octopus arm biomechanics may allow independent control of kinematics and resistance to perturbation during arm extension movements.


title: Runge-Kutta discontinuous Galerkin method using a new type of WENO limiters on unstructured meshes

abstract: In this paper we generalize a new type of limiters based on the weighted essentially non-oscillatory (WENO) finite volume methodology for the Runge–Kutta discontinuous Galerkin (RKDG) methods solving nonlinear hyperbolic conservation laws, which were recently developed in [32] for structured meshes, to two-dimensional unstructured triangular meshes. The key idea of such limiters is to use the entire polynomials of the DG solutions from the troubled cell and its immediate neighboring cells, and then apply the classical WENO procedure to form a convex combination of these polynomials based on smoothness indicators and nonlinear weights, with suitable adjustments to guarantee conservation. The main advantage of this new limiter is its simplicity in implementation, especially for the unstructured meshes considered in this paper, as only information from immediate neighbors is needed and the usage of complicated geometric information of the meshes is largely avoided. Numerical results for both scalar equations and Euler systems of compressible gas dynamics are provided to illustrate the good performance of this procedure.


title: How the eye measures reality and virtual reality

abstract: If virtual reality systems are to make good on their name, designers must know how people perceive space in natural environments, in photographs, and in cinema. Perceivers understand the layout of a cluttered natural environment through the use of nine or more sources of information, each based on different assumptions—occlusion, height in the visual field, relative size, relative density, aerial perspective, binocular disparities, accommodation, convergence, and motion perspective. The relative utility of these sources at different distances is compared, using their ordinal depth-threshold functions. From these, three classes of space around a moving observer are postulated: personal space, action space, and vista space. Within each, a smaller number of sources act in consort, with different relative strengths. Given the general ordinality of the sources, these spaces are likely to be affine in character, stretching and collapsing with viewing conditions. One of these conditions is controlled by lens length in photography and cinematography or by field-of-view commands in computer graphics. These have striking effects on many of these sources of information and, consequently, on how the layout of a scene is perceived.


title: Incremental Parsing with Minimal Features Using Bi-Directional LSTM

abstract: Recently, neural network approaches for parsing have largely automated the combination of individual features, but still rely on (often a larger number of) atomic features created from human linguistic intuition, and potentially omitting important global context. To further reduce feature engineering to the bare minimum, we use bi-directional LSTM sentence representations to model a parser state with only three sentence positions, which automatically identifies important aspects of the entire sentence. This model achieves state-of-the-art results among greedy dependency parsers for English. We also introduce a novel transition system for constituency parsing which does not require binarization, and together with the above architecture, achieves state-of-the-art results among greedy parsers for both English and Chinese.


title: Leveraging existing tests in automated test generation for web applications

abstract: To test web applications, developers currently write test cases in frameworks such as Selenium. On the other hand, most web test generation techniques rely on a crawler to explore the dynamic states of the application. The first approach requires much manual effort, but benefits from the domain knowledge of the developer writing the test cases. The second one is automated and systematic, but lacks the domain knowledge required to be as effective. We believe combining the two can be advantageous. In this paper, we propose to (1) mine the human knowledge present in the form of input values, event sequences, and assertions, in the human-written test suites, (2) combine that inferred knowledge with the power of automated crawling, and (3) extend the test suite for uncovered/unchecked portions of the web application under test. Our approach is implemented in a tool called Testilizer. An evaluation of our approach indicates that Testilizer (1) outperforms a random test generator, and (2) on average, can generate test suites with improvements of up to 150% in fault detection rate and up to 30% in code coverage, compared to the original test suite.


title: Automatic contrast enhancement of low-light images based on local statistics of wavelet coefficients

abstract: This paper describes a new method for contrast enhancement in images and image sequences of low-light or unevenly illuminated scenes based on statistical modelling of wavelet coefficients of the image. A non-linear enhancement function has been designed based on the local dispersion of the wavelet coefficients modelled as a bivariate Cauchy distribution. Within the same statistical framework, a simultaneous noise reduction in the image is performed by means of a shrinkage function, thus preventing noise amplification. The proposed enhancement method has been shown to perform very well with insufficiently illuminated and noisy imagery, outperforming other conventional methods, in terms of contrast enhancement and noise reduction in the output data.


title: Optimised Multiplication Architectures for Accelerating Fully Homomorphic Encryption

abstract: Large integer multiplication is a major performance bottleneck in fully homomorphic encryption (FHE) schemes over the integers. In this paper two optimised multiplier architectures for large integer multiplication are proposed. The first of these is a low-latency hardware architecture of an integer-FFT multiplier. Secondly, the use of low Hamming weight (LHW) parameters is applied to create a novel hardware architecture for large integer multiplication in integer-based FHE schemes. The proposed architectures are implemented, verified and compared on the Xilinx Virtex-7 FPGA platform. Finally, the proposed implementations are employed to evaluate the large multiplication in the encryption step of FHE over the integers. The analysis shows a speed improvement factor of up to 26.2 for the low-latency design compared to the corresponding original integer-based FHE software implementation. When the proposed LHW architecture is combined with the low-latency integer-FFT accelerator to evaluate a single FHE encryption operation, the performance results show that a speed improvement by a factor of approximately 130 is possible.


title: A Novel Approximation to Dynamic Time Warping allows Anytime Clustering of Massive Time Series Datasets

abstract: Given the ubiquity of time series data, the data mining community has spent significant time investigating the best time series similarity measure to use for various tasks and domains. After more than a decade of extensive efforts, there is increasing evidence that Dynamic Time Warping (DTW) is very difficult to beat. Given that, recent efforts have focused on making the intrinsically slow DTW algorithm faster. For the similarity-search task, an important subroutine in many data mining algorithms, significant progress has been made by replacing the vast majority of expensive DTW calculations with cheap-to-compute lower bound calculations. However, these lower bound based optimizations do not directly apply to clustering, and thus for some realistic problems, clustering with DTW can take days or weeks. In this work, we show that we can mitigate this untenable lethargy by casting DTW clustering as an anytime algorithm. At the heart of our algorithm is a novel data-adaptive approximation to DTW which can be quickly computed, and which produces approximations to DTW that are much better than the best currently known linear-time approximations. We demonstrate our ideas on real world problems showing that we can get virtually all the accuracy of a batch DTW clustering algorithm in a fraction of the time.


title: Consistency-preserving edit scripts in model versioning

abstract: In model-based software development, models are iteratively evolved. To optimally support model evolution, developers need adequate tools for model versioning tasks, including comparison, patching, and merging of models. A significant disadvantage of tools currently available is that they display, and operate with, low-level model changes which refer to internal model representations and which can lead to intermediate inconsistent states. Higher-level consistency-preserving edit operations including refactorings are better suited to explain changes or to resolve conflicts. This paper presents an automatic procedure which transforms a low-level difference into an executable edit script which uses consistency-preserving edit operations only. Edit scripts support consistent model patching and merging on a higher abstraction level. Our approach to edit script generation has been evaluated in a larger real-world case study.


title: Unix Domain Sockets Applied in Android Malware Should Not Be Ignored

abstract: Increasingly, malicious Android apps use various methods to steal private user data without their knowledge. Detecting the leakage of private data is the focus of mobile information security. An initial investigation found that none of the existing security analysis systems can track the flow of information through Unix domain sockets to detect the leakage of private data through such sockets, which can result in zero-day exploits in the information security field. In this paper, we conduct the first systematic study on Unix domain sockets as applied in Android apps. Then, we identify scenarios in which such apps can leak private data through Unix domain sockets, which the existing dynamic taint analysis systems do not catch. Based on these insights, we propose and implement JDroid, a taint analysis system that can track information flows through Unix domain sockets effectively to detect such privacy leaks.


