title: Outfit Recommendation with Deep Sequence Learning

abstract: Choosing a proper clothing collocation requires the sense of fashion. Yet modeling how people select items is challenging: the items in a collocation should be compatible but there are too many attributes to consider (e.g., color, texture, style) for each kind of fashion items. In this paper, we propose to learn a global compatible outfit generation model from existing outfit images and text descriptions. Our approach relies on a bidirectional LSTM to model the relationship between different categories of fashion items and then predict the item based on all the other items. Meanwhile, embedded visual semantic descriptions are exploited to guide the generation with attribute information. Combining these structures, it is guaranteed that in the resulting outfit, items share a similar style and neither redundant nor missing items exist for essential categories. We demonstrate our method applied to an outfit dataset containing about 160,000 fashion items. Experimental results indicate that a good sense of fashion is obtained by the proposed method.


Outperforming Neural Readers Using Broad-Context Discriminative Language Modeling on the LAMBADA Word Prediction Task


title: Soft gelatin capsules (softgels).

abstract: It is estimated that more than 40% of new chemical entities (NCEs) coming out of the current drug discovery process have poor biopharmaceutical properties, such as low aqueous solubility and/or permeability. These suboptimal properties pose significant challenges for the oral absorption of the compounds and for the development of orally bioavailable dosage forms. Development of soft gelatin capsule (softgel) dosage form is of growing interest for the oral delivery of poorly water soluble compounds (BCS class II or class IV). The softgel dosage form offers several advantages over other oral dosage forms, such as delivering a liquid matrix designed to solubilize and improve the oral bioavailability of a poorly soluble compound as a unit dose solid dosage form, delivering low and ultra-low doses of a compound, delivering a low melting compound, and minimizing potential generation of dust during manufacturing and thereby improving the safety of production personnel. However, due to the very dynamic nature of the softgel dosage form, its development and stability during its shelf-life are fraught with several challenges. The goal of the current review is to provide an in-depth discussion on the softgel dosage form to formulation scientists who are considering developing softgels for therapeutic compounds.


InferLite: Simple Universal Sentence Representations from Natural Language Inference Data.


Modeling Skip-Grams for Event Detection with Convolutional Neural Networks


title: Object Contour Detection with a Fully Convolutional Encoder-Decoder Network

abstract: We develop a deep learning algorithm for contour detection with a fully convolutional encoder-decoder network. Different from previous low-level edge detection, our algorithm focuses on detecting higher-level object contours. Our network is trained end-to-end on PASCAL VOC with refined ground truth from inaccurate polygon annotations, yielding much higher precision in object contour detection than previous methods. We find that the learned model generalizes well to unseen object classes from the same supercategories on MS COCO and can match state-of-the-art edge detection on BSDS500 with fine-tuning. By combining with the multiscale combinatorial grouping algorithm, our method can generate high-quality segmented object proposals, which significantly advance the state-of-the-art on PASCAL VOC (improving average recall from 0.62 to 0.67) with a relatively small amount of candidates (~1660 per image).


title: Quantitative Evaluation of Software Quality Metrics in Open-Source Projects

abstract: The validation of software quality metrics lacks statistical significance. One reason for this is that the data collection requires quite some effort. To help solve this problem, we develop tools for metrics analysis of a large number of software projects (146 projects with ca. 70.000 classes and interfaces and over 11 million lines of code). Moreover, validation of software quality metrics should focus on relevant metrics, i.e., correlated metrics need not to be validated independently. Based on our statistical basis, we identify correlation between several metrics from well-known object-oriented metrics suites. Besides, we present early results of typical metrics values and possible thresholds.


title: Binary Tree Slotted ALOHA for Passive RFID Tag Anticollision

abstract: In order to enhance the efficiency of radio frequency identification (RFID) and lower system computational complexity, this paper proposes three novel tag anticollision protocols for passive RFID systems. The three proposed protocols are based on a binary tree slotted ALOHA (BTSA) algorithm. In BTSA, tags are randomly assigned to slots of a frame and if some tags collide in a slot, the collided tags in the slot will be resolved by binary tree splitting while the other tags in the subsequent slots will wait. The three protocols utilize a dynamic, an adaptive, and a splitting method to adjust the frame length to a value close to the number of tags, respectively. For BTSA, the identification efficiency can achieve an optimal value only when the frame length is close to the number of tags. Therefore, the proposed protocols efficiency is close to the optimal value. The advantages of the protocols are that, they do not need the estimation of the number of tags, and their efficiency is not affected by the variance of the number of tags. Computer simulation results show that splitting BTSA's efficiency can achieve 0.425, and the other two protocols efficiencies are about 0.40. Also, the results show that the protocols efficiency curves are nearly horizontal when the number of tags increases from 20 to 4,000.


title: Green HetNet CoMP: Energy Efficiency Analysis and Optimization

abstract: This paper investigates advanced energy-efficient wireless systems in orthogonal frequency-division multiple access (OFDMA) downlink networks using coordinated multipoint (CoMP) transmissions between the base stations (BSs) in a heterogeneous network (HetNet), which is adopted by Third-Generation Partnership Project (3GPP) Long-Term Evolution (LTE)-Advanced to meet International Mobile Telecommunications-Advanced targets. HetNet CoMP has received significant attention as a way of achieving spectral efficiency (SE) and energy efficiency (EE). Usually, in the literature, the total network power consumption is restricted to the sum of the power consumption of all BSs. The significance of the power consumption of the backhaul links in wireless networks is normally omitted for its trivial effect with respect to that of the radio BSs. For SE and EE analysis of HetNet CoMP, the energy and bandwidth consumption of the backhaul is considered, without which, the investigation remains incomplete. However, SE and EE are design criteria in conflict with each other, and a careful study of their tradeoff is mandatory for designing future wireless communication systems. The EE is measured as “throughput (bits) per joule,” whereas the power consumption model includes RF transmit (radiated), circuit, and backhaul power. Furthermore, a nonideal backhaul model such as a microwave link is also investigated within intra-HetNet-CoMP (inside one cell), where an implementing fiber is not feasible. An intercell interference (ICI) coordination method is also studied to mitigate ICI. At the end, a novel resource allocation algorithm is proposed—modeled as an optimization problem—which takes into account the total power consumption, including radiated, circuit, and backhaul power, and the minimum required data rate to maximize EE. Given an SE requirement, the EE optimization problem is formulated as a constrained optimization problem. The considered optimization problem is transformed into a convex optimization problem by redefining the constraint using cubic inequality, which results in an efficient iterative resource allocation algorithm. In each iteration, the transformed problem is solved by using dual decomposition with a projected gradient method. Simulations results demonstrate how backhaul has a significant impact on total power consumption and the effectiveness of the studied schemes. In addition, the results demonstrate that the proposed iterative resource allocation algorithm converges within a small number of iterations and illustrate the fundamental tradeoffs between SE and EE. Our analytical results shed light on future “green” network planning in advanced OFDMA wireless systems like those envisioned for a fifth-generation (5G) system.


