title: Autoencoder in Time-Series Analysis for Unsupervised Tissues Characterisation in a Large Unlabelled Medical Image Dataset

abstract: The topic of deep-learning has recently received considerable attention in the machine learning research community, having great potential to liberate computer scientists from hand-engineering training datasets, because the method can learn the desired features automatically. This is particularly beneficial in medical research applications of machine learning, where getting good hand labelling of data is especially expensive. We propose application of a single-layer sparse-auto encoder to dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for fully automatic classification of tissue types in a large unlabelled dataset with minimal human interference -- in a manner similar to data-mining. DCE-MRI analysis, looking at the change of the MR contrast-agent concentration over successively acquired images, is time-series analysis. We analyse the change of brightness (which is related to the contrast-agent concentration) of the DCE-MRI images over time to classify different tissue types in the images. Therefore our system is an application of an auto encoder to time-series analysis while the demonstrated result and further possible successive application areas are in computer vision. We discuss the important factors affecting performance of the system in applying the auto encoder to the time-series analysis of DCE-MRI medical image data.


title: Finite Element Analysis and Comparison of Two Hybrid Robots-the Tricept and the TriVariant

abstract: This paper deals with the finite element analysis (FEA) of two 5-DOF reconfigurable hybrid robots, the Tricept and the TriVariant, by means of ANSYS Parametric Design Language (APDL). The research interests are focused on: (1) precise FEA formulation of different types of joints by setting of contact elements, and (2) the development of an effective modeling strategy for the rapid stiffness and natural frequencies estimation associated with different configurations. The computational results show that two robots are of similar static and dynamic performances provided that they have identical geometrical dimensions, elastical and inertial parameters.


abstract: Ternier, S., Klemke, R., Kalz, M., Van Ulzen, P., & Specht, M. (2012). ARLearn: augmented reality meets augmented virtuality [Special issue]. Journal of Universal Computer Science - Technology for learning across physical and virtual spaces, 18(15), 2143-2164.


SURVEY PAPER : MULTI SENSOR DATA FUSION FOR SENSOR VALIDATION


title: Impact of podcasting on student motivation in the online learning environment

abstract: Researchers investigated the impact of podcasting on student motivation in the online environment during fall 2008 and spring 2009. Data were collected from students enrolled in fourteen online courses at a research university in the United States. One hundred and ninety-one students completed a modified version of the Instructional Materials Motivation Survey (Keller, 2006); it has four subscales: attention, relevance, confidence, and satisfaction. Strong positive relationships between all subscales were detected. Results indicate students were moderately motivated by the use of podcasts in their online courses. Statistically significant differences in student motivation based on gender, class standing, and prior online learning experience were found. Benefits of using podcasts and recommendations for improvement of the multimedia files were offered by users.


title: Deep Scattering Spectrum

abstract: A scattering transform defines a locally translation invariant representation which is stable to time-warping deformation. It extends MFCC representations by computing modulation spectrum coefficients of multiple orders, through cascades of wavelet convolutions and modulus operators. Second-order scattering coefficients characterize transient phenomena such as attacks and amplitude modulation. A frequency transposition invariant representation is obtained by applying a scattering transform along log-frequency. State-the-of-art classification results are obtained for musical genre and phone classification on GTZAN and TIMIT databases, respectively.


title: Context aware topic model for scene recognition

abstract: We present a discriminative latent topic model for scene recognition. The capacity of our model is originated from the modeling of two types of visual contexts, i.e., the category specific global spatial layout of different scene elements, and the reinforcement of the visual coherence in uniform local regions. In contrast, most previous methods for scene recognition either only modeled one of these two visual contexts, or just totally ignored both of them. We cast these two coupled visual contexts in a discriminative Latent Dirichlet Allocation framework, namely context aware topic model. Then scene recognition is achieved by Bayesian inference given a target image. Our experiments on several scene recognition benchmarks clearly demonstrated the advantages of the proposed model.


title: Analysis and design of a control approach for a boost converter with voltage multiplier cell

abstract: This paper presents the design of a control system for the boost converter with voltage multiplier cell. The adopted strategy employs an inner loop for the input current control and an outer loop for the output voltage regulation. As with the conventional boost converter, this strategy allows the improvement of the dynamic response of the converter and avoid the restrictions imposed by the non-minimum phase zero in the transfer function of the output voltage by the duty cycle. Simulation and experimental results are presented, showing the robustness of the proposed controller subject to load and input voltage disturbances. Besides, it is demonstrated that the controller designed with the aid of the reduced order model is capable to adequately control the converter with the inclusion of the resonant inductor.


title: Four decades of conjoined twins at Red Cross Children's Hospital--lessons learned.

abstract: Conjoined twins represent a rare but fascinating congenital condition, the aetiology of which remains obscure. Over the past four decades, the paediatric surgeons at Red Cross Children's Hospital have been involved in the management of 4;6 pairs of conjoined twins, of which 33 have been symmetrical and 12 asymmetrical. Seventeen symmetrical twins have undergone separation with 22 children (65%) surviving; all of the live asymmetrical twins survived separation. We describe the important features of this unique cohort, outline our approach to management and present the results of this approach. We consider some of the ethfcal and moral dilemmas we have confronted, and discuss the prenatal diagnosis, obstetric implications and postnatal care of these children, including the relevant investigations and anaesthetic and surgical management. Specific aspects related to the cardiovascular system, hepatobiliary and gastrointestinal tracts, urogenital tract, central nervous system and musculoskeletal system are highlighted.


title: Exploring Representations from Unlabeled Data with Co-training for Chinese Word Segmentation

abstract: Nowadays supervised sequence labeling models can reach competitive performance on the task of Chinese word segmentation. However, the ability of these models is restricted by the availability of annotated data and the design of features. We propose a scalable semi-supervised feature engineering approach. In contrast to previous works using pre-defined taskspecific features with fixed values, we dynamically extract representations of label distributions from both an in-domain corpus and an out-of-domain corpus. We update the representation values with a semi-supervised approach. Experiments on the benchmark datasets show that our approach achieve good results and reach an f-score of 0.961. The feature engineering approach proposed here is a general iterative semi-supervised method and not limited to the word segmentation task.


title: Glowworm swarm optimization for simultaneous capture of multiple local optima of multimodal functions

abstract: This paper presents glowworm swarm optimization (GSO), a novel algorithm for the simultaneous computation of multiple optima of multimodal functions. The algorithm shares a few features with some better known swarm intelligence based optimization algorithms, such as ant colony optimization and particle swarm optimization, but with several significant differences. The agents in GSO are thought of as glowworms that carry a luminescence quantity called luciferin along with them. The glowworms encode the fitness of their current locations, evaluated using the objective function, into a luciferin value that they broadcast to their neighbors. The glowworm identifies its neighbors and computes its movements by exploiting an adaptive neighborhood, which is bounded above by its sensor range. Each glowworm selects, using a probabilistic mechanism, a neighbor that has a luciferin value higher than its own and moves toward it. These movements—based only on local information and selective neighbor interactions—enable the swarm of glowworms to partition into disjoint subgroups that converge on multiple optima of a given multimodal function. We provide some theoretical results related to the luciferin update mechanism in order to prove the bounded nature and convergence of luciferin levels of the glowworms. Experimental results demonstrate the efficacy of the proposed glowworm based algorithm in capturing multiple optima of a series of standard multimodal test functions and more complex ones, such as stair-case and multiple-plateau functions. We also report the results of tests in higher dimensional spaces with a large number of peaks. We address the parameter selection problem by conducting experiments to show that only two parameters need to be selected by the user. Finally, we provide some comparisons of GSO with PSO and an experimental comparison with Niche-PSO, a PSO variant that is designed for the simultaneous computation of multiple optima.


title: Countering terrorism through dark web analysis

abstract: Dark Web analysis is an important aspect in field of counter terrorism (CT). In the present scenario terrorist attacks are biggest problem for the mankind and whole world is under constant threat from these well-planned, sophisticated and coordinated terrorist operations. Terrorists anonymously set up various web sites embedded in the public Internet, exchanging ideology, spreading propaganda, and recruiting new members. Dark web is a hotspot where terrorists are communicating and spreading their messages. Now every country is focusing for CT. Dark web analysis can be an efficient proactive method for CT by detecting and avoiding terrorist threats/attacks. In this paper we have proposed dark web analysis model that analyzes dark web forums for CT and connecting the dots to prevent the country from terrorist attacks.


title: A hierarchical fair service curve algorithm for link-sharing, real-time and priority services

abstract: In this paper, we study hierarchical resource management models and algorithms that support both link-sharing and guaranteed real-time services with decoupled delay (priority) and bandwidth allocation. We extend the service curve based QoS model, which defines both delay and bandwidth requirements of a class, to include fairness, which is important for the integration of real-time and hierarchical link-sharing services. The resulting Fair Service Curve link-sharing model formalizes the goals of link-sharing and real-time services and exposes the fundamental tradeoffs between these goals. In particular, with decoupled delay and band-width allocation, it is impossible to simultaneously provide guaranteed real-time service and achieve perfect link-sharing. We propose a novel scheduling algorithm called Hierarchical Fair Service Curve (H-FSC) that approximates the model closely and efficiently. The algorithm always guarantees the performance for leaf classes, thus ensures real-time services, while minimizing the discrepancy between the actual services provided to the interior classes and the services defined by the Fair Service Curve link-sharing model. We have implemented the H-FSC scheduler in the NetBSD environment. By performing simulation and measurement experiments, we evaluate the link-sharing and real-time performances of H-FSC, and determine the computation over-head.


title: A Stable-Isotope Dilution GC-MS Approach for the Analysis of DFRC (Derivatization Followed by Reductive Cleavage) Monomers from Low-Lignin Plant Materials

abstract: The derivatization followed by reductive cleavage (DFRC) method is a well-established tool to characterize the lignin composition of plant materials. However, the application of the original procedure, especially the chromatographic determination of the DFRC monomers, is problematic for low-lignin foods. To overcome these problems a modified sample cleanup and a stable-isotope dilution approach were developed and validated. To quantitate the diacetylated DFRC monomers, their corresponding hexadeuterated analogs were synthesized and used as internal standards. By using the selected-ion monitoring mode, matrix-associated interferences can be minimized resulting in higher selectivity and sensitivity. The modified method was applied to four low-lignin samples. Lignin from carrot fibers was classified as guaiacyl-rich whereas the lignins from radish, pear, and asparagus fibers where classified as balanced lignins (guaiacyl/syringyl ratio=1–2).