An Improved Genetic Algorithm for Vehicle Routing Problem with Time Windows


title: Architectural Limitations in Multi-User Computer-Aided Engineering Applications

abstract: The engineering design process evolves products by a collaborative synthesis of specifications, personnel and organizations. Unfortunately, collaborative effectiveness is thwarted by existing single-user computer-aided applications like computer-aided design, computer-aided analysis, and others. These applications and associated file management systems assign editing rights to one technical person, e.g., a designer, analyst, or a process planner. In the absence of collaborative computer-aided engineering applications, we conducted a survey to establish that product collaboration is limited to interactive, either formal or ad-hoc design sessions, social communication tools, serial model sharing, terminal/screen sharing, and to conference call interactions. Current computer-aided (CAx) tools do not permit simultaneous model changes by a collaborative team editing the same model. Although over a decade of prior research has demonstrated multi-user feasibility for computer-aided applications, the architectural breadth of this research has apparently not yet compelled developers and end-users to develop and adopt new multi-user computer-aided applications devoted to product development. Why have collaborative engineering CAx tools not been commercialized for mainstream use? This paper uses several multi-user prototypes, including the first Computer-Aided Engineering multi-user prototype called CUBIT Connect, to expose additional architectural hurdles to implementing new multi-user collaborative paradigms. These challenges relate to variable algorithmic performance times, multi-threading and event driven client notification processes, distributed access level security, and model change management in design sessions.


title: Early detection and treatment of severe sepsis in the emergency department: identifying barriers to implementation of a protocol-based approach.

abstract: INTRODUCTION ::: Despite evidence to support efficacy of early goal-directed therapy for resuscitation of patients with severe sepsis and septic shock in the emergency department, implementation remains incomplete. To identify and address specific barriers at our institution and maximize benefits of a planned sepsis treatment initiative, a baseline assessment of knowledge, attitudes, and behaviors regarding detection and treatment of severe sepsis was performed. ::: ::: ::: METHODS ::: An online survey was offered to nurses and physicians in the emergency department of a major urban academic medical center. The questionnaire was designed to assess (1) baseline knowledge and self-reported confidence in identification of systemic inflammatory response syndrome and sepsis; (2) current practices in treatment; (3) difficulties encountered in managing sepsis cases; (4) perceived barriers to implementation of a clinical pathway based on early quantitative resuscitation goals; and (5) to elicit suggestions for improvement of sepsis treatment within the department. ::: ::: ::: RESULTS ::: Respondents (n = 101) identified barriers to a quantitative resuscitation protocol for sepsis. These barriers included the inability to perform central venous pressure/central venous oxygen saturation monitoring, limited physical space in the emergency department, and lack of sufficient nursing staff. Among nurses, the greatest perceived contributor to delays in treatment was a delay in diagnosis by physicians; among physicians, a delay in availability of ICU beds and nursing delays were the greatest barriers. Despite these issues, respondents indicated that a written protocol would be helpful to them. ::: ::: ::: DISCUSSION ::: Knowledge gaps and procedural hurdles identified by the survey will inform both educational and process components of an initiative to improve sepsis care in the emergency department.


title: MNE software for processing MEG and EEG data

abstract: Magnetoencephalography and electroencephalography (M/EEG) measure the weak electromagnetic signals originating from neural currents in the brain. Using these signals to characterize and locate brain activity is a challenging task, as evidenced by several decades of methodological contributions. MNE, whose name stems from its capability to compute cortically-constrained minimum-norm current estimates from M/EEG data, is a software package that provides comprehensive analysis tools and workflows including preprocessing, source estimation, time–frequency analysis, statistical analysis, and several methods to estimate functional connectivity between distributed brain regions. The present paper gives detailed information about the MNE package and describes typical use cases while also warning about potential caveats in analysis. The MNE package is a collaborative effort of multiple institutes striving to implement and share best methods and to facilitate distribution of analysis pipelines to advance reproducibility of research. Full documentation is available at http://martinos.org/mne.


title: Large Scale Distributed Distance Metric Learning

abstract: In large scale machine learning and data mining problems with high feature dimensionality, the Euclidean distance between data points can be uninformative, and Distance Metric Learning (DML) is often desired to learn a proper similarity measure (using side information such as example data pairs being similar or dissimilar). However, high dimensionality and large volume of pairwise constraints in modern big data can lead to prohibitive computational cost for both the original DML formulation in Xing et al. (2002) and later extensions. In this paper, we present a distributed algorithm for DML, and a large-scale implementation on a parameter server architecture. Our approach builds on a parallelizable reformulation of Xing et al. (2002), and an asynchronous stochastic gradient descent optimization procedure. To our knowledge, this is the first distributed solution to DML, and we show that, on a system with 256 CPU cores, our program is able to complete a DML task on a dataset with 1 million data points, 22-thousand features, and 200 million labeled data pairs, in 15 hours; and the learned metric shows great effectiveness in properly measuring distances.


title: Panoramic video textures

abstract: This paper describes a mostly automatic method for taking the output of a single panning video camera and creating a panoramic video texture (PVT): a video that has been stitched into a single, wide field of view and that appears to play continuously and indefinitely. The key problem in creating a PVT is that although only a portion of the scene has been imaged at any given time, the output must simultaneously portray motion throughout the scene. Like previous work in video textures, our method employs min-cut optimization to select fragments of video that can be stitched together both spatially and temporally. However, it differs from earlier work in that the optimization must take place over a much larger set of data. Thus, to create PVTs, we introduce a dynamic programming step, followed by a novel hierarchical min-cut optimization algorithm. We also use gradient-domain compositing to further smooth boundaries between video fragments. We demonstrate our results with an interactive viewer in which users can interactively pan and zoom on high-resolution PVTs.