title: Tailored displays to compensate for visual aberrations

abstract: We introduce tailored displays that enhance visual acuity by decomposing virtual objects and placing the resulting anisotropic pieces into the subject's focal range. The goal is to free the viewer from needing wearable optical corrections when looking at displays. Our tailoring process uses aberration and scattering maps to account for refractive errors and cataracts. It splits an object's light field into multiple instances that are each in-focus for a given eye sub-aperture. Their integration onto the retina leads to a quality improvement of perceived images when observing the display with naked eyes. The use of multiple depths to render each point of focus on the retina creates multi-focus, multi-depth displays. User evaluations and validation with modified camera optics are performed. We propose tailored displays for daily tasks where using eyeglasses are unfeasible or inconvenient (e.g., on head-mounted displays, e-readers, as well as for games); when a multi-focus function is required but undoable (e.g., driving for farsighted individuals, checking a portable device while doing physical activities); or for correcting the visual distortions produced by high-order aberrations that eyeglasses are not able to.


title: Online Gaming Adoption in Competitive Social Networks: Combining the Theory of Planned Behavior and Social Network Theory

abstract: Online gaming has been the most rapidly growing segment of the video game market within the last ten years. Its market penetration has increased steadily attracting consumers worldwide. Even though, it occurs in a particular environment of competitive social networks, its adoption has gained only little scientific interest. To fill that gap, we suggest a model of online gaming adoption in competitive social networks. For that purpose, we combine the Theory of Planned Behavior with Social Network Theory, introducing external influences in terms of social interaction design and network exposure.


title: A Survey of Underwater Optical Wireless Communications

abstract: Underwater wireless communications refer to data transmission in unguided water environment through wireless carriers, i.e., radio-frequency (RF) wave, acoustic wave, and optical wave. In comparison to RF and acoustic counterparts, underwater optical wireless communication (UOWC) can provide a much higher transmission bandwidth and much higher data rate. Therefore, we focus, in this paper, on the UOWC that employs optical wave as the transmission carrier. In recent years, many potential applications of UOWC systems have been proposed for environmental monitoring, offshore exploration, disaster precaution, and military operations. However, UOWC systems also suffer from severe absorption and scattering introduced by underwater channels. In order to overcome these technical barriers, several new system design approaches, which are different from the conventional terrestrial free-space optical communication, have been explored in recent years. We provide a comprehensive and exhaustive survey of the state-of-the-art UOWC research in three aspects: 1) channel characterization; 2) modulation; and 3) coding techniques, together with the practical implementations of UOWC.


title: Ontology-based integration of data sources

abstract: Many applications, e.g., data/information fusion, data mining, and decision aids, need to access multiple heterogeneous data sources. These data sources may come from internal and external databases. They have to evolve due to requirement changes. Any change in an application domain induces semantics change in the data sources. The integration of these data sources raises several semantic heterogeneity problems. This has traditionally been the subject of data/schema integration and mapping. However, many heterogeneity conflicts remain in information integration due to lack of semantics. Therefore, richer semantics of data are needed to resolve the heterogeneity problems. Ontological approaches now offer new solution avenues to this interoperability limitation. In this perspective, we propose an ontology- based information integration with a local to global ontology mapping as an approach to the integration of heterogeneous data sources.


title: Real-time recognition system of traffic light in urban environment

abstract: Detection of arrow traffic light is a focal point research in autonomous vehicle, and in urban environment it is the basic technique. However, most researches mainly concern the circular traffic lights. A novel algorithm is proposed in this paper to resolve the problems of detection and recognition of arrow traffic lights. Two sub-modules, detection module and recognition module, are introduced in the main framework. In detection submodule, the color space conversion, binarization and morphology features filtering methods are performed to get the regions of candidates of blackboards. For getting the regions of arrow of traffic lights, segmentation based on the YCbCr color space is used in the cropping image, which is cropped from original image by the region of blackboard. In recognition sub-module, Gabor wavelet transform and 2D independent component analysis(2DICA) are used to extract traffic light candidate's features for features of the arrow traffic lights. A library for recognition has been built, and experimental results show that rate of recognition exceeds 91%.


title: Dynamic Queries for Visual Information Seeking

abstract: Considers how dynamic queries allow users to "fly through" databases by adjusting widgets and viewing the animated results. In studies, users reacted to this approach with an enthusiasm more commonly associated with video games. Adoption requires research into retrieval and display algorithms and user-interface design. The author discusses how experts may benefit from visual interfaces because they will be able to formulate more complex queries and interpret intricate results. >


title: Temporomandibular disorders, head and orofacial pain: cervical spine considerations.

abstract: Head and orofacial pain originates from dental, neurologic, musculoskeletal, otolaryngologic, vascular, metaplastic, or infectious disease. It is treated by many health care practitioners, such as dentists, oral surgeons, and physicians. The article focuses on the nonpathologic involvement of the musculoskeletal system as a source of head and orofacial pain. The areas of the musculoskeletal system that are reviewed include the temporomandibular joint and muscles of mastication--collectively referred to as temporomandibular disorders (TMDs) and cervical spine disorders. The first part of the article highlights the role of physical therapy in the treatment of TMDs. The second part discusses cervical spine considerations in the management of TMDs and head and orofacial symptoms. It concludes with and overview of the evaluation and treatment of the cervical spine.


title: Named Entity Recognition with stack residual LSTM and trainable bias decoding

abstract: Recurrent Neural Network models are the state-of-the-art for Named Entity Recognition (NER). We present two innovations to improve the performance of these models. The first innovation is the introduction of residual connections between the Stacked Recurrent Neural Network model to address the degradation problem of deep neural networks. The second innovation is a bias decoding mechanism that allows the trained system to adapt to non-differentiable and externally computed objectives, such as the entity-based F-measure. Our work improves the state-of-the-art results for both Spanish and English languages on the standard train/development/test split of the CoNLL 2003 Shared Task NER dataset.


title: 3D printing for soft robotics – a review

abstract: AbstractSoft robots have received an increasing attention due to their advantages of high flexibility and safety for human operators but the fabrication is a challenge. Recently, 3D printing has be...


title: A Localization Database Establishment Method Based on Crowdsourcing Inertial Sensor Data and Quality Assessment Criteria

abstract: Aimed at the challenge of generating indoor localization databases with daily life crowdsourcing-based inertial sensor data, this paper proposes an anchor point-based forward–backward smoothing method to obtain reliable localization solutions. More importantly, a quantitative framework is proposed to evaluate the quality of smartphone-based inertial sensor data automatically without user intervention. Through this framework, the reliability of each inertial sensor data can be evaluated and sorted. Tests with multiple people and multiple smartphones in a public office building and a shopping mall illustrate that the proposed method can provide a WiFi fingerprinting database that has similar accuracy to that generated by a supervised map-aided database-generation method. Therefore, the proposed method and framework can guide the promotion of crowdsourcing-based Internet of Things applications in the context of big data.


