title: Deep Recurrent Neural Networks for Product Attribute Extraction in eCommerce

abstract: Extracting accurate attribute qualities from product titles is a vital component in delivering eCommerce customers with a rewarding online shopping experience via an enriched faceted search. We demonstrate the potential of Deep Recurrent Networks in this domain, primarily models such as Bidirectional LSTMs and Bidirectional LSTM-CRF with or without an attention mechanism. These have improved overall F1 scores, as compared to the previous benchmarks (More et al.) by at least 0.0391, showcasing an overall precision of 97.94%, recall of 94.12% and the F1 score of 0.9599. This has made us achieve a significant coverage of important facets or attributes of products which not only shows the efficacy of deep recurrent models over previous machine learning benchmarks but also greatly enhances the overall customer experience while shopping online.


Video Object Segmentation via Tracking Edges and Classifying Segments


Data sets for author name disambiguation: an empirical analysis and a new resource


title: Factors influencing e-marketplace adoption in agricultural micro-enterprises

abstract: Electronic marketplaces (e-marketplaces) present a business-tobusiness (B2B) trading environment where firms can benefit from increased choice among trading partners, and other efficiencies gained through electronic exchange. B2B e-marketplaces have only recently emerged in New Zealand; however, there is already doubt whether predicted benefits are being realised. The study draws on Tornatzky and Fleischer's (1990) adoption model to explore motivations and barriers to e-marketplace adoption that agricultural micro-enterprises experience. Key barriers include conflict with pre-existing relationships, risk perceptions, ease of use and infrastructure problems. Dominant motivations include the ability to exclude agent middlemen, a reduction in transaction costs, and market reach.


title: Deep Unsupervised Intrinsic Image Decomposition by Siamese Training

abstract: We harness modern intrinsic decomposition tools based on deep learning to increase their applicability on realworld use cases. Traditional techniques are derived from the Retinex theory: handmade prior assumptions constrain an optimization to yield a unique solution that is qualitatively satisfying on a limited set of examples. Modern techniques based on supervised deep learning leverage largescale databases that are usually synthetic or sparsely annotated. Decomposition quality on images in the wild is therefore arguable. We propose an end-to-end deep learning solution that can be trained without any ground truth supervision, as this is hard to obtain. Time-lapses form an ubiquitous source of data that (under a scene staticity assumption) capture a constant albedo under varying shading conditions. We exploit this natural relationship to train in an unsupervised siamese manner on image pairs. Yet, the trained network applies to single images at inference time. We present a new dataset to demonstrate our siamese training on, and reach results that compete with the state of the art, despite the unsupervised nature of our training scheme. As evaluation is difficult, we rely on extensive experiments to analyze the strengths and weaknesses of our and related methods.


title: Attacks Against BLE Devices by Co-located Mobile Applications

abstract: Bluetooth Low Energy (BLE) is a fast-growing wireless technology with a large number of potential use cases, particularly in the IoT domain. With many of these use cases, the BLE device stores sensitive user data or critical device controls, which may be accessed by an augmentative Android or iOS application. Uncontrolled access to such data could violate a user's privacy, cause a device to malfunction, or even endanger lives. The BLE specification aims to solve this with network layer security mechanisms such as pairing and bonding. Unfortunately, this doesn't take into account the fact that many applications may be co-located on the same mobile device, which introduces the possibility of unauthorised applications being able to access and modify sensitive data stored on a BLE device. In this paper, we present an attack in which an unauthorised Android application can access pairing-protected data from a BLE device by exploiting the bonding relationship previously triggered by an authorised application. We discuss possible mitigation strategies, and perform an analysis over 13,500+ BLE-enabled Android applications to identify how many of them implement such strategies to avoid this attack. Our results indicate that over 60% of these applications do not have mitigation strategies in place in the form of application-layer security, and that cryptography is sometimes implemented incorrectly in those that do. This implies that the corresponding BLE devices are potentially vulnerable to unauthorised data access by malicious applications.


title: Mining Probabilistic Representative Frequent Patterns From Uncertain Data.

abstract: Probabilistic frequent pattern mining over uncertain data has received a great deal of attention recently due to the wide applications of uncertain data. Similar to its counterpart in deterministic databases, however, probabilistic frequent pattern mining suffers from the same problem of generating an exponential number of result patterns. The large number of discovered patterns hinders further evaluation and analysis, and calls for the need to find a small number of representative patterns to approximate all other patterns. This paper formally defines the problem of probabilistic representative frequent pattern (P-RFP) mining, which aims to find the minimal set of patterns with sufficiently high probability to represent all other patterns. The problem’s bottleneck turns out to be checking whether a pattern can probabilistically represent another, which involves the computation of a joint probability of supports of two patterns. To address the problem, we propose a novel and efficient dynamic programming-based approach. Moreover, we have devised a set of effective optimization strategies to further improve the computation efficiency. Our experimental results demonstrate that the proposed P-RFP mining effectively reduces the size of probabilistic frequent patterns. Our proposed approach not only discovers the set of P-RFPs efficiently, but also restores the frequency probability information of patterns with an error guarantee.


title: Level set surface editing operators

abstract: We present a level set framework for implementing editing operators for surfaces. Level set models are deformable implicit surfaces where the deformation of the surface is controlled by a speed function in the level set partial differential equation. In this paper we define a collection of speed functions that produce a set of surface editing operators. The speed functions describe the velocity at each point on the evolving surface in the direction of the surface normal. All of the information needed to deform a surface is encapsulated in the speed function, providing a simple, unified computational framework. The user combines pre-defined building blocks to create the desired speed function. The surface editing operators are quickly computed and may be applied both regionally and globally. The level set framework offers several advantages. 1) By construction, self-intersection cannot occur, which guarantees the generation of physically-realizable, simple, closed surfaces. 2) Level set models easily change topological genus, and 3) are free of the edge connectivity and mesh quality problems associated with mesh models. We present five examples of surface editing operators: blending, smoothing, sharpening, openings/closings and embossing. We demonstrate their effectiveness on several scanned objects and scan-converted models.


title: Prediction of Breast Cancer Survival Through Knowledge Discovery in Databases

abstract: The collection of large volumes of medical data has offered an opportunity to develop prediction models for survival by the medical research community. Medical researchers who seek to discover and extract hidden patterns and relationships among large number of variables use knowledge discovery in databases (KDD) to predict the outcome of a disease. The study was conducted to develop predictive models and discover relationships between certain predictor variables and survival in the context of breast cancer. This study is Cross sectional. After data preparation, data of 22,763 female patients, mean age 59.4 years, stored in the Surveillance Epidemiology and End Results (SEER) breast cancer dataset were analyzed anonymously. IBM SPSS Statistics 16, Access 2003 and Excel 2003 were used in the data preparation and IBM SPSS Modeler 14.2 was used in the model design. Support Vector Machine (SVM) model outperformed other models in the prediction of breast cancer survival. Analysis showed SVM model detected ten important predictor variables contributing mostly to prediction of breast cancer survival. Among important variables, behavior of tumor as the most important variable and stage of malignancy as the least important variable were identified. In current study, applying of the knowledge discovery method in the breast cancer dataset predicted the survival condition of breast cancer patients with high confidence and identified the most important variables participating in breast cancer survival.


title: Incremental Discovery of Inclusion Dependencies

abstract: Inclusion dependencies form one of the most fundamental classes of integrity constraints. Their importance in classical data management is reinforced by modern applications such as data profiling, data cleaning, entity resolution and schema matching. Their discovery in an unknown dataset is at the core of any data analysis effort. Therefore, several research approaches have focused on their efficient discovery in a given, static dataset. However, none of these approaches are appropriate for applications on dynamic datasets, such as transactional datasets, scientific applications, and social network. In these cases, discovery techniques should be able to efficiently update the inclusion dependencies after an update in the dataset, without reprocessing the entire dataset. We present the first approach for incrementally updating the unary inclusion dependencies. In particular, our approach is based on the concept of attribute clustering from which the unary inclusion dependencies are efficiently derivable. We incrementally update the clusters after each update of the dataset. Updating the clusters does not need to access the dataset because of special data structures designed to efficiently support the updating process. We perform an exhaustive analysis of our approach by applying it to large datasets with several hundred attributes and more than 116,200,000 million tuples. The results show that the incremental discovery significantly reduces the runtime needed by the static discovery. This reduction in the runtime is up to 99.9996 % for both the insert and the delete.


title: DNS Unchained: Amplified Application-Layer DoS Attacks Against DNS Authoritatives

abstract: We present DNS Unchained, a new application-layer DoS attack against core DNS infrastructure that for the first time uses amplification. To achieve an attack amplification of 8.51, we carefully chain CNAME records and force resolvers to perform deep name resolutions—effectively overloading a target authoritative name server with valid requests. We identify 178 508 potential amplifiers, of which 74.3% can be abused in such an attack due to the way they cache records with low Time-to-Live values. In essence, this allows a single modern consumer uplink to downgrade availability of large DNS setups. To tackle this new threat, we conclude with an overview of countermeasures and suggestions for DNS servers to limit the impact of DNS chaining attacks.