Revitalization of Elderly Community in Aging Society


title: ALEX III: A novel robotic platform with 12 DOFs for human gait training

abstract: ALEX III is a bilateral exoskeleton for gait rehabilitation. It is an evolution of two previous prototypes - ALEX and ALEX II - developed at the University of Delaware. The new robot comprises a support platform and two robotic legs. Its unique characteristic is the possibility to actively control 12 degrees-of-freedom: 4 at the pelvis and 4 for each leg. This paper focuses on the design and fabrication of the robotic leg. Results from early evaluations are presented where the robotic leg is attached to a fixed frame and controlled with a zero-interaction controller.


title: How gamification can influence the web design and the customer to use the e-banking systems

abstract: The gamification is growing in e-business and the banks are looking for new ways to get more customers on their websites. Therefore, it is important to study what are the most appreciated features of the website that could influence the behaviour of the customer to use an electronic banking system with game features. The gamified e-banking suggests that rich elements/features associated with the games could influence other variables and therefore increasing the client loyalty, to spend more time and increasing the transactions on the website. The aim of this study is to look into the influence of gamification in the e-banking system. Based on the research of 180 publications and 210 variables that could influence the intention to use a certain technology this study develops a theoretical model representing the gamification influence on ease of use, information, web pages characteristics, web design and on the intention to use an e-banking with game features. The results from an online survey of 219 e-banking customers show that the gamification had a positive impact on all variables; special has a medium positive influence in web design and information and a large positive influence on customer intentions to use. Further analysis shows that the website ease of use plays has also a medium positive influence on the intention to use an e-banking gamified. Our findings also show that the clients give more importance to an attractive graphical and architecture website design, and less to web pages with so much information or having pleasure in using an e-banking system.


title: The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo

abstract: Hamiltonian Monte Carlo (HMC) is a Markov chain Monte Carlo (MCMC) algorithm that avoids the random walk behavior and sensitivity to correlated parameters that plague many MCMC methods by taking a series of steps informed by first-order gradient information. These features allow it to converge to high-dimensional target distributions much more quickly than simpler methods such as random walk Metropolis or Gibbs sampling. However, HMC's performance is highly sensitive to two user-specified parameters: a step size {\epsilon} and a desired number of steps L. In particular, if L is too small then the algorithm exhibits undesirable random walk behavior, while if L is too large the algorithm wastes computation. We introduce the No-U-Turn Sampler (NUTS), an extension to HMC that eliminates the need to set a number of steps L. NUTS uses a recursive algorithm to build a set of likely candidate points that spans a wide swath of the target distribution, stopping automatically when it starts to double back and retrace its steps. Empirically, NUTS perform at least as efficiently as and sometimes more efficiently than a well tuned standard HMC method, without requiring user intervention or costly tuning runs. We also derive a method for adapting the step size parameter {\epsilon} on the fly based on primal-dual averaging. NUTS can thus be used with no hand-tuning at all. NUTS is also suitable for applications such as BUGS-style automatic inference engines that require efficient"turnkey"sampling algorithms.


title: Point Pair Feature-Based Pose Estimation with Multiple Edge Appearance Models (PPF-MEAM) for Robotic Bin Picking

abstract: Automation of the bin picking task with robots entails the key step of pose estimation, which identifies and locates objects so that the robot can pick and manipulate the object in an accurate and reliable way. This paper proposes a novel point pair feature-based descriptor named Boundary-to-Boundary-using-Tangent-Line (B2B-TL) to estimate the pose of industrial parts including some parts whose point clouds lack key details, for example, the point cloud of the ridges of a part. The proposed descriptor utilizes the 3D point cloud data and 2D image data of the scene simultaneously, and the 2D image data could compensate the missing key details of the point cloud. Based on the descriptor B2B-TL, Multiple Edge Appearance Models (MEAM), a method using multiple models to describe the target object, is proposed to increase the recognition rate and reduce the computation time. A novel pipeline of an online computation process is presented to take advantage of B2B-TL and MEAM. Our algorithm is evaluated against synthetic and real scenes and implemented in a bin picking system. The experimental results show that our method is sufficiently accurate for a robot to grasp industrial parts and is fast enough to be used in a real factory environment.


title: The Value of Merge-Join and Hash-Join in SQL Server

abstract: Microsoft SQL Server was successful for many years for transaction processing and decision sup- port workloads with neither merge join nor hash join, relying entirely on nested loops and index nested loops join. How much difference do addi- tional join algorithms really make, and how much system performance do they actually add? In a pure OLTP workload that requires only record-to-record navigation, intuition agrees that index nested loops join is sufficient. For a DSS workload, however, the question is much more complex. To answer this question, we have analyzed TPC-D query perform- ance using an internal build of SQL Server with merge-join and hash-join enabled and disabled. It shows that merge join and hash join are both re- quired to achieve the best performance for decision support workloads.


title: Virtual network functions placement and routing optimization

abstract: Network Functions Virtualization (NFV) is incrementally deployed by Internet Service Providers (ISPs) in their carrier networks, by means of Virtual Network Function (VNF) chains, to address customers' demands. The motivation is the increasing manageability, reliability and performance of NFV systems, the gains in energy and space granted by virtualization, at a cost that becomes competitive with respect to legacy physical network function nodes. From a network optimization perspective, the routing of VNF chains across a carrier network implies key novelties making the VNF chain routing problem unique with respect to the state of the art: the bitrate of each demand flow can change along a VNF chain, the VNF processing latency and computing load can be a function of the demands traffic, VNFs can be shared among demands, etc. In this paper, we provide an NFV network model suitable for ISP operations. We define the generic VNF chain routing optimization problem and devise a mixed integer linear programming formulation. By extensive simulation on realistic ISP topologies, we draw conclusions on the trade-offs achievable between legacy Traffic Engineering (TE) ISP goals and novel combined TE-NFV goals.


title: Cross-database non-frontal facial expression recognition based on transductive deep transfer learning

abstract: Cross-database non-frontal expression recognition is a very meaningful but rather difficult subject in the fields of computer vision and affect computing. In this paper, we proposed a novel transductive deep transfer learning architecture based on widely used VGGface16-Net for this problem. In this framework, the VGGface16-Net is used to jointly learn an common optimal nonlinear discriminative features from the non-frontal facial expression samples between the source and target databases and then we design a novel transductive transfer layer to deal with the cross-database non-frontal facial expression classification task. In order to validate the performance of the proposed transductive deep transfer learning networks, we present extensive crossdatabase experiments on two famous available facial expression databases, namely the BU-3DEF and the Multi-PIE database. The final experimental results show that our transductive deep transfer network outperforms the state-of-the-art cross-database facial expression recognition methods.


title: A hybrid system for reducing the false alarm rate of anomaly intrusion detection system

abstract: In this paper, we propose a hybrid intrusion detection system that combines k-Means, and two classifiers: K-nearest neighbor and Naive Bayes for anomaly detection. It consists of selecting features using an entropy based feature selection algorithm which selects the important attributes and removes the irredundant attributes. This algorithm operates on the KDD-99 Data set; this data set is used worldwide for evaluating the performance of different intrusion detection systems. The next step is clustering phase using k-Means. We have used the KDD99 (knowledge Discovery and Data Mining) intrusion detection contest. This system can detect the intrusions and further classify them into four categories: Denial of Service (DoS), U2R (User to Root), R2L (Remote to Local), and probe. The main goal is to reduce the false alarm rate of IDS1.


title: Anatomy of the Ankle and Foot

abstract: The ankle and foot complex at times must be mobile and at other times must be quite stable. The bony, ligamentous, and muscular structures of the ankle and foot are presented with respect to their mobility and stability functions. Innervation and vascular supply are discussed. Normal anatomy provides a basis and a reference point for discussion of dysfunction of the ankle and foot.


title: Information Theoretic Model Validation for Spectral Clustering

abstract: Model validation constitutes a fundamental step in data clustering. The central question is: Which cluster model and how many clusters are most appropriate for a certain application? In this study, we introduce a method for the validation of spectral clustering based upon approximation set coding. In particular, we compare correlation and pairwise clustering to analyze the correlations of temporal gene expression profiles. To evaluate and select clustering models, we calculate their reliable informativeness. Experimental results in the context of gene expression analysis show that pairwise clustering yields superior amounts of reliable information. The analysis results are consistent with the Bayesian Information Criterion (BIC), and exhibit higher generality than BIC.