Bayesian Approaches to Therapeutic Drug Monitoring


title: A Mathematical Formalization of Hierarchical Temporal Memory’s Spatial Pooler

abstract: Hierarchical temporal memory (HTM) is an emerging machine learning algorithm, with the potential to provide a means to perform predictions on spatiotemporal data. The algorithm, inspired by the neocortex, currently does not have a comprehensive mathematical framework. This work brings together all aspects of the spatial pooler (SP), a critical learning component in HTM, under a single unifying framework. The primary learning mechanism is explored, where a maximum likelihood estimator for determining the degree of permanence update is proposed. The boosting mechanisms are studied and found to be only relevant during the initial few iterations of the network. Observations are made relating HTM to well-known algorithms such as competitive learning and attribute bagging. Methods are provided for using the SP for classification as well as dimensionality reduction. Empirical evidence verifies that given the proper parameterizations, the SP may be used for feature learning.


title: A 50-GHz Phase-Locked Loop in 0.13-$\mu$ m CMOS

abstract: A 50-GHz charge pump phase-locked loop (PLL) utilizing an LC-oscillator-based injection-locked frequency divider (ILFD) was fabricated in 0.13-mum logic CMOS process. The PLL can be locked from 45.9 to 50.5 GHz and output power level is around -10 dBm. The operating frequency range is increased by tracking the self-oscillation frequencies of the voltage-controlled oscillator (VCO) and the frequency divider. The PLL including buffers consumes 57 mW from 1.5/0.8-V supplies. The phase noise at 50 kHz, 1 MHz, and 10 MHz offset from the carrier is -63.5, -72, and -99 dBc/Hz, respectively. The PLL also outputs second-order harmonics at frequencies between 91.8 and 101 GHz. The output frequency of 101 GHz is the highest for signals locked by a PLL fabricated using the silicon integrated circuits technology.


title: 3-D Mapping With an RGB-D Camera

abstract: In this paper, we present a novel mapping system that robustly generates highly accurate 3-D maps using an RGB-D camera. Our approach requires no further sensors or odometry. With the availability of low-cost and light-weight RGB-D sensors such as the Microsoft Kinect, our approach applies to small domestic robots such as vacuum cleaners, as well as flying robots such as quadrocopters. Furthermore, our system can also be used for free-hand reconstruction of detailed 3-D models. In addition to the system itself, we present a thorough experimental evaluation on a publicly available benchmark dataset. We analyze and discuss the influence of several parameters such as the choice of the feature descriptor, the number of visual features, and validation methods. The results of the experiments demonstrate that our system can robustly deal with challenging scenarios such as fast camera motions and feature-poor environments while being fast enough for online operation. Our system is fully available as open source and has already been widely adopted by the robotics community.


title: Fast geometric point labeling using conditional random fields

abstract: In this paper we present a new approach for labeling 3D points with different geometric surface primitives using a novel feature descriptor - the Fast Point Feature Histograms, and discriminative graphical models. To build informative and robust 3D feature point representations, our descriptors encode the underlying surface geometry around a point p using multi-value histograms. This highly dimensional feature space copes well with noisy sensor data and is not dependent on pose or sampling density. By defining classes of 3D geometric surfaces and making use of contextual information using Conditional Random Fields (CRFs), our system is able to successfully segment and label 3D point clouds, based on the type of surfaces the points are lying on. We validate and demonstrate the method's efficiency by comparing it against similar initiatives as well as present results for table setting datasets acquired in indoor environments.


title: Autonomous robotic exploration using occupancy grid maps and graph SLAM based on Shannon and Rényi Entropy

abstract: In this paper we examine the problem of autonomously exploring and mapping an environment using a mobile robot. The robot uses a graph-based SLAM system to perform mapping and represents the map as an occupancy grid. In this setting, the robot must trade-off between exploring new area to complete the task and exploiting the existing information to maintain good localization. Selecting actions that decrease the map uncertainty while not significantly increasing the robot's localization uncertainty is challenging. We present a novel information-theoretic utility function that uses both Shannon's and Renyi's definitions of entropy to jointly consider the uncertainty of the robot and the map. This allows us to fuse both uncertainties without the use of manual tuning. We present simulations and experiments comparing the proposed utility function to state-of-the-art utility functions, which only use Shannon's entropy. We show that by using the proposed utility function, the robot and map uncertainties are smaller than using other existing methods.


title: Going Wild: Large-Scale Classification of Open DNS Resolvers

abstract: Since several years, millions of recursive DNS resolvers are-deliberately or not-open to the public. This, however, is counter-intuitive, since the operation of such openly accessible DNS resolvers is necessary in rare cases only. Furthermore, open resolvers enable both amplification DDoS and cache snooping attacks, and can be abused by attackers in multiple other ways. We thus find open recursive DNS resolvers to remain one critical phenomenon on the Internet. In this paper, we illuminate this phenomenon by analyzing it from two different angles. On the one hand, we study the landscape of DNS resolvers based on empirical data we collected for over a year. We analyze the changes over time and classify the resolvers according to device type and software version. On the other hand, we take the viewpoint of a client and measure the response authenticity of these resolvers. Besides legitimate redirections (e.g., to captive portals or router login pages), we find millions of resolvers to deliberately manipulate DNS resolutions (i.e., return bogus IP address information). To understand this threat in more detail, we systematically analyze non-legitimate DNS responses and reveal open DNS resolvers that manipulate DNS resolutions to censor communication channels, inject advertisements, serve malicious files, perform phishing, or redirect to other kinds of suspicious or malicious activities.


title: Fast Linearization of Tree Kernels over Large-Scale Data

abstract: Convolution tree kernels have been successfully applied to many language processing tasks for achieving state-of-the-art accuracy. Unfortunately, higher computational complexity of learning with kernels w.r.t. using explicit feature vectors makes them less attractive for large-scale data. In this paper, we study the latest approaches to solve such problems ranging from feature hashing to reverse kernel engineering and approximate cutting plane training with model compression. We derive a novel method that relies on reverse-kernel engineering together with an efficient kernel learning method. The approach gives the advantage of using tree kernels to automatically generate rich structured feature spaces and working in the linear space where learning and testing is fast. We experimented with training sets up to 4 million examples from Semantic Role Labeling. The results show that (i) the choice of correct structural features is essential and (ii) we can speed-up training from weeks to less than 20 minutes.


title: Coverage path planning on three-dimensional terrain for arable farming

abstract: Field operations should be done in a manner that minimizes time and travels over the field surface and is coordinated with topographic land features. Automated path planning can help to find the best coverage path so that the field operation costs can be minimized. Intelligent algorithms are desired for both two-dimensional (2D) and three-dimensional (3D) terrain field coverage path planning. The algorithm of generating an optimized full coverage pattern for a given 2D planar field by using boustrophedon paths has been investigated and reported before. However, a great proportion of farms have rolling terrains, which have a considerable influence on the design of coverage paths. Coverage path planning in 3D space has a great potential to further optimize field operations. This work addressed four critical tasks: terrain modeling and representation, coverage cost analysis, terrain decomposition, and the development of optimized path searching algorithm. The developed algorithms and methods have been successfully implemented and tested using 3D terrain maps of farm fields with various topographic features. Each field was decomposed into subregions based on its terrain features. A recommended “seed curve” based on a customized cost function was searched for each subregion, and parallel coverage paths were generated by offsetting the found “seed curve” toward its two sides until the whole region was completely covered. Compared with the 2D planning results, the experimental results of 3D coverage path planning showed its superiority in reducing both headland turning cost and soil erosion cost. On the tested fields, on average the 3D planning algorithm saved 10.3% on headland turning cost, 24.7% on soil erosion cost, 81.2% on skipped area cost, and 22.0% on the weighted sum of these costs, where their corresponding weights were 1, 1, and 0.5, respectively. © 2011 Wiley Periodicals, Inc. © 2011 Wiley Periodicals, Inc.