title: Performance assessment and uncertainty quantification of predictive models for smart manufacturing systems

abstract: We review in this paper several methods from Statistical Learning Theory (SLT) for the performance assessment and uncertainty quantification of predictive models. Computational issues are addressed so to allow the scaling to large datasets and the application of SLT to Big Data analytics. The effectiveness of the application of SLT to manufacturing systems is exemplified by targeting the derivation of a predictive model for quality forecasting of products on an assembly line.


title: Text and Data Mining Techniques in Adverse Drug Reaction Detection

abstract: We review data mining and related computer science techniques that have been studied in the area of drug safety to identify signals of adverse drug reactions from different data sources, such as spontaneous reporting databases, electronic health records, and medical literature. Development of such techniques has become more crucial for public heath, especially with the growth of data repositories that include either reports of adverse drug reactions, which require fast processing for discovering signals of adverse reactions, or data sources that may contain such signals but require data or text mining techniques to discover them. In order to highlight the importance of contributions made by computer scientists in this area so far, we categorize and review the existing approaches, and most importantly, we identify areas where more research should be undertaken.


title: Stiffness control of 2-DOF exoskeleton for brain-machine interfaces

abstract: Previous demonstrations of brain-machine interfaces have shown the potential for controlling a neuroprosthesis under pure motion control, i.e. predicting end effector kinematics from neural ensemble activity. For real world tasks, however, pure motion control lacks the information required for versatile manipulation in which the dynamic interactions of forces and torques between the musculoskeletal system and the environment play a crucial role. Thus, our current efforts aim at enabling a subject using a brain-machine interface to volitionally control the mechanical impedance of the prosthetic device. Here we propose the use of a two-link arm exoskeleton to investigate upper limb stiffness in non-human primates. We show that the device can be used to experimentally measure end-point limb stiffness, as well as to control the stiffness when the exoskeleton is used in slave-robot mode. Experimental results show that this platform allows for both stiffness measurement and control of the robotic device.


title: A Reference Architecture for Improving Security and Privacy in Internet of Things Applications

abstract: As the promise of the Internet of Things (IoT) materializes in our everyday lives, we are often challenged with a number of concerns regarding the efficacy of the current data privacy solutions that support the pervasive components at play in IoT. The privacy and security concerns surrounding IoT often manifests themselves as a treat to end-user adoption and negatively impacts trust among end-users in these solutions. In this paper, we present a reference software architecture for building cloud-enabled IoT applications in support of collaborative pervasive systems aimed at achieving trustworthiness among end-users in IoT scenarios. We present a case study that leverages this reference architecture to protect sensitive user data in an IoT application implementation and evaluate the response of an end-user study accomplished through a survey.


title: The Hebbian-LMS Learning Algorithm

abstract: Abstract-Hebbian learning is widely accepted in the fields of psychology, neurology, and neurobiology. It is one of the fundamental premises of neuroscience. The LMS (least mean square) algorithm of Widrow and Hoff is the world's most widely used adaptive algorithm, fundamental in the fields of signal processing, control systems, pattern recognition, and artificial neural networks. These are very different learning paradigms. Hebbian learning is unsupervised. LMS learning is supervised. However, a form of LMS can be constructed to perform unsupervised learning and, as such, LMS can be used in a natural way to implement Hebbian learning. Combining the two paradigms creates a new unsupervised learning algorithm that has practical engineering applications and provides insight into learning in living neural networks. A fundamental question is, how does learning take place in living neural networks? "Nature's little secret," the learning algorithm practiced by nature at the neuron and synapse level, may well be the Hebbian-LMS algorithm.


title: Hashtagger+: Efficient High-Coverage Social Tagging of Streaming News

abstract: News and social media now play a synergistic role and neither domain can be grasped in isolation. On one hand, platforms such as Twitter have taken a central role in the dissemination and consumption of news. On the other hand, news editors rely on social media for following their audience’s attention and for crowd-sourcing news stories. Twitter hashtags function as a key connection between Twitter crowds and the news media, by naturally naming and contextualizing stories, grouping the discussion of news and marking topic trends. In this work, we propose Hashtagger+, an efficient learning-to-rank framework for merging news and social streams in real-time, by recommending Twitter hashtags to news articles. We provide an extensive study of different approaches for streaming hashtag recommendation, and show that pointwise learning-to-rank is more effective than multi-class classification as well as more complex learning-to-rank approaches. We improve the efficiency and coverage of a state-of-the-art hashtag recommendation model by proposing new techniques for data collection and feature computation. In our comprehensive evaluation on real-data, we show that we drastically outperform the accuracy and efficiency of prior methods. Our prototype system delivers recommendations in under 1 minute, with a Precision@1 of 94 percent and article coverage of 80 percent. This is an order of magnitude faster than prior approaches, and brings improvements of 5 percent in precision and 20 percent in coverage. By effectively linking the news stream to the social stream via the recommended hashtags, we open the door to solving many challenging problems related to story detection and tracking. To showcase this potential, we present an application of our recommendations to automated news story tracking via social tags. Our recommendation framework is implemented in a real-time Web system available from insight4news.ucd.ie .


title: A Corpus for Modeling Word Importance in Spoken Dialogue Transcripts

abstract: Motivated by a project to create a system for people who are deaf or hard-of-hearing that would use automatic speech recognition (ASR) to produce real-time text captions of spoken English during in-person meetings with hearing individuals, we have augmented a transcript of the Switchboard conversational dialogue corpus with an overlay of word-importance annotations, with a numeric score for each word, to indicate its importance to the meaning of each dialogue turn. Further, we demonstrate the utility of this corpus by training an automatic word importance labeling model; our best performing model has an F-score of 0.60 in an ordinal 6-class word-importance classification task with an agreement (concordance correlation coefficient) of 0.839 with the human annotators (agreement score between annotators is 0.89). Finally, we discuss our intended future applications of this resource, particularly for the task of evaluating ASR performance, i.e. creating metrics that predict ASR-output caption text usability for DHH users better thanWord Error Rate (WER).


title: Extrinsic versus intrinsic motivations for consumers to shop on-line

abstract: Previous research has usually assumed that shopping on-line is a goal-oriented activity and is motivated by extrinsic factors of the customers. On the other hand, intrinsic factors, such as entertainment, have been found to be a major reason for peoples to use the Internet. This study examined whether such intrinsic motivations can be used to explain consumers' acceptance of on-line shopping. A theoretical model, based on the technology acceptance model, was proposed to describe the intrinsic and extrinsic motivations of consumers to shop on-line. Results of this empirical study showed that perceived usefulness is not an antecedent of on-line shopping, while fashion and a cognitive absorption experiences on the web were more important than their extrinsic factors in explaining on-line consuming behavior. Implications and limitations were discussed.