Building compositional semantics and higher-order inference system for a wide-coverage Japanese CCG parser


title: Systematic reviews of the evidence on the nature, extent and effects of food marketing to children. A retrospective summary

abstract: A 2009 systematic review of the international evidence on food and beverage marketing to children is the most recent internationally comprehensive review of the evidence base. Its findings are consistent with other independent, rigorous reviews conducted during the period 2003–2012. Food promotions have a direct effect on children’s nutrition knowledge, preferences, purchase behaviour, consumption patterns and diet-related health. Current marketing practice predominantly promotes low nutrition foods and beverages. Rebalancing the food marketing landscape’ is a recurring policy aim of interventions aimed at constraining food and beverage promotions to children. The collective review evidence on marketing practice indicates little progress towards policy aims has been achieved during the period 2003–2012. There is a gap in the evidence base on how substantive policy implementation can be achieved. We recommend a priority for future policy relevant research is a greater emphasis on translational research. A global framework for co-ordinated intervention to constrain unhealthy food marketing which has received high level support provides valuable insight on some aspects of immediate implementation research priorities.


title: Data Mining in Education

abstract: Data mining techniques are used to extract useful ::: knowledge from raw data. The extracted knowledge is valuable ::: and significantly affects the decision maker. Educational data ::: mining (EDM) is a method for extracting useful information ::: that could potentially affect an organization. The increase of ::: technology use in educational systems has led to the storage ::: of large amounts of student data, which makes it important ::: to use EDM to improve teaching and learning processes. EDM ::: is useful in many different areas including identifying at-risk ::: students, identifying priority learning needs for different groups ::: of students, increasing graduation rates, effectively assessing ::: institutional performance, maximizing campus resources, and ::: optimizing subject curriculum renewal. This paper surveys the ::: relevant studies in the EDM field and includes the data and ::: methodologies used in those studies


title: Acute effects of Surya Namaskar on the cardiovascular & metabolic system

abstract: Summary With the recent rise in obesity awareness and the increased understanding of the importance of physical activity in promoting overall health, greater emphasis has been placed on improving physical fitness to enhance quality of life. Surya Namaskar, a component of Hatha Yoga, has been practiced by Asian Indians for hundreds of years and is often used in place of a typical fitness program. It consists of a series of postures (asanas) that are repeated 12 times per round. Only one published study has looked specifically at Surya Namaskar, measuring the energy cost of individual asanas ( Sinha et al., 2004 ). However, practitioners typically perform several rounds of the asanas during a session. Purpose To assess the cardiorespiratory and metabolic responses of four rounds of Surya Namaskar, a typical amount performed by practitioners, to determine its potential as a training and weight loss tool. Methods Six healthy Asian Indian men and women (18–22 years) who had trained in Surya Namaskar for over two years participated in the study. Testing was completed in a single session lasting about 30 min. To measure heart rate and oxygen consumption while performing the four rounds, participants were connected to a heart rate monitor and the Oxycon Mobile Metabolic System. Results Participants exercised at 80% of age-predicted maximal heart rate (HRmax) during Round 2, 84% during Round 3, and 90% during Round 4. Average intensity during the four rounds was 80% HRmax, sufficient to elicit a cardiorespiratory training effect. Oxygen consumption averaged 26 ml/kg/min during each round, resulting in an energy expenditure of 230 kcals during a 30 min session for a 60 kg individual. Conclusion Regular practice of Surya Namaskar may maintain or improve cardiorespiratory fitness, as well as promote weight management.


title: Simulations of Ar gas-puff Z-pinch radiation sources with double shells and central jets on the Z generator

abstract: Radiation-magnetohydrodynamic simulations using the non-local thermodynamic equilibrium Mach2-Tabular Collisional-Radiative Equilibrium code in (r, z) geometry are performed for two pairs of recent Ar gas-puff Z-pinch experiments on the refurbished Z generator with an 8 cm diameter nozzle. One pair of shots had an outer-to-inner shell mass ratio of 1:1.6 and a second pair had a ratio of 1:1. In each pair, one of the shots had a central jet. The experimental trends in the Ar K-shell yield and power are reproduced in the calculations. However, the K-shell yield and power are significantly lower than the other three shots for the case of a double-shell puff of 1:1 mass ratio and no central jet configuration. Further simulations of a hypothetical experiment with the same relative density profile of this configuration, but higher total mass, show that the coupled energy from the generator and the K-shell yield can be increased to levels achieved in the other three configurations, but not the K-shell power. Bas...


Classifying Land Cover from Satellite Images Using Time Series Analytics


title: Automotive radar target characterization from 22 to 29 GHz and 76 to 81 GHz

abstract: The radar signatures of automotive targets were measured from 22 to 29 GHz and 76 to 81 GHz inside an anechoic chamber using a vector network analyzer. Radar cross section maps of a sedan, truck, van, pedestrian, motorcycle, and bicycle as a function of effective radar sensor bandwidth, center frequency, and polarization are presented.


title: Privacy-Preserving Public Auditing for Data Storage Security in Cloud Computing

abstract: Cloud Computing is the long dreamed vision of computing as a utility, where users can remotely store their data into the cloud so as to enjoy the on-demand high quality applications and services from a shared pool of configurable computing resources. By data outsourcing, users can be relieved from the burden of local data storage and maintenance. However, the fact that users no longer have physical possession of the possibly large size of outsourced data makes the data integrity protection in Cloud Computing a very challenging and potentially formidable task, especially for users with constrained computing resources and capabilities. Thus, enabling public auditability for cloud data storage security is of critical importance so that users can resort to an external audit party to check the integrity of outsourced data when needed. To securely introduce an effective third party auditor (TPA), the following two fundamental requirements have to be met: 1) TPA should be able to efficiently audit the cloud data storage without demanding the local copy of data, and introduce no additional on-line burden to the cloud user; 2) The third party auditing process should bring in no new vulnerabilities towards user data privacy. In this paper, we utilize and uniquely combine the public key based homomorphic authenticator with random masking to achieve the privacy-preserving public cloud data auditing system, which meets all above requirements. To support efficient handling of multiple auditing tasks, we further explore the technique of bilinear aggregate signature to extend our main result into a multi-user setting, where TPA can perform multiple auditing tasks simultaneously. Extensive security and performance analysis shows the proposed schemes are provably secure and highly efficient.


title: A Tour of TensorFlow

abstract: Deep learning is a branch of artificial intelligence employing deep neural network architectures that has significantly advanced the state-of-the-art in computer vision, speech recognition, natural language processing and other domains. In November 2015, Google released $\textit{TensorFlow}$, an open source deep learning software library for defining, training and deploying machine learning models. In this paper, we review TensorFlow and put it in context of modern deep learning concepts and software. We discuss its basic computational paradigms and distributed execution model, its programming interface as well as accompanying visualization toolkits. We then compare TensorFlow to alternative libraries such as Theano, Torch or Caffe on a qualitative as well as quantitative basis and finally comment on observed use-cases of TensorFlow in academia and industry.