title: "What is Relevant in a Text Document?": An Interpretable Machine Learning Approach

abstract: Text documents can be described by a number of abstract concepts such as semantic category, writing style, or sentiment. Machine learning (ML) models have been trained to automatically map documents to these abstract concepts, allowing to annotate very large text collections, more than could be processed by a human in a lifetime. Besides predicting the text’s category very accurately, it is also highly desirable to understand how and why the categorization process takes place. In this paper, we demonstrate that such understanding can be achieved by tracing the classification decision back to individual words using layer-wise relevance propagation (LRP), a recently developed technique for explaining predictions of complex non-linear classifiers. We train two word-based ML models, a convolutional neural network (CNN) and a bag-of-words SVM classifier, on a topic categorization task and adapt the LRP method to decompose the predictions of these models onto words. Resulting scores indicate how much individual words contribute to the overall classification decision. This enables one to distill relevant information from text documents without an explicit semantic information extraction step. We further use the word-wise relevance scores for generating novel vector-based document representations which capture semantic information. Based on these document vectors, we introduce a measure of model explanatory power and show that, although the SVM and CNN models perform similarly in terms of classification accuracy, the latter exhibits a higher level of explainability which makes it more comprehensible for humans and potentially more useful for other applications.


title: TnT - A Statistical Part-Of-Speech Tagger

abstract: Trigrams'n'Tags (TnT) is an efficient statistical part-of-speech tagger. Contrary to claims found elsewhere in the literature, we argue that a tagger based on Markov models performs at least as well as other current approaches, including the Maximum Entropy framework. A recent comparison has even shown that TnT performs significantly better for the tested corpora. We describe the basic model of TnT, the techniques used for smoothing and for handling unknown words. Furthermore, we present evaluations on two corpora.


title: Accurate online power estimation and automatic battery behavior based power model generation for smartphones

abstract: This paper describes PowerBooter, an automated power model construction technique that uses built-in battery voltage sensors and knowledge of battery discharge behavior to monitor power consumption while explicitly controlling the power management and activity states of individual components. It requires no external measurement equipment. We also describe PowerTutor, a component power management and activity state introspection based tool that uses the model generated by PowerBooter for online power estimation. PowerBooter is intended to make it quick and easy for application developers and end users to generate power models for new smartphone variants, which each have different power consumption properties and therefore require different power models. PowerTutor is intended to ease the design and selection of power efficient software for embedded systems. Combined, PowerBooter and PowerTutor have the goal of opening power modeling and analysis for more smartphone variants and their users.


title: Paraphrase Identification Based on Weighted URAE, Unit Similarity and Context Correlation Feature

abstract: A deep learning model adaptive to both sentence-level and article-level paraphrase identification is proposed in this paper. It consists of pairwise unit similarity feature and semantic context correlation feature. In this model, sentences are represented by word and phrase embedding while articles are represented by sentence embedding. Those phrase and sentence embedding are learned from parse trees through Weighted Unfolding Recursive Autoencoders (WURAE), an unsupervised learning algorithm. Then, unit similarity matrix is calculated by matching the pairwise lists of embedding. It is used to extract the pairwise unit similarity feature through CNN and k-max pooling layers. In addition, semantic context correlation feature is taken into account, which is captured by the combination of CNN and LSTM. CNN layers learn collocation information between adjacent units while LSTM extracts the long-term dependency feature of the text based on the output of CNN. This model is experimented on a famous English sentence paraphrase corpus, MSRPC, and a Chinese article paraphrase corpus. The results show that the deep semantic feature of text could be extracted based on WURAE, unit similarity and context correlation feature. We release our code of WURAE, deep learning model for paraphrase identification and pre-trained phrase end sentence embedding data for use by the community.


title: The SMM Rootkit Revisited: Fun with USB

abstract: System Management Mode (SMM) in x86 has enabled a new class of malware with incredible power to control physical hardware that is virtually impossible to detect by the host operating system. Previous SMM root kits have only scratched the surface by modifying kernel data structures and trapping on I/O registers to implement PS/2 key loggers. In this paper, we present new SMM-based malware that hijacks Universal Serial Bus (USB) host controllers to intercept USB events. This enables SMM root kits to control USB devices directly without ever permitting the OS kernel to receive USB-related hardware interrupts. Using this approach, we created a proof-of-concept USB key logger that is also more difficult to detect than prior SMM-based key loggers that are triggered on OS actions like port I/O. We also propose additional extensions to this technique and methods to prevent and mitigate such attacks.


title: Ultra Linear 3.5GHz RF Front-End for OFDM System

abstract: This paper presents the design and performance of chips suitable for an RF front-end module, for WiMAX applications. The chips include a low cost single pole double throw (SPDT) switch and low noise amplifier as well as a 10-Watt 3.5GHz power amplifier MMIC implemented in a plastic air cavity QFN 8times6 mm2 package. Plastic packaging is used for the switch and the low noise amplifier. The broadband performance of these parts allow coverage from 3.3 GHz up to 3.8 GHz, over temperature range and process spread. The transmit path exhibits over 15dB of gain, thanks to very low insertion loss on the switch, manufactured on a high yield and reliable process. The error vector magnitude (EVM) is maintained below 2.5% up to 30dBm transmit power, for OFDM signal with PAR=10dB. On the receive path, the LNA provides 2 modes: a low noise operation with NF of 2.5dB and a bypass mode with 28dBm output PldB


title: Efficiency estimation in digitally-controlled dc-dc buck converters based on single current sensing

abstract: This paper proposes an efficiency estimator for dc-dc buck converters suitable for on-chip integration in the digital IC controller. The proposed solution is tailored for distributed power architectures, where the on-line efficiency measurement or estimation of each dc-dc converter is useful in order to perform a system power management and to monitor the status of each point of load (POL) converter. Existing solutions relay on the measurement of the input voltage and current, output voltage and current, the latter usually based on the output inductor current sensing (at least for buck-type converters). The current sensing is the main drawback since it requires two current sensors and, more importantly, a precise matching, tuning and temperature compensation in the two current sensors. This paper proposes an indirect estimation of the ratio between the output and input current based on a single current sensing, thus avoiding precise gain tuning. The current sensing is based on the input bus voltage and a filter matched with the impedance seen by the dc-dc converter. Experimental results on a synchronous buck converter with the efficiency estimator implemented in a field programmable gate array (FPGA) show the properties as well as the limitations of the proposed method.


title: Predicting bacteremia in children with cancer and fever in chemotherapy-induced neutropenia : results of the prospective multicenter SPOG 2003 FN study

abstract: To develop a score predicting the risk of bacteremia in cancer patients with fever and neutropenia (FN), and to evaluate its performance.


title: Tips and Tricks for Visual Question Answering: Learnings from the 2017 Challenge

abstract: This paper presents a state-of-the-art model for visual question answering (VQA), which won the first place in the 2017 VQA Challenge. VQA is a task of significant importance for research in artificial intelligence, given its multimodal nature, clear evaluation protocol, and potential real-world applications. The performance of deep neural networks for VQA is very dependent on choices of architectures and hyperparameters. To help further research in the area, we describe in detail our high-performing, though relatively simple model. Through a massive exploration of architectures and hyperparameters representing more than 3,000 GPU-hours, we identified tips and tricks that lead to its success, namely: sigmoid outputs, soft training targets, image features from bottom-up attention, gated tanh activations, output embeddings initialized using GloVe and Google Images, large mini-batches, and smart shuffling of training data. We provide a detailed analysis of their impact on performance to assist others in making an appropriate selection.


title: Personalized Search Over Encrypted Data With Efficient and Secure Updates in Mobile Clouds

abstract: Mobile cloud computing has been involved as a key enabling technology to overcome the physical limitations of mobile devices toward scalable and flexible mobile services. In the mobile cloud environment, searchable encryption, which enables direct search over encrypted data, is a key technique to maintain both the privacy and usability of outsourced data in cloud. On addressing the issue, many research efforts resolve to using the searchable symmetric encryption (SSE) and searchable public-key encryption (SPE). In this paper, we improve the existing works by developing a more practical searchable encryption technique, which can support dynamic updating operations in the mobile cloud applications. Specifically, we make our efforts on taking the advantages of both the SSE and SPE techniques, and propose PSU, a Personalized Search scheme over encrypted data with efficient and secure Updates in mobile cloud. By giving thorough security analysis, we demonstrate that the PSU can achieve a high security level. Using extensive experiments in a real-world mobile environment, we show that the PUS is more efficient compared with the existing proposals.


title: Me, My Echo Chamber, and I: Introspection on Social Media Polarization

abstract: Homophily—our tendency to surround ourselves with others who share our perspectives and opinions about the world—is both a part of human nature and an organizing principle underpinning many of our digital social networks. However, when it comes to politics or culture, homophily can amplify tribal mindsets and produce “”echo chambers”” that degrade the quality, safety, and diversity of discourse online. While several studies have empirically proven this point, few have explored how making users aware of the extent and nature of their political echo chambers influences their subsequent beliefs and actions. In this paper, we introduce Social Mirror, a social network visualization tool that enables a sample of Twitter users to explore the politically-active parts of their social network. We use Social Mirror to recruit Twitter users with a prior history of political discourse to a randomized experiment where we evaluate the effects of different treatments on participants’ i) beliefs about their network connections, ii) the political diversity of who they choose to follow, and iii) the political alignment of the URLs they choose to share. While we see no effects on average political alignment of shared URLs, we find that recommending accounts of the opposite political ideology to follow reduces participants’ beliefs in the political homogeneity of their network connections but still enhances their connection diversity one week after treatment. Conversely, participants who enhance their belief in the political homogeneity of their Twitter connections have less diverse network connections 2-3 weeks after treatment. We explore the implications of these disconnects between beliefs and actions on future efforts to promote healthier exchanges in our digital public spheres.