title: One-Shot Video Object Segmentation

abstract: This paper tackles the task of semi-supervised video object segmentation, i.e., the separation of an object from the background in a video, given the mask of the first frame. We present One-Shot Video Object Segmentation (OSVOS), based on a fully-convolutional neural network architecture that is able to successively transfer generic semantic information, learned on ImageNet, to the task of foreground segmentation, and finally to learning the appearance of a single annotated object of the test sequence (hence one-shot). Although all frames are processed independently, the results are temporally coherent and stable. We perform experiments on two annotated video segmentation databases, which show that OSVOS is fast and improves the state of the art by a significant margin (79.8% vs 68.0%).


title: Sampling properties of random graphs: The degree distribution

abstract: We discuss two sampling schemes for selecting random subnets from a network, random sampling and connectivity dependent sampling, and investigate how the degree distribution of a node in the network is affected by the two types of sampling. Here we derive a necessary and sufficient condition that guarantees that the degree distributions of the subnet and the true network belong to the same family of probability distributions. For completely random sampling of nodes we find that this condition is satisfied by classical random graphs; for the vast majority of networks this condition will, however, not be met. We furthermore discuss the case where the probability of sampling a node depends on the degree of a node and we find that even classical random graphs are no longer closed under this sampling regime. We conclude by relating the results to real Eschericia coli protein interaction network data.


title: Automatic testing of object-oriented software

abstract: Effective testing involves preparing test oracles and test cases, two activities which are too tedious to be effectively performed by humans, yet for the most part remain manual. The AutoTest unit testing framework automates both, by using Eiffel contracts -- already present in the software -- as test oracles, and generating objects and routine arguments to exercise all given classes; manual tests can also be added, and all failed test cases are automatically retained for regression testing, in a "minimized" form retaining only the relevant instructions. AutoTest has already detected numerous hitherto unknown bugs in production software.


title: Combining Computational Analyses and Interactive Visualization for Document Exploration and Sensemaking in Jigsaw

abstract: Investigators across many disciplines and organizations must sift through large collections of text documents to understand and piece together information. Whether they are fighting crime, curing diseases, deciding what car to buy, or researching a new field, inevitably investigators will encounter text documents. Taking a visual analytics approach, we integrate multiple text analysis algorithms with a suite of interactive visualizations to provide a flexible and powerful environment that allows analysts to explore collections of documents while sensemaking. Our particular focus is on the process of integrating automated analyses with interactive visualizations in a smooth and fluid manner. We illustrate this integration through two example scenarios: An academic researcher examining InfoVis and VAST conference papers and a consumer exploring car reviews while pondering a purchase decision. Finally, we provide lessons learned toward the design and implementation of visual analytics systems for document exploration and understanding.


title: Improved texture and appearance of human facial skin after daily topical application of barley produced, synthetic, human-like epidermal growth factor (EGF) serum.

abstract: A three month, open-label, single center study was conducted to determine whether a uniquely derived serum containing barley bioengineered, human-like epidermal growth factor protein could improve visible signs of photodamage and aging in facial skin. Twenty-nine females, aged 39 to 75 years, with mild to severe, fine and course rhytids, photodamage, and pigmentation were enrolled. Subjects then applied the treatment serum per the prescribed protocol twice-daily for 3 months, in addition to the use of a basic sunscreen and facial cleanser. In-person clinical evaluations and subject self-assessment questionnaires were administered at each follow up visit. In addition, clinical photography was completed at baseline, and each subsequent visit. Clinical evaluations showed statistically significant improvement in the appearance of fine lines and rhytids, skin texture, pore size, and various dyschromatic conditions apparent within the first month of use, and continuing improvement trends for the duration of the study. The treatment serum was well tolerated with minimal treatment-related complications reported throughout. Efficacy of this novel serum and treatment protocol resulted in meaningful improvements in photodamage and visible signs of aging.


title: Image Captioning with Affective Guiding and Selective Attention

abstract: Image captioning is an increasingly important problem associated with artificial intelligence, computer vision, and natural language processing. Recent works revealed that it is possible for a machine to generate meaningful and accurate sentences for images. However, most existing methods ignore latent emotional information in an image. In this article, we propose a novel image captioning model with Affective Guiding and Selective Attention Mechanism named AG-SAM. In our method, we aim to bridge the affective gap between image captioning and the emotional response elicited by the image. First, we introduce affective components that capture higher-level concepts encoded in images into AG-SAM. Hence, our language model can be adapted to generate sentences that are more passionate and emotive. In addition, a selective gate acting on the attention mechanism controls the degree of how much visual information AG-SAM needs. Experimental results have shown that our model outperforms most existing methods, clearly reflecting an association between images and emotional components that is usually ignored in existing works.


title: Education Analytics: Challenges and Approaches

abstract: We present a survey on the current status of education analytics. Over 80 state-of-the-art research projects and reports, published from 2013 to 2017 and archived in IEEE Xplore, are listed. Focusing on student performance prediction and intervention, we highlight the data sets used, techniques employed, algorithms utilized, and particular information in each paper/project.


title: Dynamic Bike Reposition: A Spatio-Temporal Reinforcement Learning Approach

abstract: Bike-sharing systems are widely deployed in many major cities, while the jammed and empty stations in them lead to severe customer loss. Currently, operators try to constantly reposition bikes among stations when the system is operating. However, how to efficiently reposition to minimize the customer loss in a long period remains unsolved. We propose a spatio-temporal reinforcement learning based bike reposition model to deal with this problem. Firstly, an inter-independent inner-balance clustering algorithm is proposed to cluster stations into groups. Clusters obtained have two properties, i.e. each cluster is inner-balanced and independent from the others. As there are many trikes repositioning in a very large system simultaneously, clustering is necessary to reduce the problem complexity. Secondly, we allocate multiple trikes to each cluster to conduct inner-cluster bike reposition. A spatio-temporal reinforcement learning model is designed for each cluster to learn a reposition policy in it, targeting at minimizing its customer loss in a long period. To learn each model, we design a deep neural network to estimate its optimal long-term value function, from which the optimal policy can be easily inferred. Besides formulating the model in a multi-agent way, we further reduce its training complexity by two spatio-temporal pruning rules. Thirdly, we design a system simulator based on two predictors to train and evaluate the reposition model. Experiments on real-world datasets from Citi Bike are conducted to confirm the effectiveness of our model.


title: A Fully Onchip Binarized Convolutional Neural Network FPGA Impelmentation with Accurate Inference

abstract: Deep convolutional neural network has taken an important role in machine learning algorithm which has been widely used in computer vision tasks. However, its enormous model size and massive computation cost have became the main obstacle for deployment of such powerful algorithm in low power and resource limited embedded system, such as FPGA. Recent works have shown the binarized neural networks (BNN), utilizing binarized (i.e. +1 and -1) convolution kernel and binary activation function, can significantly reduce the model size and computation complexity, which paves a new road for energy-efficient FPGA implementation. In this work, we first propose a new BNN algorithm, called Parallel-Convolution BNN (i.e. PC-BNN), which replaces the original binary convolution layer in conventional BNN with two parallel binary convolution layers. PC-BNN achieves ~86% on CIFAR-10 dataset with only 2.3Mb parameter size. We then deploy our proposed PC-BNN into the Xilinx PYNQ Z1 FPGA board with only 4.9Mb on-chip RAM. Since the ultra-small network parameter, it is feasible to store the whole network parameter into on-chip RAM, which could greatly reduce the energy and delay overhead to load network parameter from off-chip memory. Meanwhile, a new data streaming pipeline architecture is proposed in PC-BNN FPGA implementation to further improve throughput. The experiment results show that our PC-BNN based FPGA implementation achieves 930 frames per second, 387.5 FPS/Watt and 396x10-4 FPS/LUT, which are among the best throughput and energy efficiency compared to most recent works.


title: Sparse Feature Fidelity for Perceptual Image Quality Assessment

abstract: The prediction of an image quality metric (IQM) should be consistent with subjective human evaluation. As the human visual system (HVS) is critical to visual perception, modeling of the HVS is regarded as the most suitable way to achieve perceptual quality predictions. Sparse coding that is equivalent to independent component analysis (ICA) can provide a very good description of the receptive fields of simple cells in the primary visual cortex, which is the most important part of the HVS. With this inspiration, a quality metric called sparse feature fidelity (SFF) is proposed for full-reference image quality assessment (IQA) on the basis of transformation of images into sparse representations in the primary visual cortex. The proposed method is based on the sparse features that are acquired by a feature detector, which is trained on samples of natural images by an ICA algorithm. In addition, two strategies are designed to simulate the properties of the visual perception: 1) visual attention and 2) visual threshold. The computation of SFF has two stages: training and fidelity computation, in addition, the fidelity computation consists of two components: feature similarity and luminance correlation. The feature similarity measures the structure differences between the two images, whereas the luminance correlation evaluates brightness distortions. SFF also reflects the chromatic properties of the HVS, and it is very effective for color IQA. The experimental results on five image databases show that SFF has a better performance in matching subjective ratings compared with the leading IQMs.


title: Video Desnowing and Deraining Based on Matrix Decomposition

abstract: The existing snow/rain removal methods often fail for heavy snow/rain and dynamic scene. One reason for the failure is due to the assumption that all the snowflakes/rain streaks are sparse in snow/rain scenes. The other is that the existing methods often can not differentiate moving objects and snowflakes/rain streaks. In this paper, we propose a model based on matrix decomposition for video desnowing and deraining to solve the problems mentioned above. We divide snowflakes/rain streaks into two categories: sparse ones and dense ones. With background fluctuations and optical flow information, the detection of moving objects and sparse snowflakes/rain streaks is formulated as a multi-label Markov Random Fields (MRFs). As for dense snowflakes/rain streaks, they are considered to obey Gaussian distribution. The snowflakes/rain streaks, including sparse ones and dense ones, in scene backgrounds are removed by low-rank representation of the backgrounds. Meanwhile, a group sparsity term in our model is designed to filter snow/rain pixels within the moving objects. Experimental results show that our proposed model performs better than the state-of-the-art methods for snow and rain removal.