title: Evolutionary cooperative spectrum sensing game: how to collaborate?

abstract: Cooperative spectrum sensing has been shown to be able to greatly improve the sensing performance in cognitive radio networks. However, if cognitive users belong to different service providers, they tend to contribute less in sensing in order to increase their own throughput. In this paper, we propose an evolutionary game framework to answer the question of "how to collaborate" in multiuser de-centralized cooperative spectrum sensing, because evolutionary game theory provides an excellent means to address the strategic uncertainty that a user/player may face by exploring different actions, adaptively learning during the strategic interactions, and approaching the best response strategy under changing conditions and environments using replicator dynamics. We derive the behavior dynamics and the evolutionarily stable strategy (ESS) of the secondary users. We then prove that the dynamics converge to the ESS, which renders the possibility of a de-centralized implementation of the proposed sensing game. According to the dynamics, we further develop a distributed learning algorithm so that the secondary users approach the ESS solely based on their own payoff observations. Simulation results show that the average throughput achieved in the proposed cooperative sensing game is higher than the case where secondary users sense the primary user individually without cooperation. The proposed game is demonstrated to converge to the ESS, and achieve a higher system throughput than the fully cooperative scenario, where all users contribute to sensing in every time slot.


title: Empirical studies of software engineering: a roadmap

abstract: In this article we summarize the strengths and weaknesses of empirical research in software engineering. We argue that in order to improve the current situation we must create better studies and draw more credible interpretations from them. We finally present a roadmap for this improvement, which includes a general structure for software empirical studies and concrete steps for achieving these goals: designing better studies, collecting data more effectively, and involving others in our empirical enterprises.


title: Understanding the somatic consequences of depression: biological mechanisms and the role of depression symptom profile

abstract: Depression is the most common psychiatric disorder worldwide. The burden of disease for depression goes beyond functioning and quality of life and extends to somatic health. Depression has been shown to subsequently increase the risk of, for example, cardiovascular, stroke, diabetes and obesity morbidity. These somatic consequences could partly be due to metabolic, immuno-inflammatory, autonomic and hypothalamic-pituitary-adrenal (HPA)-axis dysregulations which have been suggested to be more often present among depressed patients. Evidence linking depression to metabolic syndrome abnormalities indicates that depression is especially associated with its obesity-related components (for example, abdominal obesity and dyslipidemia). In addition, systemic inflammation and hyperactivity of the HPA-axis have been consistently observed among depressed patients. Slightly less consistent observations are for autonomic dysregulation among depressed patients. The heterogeneity of the depression concept seems to play a differentiating role: metabolic syndrome and inflammation up-regulations appear more specific to the atypical depression subtype, whereas hypercortisolemia appears more specific for melancholic depression. This review finishes with potential treatment implications for the downward spiral in which different depressive symptom profiles and biological dysregulations may impact on each other and interact with somatic health decline.


On-To-Knowledge Methodology - Final Version


title: Item Response Theory: What It Is and How You Can Use the IRT Procedure to Apply It

abstract: Item response theory (IRT) is concerned with accurate test scoring and development of test items. You design test items to measure various kinds of abilities (such as math ability), traits (such as extroversion), or behavioral characteristics (such as purchasing tendency). Responses to test items can be binary (such as correct or incorrect responses in ability tests) or ordinal (such as degree of agreement on Likert scales). Traditionally, IRT models have been used to analyze these types of data in psychological assessments and educational testing. With the use of IRT models, you can not only improve scoring accuracy but also economize test administration by adaptively using only the discriminative items. These features might explain why in recent years IRT models have become increasingly popular in many other fields, such as medical research, health sciences, quality-of-life research, and even marketing research. This paper describes a variety of IRT models, such as the Rasch model, two-parameter model, and graded response model, and demonstrates their application by using real-data examples. It also shows how to use the IRT procedure, which is new in SAS/STAT ® 13.1, to calibrate items, interpret item characteristics, and score respondents. Finally, the paper explains how the application of IRT models can help improve test scoring and develop better tests. You will see the value in applying item response theory, possibly in your own organization!


title: Analysis of Multilayer Neural Networks for Object Recognition

abstract: In 2012 Krizhevsky et al. demonstrated that a Convolutional Neural Network (CNN) trained on large amount of data as part of the ImageNet challenge significantly outperformed traditional computer vision approaches on image classification. Subsequently, Girshick et al. (2013) exploited these features to establish the new state of the art on PASCAL object detection. This suggests that computer vision maybe in the process of a feature revolution akin to that following SIFT and HOG nearly a decade ago. It is therefore important to get more insights into features learned by these networks. Our work provides answer to the following four questions: (a) What happens during finetuning of a discriminatively pretrained network? (b) How much information is in the location and how much of it is in the magnitude of filter activation? (c) Does a multilayer CNN contain Grand-Mother Cells? How distributed is the code? (d) How does training of CNN progress over time? Does too much pre-training hurt generalization performance?


title: Reduced complexity channel models for IMT-advanced evaluation

abstract: Accuracy and complexity are two crucial aspects of the applicability of a channel model for wideband multiple input multiple output (MIMO) systems. For small number of antenna element pairs, correlation-based models have lower computational complexity while the geometry-based stochastic models (GBSMs) can provide more accurate modeling of real radio propagation. This paper investigates several potential simplifications of the GBSM to reduce the complexity with minimal impact on accuracy. In addition, we develop a set of broadband metrics which enable a thorough investigation of the differences between the GBSMs and the simplified models. The impact of various random variables which are employed by the original GBSM on the system level simulation are also studied. Both simulation results and a measurement campaign show that complexity can be reduced significantly with a negligible loss of accuracy in the proposed metrics. As an example, in the presented scenarios, the computational time can be reduced by up to 57% while keeping the relative deviation of 5% outage capacity within 5%.


title: A sub-µW, 1.0V CMOS temperature sensor circuit insensitive to device parameters

abstract: In this paper, we firstly propose the CMOS temperature sensor circuit operating in the subthreshold region. The circuit has advantages of 1.0 V operation and sub-µW power consumption. Furthermore, it is insensitive to the device parameter of the fabrication process. Next, we design the high sensitivity temperature sensor circuit that can be realized by cascade-connection of the proposed temperature sensor circuits. The proposed circuits were evaluated through HSPICE with a standard 0.35 µm CMOS device parameter. The simulation demonstrate the maximum power consumption is 0.27 µW under the condition that the temperature range is from 0 °C to 100 °C, the sensitivity is 1.01 mV/ °C and the maximum differential error is 0.87 %.