title: Remaining useful life estimation using long short-term memory deep learning

abstract: This paper proposes a deep learning method to estimate the remaining useful life (RUL) of aero-propulsion engines. The proposed method is based on the long short-term memory (LSTM) structure of the recurrent neural network (RNN). LSTM can effectively extract the relationship between data items that are far separated in the time series. The proposed method is applied to the NASA C-MAPSS data set for RUL estimation accuracy evaluation and is compared with the methods using the multi-layer perceptron (MLP), support vector regression (SVR), relevance vector regression (RVR) and convolutional neural network (CNN). Comparisons show that the proposed method is better than others in terms of the root mean squared error (RMSE) and the value of a scoring function.


title: ModelSeek: an effective 3D model retrieval system

abstract: Since object similarity is a subjective matter, the gap between low-level feature representations and high-level semantic concepts is a major problem in the field of content-based 3D model retrieval. This paper presents a novel composite model descriptor, which takes into account both visual and geometric characteristics of 3D models. It also proposes an original mapping mechanism from low-level model features to high-level semantic concepts based on the user's retrieval history, and so this method belongs to long-term relevance feedback algorithms for 3D model retrieval. Finally, an effective 3D model retrieval system "ModelSeek" has been built, and implemented with the introduced model descriptor and mapping mechanism. The experimental results show that the approaches above not only have significantly improved the retrieval performance, but have also achieved better retrieval effectiveness than the state-of-the-art techniques on the publicly available 3D model criterion that Princeton Shape Benchmark (PSB) and several standard evaluation measures.


title: ENVIRONMENTAL AND ECONOMIC COSTS ASSOCIATED WITH NON-INDIGENOUS SPECIES IN THE UNITED STATES

abstract: Invading non-indigenous species in the United States cause major environmental damages and losses adding up to more than $138 billion per year. There are approximately 50,000 foreign species and the number is increasing. About 42% of the species on the Threatened or Endangered species lists are at risk primarily because of non-indigenous species. In the history of the United States, approximately 50,000 non-indigenous (non-native) species are estimated to have been introduced into the United States. Introduced species, such as corn, wheat, rice, and other food crops, and cattle, poultry, and other livestock, now provide more than 98% of the U.S. food system at a value of approximately $800 billion per year (USBC 1998). Other exotic species have been introduced for landscape restoration, biological pest control, sport, pets, and food processing. Some non-indigenous species, however, have caused major economic losses in agriculture, forestry, and several other segments of the U.S. economy, in addition to harming the environment. One recent study reported approximately $97 billion in damages from 79 exotic species during the period from 1906 to 1991 (OTA 1993).


title: Outlier Robust Online Learning

abstract: We consider the problem of learning from noisy data in practical settings where the size of data is too large to store on a single machine. More challenging, the data coming from the wild may contain malicious outliers. To address the scalability and robustness issues, we present an online robust learning (ORL) approach. ORL is simple to implement and has provable robustness guarantee -- in stark contrast to existing online learning approaches that are generally fragile to outliers. We specialize the ORL approach for two concrete cases: online robust principal component analysis and online linear regression. We demonstrate the efficiency and robustness advantages of ORL through comprehensive simulations and predicting image tags on a large-scale data set. We also discuss extension of the ORL to distributed learning and provide experimental evaluations.


title: Latent Variable PixelCNNs for Natural Image Modeling

abstract: We study probabilistic models of natural images and extend the autoregressive family of PixelCNN models by incorporating latent variables. Subsequently, we describe two new generative image models that exploit different image transformations as latent variables: a quantized grayscale view of the image or a multi-resolution image pyramid. The proposed models tackle two known shortcomings of existing PixelCNN models: 1) their tendency to focus on low-level image details, while largely ignoring high-level image information, such as object shapes, and 2) their computationally costly procedure for image sampling. We experimentally demonstrate benefits of our LatentPixelCNN models, in particular showing that they produce much more realistically looking image samples than previous state-of-the-art probabilistic models.


title: Advantage and drawback of support vector machine functionality

abstract: Support Vector Machine(SVM)is one of the most efficient machine learning algorithms, which is mostly used for pattern recognition since its introduction in 1990s. SVMs vast variety of usage, such as face and speech recognition, face detection and image recognition has turned it into a very useful algorithm. This has also been applied to many pattern classification problems such as image recognition, speech recognition, text categorization, face detection, and faulty card detection.Statistics was collected from journals and electronic sources published in the period of 2000 to 2013. Pattern recognition aims to classify data based on either a priori knowledge or statistical information extracted from raw data, which is a powerful tool in data separation in many disciplines. The Support Vector Machine (SVM) is a kind of algorithms in biometrics. It is a statistics technical and used orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables.


title: Improving Stock Movement Prediction with Adversarial Training

abstract: This paper contributes a new machine learning solution for stock movement prediction, which aims to predict whether the price of a stock will be up or down in the near future. The key novelty is that we propose to employ adversarial training to improve the generalization of a recurrent neural network model. The rationality of adversarial training here is that the input features to stock prediction are typically based on stock price, which is essentially a stochastic variable and continuously changed with time by nature. As such, normal training with stationary price-based features (e.g. the closing price) can easily overfit the data, being insufficient to obtain reliable models. To address this problem, we propose to add perturbations to simulate the stochasticity of continuous price variable, and train the model to work well under small yet intentional perturbations. Extensive experiments on two real-world stock data show that our method outperforms the state-of-the-art solution with 3.11% relative improvements on average w.r.t. accuracy, verifying the usefulness of adversarial training for stock prediction task. Codes will be made available upon acceptance.


title: Watch, Think and Attend: End-to-End Video Classification via Dynamic Knowledge Evolution Modeling

abstract: Video classification has been achieved by automatically mining the underlying concepts (\eg actions, events) in videos, which plays an essential role in intelligent video analysis. However, most existing algorithms only exploit the visual cues of these concepts but ignore external knowledge information for modeling their relationships during the evolution of videos. In fact, humans have remarkable ability to utilize acquired knowledge to reason about the dynamically changing world. To narrow the knowledge gap between existing methods and humans, we propose an end-to-end video classification framework based on a structured knowledge graph, which can model the dynamic knowledge evolution in videos overtime. Here, we map the concepts of videos to the nodes of the knowledge graph. To effectively leverage the knowledge graph, we adopt a graph convLSTM model to not only identify local knowledge structures in each video shot but also model dynamic patterns of knowledge evolution across these shots. Furthermore, a novel knowledge-based attention model is designed by considering the importance of each video shot and relationships between concepts. We show that by using knowledge graphs, our framework is able to improve the performance of various existing methods. Extensive experimental results on two video classification benchmarks UCF101 and Youtube-8M demonstrate the favorable performance of the proposed framework.


title: iPCA: An Interactive System for PCA-based Visual Analytics

abstract: Principle Component Analysis (PCA) is a widely used mathematical technique in many fields for factor and trend analysis, dimension reduction, etc. However, it is often considered to be a "black box" operation whose results are difficult to interpret and sometimes counter-intuitive to the user. In order to assist the user in better understanding and utilizing PCA, we have developed a system that visualizes the results of principal component analysis using multiple coordinated views and a rich set of user interactions. Our design philosophy is to support analysis of multivariate datasets through extensive interaction with the PCA output. To demonstrate the usefulness of our system, we performed a comparative user study with a known commercial system, SAS/INSIGHT's Interactive Data Exploration. Participants in our study solved a number of high-level analysis tasks with each interface and rated the systems on ease of learning and usefulness. Based on the participants' accuracy, speed, and qualitative feedback, we observe that our system helps users to better understand relationships between the data and the calculated eigenspace, which allows the participants to more accurately analyze the data. User feedback suggests that the interactivity and transparency of our system are the key strengths of our approach.


title: Scheduler-based defenses against cross-VM side-channels

abstract: Public infrastructure-as-a-service clouds, such as Amazon EC2 and Microsoft Azure allow arbitrary clients to run virtual machines (VMs) on shared physical infrastructure. This practice of multi-tenancy brings economies of scale, but also introduces the threat of malicious VMs abusing the scheduling of shared resources. Recent works have shown how to mount cross-VM side-channel attacks to steal cryptographic secrets. The straightforward solution is hard isolation that dedicates hardware to each VM. However, this comes at the cost of reduced efficiency. ::: ::: We investigate the principle of soft isolation: reduce the risk of sharing through better scheduling. With experimental measurements, we show that a minimum run time (MRT) guarantee for VM virtual CPUs that limits the frequency of preemptions can effectively prevent existing Prime+Probe cache-based side-channel attacks. Through experimental measurements, we find that the performance impact of MRT guarantees can be very low, particularly in multi-core settings. Finally, we integrate a simple per-core CPU state cleansing mechanism, a form of hard isolation, into Xen. It provides further protection against side-channel attacks at little cost when used in conjunction with an MRT guarantee.


Augmented Reality Interface for Teleoperation via Internet ) , one of the characteristics Design and Manipulation in Augmented Reality of Fanuc 200 iC Robot


title: Workshop on Computational Personality Recognition: Shared Task

abstract: In the Workshop on Computational Personality Recognition (Shared Task), we released two datasets, varying in size and genre, annotated with gold standard personality labels. This allowed participants to evaluate features and learning techniques, and even to compare the performances of their systems for personality recognition on a common benchmark. We had 8 participants to the task. In this paper we discuss the results and compare them to previous literature.