title: Spectral Efficiency of CDMA with Random Spreading

abstract: The CDMA channel with randomly and independently chosen spreading sequences accurately models the situation where pseudonoise sequences span many symbol periods. Furthermore, its analysis provides a comparison baseline for CDMA channels with deterministic signature waveforms spanning one symbol period. We analyze the spectral efficiency (total capacity per chip) as a function of the number of users, spreading gain, and signal-to-noise ratio, and we quantify the loss in efficiency relative to an optimally chosen set of signature sequences and relative to multiaccess with no spreading. White Gaussian background noise and equal-power synchronous users are assumed. The following receivers are analyzed: (a) optimal joint processing, (b) single-user matched filtering, (c) decorrelation, and (d) MMSE linear processing.


title: Deep Recurrent Models with Fast-Forward Connections for Neural Machine Translation

abstract: Neural machine translation (NMT) aims at solving machine translation (MT) problems using neural networks and has exhibited promising results in recent years. However, most of the existing NMT models are shallow and there is still a performance gap between a single NMT model and the best conventional MT system. In this work, we introduce a new type of linear connections, named fast-forward connections, based on deep Long Short-Term Memory (LSTM) networks, and an interleaved bi-directional architecture for stacking the LSTM layers. Fast-forward connections play an essential role in propagating the gradients and building a deep topology of depth 16. On the WMT'14 English-to-French task, we achieve BLEU=37.7 with a single attention model, which outperforms the corresponding single shallow model by 6.2 BLEU points. This is the first time that a single NMT model achieves state-of-the-art performance and outperforms the best conventional model by 0.7 BLEU points. We can still achieve BLEU=36.3 even without using an attention mechanism. After special handling of unknown words and model ensembling, we obtain the best score reported to date on this task with BLEU=40.4. Our models are also validated on the more difficult WMT'14 English-to-German task.


Content-Based Based Image Retrieval using Color Moment and Gabor Texture Feature


title: Prioritizing the devices to test your app on: a case study of Android game apps

abstract: Star ratings that are given by the users of mobile apps directly impact the revenue of its developers. At the same time, for popular platforms like Android, these apps must run on hundreds of devices increasing the chance for device-specific problems. Device-specific problems could impact the rating assigned to an app, given the varying capabilities of devices (e.g., hardware and software). To fix device-specific problems developers must test their apps on a large number of Android devices, which is costly and inefficient. Therefore, to help developers pick which devices to test their apps on, we propose using the devices that are mentioned in user reviews. We mine the user reviews of 99 free game apps and find that, apps receive user reviews from a large number of devices: between 38 to 132 unique devices. However, most of the reviews (80%) originate from a small subset of devices (on average, 33%). Furthermore, we find that developers of new game apps with no reviews can use the review data of similar game apps to select the devices that they should focus on first. Finally, among the set of devices that generate the most reviews for an app, we find that some devices tend to generate worse ratings than others. Our findings indicate that focusing on the devices with the most reviews (in particular the ones with negative ratings), developers can effectively prioritize their limited Quality Assurance (QA) efforts, since these devices have the greatest impact on ratings.


title: Predicting user traits from a snapshot of apps installed on a smartphone

abstract: Third party apps are an integral component of the smartphone ecosystem. In this paper, we investigate how user traits can be inferred by observing only a single snapshot of installed apps. Using supervised learning methods and minimal external information we show that user traits such as religion, relationship status, spoken languages, countries of interest, and whether or not the user is a parent of small children, can be easily predicted. Using data collected from over 200 smartphone users, specifically the list of installed apps and the corresponding ground truth traits of the users, we show that for most traits we can achieve over 90% precision. Our inference method can be used to provide services such as personalized content delivery or recommender systems for users. We also highlight privacy loss that can occur from unrestricted access to the app lists in popular smartphone operating systems.


title: Real time asset tracking in the data center

abstract: The importance and difficultly of asset tracking make it worthy of attention. We focus on data centers consisting of vertical racks where each rack may accommodate a variety of equipment. We describe an asset tracking system which automatically detects and identifies equipment within rack; has "pinpoint" accuracy, i.e., location resolution equals asset size; relays this information to possibly several management back-ends; includes a back-end application that maintains a location history for all equipment; and uses a visualization tool to display both the current state and the history of deployment. ::: ::: The solution features a flexible architecture that simplifies the connection with both existing and future asset management applications. The architecture supports simple configuration, load balancing, and redundancy. Care has been taken to use widely recognized standards wherever possible.


title: Motion Planning Based on Learning From Demonstration for Multiple-Segment Flexible Soft Robots Actuated by Electroactive Polymers

abstract: Multiple-segment flexible and soft robotic arms composed by ionic polymer--metal composite (IPMC) flexible actuators exhibit compliance but suffer from the difficulty of path planning due to their redundant degrees of freedom, although they are promising in complex tasks such as crossing body cavities to grasp objects. We propose a learning from demonstration method to plan the motion paths of IPMC-based manipulators, by statistics machine-learning algorithms. To encode demonstrated trajectories and estimate suitable paths for the manipulators to reproduce the task, models are built based on Gaussian mixture model and Gaussian mixture regression, respectively. The forward and inverse kinematic models of IPMC-based soft robotic arm are derived for the motion control. A flexible and soft robotic manipulator is implemented with six IPMC segments, and it verifies the learned paths by successfully completing a representative task of navigating through a narrow keyhole.


title: Social Scene Understanding: End-to-End Multi-person Action Localization and Collective Activity Recognition

abstract: We present a unified framework for understanding human social behaviors in raw image sequences. Our model jointly detects multiple individuals, infers their social actions, and estimates the collective actions with a single feed-forward pass through a neural network. We propose a single architecture that does not rely on external detection algorithms but rather is trained end-to-end to generate dense proposal maps that are refined via a novel inference scheme. The temporal consistency is handled via a person-level matching Recurrent Neural Network. The complete model takes as input a sequence of frames and outputs detections along with the estimates of individual actions and collective activities. We demonstrate state-of-the-art performance of our algorithm on multiple publicly available benchmarks.


title: Knowledge sharing behavior in virtual communities: The relationship between trust, self-efficacy, and outcome expectations

abstract: There has been a growing interest in examining the factors that support or hinder one's knowledge sharing behavior in the virtual communities. However, still very few studies examined them from both personal and environmental perspectives. In order to explore the knowledge sharing behaviors within the virtual communities of professional societies, this study proposed a social cognitive theory (SCT)-based model that includes knowledge sharing self-efficacy and outcome expectations for personal influences, and multi-dimensional trusts for environmental influences. The proposed research model was then evaluated with structural equation modeling, and confirmatory factor analysis was also applied to test if the empirical data conform to the proposed model.


title: Adaptive Road Following using Self-Supervised Learning and Reverse Optical Flow

abstract: The majority of current image-based road following algorithms operate, at least in part, by assuming the presence of structural or visual cues unique to the roadway. As a result, these algorithms are poorly suited to the task of tracking unstructured roads typical in desert environments. In this paper, we propose a road following algorithm that operates in a selfsupervised learning regime, allowing it to adapt to changing road conditions while making no assumptions about the general structure or appearance of the road surface. An application of optical flow techniques, paired with one-dimensional template matching, allows identification of regions in the current camera image that closely resemble the learned appearance of the road in the recent past. The algorithm assumes the vehicle lies on the road in order to form templates of the road’s appearance. A dynamic programming variant is then applied to optimize the 1-D template match results while enforcing a constraint on the maximum road curvature expected. Algorithm output images, as well as quantitative results, are presented for three distinct road types encountered in actual driving video acquired in the California Mojave Desert.


title: Fuzzy sets and systems

abstract: The notion of fuzziness as defined in this paper relates to situations in which the source of imprecision is not a random variable or a stochastic process, but rather a class or classes which do not possess sharply defined boundaries, e.g., the “class of bald men,” or the “class of numbers which are much greater than 10,” or the “class of adaptive systems,” etc. A basic concept which makes it possible to treat fuzziness in a quantitative manner is that of a fuzzy set, that is, a class in which there may be grades of membership intermediate between full membership and non-membership. Thus, a fuzzy set is characterized by a membership function which assigns to each object its grade of membership (a number lying between 0 and 1) in the fuzzy set. After a review of some of the relevant properties of fuzzy sets, the notions of a fuzzy system and a fuzzy class of systems are introduced and briefly analyzed. The paper closes with a section dealing with optimization under fuzzy constraints in which an approach to...


title: A Survey on Credit Card Fraud Detection Using Machine Learning

abstract: Nowadays digitalization gaining popularity because of seamless, easy and convenience use of e-commerce. It became very rampant and easy mode of payment. People choose online payment and e-shopping; because of time convenience, transport convenience, etc. As the result of huge amount of e-commerce use, there is a vast increment in credit card fraud also. Fraudsters try to misuse the card and transparency of online payments. Thus to overcome with the fraudsters activity become very essential. The main aim is to secure credit card transactions; so people can use e-banking safely and easily. To detecting the credit card fraud there are various techniques which are based on Deep learning, Logistic Regression, Naive Bayesian, Support Vector Machine (SVM), Neural Network, Artificial Immune System, K Nearest Neighbor, Data Mining, Decision Tree, Fuzzy logic based System, Genetic Algorithm etc.