title: Joint 3D Scene Reconstruction and Class Segmentation

abstract: Both image segmentation and dense 3D modeling from images represent an intrinsically ill-posed problem. Strong regularizers are therefore required to constrain the solutions from being 'too noisy'. Unfortunately, these priors generally yield overly smooth reconstructions and/or segmentations in certain regions whereas they fail in other areas to constrain the solution sufficiently. In this paper we argue that image segmentation and dense 3D reconstruction contribute valuable information to each other's task. As a consequence, we propose a rigorous mathematical framework to formulate and solve a joint segmentation and dense reconstruction problem. Image segmentations provide geometric cues about which surface orientations are more likely to appear at a certain location in space whereas a dense 3D reconstruction yields a suitable regularization for the segmentation problem by lifting the labeling from 2D images to 3D space. We show how appearance-based cues and 3D surface orientation priors can be learned from training data and subsequently used for class-specific regularization. Experimental results on several real data sets highlight the advantages of our joint formulation.


title: Business value through controlled IT: toward an integrated model of IT governance success and its impact

abstract: Owing to increasing regulatory pressure and the need for aligned information technology (IT) decisions at the interface of business and IT, IT governance (ITG) has become important in both academia and practice. However, knowledge of integrating the determinants and consequences of ITG success remains scarce. Although some studies investigate individual aspects of ITG success and its impact, none combine these factors into a comprehensive and integrated model that would lead to a more complete understanding of the ITG concept. To address this gap, our research aims at understanding what factors influence and result from successful ITG, and at determining how they can be translated into a model to explain ITG success and the impact thereof. Therefore, we conducted interviews with 28 IT decision makers in 19 multinational organizations headquartered in Europe. Our study synthesizes the fragmented previous research, provides new empirical insights gathered on the basis of a clear ITG conceptualization, and suggests three innovative constructs heretofore not related to ITG. Moreover, we elucidate in a holistic model the factors that make ITG successful, how ITG contributes to an IT organization’s success, and how it eventually unfolds throughout the overall organization. The resulting model allows organizational decision makers to develop an effective ITG implementation and to explain the implications of successful ITG, thus providing a justification for the respective investments.


title: FoldingNet: Interpretable Unsupervised Learning on 3D Point Clouds

abstract: Recent deep networks that directly handle points in a point set, e.g., PointNet, have been state-of-the-art for supervised semantic learning tasks on point clouds such as classification and segmentation. In this work, a novel end-to-end deep auto-encoder is proposed to address unsupervised learning challenges on point clouds. On the encoder side, a graph-based enhancement is enforced to promote local structures on top of PointNet. Then, a novel folding-based approach is proposed in the decoder, which folds a 2D grid onto the underlying 3D object surface of a point cloud. The proposed decoder only uses about 7\% parameters of a decoder with fully-connected neural networks, yet leads to a more discriminative representation that achieves higher linear SVM classification accuracy than the benchmark. In addition, the proposed decoder structure is shown, in theory, to be a generic architecture that is able to reconstruct an arbitrary point cloud from a 2D grid. Finally, this folding-based decoder is interpretable since the reconstruction could be viewed as a fine granular warping from the 2D grid to the point cloud surface.


title: Perspectives on Leadership in Organizations Providing Services to People With Disabilities: An Exploratory Study

abstract: Abstract As leaders from the Baby Boomer generation prepare for retirement over the next decade, emerging leaders must be identified and supported in anticipation of a major organizational transiti...


title: Gated Recurrent Capsules for Visual Word Embeddings

abstract: The caption retrieval task can be defined as follows: given a set of images I and a set of describing sentences S, for each image i in I we ought to find the sentence in S that best describes i. The most commonly applied method to solve this problem is to build a multimodal space and to map each image and each sentence to that space, so that they can be compared easily. A non-conventional model called Word2VisualVec has been proposed recently: instead of mapping images and sentences to a multimodal space, they mapped sentences directly to a space of visual features. Advances in the computation of visual features let us infer that such an approach is promising. In this paper, we propose a new Recurrent Neural Network model following that unconventional approach based on Gated Recurrent Capsules (GRCs), designed as an extension of Gated Recurrent Units (GRUs). We show that GRCs outperform GRUs on the caption retrieval task. We also state that GRCs present a great potential for other applications.


Perceived racial/ethnic discrimination, hope, and social connectedness: examining the predictors of future orientation among emerging adults


title: Cell-autonomous correction of ring chromosomes in human induced pluripotent stem cells

abstract: Generation of human induced pluripotent stem cells from patient fibroblasts containing ring chromosomes with large deletions reveals that reprogrammed cells lose the abnormal chromosome and duplicate the wild-type homologue through compensatory uniparental disomy, suggesting that cellular reprogramming may hold potential for ‘chromosome therapy’.


title: Flow: Architecture and Benchmarking for Reinforcement Learning in Traffic Control

abstract: Flow is a new computational framework, built to support a key need triggered by the rapid growth of autonomy in ground traffic: controllers for autonomous vehicles in the presence of complex nonlinear dynamics in traffic. Leveraging recent advances in deep Reinforcement Learning (RL), Flow enables the use of RL methods such as policy gradient for traffic control and enables benchmarking the performance of classical (including hand-designed) controllers with learned policies (control laws). Flow integrates traffic microsimulator SUMO with deep reinforcement learning library rllab and enables the easy design of traffic tasks, including different networks configurations and vehicle dynamics. We use Flow to develop reliable controllers for complex problems, such as controlling mixed-autonomy traffic (involving both autonomous and human-driven vehicles) in a ring road. For this, we first show that state-of-the-art hand-designed controllers excel when in-distribution, but fail to generalize; then, we show that even simple neural network policies can solve the stabilization task across density settings and generalize to out-of-distribution settings.


Design and Implementation of a Wearable Gas Sensor Network for Oil and Gas Industry Workers


title: Scalable Micro-planned Generation of Discourse from Structured Data

abstract: We present a framework for generating natural language description from structured data such as tables; the problem comes under the category of data-to-text natural language generation (NLG). Moder...


title: Exploring social influence via posterior effect of word-of-mouth recommendations

abstract: Word-of-mouth has proven an effective strategy for promoting products through social relations. Particularly, existing studies have convincingly demonstrated that word-of-mouth recommendations can boost users' prior expectation and hence encourage them to adopt a certain innovation, such as buying a book or watching a movie. However, less attention has been paid to studying the posterior effect of word-of-mouth recommendations, i.e., whether or not word-of-mouth recommendations can influence users' posterior evaluation on the products or services recommended to them, the answer to which is critical to estimating user satisfaction when proposing a word-of-mouth marketing strategy. In order to fill this gap, in this paper we empirically study the above issue and verify that word-of-mouth recommendations are strongly associated with users' posterior evaluation. Through elaborately designed statistical hypothesis tests we prove the causality that word-of-mouth recommendations directly prompt the posterior evaluation of receivers. Finally, we propose a method for investigating users' social influence, namely, their ability to affect followers' posterior evaluation via word-of-mouth recommendations, by examining the number of their followers and their sensitivity of discovering good items. The experimental results on real datasets show that our method can successfully identify 78% influential friends with strong social influence.