Theoretical Constructs , Concepts , and Applications


title: Laplacian Reconstruction and Refinement for Semantic Segmentation

abstract: CNN architectures have terrific recognition performance but rely on spatial pooling which makes it difficult to adapt them to tasks that require dense pixel-accurate labeling. This paper makes two contributions: (1) We demonstrate that while the apparent spatial resolution of convolutional feature maps is low, the high-dimensional feature representation contains significant sub-pixel localization information. (2) We describe a multi-resolution reconstruction architecture, akin to a Laplacian pyramid, that uses skip connections from higher resolution feature maps to successively refine segment boundaries reconstructed from lower resolution maps. This approach yields state-of-the-art semantic segmentation results on PASCAL without resorting to more complex CRF or detection driven architectures.


title: Computer Vision based semi-intuitive Robotic arm

abstract: Robotics is one of the most growing fields of science. The number and types of tasks that are being performed by robotics is multiplying rapidly. In the field of Robotics, Robotic arms have gained a lot of importance. These are used to perform tasks which humans cannot or will not. They are extremely efficient and accurate. The current mode of control of the arm includes External Actuators, Joysticks or Wired Controllers, and even manual programming. Using Computer Vision to control a Robotic arm is a newer control mechanism, which is used in this paper. A Kinect 3-D camera is used to track the operator's movements, this is connected to a Computer with NI LabVIEW, which processes the data and transmits the coordinates wirelessly to a received module which is connected to a Robotic arm, which performs the actual movement. This is what will be discussed in detail during the course of this paper.


title: Trajectory planning for a four-wheel-steering vehicle

abstract: This paper develops a trajectory planning algorithm for a four-wheel-steering vehicle based on vehicle kinematics. The flexibility offered by the steering is utilized fully in the trajectory planning. A two-part trajectory planning algorithm consists of the steering planning and velocity planning. Limits of the vehicle mechanism and drive torque are taken into account. Simulation results are presented to illustrate the application of the proposed algorithm.


title: ARA: a robust audit to prevent free-riding in P2P networks

abstract: A number of solutions have been proposed to address the free-riding problem in peer-to-peer file sharing systems. The solutions are either imperfect-they allow some users to cheat the system with malicious behavior, or expensive-they require human intervention, require servers, or incur high mental transaction costs. The authors proposed a method to address these weaknesses. Specifically, a utility function was introduced to capture contributions made by a user and an auditing scheme to ensure the integrity of a utility function's values. The method enabled to reduce cheating by a malicious peer: it is shown that this approach can efficiently detect malicious peers with a probability over 98%.


title: Decision trees for uplift modeling with single and multiple treatments

abstract: Most classification approaches aim at achieving high prediction accuracy on a given dataset. However, in most practical cases, some action such as mailing an offer or treating a patient is to be taken on the classified objects, and we should model not the class probabilities themselves, but instead, the change in class probabilities caused by the action. The action should then be performed on those objects for which it will be most profitable. This problem is known as uplift modeling, differential response analysis, or true lift modeling, but has received very little attention in machine learning literature. An important modification of the problem involves several possible actions, when for each object, the model must also decide which action should be used in order to maximize profit. In this paper, we present tree-based classifiers designed for uplift modeling in both single and multiple treatment cases. To this end, we design new splitting criteria and pruning methods. The experiments confirm the usefulness of the proposed approaches and show significant improvement over previous uplift modeling techniques.


title: Current Approaches for Predicting a Lack of Response to Anti-EGFR Therapy in KRAS Wild-Type Patients

abstract: Targeting epidermal growth factor receptor (EGFR) has been one of the most effective colorectal cancer strategies. Anti-EGFR antibodies function by binding to the extracellular domain of EGFR, preventing its activation, and ultimately providing clinical benefit. KRAS mutations in codons 12 and 13 are recognized prognostic and predictive biomarkers that should be analyzed at the clinic prior to the administration of anti-EGFR therapy. However, still an important fraction of KRAS wild-type patients do not respond to the treatment. The identification of additional genetic determinants of primary or secondary resistance to EGFR targeted therapy for further improving the selection of patients is urgent. Herein, we review the latest published literature highlighting the most important genes that may predict resistance to anti-EGFR monoclonal antibodies in colorectal cancer patients. According to the available findings, the evaluation of BRAF, NRAS, PIK3CA, and PTEN status could be the right strategy to select patients who are likely to respond to anti-EGFR therapies. In the future, the combination of those biomarkers will help establish consensus that can be introduced into clinical practice.


title: Analysis & characterization of dual tail current based dynamic latch comparator with modified SR latch using 90nm technology

abstract: This paper presents characterization of low operating voltage, high speed and power efficient comparator used as a basic building block in speed optimized Analog to Digital Converters (ADC), such as flash ADC. Overall performance of any ADC in terms of speed, resolution and power consumption highly depends on the underlying comparator being used. In this paper, better structure of comparator is implemented and analyzed, which is a combination of modified SR latch and sense amplifier. The comparator is built up by preamplifier and positive feedback latch (back-to-back connected inverter) to enhance the speed. The output of latch is given to the modified SR latch, which provides stable output signal compare to that of conventional dynamic latch structure. The implementation is carried out in 90nm technology in Mentor Graphics' IC Studio tool and simulation is done in Eldo tool.


title: Design and implementation of smart garage- An IoT perspective

abstract: Internet of Things is a burgeoning field in the world of technology. It helps ease life for a person by means of internetworking physical devices, automotive, home appliances and other items embedded with electronics, software, sensors and actuators. Smart garage is one such device that helps ease the access of the garage doors in domestic use. Unlike most solutions, smart garage help you to access your doors with the help of a computable device such as smart phones or tablets connected to a network, and access by means of secured pins to authorize the access. We designed one such garage using Arduino web server, and a smartphone. Which we compared with existing methods. We have illustrated the building process of the setup for the prototype of our garage, and various means to improve power consumptions and alternate methods for a secured pin.


title: Comparing Virtual Reality Exposure Therapy to Present-Centered Therapy with 11 U.S. Vietnam Veterans with PTSD

abstract: Abstract Eleven Vietnam veterans with war-related posttraumatic stress disorder (PTSD) were randomly assigned to 10 sessions of either virtual reality exposure (VRE) therapy within a computer-generated virtual Vietnam environment or present-centered therapy (PCT) that avoided traumatic content and utilized a problem-solving approach. Participants were assessed at pretreatment, posttreatment, and 6 months posttreatment by an independent assessor blind to treatment condition. Nine participants completed treatment with one dropout per condition. No significant differences emerged between treatments, likely due to insufficient power. Although comparison of mean changes in PTSD symptoms for the VRE and PCT conditions yielded a moderate effect size (d = 0.56) in favor of VRE at 6 months posttreatment, changes in PTSD scores were more variable, and therefore less reliable, within the VRE condition. The utility of VRE with older veterans with PTSD is discussed.


title: Content-based concept drift detection for Email spam filtering

abstract: The continued growth of Email usage, which is naturally followed by an increase in unsolicited emails so called spams, motivates research in spam filtering area. In the context of spam filtering systems, addressing the evolving nature of spams, which leads to obsolete the related models, has been always a challenge. In this paper an adaptive spam filtering system based on language model is proposed which can detect concept drift based on computing the deviation in email contents distribution. The proposed method can be used along with any existing classifier; particularly in this paper we use Naive Bayes method as classifier. The proposed method has been evaluated with Enron data set. The results indicate the efficiency of the method in detecting concept drift and its superiority over Naive Bayes classifier in terms of accuracy.


title: SceneCut: Joint Geometric and Object Segmentation for Indoor Scenes

abstract: This paper presents SceneCut, a novel approach to jointly discover previously unseen objects and non-object surfaces using a single RGB-D image. SceneCut's joint reasoning over scene semantics and geometry allows a robot to detect and segment object instances in complex scenes where modern deep learning-based methods either fail to separate object instances, or fail to detect objects that were not seen during training. SceneCut automatically decomposes a scene into meaningful regions which either represent objects or scene surfaces. The decomposition is qualified by an unified energy function over objectness and geometric fitting. We show how this energy function can be optimized efficiently by utilizing hierarchical segmentation trees. Moreover, we leverage a pre-trained convolutional oriented boundary network to predict accurate boundaries from images, which are used to construct high-quality region hierarchies. We evaluate SceneCut on several different indoor environments, and the results show that SceneCut significantly outperforms all the existing methods.


title: Block-Matching Convolutional Neural Network for Image Denoising

abstract: There are two main streams in up-to-date image denoising algorithms: non-local self similarity (NSS) prior based methods and convolutional neural network (CNN) based methods. The NSS based methods are favorable on images with regular and repetitive patterns while the CNN based methods perform better on irregular structures. In this paper, we propose a block-matching convolutional neural network (BMCNN) method that combines NSS prior and CNN. Initially, similar local patches in the input image are integrated into a 3D block. In order to prevent the noise from messing up the block matching, we first apply an existing denoising algorithm on the noisy image. The denoised image is employed as a pilot signal for the block matching, and then denoising function for the block is learned by a CNN structure. Experimental results show that the proposed BMCNN algorithm achieves state-of-the-art performance. In detail, BMCNN can restore both repetitive and irregular structures.