title: Acculturation and environmental change impacts dietary habits among adult Hmong

abstract: Abstract Focus groups ( n = 65) were conducted with Hmong adults in St. Paul/Minneapolis, MN to determine how environmental factors, acculturation, and food insecurity influence dietary behavior, body mass index (BMI), and health. Acculturation was assessed by examining linguistic, social, and eating behavior, length of time in the US, and BMI for B-TL 1 (born in Thailand/Laos and in US ≤5 years), B-TL 2 (in Thailand/Laos through adolescence, had food memories, and in US >5 years), and B-US (born in US or in Thailand/Laos less than 8 years and no food memories from there). Acculturation was associated with years lived in the US, household size and income, and food assistance usage. Years lived in the US was correlated with food assistance usage, education, household size and income, and perception of diet. B-TL 1 and B-TL 2 were on average overweight (BMI ≥ 25


title: Evaluation of Local Features for Structure from Motion

abstract: Structure from motion (SFM) is an effective approach for reconstructing large-scale 3D scene from multiple images. In this field, many local feature methods have been proposed to detect feature point and compute descriptor. For designing a robust SFM system, how to select a good feature from existing methods is an important problem. In this paper, we aim to help different users for making decision by an experimental way for large-scale 3D reconstruction where many high resolution images are captured. To this end, we make a comprehensive evaluation of several local features on the ground truth datasets. Experimental results show that SIFT and SURF have a better performance than that of some binary features such as ORB and BRISK.


title: Smart-energy group anomaly based behavioral abnormality detection

abstract: Monitoring behavioral abnormality of individuals living independently in their own homes is a key issue for building sustainable healthcare models in smart environments. While most of the efforts have been directed towards building ambient and wearable sensors-assisted activity recognition based behavioral analysis models for remote health monitoring, energy analytics assisted behavioral abnormality prediction have rarely been investigated. In this paper, we propose a data analytic approach that helps detect energy usage anomalies corresponding to the behavioral abnormality of the residents. Our approach relies on detecting everyday appliances usage from smart meter and smart plug data traces in regular activity days and then learning the unique time segment group of each appliance’s energy consumption. We focus on detecting behavioral anomalies over a set of energy source data points rather than pinpointing individual odd points. We employ hierarchical probabilistic model-based group anomaly detection [7] to interpret the anomalous behavior and therefore, detect potential tendency towards behavioral abnormality. We apply daily activity logs to evaluate our approach using two realworld energy datasets pertaining to staged functional behaviors, and show that it is possible to detect max. 97% of anomalous days with max. 87% of meaningful micro-behavioral abnormal events generating 1.1% of false alarms. However, we show that our detected abnormality can be meaningfully represented to different stakeholders such as caregivers and family members to understand the nature and severity of abnormal human behavior for sustaining better healthcare.


title: Fast subtree kernels on graphs

abstract: In this article, we propose fast subtree kernels on graphs. On graphs with n nodes and m edges and maximum degree d, these kernels comparing subtrees of height h can be computed in O(mh), whereas the classic subtree kernel by Ramon & Gartner scales as O(n24dh). Key to this efficiency is the observation that the Weisfeiler-Lehman test of isomorphism from graph theory elegantly computes a subtree kernel as a byproduct. Our fast subtree kernels can deal with labeled graphs, scale up easily to large graphs and outperform state-of-the-art graph kernels on several classification benchmark datasets in terms of accuracy and runtime.


title: DBSeer: Resource and Performance Prediction for Building a Next Generation Database Cloud

abstract: Cloud computing is characterized by shared infrastructure and a decoupling between its operators and tenants. These two characteristics impose new challenges to databases applications hosted in the cloud, namely: (i) how to price database services, (ii) how to isolate database tenants, and (iii) how to optimize database performance on this shared infrastructure. We argue that today’s solutions, based on virtual-machines, do not properly address these challenges. We hint at new research directions to tackle these problems and argue that these three challenges share a common need for accurate predictive models of performance and resource utilization. We present our approach, called DBSeer, with our initial results on predictive models for the important class of OLTP/Web workloads and show how they can be used to address these challenges.


title: Learning to Decode Cognitive States from Brain Images

abstract: Over the past decade, functional Magnetic Resonance Imaging (fMRI) has emerged as a powerful new instrument to collect vast quantities of data about activity in the human brain. A typical fMRI experiment can produce a three-dimensional image related to the human subject's brain activity every half second, at a spatial resolution of a few millimeters. As in other modern empirical sciences, this new instrumentation has led to a flood of new data, and a corresponding need for new data analysis methods. We describe recent research applying machine learning methods to the problem of classifying the cognitive state of a human subject based on fRMI data observed over a single time interval. In particular, we present case studies in which we have successfully trained classifiers to distinguish cognitive states such as (1) whether the human subject is looking at a picture or a sentence, (2) whether the subject is reading an ambiguous or non-ambiguous sentence, and (3) whether the word the subject is viewing is a word describing food, people, buildings, etc. This learning problem provides an interesting case study of classifier learning from extremely high dimensional (105 features), extremely sparse (tens of training examples), noisy data. This paper summarizes the results obtained in these three case studies, as well as lessons learned about how to successfully apply machine learning methods to train classifiers in such settings.


title: Surge Current Ruggedness of Silicon Carbide Schottky- and Merged-PiN-Schottky Diodes

abstract: In the paper the surge current capability of different power diodes made of silicon carbide (SiC) providing Schottky- and merged-pin-Schottky (MPS) structures are investigated. The diodes were impinged with surge current pulses of different shape and time. Depending on the diodes and there structures, they provide a different responsiveness. In some cases apart from the diode design it's time constant and the case influences the destruction limit of the surge current capability. There exist different destruction mechanisms. The destruction mechanism of MPS diodes of one diode type was the temperature limitation of the metallization melting point. While the surge current pulse the SiC diodes reaches astonishingly high temperatures at the epitaxy layer and the metallization. The diodes of a second diode type show destructive hot spots.


title: Position Bias Estimation for Unbiased Learning to Rank in Personal Search

abstract: A well-known challenge in learning from click data is its inherent bias and most notably position bias. Traditional click models aim to extract the ‹query, document› relevance and the estimated bias is usually discarded after relevance is extracted. In contrast, the most recent work on unbiased learning-to-rank can effectively leverage the bias and thus focuses on estimating bias rather than relevance [20, 31]. Existing approaches use search result randomization over a small percentage of production traffic to estimate the position bias. This is not desired because result randomization can negatively impact users' search experience. In this paper, we compare different schemes for result randomization (i.e., RandTopN and RandPair) and show their negative effect in personal search. Then we study how to infer such bias from regular click data without relying on randomization. We propose a regression-based Expectation-Maximization (EM) algorithm that is based on a position bias click model and that can handle highly sparse clicks in personal search. We evaluate our EM algorithm and the extracted bias in the learning-to-rank setting. Our results show that it is promising to extract position bias from regular clicks without result randomization. The extracted bias can improve the learning-to-rank algorithms significantly. In addition, we compare the pointwise and pairwise learning-to-rank models. Our results show that pairwise models are more effective in leveraging the estimated bias.


title: Model-Based Probabilistic Collision Detection in Autonomous Driving

abstract: The safety of the planned paths of autonomous cars with respect to the movement of other traffic participants is considered. Therefore, the stochastic occupancy of the road by other vehicles is predicted. The prediction considers uncertainties originating from the measurements and the possible behaviors of other traffic participants. In addition, the interaction of traffic participants, as well as the limitation of driving maneuvers due to the road geometry, is considered. The result of the presented approach is the probability of a crash for a specific trajectory of the autonomous car. The presented approach is efficient as most of the intensive computations are performed offline, which results in a lean online algorithm for real-time application.


title: Estimation on Location, Velocity, and Acceleration With High Precision for Collision Avoidance

abstract: An approach is proposed to estimate the location, velocity, and acceleration of a target vehicle to avoid a possible collision. Radial distance, velocity, and acceleration are extracted from the hybrid linear frequency modulation (LFM)/frequency-shift keying (FSK) echoed signals and then processed using the Kalman filter and the trilateration process. This approach proves to converge fast with good accuracy. Two other approaches, i.e., an extended Kalman filter (EKF) and a two-stage Kalman filter (TSKF), are used as benchmarks for comparison. Several scenarios of vehicle movement are also presented to demonstrate the effectiveness of this approach.


title: Matrix Completion has No Spurious Local Minimum

abstract: Matrix completion is a basic machine learning problem that has wide applications, especially in collaborative filtering and recommender systems. Simple non-convex optimization algorithms are popular and effective in practice. Despite recent progress in proving various non-convex algorithms converge from a good initial point, it remains unclear why random or arbitrary initialization suffices in practice. We prove that the commonly used non-convex objective function for matrix completion has no spurious local minima -- all local minima must also be global. Therefore, many popular optimization algorithms such as (stochastic) gradient descent can provably solve matrix completion with \textit{arbitrary} initialization in polynomial time.