title: Ohmic Contacts for High Power and High Temperature Microelectronics

abstract: The increased requirements to the microelectronics regarding the device potential for work at high temperatures, high powers, and high frequencies and in harsh environments engendered the increased interest to the wide band-gap semiconductors. They are considered as a third generation materials in the semiconductor industry, after Si and Ge, and A3B5 compounds and their solid solutions. Several materials of the wide band-gap semiconductor group such as SiC, III-V nitrides (GaN, AlN, c-BN), ZnSe, and diamond are very important for the device industry. The unique combination of physical properties in these materials allows development of devices, which could be applied in fields where the devices of the first and second generations cannot be used. Whereas Si and GaAs are chemically stable at 400 0C and 650 0C, respectively, SiC and III-V nitrides are stable up to 1000 0C (Meyer & Metzger, 1996). This high thermal stability allows development of new class high temperature and high power devices with maximal working temperature of 600 0C, which is three and four times higher than this one of GaAs and Si devices, respectively. Among the wide band-gap semiconductors, SiC and GaN have been most successfully applied in the device fabrication. These semiconductors offer a higher electric breakdown field (4-20 times), a higher thermal conductivity (3-13 times), and a larger saturated electron drift velocity (2-2.5 times) in comparison with silicon. These features make them very useful materials in development of high temperature and high power devices. The advantages of SiC and III-V nitrides technologies allowed manufacture of SiC-based and GaN-based devices such as unipolar high-voltage power FETs (MOSFET, JFET and HEMT), bipolar power diodes (p-n and p-i-n) and transistors (BJT, IGBT and HBT). The existing applications present many challenges in obtaining high-performance ohmic contacts because they are limiting for device functioning. The ohmic contacts are a critical factor that could restrict the high power and high temperature device application. The high operating temperatures may cause diffusion processes in the contact layer and reactions between the contact components, which could lead to changes of the contact properties during operation at high temperatures, and deterioration of the devices. If the contact resistivity is not sufficiently low inadmissible high voltage drop could arise due to the high current density in the contact of the high power devices. Hence, the following requirements to the ohmic contacts are decisive for application in high power and high temperature microelectronics:


title: Distance dependent centroid to centroid force fields using high resolution decoys

abstract: Simplified force fields play an important role in protein structure prediction and de novo protein design by requiring less computational effort than detailed atomistic potentials. A side chain centroid based, distance dependent pairwise interaction potential has been developed. A linear programming based formulation was used in which non-native “decoy” conformers are forced to take a higher energy compared with the corresponding native structure. This model was trained on an enhanced and diverse protein set. High quality decoy structures were generated for ∼1400 nonhomologous proteins using torsion angle dynamics along with restricted variations of the hydrophobic cores of the native structure. The resulting decoy set was used to train the model yielding two different side chain centroid based force fields that differ in the way distance dependence has been used to calculate energy parameters. These force fields were tested on an independent set of 148 test proteins with 500 decoy structures for each protein. The side chain centroid force fields were successful in correctly identifying ∼86% native structures. The Z-scores produced by the proposed centroid–centroid distance dependent force fields improved compared with other distance dependent Cα-Cα or side chain based force fields. Proteins 2008. © 2007 Wiley-Liss, Inc.


title: Model Predictive Direct Torque Control With Finite Control Set for PMSM Drive Systems, Part 2: Field Weakening Operation

abstract: The direct torque control approach called model predictive direct torque control (MP-DTC) is extended to field-weakening operation in this research. The controller is of the finite control set (FCS) type, which takes the discrete states of the voltage source inverter (VSI) into account. Each sampling period, the future behavior of the plant is predicted and inputs (voltage vectors) are selected using an optimization criterion, i.e., a cost function. Optimization, however, depend on the operation region. Maximum torque per ampere (MTPA) tracking for high electrical efficiency is aimed below rated speed, operation off the MTPA trajectory is necessary for obtaining field-weakening. In this work, a cost function is designed, which is suitable for operation at high speeds without penalizing operation below rated speed. MP-DTC is applied to permanent magnet synchronous machine (PMSM) drive systems. High control performances, i.e., dynamics, is obtained without increasing the switching frequency nor reducing significantly the torque quality. This feature is interesting above all for high power applications. The proposed control strategy has been evaluated on a small-scale (PMSM) drive system with two-level VSI for demonstration showing promising results.


title: Unsupervised Entity Resolution on Multi-type Graphs

abstract: Entity resolution is the task of identifying all mentions that represent the same real-world entity within a knowledge base or across multiple knowledge bases. We address the problem of performing entity resolution on RDF graphs containing multiple types of nodes, using the links between instances of different types to improve the accuracy. For example, in a graph of products and manufacturers the goal is to resolve all the products and all the manufacturers. We formulate this problem as a multi-type graph summarization problem, which involves clustering the nodes in each type that refer to the same entity into one super node and creating weighted links among super nodes that summarize the inter-cluster links in the original graph. Experiments show that the proposed approach outperforms several state-of-the-art generic entity resolution approaches, especially in data sets with missing values and one-to-many, many-to-many relations.


title: MODELLING SPEECH ACTS IN CONVERSATIONAL DISCOURSE

abstract: Computational pragmatics and dialogue analysis is currently a rapid growing area of research in computational linguistics. Over the past five years or so, initiatives in modelling pragmatic aspects of dialogue have led to considerably improved spoken language dialogue systems i?½ so much so in fact that constrained human-computer interaction no longer seems out of the question. ::: ::: One of the main drawbacks to such systems however is highlighted by the word "constrained". Human communication is seldom confined to answering questions or solving problems within a restricted field (such as train timetable enquiries, or route finding, for instance). How can one tell whether theories of dialogue that work well in domain specific, task-oriented dialogue, can be scaled up or expanded to deal with natural conversation? ::: ::: In this dissertation I have carried out a critical survey of the various approaches to speech act modelling, detailing what I think are the strengths and weaknesses in the current theories. One very promising approach is that of using speech act analysis as a means of interpreting a speaker's intentions in producing an utterance. This then forms the basis for determining a hearer's response (following certain rules of conversational co-operation). I go on to present what is intended as a preliminary model, which is designed to capture the characteristic relationship and interaction of speech acts in conversational dialogue, especially those features which preceding research has failed to represent. Speech acts are defined by means of schemata that match the state of the prevailing conversational context space. Each possible contexgt space is specified in the model for the performance of a particular speech act or acts; the representation of the context space is then updated accordingly. ::: ::: I illustrate the theoretical model using real conversation, collected during the course of this research, and compare its performance against the analysis of a "benchmark" conversation, highlighting where the model falls short and how it could be improved in the future. I will argue that the model provides a powerful formalism for the characterisation of a wide variety of different basic speech acts.


title: Resilience to natural hazards: How useful is this concept?

abstract: Resilience is widely seen as a desirable system property in environmental management. This paper explores the concept of resilience to natural hazards, using weather-related hazards in coastal megacities as an example. The paper draws on the wide literature on megacities, coastal hazards, hazard risk reduction strategies, and resilience within environmental management. Some analysts define resilience as a system attribute, whilst others use it as an umbrella concept for a range of system attributes deemed desirable. These umbrella concepts have not been made operational to support planning or management. It is recommended that resilience only be used in a restricted sense to describe specific system attributes concerning (i) the amount of disturbance a system can absorb and still remain within the same state or domain of attraction and (ii) the degree to which the system is capable of self-organisation. The concept of adaptive capacity, which has emerged in the context of climate change, can then be adopted as the umbrella concept, where resilience will be one factor influencing adaptive capacity. This improvement to conceptual clarity would foster much-needed communication between the natural hazards and the climate change communities and, more importantly, offers greater potential in application, especially when attempting to move away from disaster recovery to hazard prediction, disaster prevention, and preparedness.