Unipolar and Bipolar PWM Inverter


title: Quantification of opiate receptors in two patients with mesiobasal temporal lobe epilepsy, before and after selective amygdalo- hippocampectomy, using positron emission tomography

abstract: Abstract Using PET, reduced glucose metabolism (rCMRglu) and increased binding of the mu opiate receptor ligand 11 C-carfentanil have been demonstrated in lateral temporal cortex overlying mesial temporal epileptic foci. Binding of the non-specific opiate receptor ligand 11 C-diprenorphine (DPN) to lateral temporal cortex has not shown consistent asymmetries. We measured rCMRglu with 18 F-FDG and binding of 11 C-diprenorphine in two patients with temporal lobe epilepsy (TLE) before and 5 months after selective amygdalo-hippocampectomy (both patients were seizure-free post-operatively). Pre-operatively, in both patients rCMRglu was decreased in mesial temporal lobe (MTL) (asymmetry index AI = −9.1 and 9.0) ipsilateral to the EEG focus. A more marked reduction was seen in ipsilateral lateral temporal cortex (LTC) ( AI = −32.0 and 18.9). DPN binding was reduced in MTL and LTC ( AI MTL = −9.3 and 16.2; AI LTC = −8.0 and 5.5) ipsilateral to the focus, but was within 2 SD of the normal range. Post-operatively, the reduction of rCMRglu in LTC was accentuated in one patient and decreased in the other ( AI = −23.1 and 45.7) while there was a further reduction of DPN binding in LTC in both patients ( AI = −27.8 and 9.8). These preliminary results in only two patients are compatible with downregulation of opiate receptors in LTC after removal of the epileptic focus or post-operative neuronal dysfunction.


title: Modelling and simulation studies on the mobile robot with self-leveling chassis

abstract: The mobile robot presented in the article is a hybrid system combining efficient travel on wheels on a flat terrain with the capability of surmounting obstacles by walking. The research is focused on designing a control system maintaining the robot chassis at a constant position to the ground. The aims of this research are: creation of the computational model of the control system for the levelling system of designed mobile robots and realization of ::: simulation studies on the robots travel in terrain with obstacles. The simulations aimed at determination of basic dynamic and kinematic properties.


title: Hilbert modular forms and the Gross–Stark conjecture

abstract: Let F be a totally real eld and an abelian totally odd character of F . In 1988, Gross stated a p-adic analogue of Stark’s conjecture that relates the value of the derivative of the p-adic L-function associated to and the p-adic logarithm of a p-unit in the extension of F cut out by . In this paper we prove Gross’s conjecture when F is a real quadratic eld and is a narrow ring class character. The main result also applies to general totally real elds for which Leopoldt’s conjecture holds, assuming that either there are at least two primes above p in F , or that a certain condition relating the L -invariants of and 1 holds. This condition on L -invariants is always satised when is quadratic.


title: Phone-aware LSTM-RNN for voice conversion

abstract: This paper investigates a new voice conversion technique using phone-aware Long Short-Term Memory Recurrent Neural Networks (LSTM-RNNs). Most existing voice conversion methods, including Joint Density Gaussian Mixture Models (JDGMMs), Deep Neural Networks (DNNs) and Bidirectional Long Short-Term Memory Recurrent Neural Networks (BLSTM-RNNs), only take acoustic information of speech as features to train models. We propose to incorporate linguistic information to build voice conversion system by using monophones generated by a speech recognizer as linguistic features. The monophones and spectral features are combined together to train LSTM-RNN based voice conversion models, reinforcing the context-dependency modelling of LSTM-RNNs. The results of the 1st voice conversion challenge shows our system achieves significantly higher performance than baseline (GMM method) and was found among the most competitive scores in similarity test. Meanwhile, the experimental results show phone-aware LSTM-RNN method obtains lower Mel-cepstral distortion and higher MOS scores than the baseline LSTM-RNNs.


title: A Multi-tenant Architecture for Business Process Executions

abstract: Cloud computing, as a concept, promises cost savings to end-users by letting them outsource their non-critical business functions to a third party in pay-as-you-go style. However, to enable economic pay-as-you-go services, we need Cloud middleware that maximizes sharing and support near zero costs for unused applications. Multi-tenancy, which let multiple tenants (user) to share a single application instance securely, is a key enabler for building such a middleware. On the other hand, Business processes capture Business logic of organizations in an abstract and reusable manner, and hence play a key role in most organizations. This paper presents the design and architecture of a Multi-tenant Workflow engine while discussing in detail potential use cases of such architecture. Primary contributions of this paper are motivating workflow multi-tenancy, and the design and implementation of multi-tenant workflow engine that enables multiple tenants to run their workflows securely within the same workflow engine instance without modifications to the workflows.


title: 2002 Special Issue Recursive self-organizing maps

abstract: This paper explores the combination of self-organizing map (SOM) and feedback, in order to represent sequences of inputs. In general, neural networks with time-delayed feedback represent time implicitly, by combining current inputs and past activities. It has been difficult to apply this approach to SOM, because feedback generates instability during learning. We demonstrate a solution to this problem, based on a nonlinearity. The result is a generalization of SOM that learns to represent sequences recursively. We demonstrate that the resulting representations are adapted to the temporal statistics of the input series.


title: Efficient Multipattern Event Processing Over High-Speed Train Data Streams

abstract: Big data is becoming a key basis for productivity growth, innovation, and consumer surplus, but also bring us great challenges in its volume, velocity, variety, value, and veracity. The notion of event is an important cornerstone to manage big data. High-speed railway is one of the most typical application domains for event-based system, especially for the train onboard system. There are usually numerous complex event patterns subscribed in system sharing the same prefix, suffix, or subpattern; consequently, multipattern complex event detection often results in plenty of redundant detection operations and computations. In this paper, we propose a multipattern complex event detection model, multipattern event processing (MPEP), constructed by three parts: 1) multipattern state transition; 2) failure transition; and 3) state output. Based on MPEP, an intelligent onboard system for high-speed train is preliminarily implemented. The system logic is described using our proposed complex event description model and compiled into a multipattern event detection model. Experimental results show that MPEP can effectively optimize the complex event detection process and improve its throughput by eliminating duplicate automata states and redundant computations. This intelligent onboard system also provides better detection ability than other models when processing real-time events stored in high-speed train Juridical Recording Unit (JRU).


title: Conceptual graphs for a data base interface

abstract: A data base system that supports natural language queries is not really natural if it requires the user to know how the data are represented. This paper defines a formalism, called conceptual graphs, that can describe data according to the user's view and access data according to the system's view. In addition, the graphs can represent functional dependencies in the data base and support inferences and computations that are not explicit in the initial query.


title: Pricing via processing or combatting junk mail

abstract: We present a computational technique for combatting junk mail in particular and controlling access to a shared resource in general. The main idea is to require a user to compute a moderately hard, but not intractable, function in order to gain access to the resource, thus preventing frivolous use. To this end we suggest several pricing Junctions, based on, respectively, extracting square roots modulo a prime, the Fiat-Shamir signature scheme, and the Ong-Schnorr-Shamir (cracked) signature scheme.