title: Cryptonite: A Secure and Performant Data Repository on Public Clouds

abstract: Cloud storage has become immensely popular for maintaining synchronized copies of files and for sharing documents with collaborators. However, there is heightened concern about the security and privacy of Cloud-hosted data due to the shared infrastructure model and an implicit trust in the service providers. Emerging needs of secure data storage and sharing for domains like Smart Power Grids, which deal with sensitive consumer data, require the persistence and availability of Cloud storage but with client-controlled security and encryption, low key management overhead, and minimal performance costs. Cryptonite is a secure Cloud storage repository that addresses these requirements using a Strongbox model for shared key management. We describe the Cryptonite service and desktop client, discuss performance optimizations, and provide an empirical analysis of the improvements. Our experiments shows that Cryptonite clients achieve a 40% improvement in file upload bandwidth over plaintext storage using the Azure Storage Client API despite the added security benefits, while our file download performance is 5 times faster than the baseline for files greater than 100MB.


title: Associative Adversarial Networks

abstract: We propose a higher-level associative memory for learning adversarial networks. Generative adversarial network (GAN) framework has a discriminator and a generator network. The generator (G) maps white noise (z) to data samples while the discriminator (D) maps data samples to a single scalar. To do so, G learns how to map from high-level representation space to data space, and D learns to do the opposite. We argue that higher-level representation spaces need not necessarily follow a uniform probability distribution. In this work, we use Restricted Boltzmann Machines (RBMs) as a higher-level associative memory and learn the probability distribution for the high-level features generated by D. The associative memory samples its underlying probability distribution and G learns how to map these samples to data space. The proposed associative adversarial networks (AANs) are generative models in the higher-levels of the learning, and use adversarial non-stochastic models D and G for learning the mapping between data and higher-level representation spaces. Experiments show the potential of the proposed networks.


title: Eliminating redundant fragment shader executions on a mobile GPU via hardware memoization

abstract: Redundancy is at the heart of graphical applications. In fact, generating an animation typically involves the succession of extremely similar images. In terms of rendering these images, this behavior translates into the creation of many fragment programs with the exact same input data. We have measured this fragment redundancy for a set of commercial Android applications, and found that more than 40% of the fragments used in a frame have been already computed in a prior frame. In this paper we try to exploit this redundancy, using fragment memoization. Unfortunately, this is not an easy task as most of the redundancy exists across frames, rendering most HW based schemes unfeasible. We thus first take a step back and try to analyze the temporal locality of the redundant fragments, their complexity, and the number of inputs typically seen in fragment programs. The result of our analysis is a task level memoization scheme, that easily outperforms the current state-of-the-art in low power GPUs More specifically, our experimental results show that our scheme is able to remove 59.7% of the redundant fragment computations on average. This materializes to a significant speedup of 17.6% on average, while also improving the overall energy efficiency by 8.9% on average.


title: 3D Graph Embedding Learning with a Structure-aware Loss Function for Point Cloud Semantic Instance Segmentation

abstract: This paper introduces a novel approach for 3D semantic instance segmentation on point clouds. A 3D convolutional neural network called submanifold sparse convolutional network is used to generate semantic predictions and instance embeddings simultaneously. To obtain discriminative embeddings for each 3D instance, a structure-aware loss function is proposed which considers both the structure information and the embedding information. To get more consistent embeddings for each 3D instance, attention-based k nearest neighbour (KNN) is proposed to assign different weights for different neighbours. Based on the attention-based KNN, we add a graph convolutional network after the sparse convolutional network to get refined embeddings. Our network can be trained end-to-end. A simple mean-shift algorithm is utilized to cluster refined embeddings to get final instance predictions. As a result, our framework can output both the semantic prediction and the instance prediction. Experiments show that our approach outperforms all state-of-art methods on ScanNet benchmark and NYUv2 dataset.


title: A preliminary study on hybrid sentiment model for customer purchase intention analysis in socialcommerce

abstract: With the usage of Web 2.0 technologies, the internet users share their views via social media resulting in a large amount of raw data for which data mining techniques are needed to extract valuable knowledge. An example of people's views that can be extracted from the users' comments(tweets) is their interest or preference on different products, services, events etc. This research work aims to study on the customer intention to purchase in social-commerce(s-commerce). Finding out the intention of customer to purchase in s-commerce is important due to the major role being brought by the customers in marketing. Sentiment analysis is seen to be the most appropriate method to extract customer's opinions. This paper presents the fundamental part of the research for developing a hybrid sentiment analysis model by using machine learning approach and lexicon-based approach to analyze communications especially using Malay language on social network portals to determine the customer intention to purchase in s-commerce. Precision, recall, accuracy and f-measure metrics are proposed for the computing and comparing the results of the experiments.


title: Mirror Descent Search and Acceleration

abstract: Abstract In recent years, attention has been focused on the relationship between black-box optimization problem and reinforcement learning problem. In this research, we propose the Mirror Descent Search (MDS) algorithm which is applicable both for black box optimization problems and reinforcement learning problems. Our method is based on the mirror descent method, which is a general optimization algorithm. The contribution of this research is roughly twofold. We propose two essential algorithms, called MDS and Accelerated Mirror Descent Search (AMDS), and two more approximate algorithms: Gaussian Mirror Descent Search (G-MDS) and Gaussian Accelerated Mirror Descent Search (G-AMDS). This research shows that the advanced methods developed in the context of the mirror descent research can be applied to reinforcement learning problem. We also clarify the relationship between an existing reinforcement learning algorithm and our method. With two evaluation experiments, we show our proposed algorithms converge faster than some state-of-the-art methods.


title: Empathy and the Somatotopic Auditory Mirror System in Humans

abstract: How do we understand the actions of other individuals if we can only hear them? Auditory mirror neurons respond both while monkeys perform hand or mouth actions and while they listen to sounds of similar actions . This system might be critical for auditory action understanding and language evolution . Preliminary evidence suggests that a similar system may exist in humans . Using fMRI, we searched for brain areas that respond both during motor execution and when individuals listened to the sound of an action made by the same effector. We show that a left hemispheric temporo-parieto-premotor circuit is activated in both cases, providing evidence for a human auditory mirror system. In the left premotor cortex, a somatotopic pattern of activation was also observed: A dorsal cluster was more involved during listening and execution of hand actions, and a ventral cluster was more involved during listening and execution of mouth actions. Most of this system appears to be multimodal because it also responds to the sight of similar actions. Finally, individuals who scored higher on an empathy scale activated this system more strongly, adding evidence for a possible link between the motor mirror system and empathy.


title: Usability evaluation methods: mind the gaps

abstract: The strengths and weaknesses of heuristic evaluation have been well researched. Despite known weaknesses, heuristic evaluation is still widely used since formal usability testing (also referred to as empirical user testing) is more costly and time consuming. What has received less attention is the type of information heuristic evaluation conveys in comparison to empirical user testing supported by eye tracking and user observation. If usability methods are combined, it becomes even more important to distinguish the information contribution by each method. This paper investigates the application of two usability evaluation methods, namely heuristic evaluation and empirical user testing supported by eye tracking, to the website of a learning management system with the intent of discovering the difference in the usability information yielded. Heuristic evaluation as an inspection method is accepted to be fundamentally different from empirical user testing. This paper contributes to a deeper understanding of the nature of the differences by identifying the kind of usability problems identified through each method. The findings should be of interest to researchers, designers and usability practitioners involved in website design and evaluation.


title: Skin whitening agents: medicinal chemistry perspective of tyrosinase inhibitors

abstract: AbstractMelanogenesis is a process to synthesize melanin, which is a primary responsible for the pigmentation of human skin, eye and hair. Although numerous enzymatic catalyzed and chemical reactions are involved in melanogenesis process, the enzymes such as tyrosinase and tyrosinase-related protein-1 (TRP-1) and TRP-2 played a major role in melanin synthesis. Specifically, tyrosinase is a key enzyme, which catalyzes a rate-limiting step of the melanin synthesis, and the downregulation of tyrosinase is the most prominent approach for the development of melanogenesis inhibitors. Therefore, numerous inhibitors that target tyrosinase have been developed in recent years. The review focuses on the recent discovery of tyrosinase inhibitors that are directly involved in the inhibition of tyrosinase catalytic activity and functionality from all sources, including laboratory synthetic methods, natural products, virtual screening and structure-based molecular docking studies.


title: The social dilemma of autonomous vehicles

abstract: Autonomous vehicles (AVs) should reduce traffic accidents, but they will sometimes have to choose between two evils, such as running over pedestrians or sacrificing themselves and their passenger to save the pedestrians. Defining the algorithms that will help AVs make these moral decisions is a formidable challenge. We found that participants in six Amazon Mechanical Turk studies approved of utilitarian AVs (that is, AVs that sacrifice their passengers for the greater good) and would like others to buy them, but they would themselves prefer to ride in AVs that protect their passengers at all costs. The study participants disapprove of enforcing utilitarian regulations for AVs and would be less willing to buy such an AV. Accordingly, regulating for utilitarian algorithms may paradoxically increase casualties by postponing the adoption of a safer technology.


title: Using covariates for improving the minimum Redundancy Maximum Relevance feature selection method

abstract: Maximizing the joint dependency with a minimum size of variables is generally the main task of feature selection. For obtaining a minimal subset, while trying to maximize the joint dependency with the target variable, the redundancy among selected variables must be reduced to a minimum. In this paper, we propose a method based on recently popular minimum Redundancy-Maximum Relevance (mRMR) criterion. The experimental results show that instead of feeding the features themselves into mRMR, feeding the covariates improves the feature selection capability and provides more expressive variable subsets.