title: SmartCrawler: A Two-Stage Crawler for Efficiently Harvesting Deep-Web Interfaces

abstract: As deep web grows at a very fast pace, there has been increased interest in techniques that help efficiently locate deep-web interfaces. However, due to the large volume of web resources and the dynamic nature of deep web, achieving wide coverage and high efficiency is a challenging issue. We propose a two-stage framework, namely SmartCrawler , for efficient harvesting deep web interfaces. In the first stage, SmartCrawler performs site-based searching for center pages with the help of search engines, avoiding visiting a large number of pages. To achieve more accurate results for a focused crawl, SmartCrawler ranks websites to prioritize highly relevant ones for a given topic. In the second stage, SmartCrawler achieves fast in-site searching by excavating most relevant links with an adaptive link-ranking. To eliminate bias on visiting some highly relevant links in hidden web directories, we design a link tree data structure to achieve wider coverage for a website. Our experimental results on a set of representative domains show the agility and accuracy of our proposed crawler framework, which efficiently retrieves deep-web interfaces from large-scale sites and achieves higher harvest rates than other crawlers.


title: Deep Brain Stimulation: A Paradigm Shifting Approach to Treat Parkinson's Disease

abstract: Parkinson disease (PD) is a chronic and progressive movement disorder classically characterized by slowed voluntary movements, resting tremor, muscle rigidity, and impaired gait and balance. Medical treatment is highly successful early on, though the majority of people experience significant complications in later stages. In advanced PD, when medications no longer adequately control motor symptoms, deep brain stimulation (DBS) offers a powerful therapeutic alternative. DBS involves the surgical implantation of one or more electrodes into specific areas of the brain, which modulate or disrupt abnormal patterns of neural signaling within the targeted region. Outcomes are often dramatic following DBS, with improvements in motor function and reductions motor complications having been repeatedly demonstrated. Given such robust responses, emerging indications for DBS are being investigated. In parallel with expansions of therapeutic scope, advancements within the areas of neurosurgical technique and the precision of stimulation delivery have recently broadened as well. This review focuses on the revolutionary addition of DBS to the therapeutic armamentarium for PD, and summarizes the technological advancements in the areas of neuroimaging and biomedical engineering intended to improve targeting, programming and overall management.


title: Color-SURF: A surf descriptor with local kernel color histograms

abstract: SIFT (Scale Invariant Feature Transform) is an important local invariant feature descriptor. Since its expensive computation, SURF (Speeded-Up Robust Features) is proposed. Both of them are designed mainly for gray images. However, color provides valuable information in object description and matching tasks. To overcome the drawback and to increase the descriptor's distinctiveness, this paper presents a novel feature descriptor which combines local kernel color histograms and Haar wavelet responses to construct the feature vector. So the descriptor is a two elements vector. In image matching process, SURF descriptor is first compared, then the unmatched points are computed by Bhattacharyya distance between their local kernel color histograms. Extensive experimental evaluations show that the method has better robustness than the original SURF. The ratio of correct matches is increased by about 8.9% in the given dataset.


title: OCCUPATIONAL THERAPY USING A SENSORY INTEGRATIVE APPROACH FOR CHILDREN WITH DEVELOPMENTAL DISABILITIES

abstract: This article provides an introduction and overview of sensory integration theory as it is used in occupational therapy practice for children with developmental disabilities. This review of the theoretical tenets of the theory, its historical foundations, and early research provides the reader with a basis for exploring current uses and applications. The key principles of the sensory integrative approach, including concepts such as “the just right challenge” and “the adaptive response” as conceptualized by A. Jean Ayres, the theory's founder, are presented to familiarize the reader with the approach. The state of research in this area is presented, including studies underway to further delineate the subtypes of sensory integrative dysfunction, the neurobiological mechanisms of poor sensory processing, advances in theory development, and the development of a fidelity measure for use in intervention studies. Finally, this article reviews the current state of the evidence to support this approach and suggests that consensual knowledge and empirical research are needed to further elucidate the theory and its utility for a variety of children with developmental disabilities. This is especially critical given the public pressure by parents of children with autism and other developmental disabilities to obtain services and who have anecdotally noted the utility of sensory integration therapy for helping their children function more independently. Key limiting factors to research include lack of funding, paucity of doctorate trained clinicians and researchers in occupational therapy, and the inherent heterogeneity of the population of children affected by sensory integrative dysfunction. A call to action for occupational therapy researchers, funding agencies, and other professions is made to support ongoing efforts and to develop initiatives that will lead to better diagnoses and effective intervention for sensory integrative dysfunction, which will improve the lives of children and their families. © 2005 Wiley-Liss, Inc. MRDD Research Reviews 2005;11:143–148.


title: The Impact of Improvement in Iran Iron and Steel Production Technology on Environment Pollution

abstract: Abstract Iron and steel industry is the most energy intensive sector in Iran's economy. In recent years this industry has experienced a high rate of growth. So it is expected that the growth of energy consumption in this industry and its environmental pollution will increase quickly in future. Thus, the estimation of long run energy consumption in iron and steel energy intensive industry and its environmental pollution, specifying the potentialities of energy saving as a result of technological progress in this industry, and analyzing of the impact of improvement in production technology in iron and steel industry on environment; seem to be considerably crucial. These subjects are analyzed in this paper using a techno - economy model. The finding of executing Model for Energy Demand Evaluation (MEDEE), reveals that through utilization of improved production technology in iron and steel industry, the level of energy consumption in this industry reduce about 13.5 and 45.8 MBOE in 2015 and 2025 respectively. So, the environmental pollution as the result of this reduction in energy consumption in iron and steel industry, is very considerable.


title: Visual saliency detection for RGB-D images under a Bayesian framework

abstract: In this paper, we propose a saliency detection model for RGB-D images based on the deep features of RGB images and depth images within a Bayesian framework. By analysing 3D saliency in the case of RGB images and depth images, the class-conditional mutual information is computed for measuring the dependence of deep features extracted using a convolutional neural network; then, the posterior probability of the RGB-D saliency is formulated by applying Bayes’ theorem. By assuming that deep features are Gaussian distributions, a discriminative mixed-membership naive Bayes (DMNB) model is used to calculate the final saliency map. The Gaussian distribution parameters can be estimated in the DMNB model by using a variational inference-based expectation maximization algorithm. The experimental results on RGB-D images from the NLPR dataset and NJU-DS400 dataset show that the proposed model performs better than other existing models.


title: Mining stackoverflow for program repair

abstract: In recent years, automatic program repair has been a hot research topic in the software engineering community, and many approaches have been proposed. Although these approaches produce promising results, some researchers criticize that existing approaches are still limited in their repair capability, due to their limited repair templates. Indeed, it is quite difficult to design effective repair templates. An award-wining paper analyzes thousands of manual bug fixes, but summarizes only ten repair templates. Although more bugs are thus repaired, recent studies show such repair templates are still insufficient. We notice that programmers often refer to Stack Overflow, when they repair bugs. With years of accumulation, Stack Overflow has millions of posts that are potentially useful to repair many bugs. The observation motives our work towards mining repair templates from Stack Overflow. In this paper, we propose a novel approach, called SOFix, that extracts code samples from Stack Overflow, and mines repair patterns from extracted code samples. Based on our mined repair patterns, we derived 13 repair templates. We implemented these repair templates in SOFix, and conducted evaluations on the widely used benchmark, Defects4J. Our results show that SOFix repaired 23 bugs, which are more than existing approaches. After comparing repaired bugs and templates, we find that SOFix repaired more bugs, since it has more repair templates. In addition, our results also reveal the urgent need for better fault localization techniques.


title: High-Performance Chemical Sensing Using Schottky-Contacted Chemical Vapor Deposition Grown Monolayer MoS2 Transistors

abstract: Trace chemical detection is important for a wide range of practical applications. Recently emerged two-dimensional (2D) crystals offer unique advantages as potential sensing materials with high sensitivity, owing to their very high surface-to-bulk atom ratios and semiconducting properties. Here, we report the first use of Schottky-contacted chemical vapor deposition grown monolayer MoS2 as high-performance room temperature chemical sensors. The Schottky-contacted MoS2 transistors show current changes by 2–3 orders of magnitude upon exposure to very low concentrations of NO2 and NH3. Specifically, the MoS2 sensors show clear detection of NO2 and NH3 down to 20 ppb and 1 ppm, respectively. We attribute the observed high sensitivity to both well-known charger transfer mechanism and, more importantly, the Schottky barrier modulation upon analyte molecule adsorption, the latter of which is made possible by the Schottky contacts in the transistors and is not reported previously for MoS2 sensors. This study show...


title: Enhancing Detection Model for Multiple Hypothesis Tracking

abstract: Tracking-by-detection has become a popular tracking paradigm in recent years. Due to the fact that detections within this framework are regarded as points in the tracking process, it brings data association ambiguities, especially in crowded scenarios. To cope with this issue, we extended the multiple hypothesis tracking approach by incorporating a novel enhancing detection model that included detection-scene analysis and detection-detection analysis; the former models the scene by using dense confidential detections and handles false trajectories, while the latter estimates the correlations between individual detections and improves the ability to deal with close object hypotheses in crowded scenarios. Our approach was tested on the MOT16 benchmark and achieved competitive results with current state-of-the-art trackers.