title: Handwritten digit recognition: benchmarking of state-of-the-art techniques

abstract: This paper presents the results of handwritten digit recognition on well-known image databases using state-of-the-art feature extraction and classification techniques. The tested databases are CENPARMI, CEDAR, and MNIST. On the test data set of each database, 80 recognition accuracies are given by combining eight classifiers with ten feature vectors. The features include chaincode feature, gradient feature, profile structure feature, and peripheral direction contributivity. The gradient feature is extracted from either binary image or gray-scale image. The classifiers include the k-nearest neighbor classifier, three neural classifiers, a learning vector quantization classifier, a discriminative learning quadratic discriminant function (DLQDF) classifier, and two support vector classifiers (SVCs). All the classifiers and feature vectors give high recognition accuracies. Relatively, the chaincode feature and the gradient feature show advantage over other features, and the profile structure feature shows efficiency as a complementary feature. The SVC with RBF kernel (SVC-rbf) gives the highest accuracy in most cases but is extremely expensive in storage and computation. Among the non-SV classifiers, the polynomial classifier and DLQDF give the highest accuracies. The results of non-SV classifiers are competitive to the best ones previously reported on the same databases.


title: Adaptive One-Class Support Vector Machine

abstract: In this correspondence, we derive an online adaptive one-class support vector machine. The machine structure is updated via growing and pruning mechanisms and the weights are updated using structural risk minimization principles underlying support vector machines. Our approach leads to very compact machines compared to other online kernel methods whose size, unless truncated, grows almost linearly with the number of observed patterns. The proposed method is online in the sense that every pattern is only presented once to the machine and there is no need to store past samples and adaptive in the sense that it can forget past input patterns and adapt to the new characteristics of the incoming data. Thus, the characterizing properties of our algorithm are compactness, adaptiveness and real-time processing capabilities, making it especially well-suited to solve online novelty detection problems. Regarding algorithm performance, we have carried out experiments in a time series segmentation problem, obtaining favorable results in both accuracy and model complexity with respect to two existing state-of-the-art methods.


title: Alpha matting using robust color sampling and fully connected conditional random fields

abstract: Alpha matting refers to the problem of softly extracting the foreground from a given image. Previous matting approaches often focused on using naive color sampling methods to estimate foreground and background colors for unknown pixels. Existing sampling-based matting methods often collect samples only near the unknown pixels, which may yield poor results if the true foreground and background samples are not found. In this paper, we present novel approach to extract foreground elements from an image through color and opacity (i.e., alpha) estimations, which consider available samples in a search window of variable size for each unknown pixel. Our proposed sampling method is robust in that similar sampling results can be generated for input trimaps of different unknown regions. Further, after the initial estimation of the alpha matte, a fully connected conditional random field (CRF) is used to correct the predicted matte at the pixel level. Our experiments show that visually plausible alpha mattes can indeed be produced.


title: 2:1 Multiplexer based design for ternary logic circuits

abstract: This paper presents a design methodology using multiplexers to implement any ternary logic function with carbon nanotube field effect transistors (CNFETs). Ternary logic is one of the promising alternatives to conventional binary logic, since it is possible to achieve simplicity and low power dissipation due to the reduced circuit such as interconnects and chip area. The paper presents a design methodology which uses the combination of Binary 2:1 multiplexers and Ternary multiplexers, to implement Ternary logic circuits. A 1-bit half adder circuits is implemented using the proposed methodology. The proposed implementations are compared with the existing designs for parameters like delay, power etc. Two kinds of 1-bit half adders i.e., delay optimized and power optimized half adders have been designed. Simulation results indicate that the proposed multiplexer based 1-bit half adder design results in 58% average power reduction and 64% power delay product reduction when compared to the existing multiplexer based design.


title: Quiet Moment around the Campfire

abstract: Suggested citation for this article: Breedlove B. Quiet moment around the campfire. Emerg Infect Dis [Internet]. 2014 Jun [date cited]. http://dx.doi.org/10.3201/eid2006.AC2006


title: Recurrence to the Rescue: Towards Causal Spatiotemporal Representations

abstract: Recently, three dimensional (3D) convolutional neural networks (CNNs) have emerged as dominant methods to capture spatiotemporal representations, by adding to pre-existing 2D CNNs a third, temporal dimension. Such 3D CNNs, however, are anti-causal (i.e., they exploit information from both the past and the future to produce feature representations, thus preventing their use in online settings), constrain the temporal reasoning horizon to the size of the temporal convolution kernel, and are not temporal resolution-preserving for video sequence-to-sequence modelling, as, e.g., in spatiotemporal action detection. To address these serious limitations, we present a new architecture for the causal/online spatiotemporal representation of videos. Namely, we propose a recurrent convolutional network (RCN), which relies on recurrence to capture the temporal context across frames at every level of network depth. Our network decomposes 3D convolutions into (1) a 2D spatial convolution component, and (2) an additional hidden state $1\times 1$ convolution applied across time. The hidden state at any time $t$ is assumed to depend on the hidden state at $t-1$ and on the current output of the spatial convolution component. As a result, the proposed network: (i) provides flexible temporal reasoning, (ii) produces causal outputs, and (iii) preserves temporal resolution. Our experiments on the large-scale large "Kinetics" dataset show that the proposed method achieves superior performance compared to 3D CNNs, while being causal and using fewer parameters.


title: Hybrid approaches based on SARIMA and artificial neural networks for inspection time series forecasting

abstract: In this paper, the number of goods subject to inspection at European Border Inspections Post are predicted using a hybrid two-step procedure. A hybridization methodology based on integrating the data obtained from autoregressive integrated moving averages (SARIMA) model in the artificial neural network model (ANN) to predict the number of inspections is proposed. Several hybrid approaches are compared and the results indicate that the hybrid models outperform either of the models used separately. This methodology may become a powerful decision-making tool at other inspection facilities of international seaports or airports.


title: Unsupervised intra-speaker variability compensation based on Gestalt and model adaptation in speaker verification with telephone speech

abstract: In this paper, an unsupervised intra-speaker variability compensation (ISVC) method based on Gestalt is proposed to address the problem of limited enrolling data and noise robustness in text-dependent speaker verification (SV). Experiments with two databases show that: ISVC can lead to reductions in EER as high as 20% or 40% and ISCV provides reductions in the integral below the ROC curve between 30% and 60%. Also, the observed improvements are independent of the number of enrolling utterances. In contrast to model adaptation methods, ISVC is memoryless with respect to previous verification attempts. As shown here, unsupervised model adaptation can lead to substantial improvements in EER but is highly dependent on the sequence of client/impostor verification events. In adverse scenarios, such as massive impostor attacks and verification from alternated telephone line, unsupervised model adaptation might even provide reductions in verification accuracy when compared with the baseline system. In those cases, ISVC can even outperform adaptation schemes. It is worth emphasizing that ISVC and unsupervised model adaptation are compatible and the combination of both methods always improves the performance of model adaptation. The combination of both schemes can lead to improvements in EER as high as 34%. Due to the restrictions of commercially available databases for text-dependent SV research, the results presented here are based on local databases in Spanish. By doing so, the visibility of research in Iberian Languages is highlighted.