title: Goal-oriented requirements enginering: a roundtrip from research to practice [enginering read engineering]

abstract: The software industry is more than ever facing the challenge of delivering WYGIWYW software (What You Get Is What You Want). A well-structured document specifying adequate, complete, consistent, precise, and measurable requirements is a critical prerequisite for such software. Goals have been recognized to be among the driving forces for requirements elicitation, elaboration, organization, analysis, negotiation, documentation, and evolution. Growing experience with goal-oriented requirements engineering suggests synergistic links between research in this area and good practice. We discuss one journey along this road from influencing ideas and research results to tool developments to good practice in industrial projects. On the way, we discuss some lessons learnt, obstacles to technonogy transfer, and challenges for better requirements engineering research and practice.


title: A Blockchain-Based Framework for Data Sharing With Fine-Grained Access Control in Decentralized Storage Systems

abstract: In traditional cloud storage systems, attribute-based encryption (ABE) is regarded as an important technology for solving the problem of data privacy and fine-grained access control. However, in all ABE schemes, the private key generator has the ability to decrypt all data stored in the cloud server, which may bring serious problems such as key abuse and privacy data leakage. Meanwhile, the traditional cloud storage model runs in a centralized storage manner, so single point of failure may leads to the collapse of system. With the development of blockchain technology, decentralized storage mode has entered the public view. The decentralized storage approach can solve the problem of single point of failure in traditional cloud storage systems and enjoy a number of advantages over centralized storage, such as low price and high throughput. In this paper, we study the data storage and sharing scheme for decentralized storage systems and propose a framework that combines the decentralized storage system interplanetary file system, the Ethereum blockchain, and ABE technology. In this framework, the data owner has the ability to distribute secret key for data users and encrypt shared data by specifying access policy, and the scheme achieves fine-grained access control over data. At the same time, based on smart contract on the Ethereum blockchain, the keyword search function on the cipher text of the decentralized storage systems is implemented, which solves the problem that the cloud server may not return all of the results searched or return wrong results in the traditional cloud storage systems. Finally, we simulated the scheme in the Linux system and the Ethereum official test network Rinkeby, and the experimental results show that our scheme is feasible.


title: CoHadoop: Flexible Data Placement and Its Exploitation in Hadoop

abstract: Hadoop has become an attractive platform for large-scale data analytics. In this paper, we identify a major performance bottleneck of Hadoop: its lack of ability to colocate related data on the same set of nodes. To overcome this bottleneck, we introduce CoHadoop, a lightweight extension of Hadoop that allows applications to control where data are stored. In contrast to previous approaches, CoHadoop retains the flexibility of Hadoop in that it does not require users to convert their data to a certain format (e.g., a relational database or a specific file format). Instead, applications give hints to CoHadoop that some set of files are related and may be processed jointly; CoHadoop then tries to colocate these files for improved efficiency. Our approach is designed such that the strong fault tolerance properties of Hadoop are retained. Colocation can be used to improve the efficiency of many operations, including indexing, grouping, aggregation, columnar storage, joins, and sessionization. We conducted a detailed study of joins and sessionization in the context of log processing---a common use case for Hadoop---, and propose efficient map-only algorithms that exploit colocated data partitions. In our experiments, we observed that CoHadoop outperforms both plain Hadoop and previous work. In particular, our approach not only performs better than repartition-based algorithms, but also outperforms map-only algorithms that do exploit data partitioning but not colocation. 8.


title: Good Background Colors for Readers: A Study of People with and without Dyslexia

abstract: The use of colors to enhance the reading of people with dyslexia have been broadly discussed and is often recommended, but evidence of the effectiveness of this approach is lacking. This paper presents a user study with 341 participants (89 with dyslexia) that measures the effect of using background colors on screen readability. Readability was measured via reading time and distance travelled by the mouse. Comprehension was used as a control variable. The results show that using certain background colors have a significant impact on people with and without dyslexia. Warm background colors, Peach, Orange and Yellow, significantly improved reading performance over cool background colors, Blue, Blue Grey and Green. These results provide evidence to the practice of using colored backgrounds to improve readability; people with and without dyslexia benefit, but people with dyslexia may especially benefit from the practice given the difficulty they have in reading in general.


title: Synthetic Social Media Data Generation

abstract: This paper presents a novel system, synthetic high-fidelity social media data generator (SHIELD), for generating the synthetic social media data. SHIELD jointly generates time-varying, directed and weighted interaction graph structures and topic-driven text features similar to the input social media data. A synthetic interaction graph is generated by a social network model to minimize the distance to real graph and is enhanced by adding various patterns, such as anomalies and information cascades, interaction types, and temporal dynamics. A synthetic text generator based on the $n$ -gram Markov model is trained under each topic identified by topic modeling. Synthetic text and graph structures are combined through the assignment of synthetic social media entities. Extensive performance evaluation via a graph and text analysis is provided to demonstrate the statistical fidelity of large-scale synthetic data generated by SHIELD. A data evaluation exercise with human participants is executed to identify how difficult it is for a human to distinguish between tweets that were generated by SHIELD and tweets that were posted by real users. Experimental results followed by a statistical significance analysis showed that human participants cannot reliably distinguish between real and synthetic tweets.


title: Model-Based Reinforcement Learning via Meta-Policy Optimization

abstract: Model-based reinforcement learning approaches carry the promise of being data efficient. However, due to challenges in learning dynamics models that sufficiently match the real-world dynamics, they struggle to achieve the same asymptotic performance as model-free methods. We propose Model-Based Meta-Policy-Optimization (MB-MPO), an approach that foregoes the strong reliance on accurate learned dynamics models. Using an ensemble of learned dynamic models, MB-MPO meta-learns a policy that can quickly adapt to any model in the ensemble with one policy gradient step. This steers the meta-policy towards internalizing consistent dynamics predictions among the ensemble while shifting the burden of behaving optimally w.r.t. the model discrepancies towards the adaptation step. Our experiments show that MB-MPO is more robust to model imperfections than previous model-based approaches. Finally, we demonstrate that our approach is able to match the asymptotic performance of model-free methods while requiring significantly less experience.


title: Grasp Quality Evaluation with Whole Arm Kinematic Noise Propagation

abstract: In this paper we propose a new approach to evaluate grasps that accounts for both the kinematic structure of the robot and the noise at its joints. Our starting observation is that with a redundant robot the same grasp can be implemented with different arm configurations, and these may display significant differences in terms of robustness to disturbances. Consequently, the grasp quality metric is seen as a random variable depending on the arm configuration. Starting from a first order approximation for the error, we introduce the high probability force closure region as a tool to evaluate the local robustness of an arm configuration, and we then introduce a new metric $Q_{arm}$ to rank different configurations according to the robustness to noise. By combining this method in an offline/online framework, we demonstrate through large scale simulations that this approach successfully captures aspects that were neglected in former literature regarding grasp evaluation, and can successfully be integrated into future grasp planners.


Ranking in context using vector spaces


title: An Epidemiological Study of Malware Encounters in a Large Enterprise

abstract: We present an epidemiological study of malware encounters in a large, multi-national enterprise. Our data sets allow us to observe or infer not only malware presence on enterprise computers, but also malware entry points, network locations of the computers (i.e., inside the enterprise network or outside) when the malware were encountered, and for some web-based malware encounters, web activities that gave rise to them. By coupling this data with demographic information for each host's primary user, such as his or her job title and level in the management hierarchy, we are able to paint a reasonably comprehensive picture of malware encounters for this enterprise. We use this analysis to build a logistic regression model for inferring the risk of hosts encountering malware; those ranked highly by our model have a >3x higher rate of encountering malware than the base rate. We also discuss where our study confirms or refutes other studies and guidance that our results suggest.


title: Nested Hierarchical Dirichlet Processes

abstract: We develop a nested hierarchical Dirichlet process (nHDP) for hierarchical topic modeling. The nHDP generalizes the nested Chinese restaurant process (nCRP) to allow each word to follow its own path to a topic node according to a per-document distribution over the paths on a shared tree. This alleviates the rigid, single-path formulation assumed by the nCRP, allowing documents to easily express complex thematic borrowings. We derive a stochastic variational inference algorithm for the model, which enables efficient inference for massive collections of text documents. We demonstrate our algorithm on 1.8 million documents from The New York Times and 2.7 million documents from Wikipedia .


title: Interaction with a projection screen using a camera-tracked laser pointer

abstract: A system for direct interaction with a video projection screen using a laser pointer is presented. The laser point on the screen is captured by a video camera, and its location recognized by image processing techniques. The behavior of the point is translated into signals sent to the mouse input of the computer causing the same reactions as if they came from the mouse. More complex interaction paradigms are composed from the elementary operations "switch on/off" and pointing of the laser pen.


title: Hydrogel swelling as a trigger to release biodegradable polymer microneedles in skin.