title: Generalized Attacker and Attack Models for Cyber Physical Systems

abstract: An attacker model is proposed for Cyber Physical Systems (CPS). The attack models derived from the attacker model are used to generate parameterized attack procedures and functions that target a specific CPS. The proposed models capture both physical and cyber attacks and unify a number of existing attack models into a common framework useful for researchers in the experimental assessment of attack detection techniques. The generality of the models is shown by mapping a broad variety of existing attack models to the models proposed here, as well as generating attacks that are not found in the CPS design literature. The models have been used extensively in understanding the impact of cyber attacks on a water treatment system and in the design and assessment of detection mechanisms.


title: Control of elastic soft robots based on real-time finite element method

abstract: In this paper, we present a new method for the control of soft robots with elastic behavior, piloted by several actuators. The central contribution of this work is the use of the Finite Element Method (FEM), computed in real-time, in the control algorithm. The FEM based simulation computes the nonlinear deformations of the robots at interactive rates. The model is completed by Lagrange multipliers at the actuation zones and at the end-effector position. A reduced compliance matrix is built in order to deal with the necessary inversion of the model. Then, an iterative algorithm uses this compliance matrix to find the contribution of the actuators (force and/or position) that will deform the structure so that the terminal end of the robot follows a given position. Additional constraints, like rigid or deformable obstacles, or the internal characteristics of the actuators are integrated in the control algorithm. We illustrate our method using simulated examples of both serial and parallel structures and we validate it on a real 3D soft robot made of silicone.


title: Exact (exponential) algorithms for the dominating set problem

abstract: We design fast exact algorithms for the problem of computing a minimum dominating set in undirected graphs. Since this problem is NP-hard, it comes with no big surprise that all our time complexities are exponential in the number n of vertices. The contribution of this paper are 'nice' exponential time complexities that are bounded by functions of the form c n with reasonably small constants c < 2: For arbitrary graphs we get a time complexity of 1.93782 n . And for the special cases of split graphs, bipartite graphs, and graphs of maximum degree three, we reach time complexities of 1.41422 n , 1.73206 n , and 1.51433, respectively.


title: Bio-inspired trajectory generation for UAV perching

abstract: This paper represents a bio-inspired trajectory generation (path planning) method for UAV perching. The method is based on the tau theory which was established from studying the natural motion behaviors when animals (including humans) approaching and catching a fixed or moving object. In our research, the tau theory is applied to the trajectory generation problem of a UAV perching on a target object. Two perching scenarios have been studied, one from a flight state (with non-zero initial velocity) and one from a hovering state (with zero initial velocity). Three strategies, namely, perching with a straight-line trajectory, perching with pitch angle coupling, and perching with pitch/yaw angular coupling, are demonstrated with each of the two scenarios. The simulation results show that the resulting flight trajectories meet all the desired requirements for a rotary UAV to perch on an object.


title: Passing a Hide-and-Seek Third-Person Turing Test

abstract: Hiding and seeking are cognitive abilities frequently demonstrated by humans in both real life and video games. To determine to which extent these abilities can be replicated with AI, we introduce a specialized version of the Turing test for hiding and seeking. We then develop a computer agent that passes the test by appearing indistinguishable from human behavior to a panel of human judges. We analyze the AI techniques that enable the agent to imitate human hide-and-seek behavior and their relative contribution to the agent's performance.


title: Online allocation of display ads with smooth delivery

abstract: Display ads on the Internet are often sold in bundles of thousands or millions of impressions over a particular time period, typically weeks or months. Ad serving systems that assign ads to pages on behalf of publishers must satisfy these contracts, but at the same time try to maximize overall quality of placement. This is usually modeled in the literature as an online allocation problem, where contracts are represented by overall delivery constraints over a finite time horizon. However this model misses an important aspect of ad delivery: time homogeneity. Advertisers who buy these packages expect their ad to be shown smoothly throughout the purchased time period, in order to reach a wider audience, to have a sustained impact, and to support the ads they are running on other media (e.g., television). In this paper we formalize this problem using several nested packing constraints, and develop a tight (1-1/e)-competitive online algorithm for this problem. Our algorithms and analysis require novel techniques as they involve online computation of multiple dual variables per ad. We then show the effectiveness of our algorithms through exhaustive simulation studies on real data sets.


title: Popular Reality: Journalism, Modernity, Popular Culture

abstract: This work provides theoretical analysis and historical detail to reconceptualize the significance of modern journalism from the point of view of its greatest creation - popular readerships. It traces the links between journalism, politics and popular culture, showing how liberty, fraternity and equality are unthinkable without suburbia, sexualization and kissing. The book also provides a critique of academic and professional discourses on popular journalism, and provides new bridges between contemporary journalism and contemporary theory. The work is illustrated from the popular media new and old.


title: Ego-motion estimation concepts, algorithms and challenges: an overview

abstract: Ego-motion technology holds great significance for computer vision applications, robotics, augmented reality and visual simultaneous localization and mapping. This paper is a study of ego-motion estimation basic concepts, equipment, algorithms, challenges and its real world applications. First, we provide an overview for motion estimation in general with special focus on ego-motion estimation and its basic concepts. For ego-motion estimation it's necessary to understand the notion of independent moving objects, focus of expansion, motion field, and optical flow. Vital algorithms that are used for ego-motion estimation are critically discussed in the following section of the paper. Various camera setups and their potential weakness and strength are also studied in context of ego-motion estimation. We also briefly specify some ego-motion applications used in the real world. We conclude the paper by discussing some open problems, provide some future directions and finally summarize the entire paper in the conclusions.


title: Investigating factors influencing local government decision makers while adopting integration technologies (IntTech)

abstract: The emergence of innovative and revolutionary Integration Technologies (IntTech) has highly influenced the local government authorities (LGAs) in their decision-making process. LGAs that plan to adopt such IntTech may consider this as a serious investment. Advocates, however, claim that such IntTech have emerged to overcome the integration problems at all levels (e.g. data, object and process). With the emergence of electronic government (e-Government), LGAs have turned to IntTech to fully automate and offer their services on-line and integrate their IT infrastructures. While earlier research on the adoption of IntTech has considered several factors (e.g. pressure, technological, support, and financial), inadequate attention and resources have been applied in systematically investigating the individual, decision and organisational context factors, influencing top management's decisions for adopting IntTech in LGAs. It is a highly considered phenomenon that the success of an organisation's operations relies heavily on understanding an individual's attitudes and behaviours, the surrounding context and the type of decisions taken. Based on empirical evidence gathered through two intensive case studies, this paper attempts to investigate the factors that influence decision makers while adopting IntTech. The findings illustrate two different doctrines - one inclined and receptive towards taking risky decisions, the other disinclined. Several underlying rationales can be attributed to such mind-sets in LGAs. The authors aim to contribute to the body of knowledge by exploring the factors influencing top management's decision-making process while adopting IntTech vital for facilitating LGAs' operational reforms.


title: Auto-encoder based bagging architecture for sentiment analysis

abstract: Sentiment analysis has long been a hot topic for understanding users statements online. Previously many machine learning approaches for sentiment analysis such as simple feature-oriented SVM or more complicated probabilistic models have been proposed. Though they have demonstrated capability in polarity detection, there exist one challenge called the curse of dimensionality due to the high dimensional nature of text-based documents. In this research, inspired by the dimensionality reduction and feature extraction capability of auto-encoders, an auto-encoder-based bagging prediction architecture (AEBPA) is proposed. The experimental study on commonly used datasets has shown its potential. It is believed that this method can offer the researchers in the community further insight into bagging oriented solution for sentimental analysis. HighlightsThis research addresses the curse of dimensionality challenge by employing auto-encoders.Bagging method is employed to further improve performance.An auto-encoder-based bagging architecture is proposed and experiment on real-world datasets reveals its potential.