title: Identification of regional atmospheric PM10 transport pathways using HYSPLIT, MM5-CMAQ and synoptic pressure pattern analysis

abstract: Air pollution in urban cites is caused not only by local emission sources but also significantly by regional atmospheric pollutant transport from surrounding areas. This study is to identify regional atmospheric PM"1"0 transport pathways through an integrated modeling and synoptic pressure pattern analysis approach with a case study in Beijing, northern China. Beijing is a city sensitive to trans-boundary transport of aerosols from its surrounding provinces. The pathway identification was conducted through tracking air masses and analyzing dominant transport patterns leading to air pollution episode in Beijing. Trajectories were calculated using NOAA-HYSPLIT model based on the meteorological field of MM5 outputs in October 2002. A k-means clustering algorithm was applied to group these trajectories into different transport patterns. Monitored PM"1"0 levels during each transport pattern were then examined to evaluate its influence on atmospheric PM"1"0 in Beijing. The southwest transport pathway was identified to be closely associated with the increasing phase of PM"1"0. An integrated MM5-CMAQ modeling system was then applied to simulate PM"1"0 concentrations in Beijing and its surrounding provinces for the southwest transport period. It was found that a convergence flow field with high PM"1"0 concentrations frequently appeared between northwest mountain breeze and southwest plain breeze on the lee of the Taihang Mountains. Further analysis indicated that high-pressure systems accompanied with thermal inversion in the boundary layer were the governing synoptic patterns in southwest transport period. Trans-boundary transport along with the convergence zone induced by mesoscale low pressure system in front of the Taihang Mountains, which was generated by topographical dynamics and thermal effects, proved to be the main cause of high PM"1"0 levels in Beijing.


CS224N Final Project: QA


title: Submodular Hypergraphs: p-Laplacians, Cheeger Inequalities and Spectral Clustering

abstract: We introduce submodular hypergraphs, a family of hypergraphs that have different submodular weights associated with different cuts of hyperedges. Submodular hypergraphs arise in clustering applications in which higher-order structures carry relevant information. For such hypergraphs, we define the notion of p-Laplacians and derive corresponding nodal domain theorems and k-way Cheeger inequalities. We conclude with the description of algorithms for computing the spectra of 1- and 2-Laplacians that constitute the basis of new spectral hypergraph clustering methods.


title: Rubber Hand Illusion under Delayed Visual Feedback

abstract: Background: Rubber hand illusion (RHI) is a subject’s illusion of the self-ownership of a rubber hand that was touched synchronously with their own hand. Although previous studies have confirmed that this illusion disappears when the rubber hand was touched asynchronously with the subject’s hand, the minimum temporal discrepancy of these two events for attenuation of RHI has not been examined. Methodology/Principal Findings: In this study, various temporal discrepancies between visual and tactile stimulations were introduced by using a visual feedback delay experimental setup, and RHI effects in each temporal discrepancy condition were systematically tested. The results showed that subjects felt significantly greater RHI effects with temporal discrepancies of less than 300 ms compared with longer temporal discrepancies. The RHI effects on reaching performance (proprioceptive drift) showed similar conditional differences. Conclusions/Significance: Our results first demonstrated that a temporal discrepancy of less than 300 ms between visual stimulation of the rubber hand and tactile stimulation to the subject’s own hand is preferable to induce strong sensation of RHI. We suggest that the time window of less than 300 ms is critical for multi-sensory integration processes constituting the self-body image.


title: Modeling of physical human–robot interaction: Admittance controllers applied to intelligent assist devices with large payload

abstract: Enhancement of human performance using an intelligent assist device is becoming more common. In order to achieve effective augmentation of human capacity, cooperation between human and robot must be safe and very intuitive. Ensuring such collaboration remains a challenge, especially when admittance control is used. This paper addresses the issues of transparency and human perception coming from vibration in admittance control schemes. Simulation results obtained with our suggested improved model using an admittance controller are presented, then four models using transfer functions are discussed in detail and evaluated as a means of simulating physical human–robot interaction using admittance control. The simulation and experimental results are then compared in order to assess the validity and limitations of the proposed models in the case of a four-degree-of-freedom intelligent assist device designed for large payload.


title: A COMPARISON OF GPU BOX-PLANE INTERSECTION ALGORITHMS FOR DIRECT VOLUME RENDERING

abstract: In order to avoid load imbalances on the GPU during direct volume rendering, a common scheme was to move the generation of proxy geometry, that quite often consists of polygons retrieved through box-plane intersections, from the CPU to the vertex stage of the GPU. Nowadays, with the unified shader architectures implemented by modern graphics hardware, usually non of the programmable stages will starve anymore. Nevertheless, redistributing calculations for proxy geometry generation from the vertex stage to the newly introduced geometry stage of the graphics hardware results in some serial computations that can be performed more finely grained, thus hinting the graphics driver to schedule more tasks in parallel. We propose different implementations of box-plane intersection algorithms on the GPU that use the vertex- as well as the geometry stage and compare their performance on modern graphics hardware.


title: A unique congenital mullerian anomaly: Robert’s uterus

abstract: Roberts uterus or asymmetric septate uterus is an uncommon uterine mullerian anomaly first reported by Robert in 1970. It has two uterine cavities with one being blind causing menstrual retention. Till date only 17 such cases have been reported in literature. A unique congenital mullerian anomaly; asymmetric septate uterus with noncommunicating right sided hemicavity and haematometra a normal extrauterine morphology with right ovarian maldescent and stage III endometriosis was diagnosed on laparotomy. (excerpt)


MIMO and CoMP in LTE-Advanced


title: Considerations in planning vegan diets

abstract: Abstract Appropriately planned vegan diets can satisfy nutrient needs of infants. The American Dietetic Association and The American Academy of Pediatrics state that vegan diets can promote normal infant growth. It is important for parents to provide appropriate foods for vegan infants, using guidelines like those in this article. Key considerations when working with vegan families include composition of breast milk from vegan women, appropriate breast milk substitutes, supplements, type and amount of dietary fat, and solid food introduction. Growth of vegan infants appears adequate with post-weaning growth related to dietary adequacy. Breast milk composition is similar to that of non-vegetarians except for fat composition. For the first 4 to 6 months, breast milk should be the sole food with soy-based infant formula as an alternative. Commercial soymilk should not be the primary beverage until after age 1 year. Breastfed vegan infants may need supplements of vitamin B-12 if maternal diet is inadequate; older infants may need zinc supplements and reliable sources of iron and vitamins D and B-12. Timing of solid food introduction is similar to that recommended for non-vegetarians. Tofu, dried beans, and meat analogs are introduced as protein sources around 7-8 months. Vegan diets can be planned to be nutritionally adequate and support growth for infants. J Am Diet Assoc. 2001; 101:670–677 .