title: De Novo Hair Follicle Morphogenesis and Hair Tumors in Mice Expressing a Truncated β-Catenin in Skin

abstract: An effector of intercellular adhesion, beta-catenin also functions in Wnt signaling, associating with Lef-1/Tcf DNA-binding proteins to form a transcription factor. We report that this pathway operates in keratinocytes and that mice expressing a stabilized beta-catenin controlled by an epidermal promoter undergo a process resembling de novo hair morphogenesis. The new follicles formed sebaceous glands and dermal papilla, normally established only in embryogenesis. As in embryologically initiated hair germs, transgenic follicles induce Lef-1, but follicles are disoriented and defective in sonic hedgehog polarization. Additionally, proliferation continues unchecked, resulting in two types of tumors also found in humans. Our findings suggest that transient beta-catenin stabilization may be a key player in the long-sought epidermal signal leading to hair development and implicate aberrant beta-catenin activation in hair tumors.


title: Multimodal 2D and 3D biometrics for face recognition

abstract: Results are presented for the largest experimental study to date that investigates the comparison and combination of 2D and 3D face data for biometric recognition. To our knowledge, this is also the only such study to incorporate significant time lapse between gallery and probe image acquisition. Recognition results are presented for gallery and probe datasets of 166 subjects imaged in both 2D and 3D, with six to thirteen weeks time lapse between gallery and probe images of a given subject. Using a PCA-based approach tuned separately for 2D and for 3D, we find no statistically significant difference between the rank-one recognition rates of 83.1% for 2D and 83.7% for 3D. Using a certainty-weighted sum-of-distance approach to combining 2D and 3D, we find a multimodal rank-one recognition rate of 92.8%, which is statistically significantly greater than either 2D or 3D alone.


title: SWAT: A System for Detecting Salient Wikipedia Entities in Texts

abstract: We study the problem of entity salience by proposing the design and implementation of SWAT, a system that identifies the salient Wikipedia entities occurring in an input document. SWAT consists of several modules that are able to detect and classify on-the-fly Wikipedia entities as salient or not, based on a large number of syntactic, semantic and latent features properly extracted via a supervised process which has been trained over millions of examples drawn from the New York Times corpus. The validation process is performed through a large experimental assessment, eventually showing that SWAT improves known solutions over all publicly available datasets. We release SWAT via an API that we describe and comment in the paper in order to ease its use in other software.


title: Incivility and Bullying in the Workplace and Nurses’ Shame Responses

abstract: Incivility and bullying in the workplace are intimidating forces that result in shame responses and threaten the well-being of nurses. Some nurses are accustomed to tolerating behaviors that are outside the realm of considerate conduct and are unaware that they are doing so. These behaviors affect the organizational climate, and their negative effects multiply if left unchecked. Interventions for incivility and bullying behaviors are needed at both individual and administrative levels.


title: Multi-object detection and tracking by stereo vision

abstract: This paper presents a new stereo vision-based model for multi-object detection and tracking in surveillance systems. Unlike most existing monocular camera-based systems, a stereo vision system is constructed in our model to overcome the problems of illumination variation, shadow interference, and object occlusion. In each frame, a sparse set of feature points are identified in the camera coordinate system, and then projected to the 2D ground plane. A kernel-based clustering algorithm is proposed to group the projected points according to their height values and locations on the plane. By producing clusters, the number, position, and orientation of objects in the surveillance scene can be determined for online multi-object detection and tracking. Experiments on both indoor and outdoor applications with complex scenes show the advantages of the proposed system.


title: Partially Overlapping Neural Networks for Real and Imagined Hand Movements

abstract: Neuroimagery findings have shown similar cerebral networks associated with imagination and execution of a movement. On the other hand, neuropsychological studies of parietal-lesioned patients suggest that these networks may be at least partly distinct. In the present study, normal subjects were asked to either imagine or execute auditory-cued hand movements. Compared with rest, imagination and execution showed overlapping networks, including bilateral premotor and parietal areas, basal ganglia and cerebellum. However, direct comparison between the two experimental conditions showed that specific cortico-subcortical areas were more engaged in mental simulation, including bilateral premotor, prefrontal, supplementary motor and left posterior parietal areas, and the caudate nuclei. These results suggest that a specific neuronal substrate is involved in the processing of hand motor representations.


title: Dynamic Few-Shot Visual Learning Without Forgetting

abstract: The human visual system has the remarkably ability to be able to effortlessly learn novel concepts from only a few examples. Mimicking the same behavior on machine learning vision systems is an interesting and very challenging research problem with many practical advantages on real world vision applications. In this context, the goal of our work is to devise a few-shot visual learning system that during test time it will be able to efficiently learn novel categories from only a few training data while at the same time it will not forget the initial categories on which it was trained (here called base categories). To achieve that goal we propose (a) to extend an object recognition system with an attention based few-shot classification weight generator, and (b) to redesign the classifier of a ConvNet model as the cosine similarity function between feature representations and classification weight vectors. The latter, apart from unifying the recognition of both novel and base categories, it also leads to feature representations that generalize better on"unseen"categories. We extensively evaluate our approach on Mini-ImageNet where we manage to improve the prior state-of-the-art on few-shot recognition (i.e., we achieve 56.20% and 73.00% on the 1-shot and 5-shot settings respectively) while at the same time we do not sacrifice any accuracy on the base categories, which is a characteristic that most prior approaches lack. Finally, we apply our approach on the recently introduced few-shot benchmark of Bharath and Girshick [4] where we also achieve state-of-the-art results. The code and models of our paper will be published on: https://github.com/gidariss/FewShotWithoutForgetting


title: Learning to See the Invisible: End-to-End Trainable Amodal Instance Segmentation

abstract: Semantic amodal segmentation is a recently proposed extension to instance-aware segmentation that includes the prediction of the invisible region of each object instance. We present the first all-in-one end-to-end trainable model for semantic amodal segmentation that predicts the amodal instance masks as well as their visible and invisible part in a single forward pass. In a detailed analysis, we provide experiments to show which architecture choices are beneficial for an all-in-one amodal segmentation model. On the COCO amodal dataset, our model outperforms the current baseline for amodal segmentation by a large margin. To further evaluate our model, we provide two new datasets with ground truth for semantic amodal segmentation, D2S amodal and COCOA cls. For both datasets, our model provides a strong baseline performance. Using special data augmentation techniques, we show that amodal segmentation on D2S amodal is possible with reasonable performance, even without providing amodal training data.


title: Control strategy of cascaded multilevel converter based on dual active bridge for wind energy conversion system

abstract: Low voltage high power wind energy conversion system (WECS) requires heavy cables and large step-up grid interfacing transformers. In recent years, power and voltage rating of offshore wind turbines have already reached 10MW and 10kV respectively, and medium voltage converters have become strongly demanded. Some scholars proposed a cascaded multilevel converter based on dual active bridge (DAB) to avoid the multi-phase generator, multi-windings transformer or large bridge inductors in traditional multilevel cascaded converter, which is suitable for medium voltage applications. However, there is little research on the strategies of current control, DC-Link voltage stability control and balance control for this topology. Thus, in this paper, the current side-split control and the voltage hierarchical control is proposed for the topology applied to medium voltage WECS. The control strategy is verified by simulation.


title: City-Scale Location Recognition

abstract: We look at the problem of location recognition in a large image dataset using a vocabulary tree. This entails finding the location of a query image in a large dataset containing 3times104 streetside images of a city. We investigate how the traditional invariant feature matching approach falls down as the size of the database grows. In particular we show that by carefully selecting the vocabulary using the most informative features, retrieval performance is significantly improved, allowing us to increase the number of database images by a factor of 10. We also introduce a generalization of the traditional vocabulary tree search algorithm which improves performance by effectively increasing the branching factor of a fixed vocabulary tree.


title: Modeling and control for smart grid integration of solar/wind energy conversion system

abstract: Performance optimization, system reliability and operational efficiency are key characteristics of smart grid systems. In this paper a novel model of smart grid-connected PV/WT hybrid system is developed. It comprises photovoltaic array, wind turbine, asynchronous (induction) generator, controller and converters. The model is implemented using MATLAB/SIMULINK software package. Perturb and observe (P&O) algorithm is used for maximizing the generated power based on maximum power point tracker (MPPT) implementation. The dynamic behavior of the proposed model is examined under different operating conditions. Solar irradiance, temperature and wind speed data is gathered from a grid connected, 28.8kW solar power system located in central Manchester. Real-time measured parameters are used as inputs for the developed system. The proposed model and its control strategy offer a proper tool for smart grid performance optimization.


title: Learning to Map Sentences to Logical Form: Structured Classification with Probabilistic Categorial Grammars

abstract: This paper addresses the problem of mapping natural language sentences to lambda–calculus encodings of their meaning. We describe a learning algorithm that takes as input a training set of sentences labeled with expressions in the lambda calculus. The algorithm induces a grammar for the problem, along with a log-linear model that represents a distribution over syntactic and semantic analyses conditioned on the input sentence. We apply the method to the task of learning natural language interfaces to databases and show that the learned parsers outperform previous methods in two benchmark database domains.


title: Insights into the catalytic properties of bamboo vacuolar invertase through mutational analysis of active site residues

abstract: Abstract Plant acid invertases, which are either associated with the cell wall or present in vacuoles, belong to family 32 of glycoside hydrolases (GH32). Homology modeling of bamboo vacuolar invertase Boβfruct3 using Arabidopsis cell-wall invertase AtcwINV1 as a template showed that its overall structure is similar to GH32 enzymes, and that the three highly conserved motifs, NDPNG, RDP and EC, are located in the active site. This study also used site-directed mutagenesis to examine the roles of the conserved amino acid residues in these three motifs, which include Asp135, Arg259, Asp260, Glu316 and Cys317, and a conserved Trp residue (Trp159) that resides between the NDPNG and RDP motifs. The mutants W159F, W159L, E316Q and C317A retained acid invertase activity, but no invertase activity was observed for the mutant E316A or mutants with changes at Asp135, Arg259, or Asp260. The apparent K m values of the four mutants with invertase activity were all higher than that of the wild-type enzyme. The mutants W159L and E316Q exhibited lower k cat values than the wild-type enzyme, but an increase in the k cat value was observed for the mutants W159F and C317A. The results of this study demonstrate that these residues have individual functions in catalyzing sucrose hydrolysis.


title: Control of soft pneumatic finger-like actuators for affective motion generation

abstract: This paper investigates the design and implementation of a finger-like robotic structure capable of reproducing human hand gestural movements performed by a multi-fingered, hand-like structure. In this work, we present a pneumatic circuit and a closed-loop controller for a finger-like soft pneumatic actuator. Experimental results demonstrate the performance of the pneumatic and control systems of the soft pneumatic actuator, and its ability to track human movement trajectories with affective content.