abstract: Abstract Biodegradable polymeric microneedles were developed as a method for achieving sustained transdermal drug release. These microneedles have potential as a patient-friendly substitute for conventional sustained release methods. However, they have limitations related to the difficulty of achieving separation of the needles into the skin. We demonstrated that microneedle separation into the skin was mediated by hydrogel swelling in response to contact with body fluid after the needles were inserted into the skin. The hydrogel microparticles were synthesized by an emulsification method using poly-N-isopropylacrylamide (PNIPAAm). The microneedles were fabricated by micromolding poly-lactic-co-glycolic acid (PLGA) after filling the cavities of the mold with the hydrogel microparticles. The failure of microneedle tips caused by hydrogel swelling was studied in regard to contact with water, insertion of microneedles into porcine cadaver skin in vitro , stress–strain behavior, and insertion into the back skin of a hairless mouse in vivo . The drug delivery property of the hydrogel particles was investigated qualitatively by inserting polymer microneedles into porcine cadaver skin in vitro, and the sustained release property of PLGA microneedles containing hydrogel microparticles was studied quantitatively using the Franz cell model. The hydrogel particles absorbed water quickly, resulting in the cracking of the microneedles due to the difference in volume expansion between the needle matrix polymer and the hydrogel particles. The swollen particles caused the microneedles to totally breakdown, leaving the microneedle tips in the porcine cadaver skin in vitro and in the hairless mouse skin in vivo . Model drugs encapsulated in biodegradable polymer microneedles and hydrogel microparticles were successfully delivered by releasing microneedles into the skin.


title: Graph-based Cross Entropy method for solving multi-robot decentralized POMDPs

abstract: This paper introduces a probabilistic algorithm for multi-robot decision-making under uncertainty, which can be posed as a Decentralized Partially Observable Markov Decision Process (Dec-POMDP). Dec-POMDPs are inherently synchronous decision-making frameworks which require significant computational resources to be solved, making them infeasible for many real-world robotics applications. The Decentralized Partially Observable Semi-Markov Decision Process (Dec-POSMDP) was recently introduced as an extension of the Dec-POMDP that uses high-level macro-actions to allow large-scale, asynchronous decision-making. However, existing Dec-POSMDP solution methods have limited scalability or perform poorly as the problem size grows. This paper proposes a cross-entropy based Dec-POSMDP algorithm motivated by the combinatorial optimization literature. The algorithm is applied to a constrained package delivery domain, where it significantly outperforms existing Dec-POSMDP solution methods.


title: Computational Design of Rubber Balloons

abstract: This paper presents an automatic process for fabrication-oriented design of custom-shaped rubber balloons. We cast computational balloon design as an inverse problem: given a target shape, we compute an optimal balloon that, when inflated, approximates the target as closely as possible. To solve this problem numerically, we propose a novel physics-driven shape optimization method, which combines physical simulation of inflatable elastic membranes with a dedicated constrained optimization algorithm. We validate our approach by fabricating balloons designed with our method and comparing their inflated shapes to the results predicted by simulation. An extensive set of manufactured sample balloons demonstrates the shape diversity that can be achieved by our method. © 2012 Wiley Periodicals, Inc.


title: Cancer biology and NuRD: a multifaceted chromatin remodelling complex

abstract: The nucleosome remodelling and histone deacetylase (NuRD; also known as Mi-2) complex regulates gene expression at the level of chromatin. The NuRD complex has been identified - using both genetic and molecular analyses - as a key determinant of differentiation in mouse embryonic stem cells and during development in various model systems. Similar to other chromatin remodellers, such as SWI/SNF and Polycomb complexes, NuRD has also been implicated in the regulation of transcriptional events that are integral to oncogenesis and cancer progression. Emerging molecular details regarding the recruitment of NuRD to specific loci during development, and the modulation of these events in cancer, are used to illustrate how the inappropriate localization of the complex could contribute to tumour biology.


title: LaNCoA: A Python toolkit for Language Networks Construction and Analysis

abstract: In this paper we describe LaNCoA, Language Networks Construction and Analysis toolkit implemented in Python. The toolkit provides various procedures for network construction from the text: on the word-level (co-occurrence networks, syntactic networks, shuffled networks), and on the subword-level (syllable networks, grapheme networks). Furthermore, we implement functions for the language networks analysis on the global and local level. The toolkit is organized in several modules that enable various aspects of language analysis: analysis of global network measures for different co-occurrence window, comparison of networks based on original and shuffled texts, comparison of networks constructed on different language levels, etc. Text manipulation methods, like corpora cleaning, lemmatization and stopwords removal, are also implemented. For the basic network representation we use available NetworkX functions and methods. However, language network analysis is specific and it requires implementation of additional functions and methods. That was the main motivation for this research.


title: Design of Artificial Dielectrics for Anti-Reflection-Coatings

abstract: With the increasing interest in new artificial materials ranging from meta-materials to photonic bandgap materials there is a growing need for accurate models of artificial materials. We examine the design of a three-layer structure with an artificial dielectric as an anti-reflection-coating on both sides of a dielectric slab. We present a field-equivalent circuit theory to calculate the effective dielectric constant of the structured dielectric. The materials discussed in this paper have a lateral periodicity smaller than /spl lambda//10 and can easily be manufactured from high density polyethylene (HDPE) for frequencies up to 20 GHz. The field-analog circuit theory can also be used to calculate the polarization dependence of the artificial dielectric. Theoretical results of three geometrically different structures were compared with waveguide measurements at X-band and showed excellent agreement. Applications such as the design of new components at millimeter and submillimeter wave frequencies are discussed, considering the X-Band measurements as a scaled model.


title: A three-stage decision framework for multi-subject emotion recognition using physiological signals

abstract: This paper investigates the potential of physiological signals as reliable channels for multi-subject emotion recognition. A three-stage decision framework is proposed for recognizing four emotions of multiple subjects. The decision framework consists of three stages: (1) in the initial stage, identifying a subject group that a test subject can be mapped to; (2) in the second stage, identifying an emotion pool that an instance of the test subject can be assigned to; and (3) in the final stage, generating the predicted emotion from the given emotion pool for the test instance. In comparison with a series of alternative methods, the high accuracy of 70.04% achieved by our proposed method clearly demonstrates the potential of the three-stage decision method in multi-subject emotion recognition.


Core Topics in Endocrinology in Anaesthesia and Critical Care


title: Spam filtering for short messages

abstract: We consider the problem of content-based spam filtering for short text messages that arise in three contexts: mobile (SMS) communication, blog comments, and email summary information such as might be displayed by a low-bandwidth client. Short messages often consist of only a few words, and therefore present a challenge to traditional bag-of-words based spam filters. Using three corpora of short messages and message fields derived from real SMS, blog, and spam messages, we evaluate feature-based and compression-model-based spam filters. We observe that bag-of-words filters can be improved substantially using different features, while compression-model filters perform quite well as-is. We conclude that content filtering for short messages is surprisingly effective.


title: A multifaceted data mining approach to understanding what factors lead college students to persist and graduate

abstract: Universities in the United States are facing the serious issue of high dropout rate and low graduation rate of four-year college students. This paper describes a host of data mining approaches to help tackle this issue. Specifically, we utilize the following approaches to identify factors that contribute to student persistence and graduation: (1) a visual analysis to identify bivariate relationships and to understand the flow of students in an educational institute; (2) an ensemble feature selection method to recognize factors that have a significant impact on a student's persistence and graduation; (3) classification and prediction algorithms to predict whether a student will persist in a given semester and ultimately graduate; and (4) a variety of association patterns to help education practitioners gain further insights into factors that affect persistence and graduation. To evaluate the above approaches, we use data originated from a local academic program. Our analyses have resulted in both interpretable and actionable outcomes. For example, the ELM (Entry Level Mathematics) score was identified as one of the most influential factors in predicting a student's third-term persistence, and furthermore graduation. This insight has in turn motivated the above program to enroll their students with low ELM scores in a remedial math course before they start their freshmen year. Among the classification algorithms under consideration in this study, we have demonstrated that Naive Bayesian is more suitable for predicting graduation, whereas AdaBoost and SVM are better at predicting persistence.


title: A profiling tool for apache hive run-time query

abstract: Apache Hive is a tool used conventionally for data warehousing and analysis. Although it is widely used, there is very less research on performance comparison and analysis. One of the reasons is, the techniques applied to supervise execution cannot be implemented to intermediate MapReduce code developed from Hive query. Since the MapReduce code is hidden from the developer, the only opportunity to view the actual execution is through run time logs. It is essential to develop an automatic tool for generating and extracting reports from runtime logs to understand the query execution behavior. A tool is designed to build an execution profile of individual hive queries for information extracted from hive and Hadoop logs. Information about MapReduce jobs, tasks and attempts belonging to a query are present in the profile. This data is saved in MongoDB as a JSON document which can be extracted as charts or table. Several experiments were conducted on queries to demonstrate that the profiling tool is capable of assisting developers for comparison of hive queries of different format and different parameters. Performance issues can be diagnosed by comparing the attempts/tasks within the same job.


title: Entangled decision forests and their application for semantic segmentation of CT images

abstract: This work addresses the challenging problem of simultaneously segmenting multiple anatomical structures in highly varied CT scans. We propose the entangled decision forest (EDF) as a new discriminative classifier which augments the state of the art decision forest, resulting in higher prediction accuracy and shortened decision time. Our main contribution is two-fold. First, we propose entangling the binary tests applied at each tree node in the forest, such that the test result can depend on the result of tests applied earlier in the same tree and at image points offset from the voxel to be classified. This is demonstrated to improve accuracy and capture long-range semantic context. Second, during training, we propose injecting randomness in a guided way, in which node feature types and parameters are randomly drawn from a learned (non-uniform) distribution. This further improves classification accuracy. We assess our probabilistic anatomy segmentation technique using a labeled database of CT image volumes of 250 different patients from various scan protocols and scanner vendors. In each volume, 12 anatomical structures have been manually segmented. The database comprises highly varied body shapes and sizes, a wide array of pathologies, scan resolutions, and diverse contrast agents. Quantitative comparisons with state of the art algorithms demonstrate both superior test accuracy and computational efficiency.