Simulated Annealing Modeling and Analog MPPT Simulation for Standalone Photovoltaic Arrays


title: FitzHugh–Nagumo Neuron Model

abstract: A CMOS circuit that emulates the FitzHugh-Nagumo neuron model is introduced. A complete derivation of the neuron model is presented, starting with the description of the fundamental biological mechanisms involved in the living neural cell, followed by the mathematical model formulation extracted from these mechanisms. A circuit theory technique for obtaining a physical IC suitable circuit that emulates the derived mathematical equations is then presented, culminating with the presentation of experimental results on a chip fabricated in a 2- mu m double-metal, double-poly CMOS process. It is emphasized that the FitzHugh-Nagumo model is very adequate for emulation of small biological systems. A reduced-complexity oscillatory model suitable for implementation of relatively large neural network architectures is also introduced with several corresponding CMOS realizations and measured results. >


title: An Intelligent Dual-Voltage Driving Method and Circuit For a Common Rail Injector for Heavy-Duty Diesel Engines

abstract: The control accuracies of the injection time and fuel quantity are the most important factors affecting the fuel economy and emissions of an engine equipped with a high-pressure common-rail fuel injection system. This paper presents an intelligent dual-voltage driving control that can simultaneously reduce the response time of the injector and improve the accuracy of the injected fuel quantity. The designed method employs a novel boost dc–dc circuit and does not require an additional independent dc–dc module that includes an inductor switch MOSFET and a controller. During the time interval between two continuous injections, the low-side MOSFET serves as a dc–dc switch, and the software in the controller controls the dual-voltage dc–dc, which uses the injector as a charging inductor of the dc–dc module. The operation principle of this intelligent driving circuit is described. Experimental results show that this method can not only significantly decrease the response time of the injector but also improve the stability of the fuel injection process.


title: Simple random search provides a competitive approach to reinforcement learning

abstract: A common belief in model-free reinforcement learning is that methods based on random search in the parameter space of policies exhibit significantly worse sample complexity than those that explore the space of actions. We dispel such beliefs by introducing a random search method for training static, linear policies for continuous control problems, matching state-of-the-art sample efficiency on the benchmark MuJoCo locomotion tasks. Our method also finds a nearly optimal controller for a challenging instance of the Linear Quadratic Regulator, a classical problem in control theory, when the dynamics are not known. Computationally, our random search algorithm is at least 15 times more efficient than the fastest competing model-free methods on these benchmarks. We take advantage of this computational efficiency to evaluate the performance of our method over hundreds of random seeds and many different hyperparameter configurations for each benchmark task. Our simulations highlight a high variability in performance in these benchmark tasks, suggesting that commonly used estimations of sample efficiency do not adequately evaluate the performance of RL algorithms.


title: Dialog act tagging with support vector machines and hidden markov models

abstract: We use a combination of linear support vector machines and hidden markov models for dialog act tagging in the HCRC MapTask corpus, and obtain better results than those previously reported. Support vector machines allow easy integration of sparse highdimensional text features and dense low-dimensional acoustic features, and produce posterior probabilities usable by sequence labelling algorithms. The relative contribution of text and acoustic features for each class of dialog act is analyzed. Index Terms: dialog acts, discourse, support vector machines, classification probabilities, Viterbi algorithm.


title: Twin-reversed arterial perfusion (TRAP) sequence: case reports and review of literature.

abstract: The twin reversed-arterial-perfusion (TRAP) sequence found in monozygotic twins is a consequence of primary or secondary cardiac development disruption and direct arterioarterial and venovenous placental anastomoses. Associated findings include the presence of a single umbilical artery (66%) and chromosomal abnormalities in the acardiac twin (33%). Morphological abnormalities in the acardiac twin are consistent with perfusion of tissues supplied by the common iliac and lower branches of the aorta with deoxygenated blood. The pump or donor twin may develop cardiac failure because of the anomalous perfusion circuit. Polyhydramnios is significantly associated with presence of renal tissue in the acardiac twin. An acardiac pump twin weight ratio (> 50%) is associated with the development of polyhydramnios and preterm labour. Identified high-risk factors for poor obstetrical outcome include: acardiac anceps, polyhydramnios, acardiac twin with ears, and pump twin cardiac failure. Management options include elective termination, observation (serial cardiotocography (CTG), ultrasonography and echocardiography) and selective nonsurgical interventions (indomethacin, digitalis, tocolysis). Additionally, surgical interventions (hysterotomy with selective delivery of the acardiac twin or ligation of the acardiac twin's umbilical cord), and ultrasound-guided embolization of the acardiac twin's umbilical artery with absolute alcohol, platinum coils, or thrombogenic coils have been reported. The most appropriate interventions for the various clinical presentations of this disorder are as yet undetermined, and conservative nonintervention is often appropriate. Long-term follow-up data on surviving pump twins are lacking. It is anticipated that centres with active study protocols for these conditions will best serve patient care and clinical research needs.


title: Solving the multiple-instance problem with axis-parallel rectangles

abstract: The multiple instance problem arises in tasks where the training examples are ambiguous: a single example object may have many alternative feature vectors (instances) that describe it, and yet only one of those feature vectors may be responsible for the observed classification of the object. This paper describes and compares three kinds of algorithms that learn axis-parallel rectangles to solve the multiple instance problem. Algorithms that ignore the multiple instance problem perform very poorly. An algorithm that directly confronts the multiple instance problem (by attempting to identify which feature vectors are responsible for the observed classifications) performs best, giving 89% correct predictions on a musk odor prediction task. The paper also illustrates the use of artificial data to debug and compare these algorithms.


Differences Between Reading Skills and Reading Strategies


title: The Vera am Mittag German audio-visual emotional speech database

abstract: The lack of publicly available annotated databases is one of the major barriers to research advances on emotional information processing. In this contribution we present a recently collected database of spontaneous emotional speech in German which is being made available to the research community. The database consists of 12 hours of audio-visual recordings of the German TV talk show ldquoVera am Mittagrdquo, segmented into broadcasts, dialogue acts and utterances. This corpus contains spontaneous and very emotional speech recorded from unscripted, authentic discussions between the guests of the talk show. In addition to the audio-visual data and the segmented utterances we provide emotion labels for a great part of the data. The emotion labels are given on a continuous valued scale for three emotion primitives: valence, activation and dominance, using a large number of human evaluators. Such data is of great interest to all research groups working on spontaneous speech analysis, emotion recognition in both speech and facial expression, natural language understanding, and robust speech recognition.