RETHINKING THE ART AND SCIENCE OF RISK ADJUSTMENT


title: An Empirical Analysis of Amazon EC2 Spot Instance Features Affecting Cost-effective Resource Procurement

abstract: Many cost-conscious public cloud workloads ("tenants") are turning to Amazon EC2's spot instances because, on average, these instances offer significantly lower prices (up to 10 times lower) than on-demand and reserved instances of comparable advertized resource capacities. To use spot instances effectively, a tenant must carefully weigh the lower costs of these instances against their poorer availability. Towards this, we empirically study four features of EC2 spot instance operation that a cost-conscious tenant may find useful to model. Using extensive evaluation based on both historical and current spot instance data, we show shortcomings in the state-of-the-art modeling of these features that we overcome. Our analysis reveals many novel properties of spot instance operation some of which offer predictive value while others do not. Using these insights, we design predictors for our features that offer a balance between computational efficiency (allowing for online resource procurement) and cost-efficacy. We explore "case studies" wherein we implement prototypes of dynamic spot instance procurement advised by our predictors for two types of workloads. Compared to the state-of-the-art, our approach achieves (i) comparable cost but much better performance (fewer bid failures) for a latency-sensitive in-memory Memcached cache, and (ii) an additional 18% cost-savings with comparable (if not better than) performance for a delay-tolerant batch workload.


title: Compensation strategy to reduce geometry and mechanics mismatches in porous biomaterials built with Selective Laser Melting

abstract: Abstract The accuracy of Additive Manufacturing processes in fabricating porous biomaterials is currently limited by their capacity to render pore morphology that precisely matches its design. In a porous biomaterial, a geometric mismatch can result in pore occlusion and strut thinning, drawbacks that can inherently compromise bone ingrowth and severely impact mechanical performance. This paper focuses on Selective Laser Melting of porous microarchitecture and proposes a compensation scheme that reduces the morphology mismatch between as-designed and as-manufactured geometry, in particular that of the pore. A spider web analog is introduced, built out of Ti–6Al–4V powder via SLM, and morphologically characterized. Results from error analysis of strut thickness are used to generate thickness compensation relations expressed as a function of the angle each strut formed with the build plane. The scheme is applied to fabricate a set of three-dimensional porous biomaterials, which are morphologically and mechanically characterized via micro Computed Tomography, mechanically tested and numerically analyzed. For strut thickness, the results show the largest mismatch (60% from the design) occurring for horizontal members, reduces to 3.1% upon application of the compensation. Similar improvement is observed also for the mechanical properties, a factor that further corroborates the merit of the design-oriented scheme here introduced.


title: Learning Representations by Recirculation

abstract: We describe a new learning procedure for networks that contain groups of nonlinear units arranged in a closed loop. The aim of the learning is to discover codes that allow the activity vectors in a "visible" group to be represented by activity vectors in a "hidden" group. One way to test whether a code is an accurate representation is to try to reconstruct the visible vector from the hidden vector. The difference between the original and the reconstructed visible vectors is called the reconstruction error, and the learning procedure aims to minimize this error. The learning procedure has two passes. On the first pass, the original visible vector is passed around the loop, and on the second pass an average of the original vector and the reconstructed vector is passed around the loop. The learning procedure changes each weight by an amount proportional to the product of the "presynaptic" activity and the difference in the post-synaptic activity on the two passes. This procedure is much simpler to implement than methods like back-propagation. Simulations in simple networks show that it usually converges rapidly on a good set of codes, and analysis shows that in certain restricted cases it performs gradient descent in the squared reconstruction error.


title: Economists' opinions on environmental policy instruments: Analysis of a survey

abstract: Incentive instruments (effluent charges) are theoretically expected to be preferred to direct regulations (individual emission ceilings) by university-employed, theoretically inclined politically right-wing, professional economists living in a market-oriented country, ceteris paribus. Public sector employed, politically left-wing, professional economists living in a country with a long tradition of government involvement prefer the regulatory approach, ceteris paribus. These theoretical hypotheses tend to be supported by a bivariate probit analysis based on a survey of more than 1400 economists in five countries. 22 references, 4 tables.


title: Towards Design Theory and Expandable Rationality: The Unfinished Program of Herbert Simon

abstract: A high-frequency band-pass filter has an input resonator, a middle resonator and an output resonator which are coupled in parallel. The input resonator and the output resonator are capacitively shortened quarter-wave line resonators. To improve the attenuation characteristics, the middle resonator is designed as a capacitively shortened half-wave resonator. The input resonator extends along a first part of the length of the middle resonator. The output resonator extends along a second part of the length of the middle resonator.


title: Current status, opportunities and challenges of augmented reality in education

abstract: Although augmented reality (AR) has gained much research attention in recent years, the term AR was given different meanings by varying researchers. In this article, we first provide an overview of definitions, taxonomies, and technologies of AR. We argue that viewing AR as a concept rather than a type of technology would be more productive for educators, researchers, and designers. Then we identify certain features and affordances of AR systems and applications. Yet, these compelling features may not be unique to AR applications and can be found in other technological systems or learning environments (e.g., ubiquitous and mobile learning environments). The instructional approach adopted by an AR system and the alignment among technology design, instructional approach, and learning experiences may be more important. Thus, we classify three categories of instructional approaches that emphasize the ''roles,'' ''tasks,'' and ''locations,'' and discuss what and how different categories of AR approaches may help students learn. While AR offers new learning opportunities, it also creates new challenges for educators. We outline technological, pedagogical, learning issues related to the implementation of AR in education. For example, students in AR environments may be cognitively overloaded by the large amount of information they encounter, the multiple technological devices they are required to use, and the complex tasks they have to complete. This article provides possible solutions for some of the challenges and suggests topics and issues for future research.


title: A systems model of leadership: WICS.

abstract: This article reviews a systems model of leadership. According to the model, effective leadership is a synthesis of wisdom, creativity, and intelligence (WICS). It is in large part a decision about how to marshal and deploy these resources. One needs creativity to generate ideas, academic (analytical) intelligence to evaluate whether the ideas are good, practical intelligence to implement the ideas and persuade others of their worth, and wisdom to balance the interests of all stakeholders and to ensure that the actions of the leader seek a common good. The article relates the current model to other extant models of leadership.


title: Internet use, transparency, and interactivity effects on trust in government

abstract: This paper asks how Internet use, citizen satisfaction with e-government and citizen trust in government are interrelated. Prior research has found that agencies stress information and service provision on the Web (one-way e-government strategy), but have generally ignore applications that would enhance citizen-government interaction (two-way e-government strategy). Based on a review of the literature, we develop hypotheses about how two facets of e-democracy - transparency and interactivity - may affect citizen trust in government. Using data obtained from the Council on Excellence in Government, we apply a two stage multiple equation model. Findings indicate that Internet use is positively associated with transparency satisfaction but negatively associated with interactivity satisfaction, and that both interactivity and transparency are positively associated with citizen trust in government. We conclude that the one-way e-transparency strategy may be insufficient, and that in the future agencies should make and effort to enhance e-interactivity.


title: Mining fine-grained code changes to detect unknown change patterns

abstract: Identifying repetitive code changes benefits developers, tool builders, and researchers. Tool builders can automate the popular code changes, thus improving the productivity of developers. Researchers can better understand the practice of code evolution, advancing existing code assistance tools and benefiting developers even further. Unfortunately, existing research either predominantly uses coarse-grained Version Control System (VCS) snapshots as the primary source of code evolution data or considers only a small subset of program transformations of a single kind - refactorings. We present the first approach that identifies previously unknown frequent code change patterns from a fine-grained sequence of code changes. Our novel algorithm effectively handles challenges that distinguish continuous code change pattern mining from the existing data mining techniques. We evaluated our algorithm on 1,520 hours of code development collected from 23 developers, and showed that it is effective, useful, and scales to large amounts of data. We analyzed some of the mined code change patterns and discovered ten popular kinds of high-level program transformations. More than half of our 420 survey participants acknowledged that eight out of ten transformations are relevant to their programming activities.