title: IEEE 802.15.6 standard in wireless body area networks from a healthcare point of view

abstract: The first standard supporting communication in wireless body area networks (WBANs) is IEEE 802.15 Task Groups 6 (TG6). IEEE 802.15.6 is a standard for short-range, low power, and highly reliable wireless communication in, on and around the human body. It supports a wide range of applications in body area networks (BANs) such as healthcare services. In WBANs, nodes are partitioned into a physical (PHY) layer and a medium access control (MAC) layer. In this paper, the MAC and PHY layers are investigated. The different types of communication supported by this standard, such as narrowband (NB), ultra-wideband (UWB), and human body communication (HBC), are further defined here. The security aspect of the standard is also discussed and investigated. Finally, using the standard and existing literature in WBAN, open issues and challenges are identified as a source of future study.


title: What has fMRI told us about the Development of Cognitive Control through Adolescence?

abstract: Cognitive control, the ability to voluntarily guide our behavior, continues to improve throughout adolescence. Below we review the literature on age-related changes in brain function related to response inhibition and working memory, which support cognitive control. Findings from studies using functional magnetic resonance imaging (fMRI) indicate that processing errors, sustaining a cognitive control state, and reaching adult levels of precision, persist through adolescence. Developmental changes in patterns of brain function suggest that core regions of the circuitry underlying cognitive control are on-line early in development. However, age-related changes in localized processes across the brain, and in establishing long range connections that support top-down modulation of behavior, more effective neural processing for optimal mature executive function. While great progress has been made in understanding the age-related changes in brain processes underlying cognitive development, there are still important challenges in developmental neuroimaging methods and the interpretation of data that need to be addressed.


title: Device Mismatch: An Analog Design Perspective

abstract: Device variability is receiving a lot of interest recently due to its important impact on the design of digital integrated systems. In analog integrated circuits, the variability of identically designed devices has long been a concern since it directly affects the attainable precision. This paper reviews the mismatch device models that are widely used in analog design as well as the fundamental impact of device mismatch on the trade-off between different performance parameters.


title: Semantic Spaces: Measuring the Distance between Different Subspaces

abstract: Semantic Space models, which provide a numerical representation of words’ meaning extracted from corpus of documents, have been formalized in terms of Hermitian operators over real valued Hilbert spaces by Bruza et al. [1]. The collapse of a word into a particular meaning has been investigated applying the notion of quantum collapse of superpositional states [2]. While the semantic association between words in a Semantic Space can be computed by means of the Minkowski distance [3] or the cosine of the angle between the vector representation of each pair of words, a new procedure is needed in order to establish relations between two or more Semantic Spaces. We address the question: how can the distance between different Semantic Spaces be computed? By representing each Semantic Space as a subspace of a more general Hilbert space, the relationship between Semantic Spaces can be computed by means of the subspace distance. Such distance needs to take into account the difference in the dimensions between subspaces. The availability of a distance for comparing different Semantic Subspaces would enable to achieve a deeper understanding about the geometry of Semantic Spaces which would possibly translate into better effectiveness in Information Retrieval tasks.


title: A User Interest Model for Web Page Navigation

abstract: Making recommendation requires predicting what is of interest to a user at a specific time. Even the same user may have different desires at different times. It is important to extract the aggregate interest of a user from his or her navigational path through the site in a session. This paper concentrates on the discovery and modelling of the user’s aggregate interest in a session. This approach relies on the premise that the visiting time of a page is an indicator of the user’s interest in that page. The proportion of times spent in a set of pages requested by the user within a single session forms the aggregate interest of that user in that session. We first partition user sessions into clusters such that only sessions which represent similar aggregate interest of users are placed in the same cluster. We employ a model-based clustering approach and partition user sessions according to similar amount of time in similar pages. In particular, we cluster sessions by learning a mixture of Poisson models using Expectation Maximization algorithm. The resulting clusters are then used to recommend pages to a user that are most likely contain the information which is of interest to that user at that time. Although the approach does not use the sequential patterns of transactions, experimental evaluation shows that the approach is quite effective in capturing a Web user’s access pattern. The model has an advantage over previous proposals in terms of speed and memory usage.


title: GaAs Mixed Signal Multi-Function X-Band MMIC with 7 Bit Phase and Amplitude Control and Integrated Serial to Parallel Converter

abstract: The design and measured performance of a GaAs multi-function X-band MMIC for space-based synthetic aperture radar (SAR) applications with 7-bit phase and amplitude control and integrated serial to parallel converter (including level conversion) is presented. The main application for the multi-function chip (MFC) is to provide full amplitude and phase control in the transmit path and the receive path of T/R modules. The MFC has been realised in the OMMIC ED02AH 0.2 ?m PHEMT process providing enhancement and depletion FET's.


title: Large-Scale Mobile Traffic Analysis: A Survey

abstract: This article surveys the literature on analyses of mobile traffic collected by operators within their network infrastructure. This is a recently emerged research field, and, apart from a few outliers, relevant works cover the period from 2005 to date, with a sensible densification over the last three years. We provide a thorough review of the multidisciplinary activities that rely on mobile traffic datasets, identifying major categories and sub-categories in the literature, so as to outline a hierarchical classification of research lines. When detailing the works pertaining to each class, we balance a comprehensive view of state-of-the-art results with punctual focuses on the methodological aspects. Our approach provides a complete introductory guide to the research based on mobile traffic analysis. It allows summarizing the main findings of the current state-of-the-art, as well as pinpointing important open research directions.


title: Predicting repeat self-harm in children

abstract: The main objective of the study was to find which variables predict repetition of deliberate self-harm in children. The study is based on a group of children who took part in a randomized control trial investigating the effects of a home-based family intervention for children who had deliberately poisoned themselves. These children had a range of baseline and outcome measures collected on two occasions (two and six months follow-up). Outcome data were collected from 149 (92 %) of the initial 162 children over the six months. Twenty-three children made a further deliberate self-harm attempt within the follow-up period. A number of variables at baseline were found to be significantly associated with repeat self-harm. Parental mental health and a history of previous attempts were the strongest predictors. A model of prediction of further deliberate self-harm combining these significant individual variables produced a high positive predictive value (86 %) but had low sensitivity (28 %). Predicting repeat self-harm in children is difficult, even with a comprehensive series of assessments over multiple time points, and we need to adapt services with this in mind. We propose a model of service provision which takes these findings into account.


title: Recent trends and future scope in the protection and comfort of fire-fighters’ personal protective clothing

abstract: Fire-fighters’ personal protective clothing is the only source of protection for fire-fighters during fire-fighting. The protective clothing should provide adequate protection as well as should be comfortable to wear. The protection and comfort requirements are always the contradicting fact in several protective clothing including fire-fighters’. Appropriate material selection, clothing design and final evaluation of the results play a critical role in predicting the clothing performance and comfort. Several researches have been done on the performance and comfort improvement of fire-fighter’s protective clothing. However, detailed review related to these parameters is not being reported in recent years. In this perspective review, we report the recent trends in the performance and comfort properties of the fire-fighters protective clothing. The clothing design and different materials used to achieve a balance between performance and comfort is illustrated. Various test standards related to the performance and comfort is also being discussed. In addition, the future scopes and challenges while designing tomorrows advanced protective clothing are cited. This would provide a guideline in terms of comfort and performance while developing and designing the fire-fighter protective clothing for different climatic conditions.


title: The Enigmatic temporal pole : a review of findings on social and emotional processing

abstract: The function of the anterior-most portion of the temporal lobes, the temporal pole, is not well understood. Anatomists have long considered it part of an extended limbic system based on its location posterior to the orbital frontal cortex and lateral to the amygdala, along with its tight connectivity to limbic and paralimbic regions. Here we review the literature in both non-human primates and humans to assess the temporal pole's putative role in social and emotional processing. Reviewed findings indicate that it has some role in both social and emotional processes, including face recognition and theory of mind, that goes beyond semantic memory. We propose that the temporal pole binds complex, highly processed perceptual inputs to visceral emotional responses. Because perceptual inputs remain segregated into dorsal (auditory), medial (olfactory) and ventral (visual) steams, the integration of emotion with perception is channel specific.


title: Emergence of Language with Multi-agent Games: Learning to Communicate with Sequences of Symbols

abstract: Learning to communicate through interaction, rather than relying on explicit supervision, is often considered a prerequisite for developing a general AI. We study a setting where two agents engage in playing a referential game and, from scratch, develop a communication protocol necessary to succeed in this game. Unlike previous work, we require that messages they exchange, both at train and test time, are in the form of a language (i.e. sequences of discrete symbols). We compare a reinforcement learning approach and one using a differentiable relaxation (straight-through Gumbel-softmax estimator) and observe that the latter is much faster to converge and it results in more effective protocols. Interestingly, we also observe that the protocol we induce by optimizing the communication success exhibits a degree of compositionality and variability (i.e. the same information can be phrased in different ways), both properties characteristic of natural languages. As the ultimate goal is to ensure that communication is accomplished in natural language, we also perform experiments where we inject prior information about natural language into our model and study properties of the resulting protocol.