title: Differentially private multidimensional data publishing

abstract: Various organizations collect data about individuals for various reasons, such as service improvement. In order to mine the collected data for useful information, data publishing has become a common practice among those organizations and data analysts, research institutes, or simply the general public. The quality of published data significantly affects the accuracy of the data analysis and thus affects decision making at the corporate level. In this study, we explore the research area of privacy-preserving data publishing, i.e., publishing high-quality data without compromising the privacy of the individuals whose data are being published. Syntactic privacy models, such as k-anonymity, impose syntactic privacy requirements and make certain assumptions about an adversary’s background knowledge. To address this shortcoming, we adopt differential privacy, a rigorous privacy model that is independent of any adversary’s knowledge and insensitive to the underlying data. The published data should preserve individuals’ privacy, yet remain useful for analysis. To maintain data utility, we propose DiffMulti, a workload-aware and differentially private algorithm that employs multidimensional generalization. We devise an efficient implementation to the proposed algorithm and use a real-life data set for experimental analysis. We evaluate the performance of our method in terms of data utility, efficiency, and scalability. When compared to closely related existing methods, DiffMulti significantly improved data utility, in some cases, by orders of magnitude.


title: Learning to Reconstruct Shapes from Unseen Classes

abstract: From a single image, humans are able to perceive the full 3D shape of an object by exploiting learned shape priors from everyday life. Contemporary single-image 3D reconstruction algorithms aim to solve this task in a similar fashion, but often end up with priors that are highly biased by training classes. Here we present an algorithm, Generalizable Reconstruction (GenRe), designed to capture more generic, class-agnostic shape priors. We achieve this with an inference network and training procedure that combine 2.5D representations of visible surfaces (depth and silhouette), spherical shape representations of both visible and non-visible surfaces, and 3D voxel-based representations, in a principled manner that exploits the causal structure of how 3D shapes give rise to 2D images. Experiments demonstrate that GenRe performs well on single-view shape reconstruction, and generalizes to diverse novel objects from categories not seen during training.


title: Curb Detection for Road and Sidewalk Detection

abstract: Curb detection, a significant area of autonomous driving, plays an important role in road detection and obstacle avoidance, etc. However, curb detection is challenging due to the problems like occlusions, shadows and the small size of the target. In this paper, we propose a curb detection paradigm for road and sidewalk detection for mobile robots using stereo vision in the urban residential region. First, the flat area is estimated based on the disparity and v-disparity maps generated from stereo matching. In the estimated flat area, to distinguish curbs from the road and obstacles, we propose an efficient 16-dimensional descriptor based on the appearance, geometry, and disparity characteristics of curbs. The curb points can be extracted by an SVM classifier with the obtained descriptors. Second, the curb points are exploited by a vanishing point constrained Dijkstra road model to find the road region, where two shortest paths, generated from the vanishing point to the last row of the cost map, are searched and regarded as the road borders. Following the curb and road region detection results, the sidewalk area is detected by a region-growing-like method based on the geometry characteristic of the sidewalk area and the layout of road scenes. The proposed method has been tested for road and sidewalk detection tasks on the KITTI dataset. The experimental results of the curb, road, and sidewalk detection demonstrate the accuracy and robustness of this method.


title: Using global mapping to create more accurate document‐level maps of research fields

abstract: We describe two general approaches to creating document‐level maps of science. To create a local map, one defines and directly maps a sample of data, such as all literature published in a set of information science journals. To create a global map of a research field, one maps “all of science” and then locates a literature sample within that full context. We provide a deductive argument that global mapping should create more accurate partitions of a research field than does local mapping, followed by practical reasons why this may not be so. The field of information science is then mapped at the document level using both local and global methods to provide a case illustration of the differences between the methods. Textual coherence is used to assess the accuracies of both maps. We find that document clusters in the global map have significantly higher coherence than do those in the local map, and that the global map provides unique insights into the field of information science that cannot be discerned from the local map. Specifically, we show that information science and computer science have a large interface and that computer science is the more progressive discipline at that interface. We also show that research communities in temporally linked threads have a much higher coherence than do isolated communities, and that this feature can be used to predict which threads will persist into a subsequent year. Methods that could increase the accuracy of both local and global maps in the future also are discussed. © 2011 Wiley Periodicals, Inc.


title: Embedding Transfer for Low-Resource Medical Named Entity Recognition: A Case Study on Patient Mobility

abstract: Functioning is gaining recognition as an important indicator of global health, but remains under-studied in medical natural language processing research. We present the first analysis of automatically extracting descriptions of patient mobility, using a recently-developed dataset of free text electronic health records. We frame the task as a named entity recognition (NER) problem, and investigate the applicability of NER techniques to mobility extraction. As text corpora focused on patient functioning are scarce, we explore domain adaptation of word embeddings for use in a recurrent neural network NER system. We find that embeddings trained on a small in-domain corpus perform nearly as well as those learned from large out-of-domain corpora, and that domain adaptation techniques yield additional improvements in both precision and recall. Our analysis identifies several significant challenges in extracting descriptions of patient mobility, including the length and complexity of annotated entities and high linguistic variability in mobility descriptions.


title: Evolution of a Performance Metric for Urban Search and Rescue Robots

abstract: Abstract : This paper discusses the advancement and proliferation of the Reference Test Arenas for Urban Search and Rescue Robots as representative search and rescue environments, which allow objective performance evaluation of mobile robot capabilities. These arenas have hosted annual rescue robot competitions sponsored by the American Association for Artificial Intelligence since 2000 and the RoboCupRescue Robot League since 2001. The rules of these competitions have evolved each year to encourage robots to negotiate complex and collapsed structures, find simulated victims, determine their condition and location, and generate human readable maps to enable victim recovery. The associated performance metric has also evolved as it attempts to quantify and encourage these and other robot capabilities pertinent to urban search and rescue applications. This paper presents the competition rules, performance metric, and generalized results of the 2003 competitions which included some inspiring robotic implementations. Performance data captured during these competitions is discussed along with recently available development tools which can quicken the pace of innovation in the field of search and rescue robotics.


title: Teaching classification boundaries to humans

abstract: Given a classification task, what is the best way to teach the resulting boundary to a human? While machine learning techniques can provide excellent methods for finding the boundary, including the selection of examples in an online setting, they tell us little about how we would teach a human the same task. We propose to investigate the problem of example selection and presentation in the context of teaching humans, and explore a variety of mechanisms in the interests of finding what may work best. In particular, we begin with the baseline of random presentation and then examine combinations of several mechanisms: the indication of an example's relative difficulty, the use of the shaping heuristic from the cognitive science literature (moving from easier examples to harder ones), and a novel kernel-based "coverage model" of the subject's mastery of the task. From our experiments on 54 human subjects learning and performing a pair of synthetic classification tasks via our teaching system, we found that we can achieve the greatest gains with a combination of shaping and the coverage model.


title: The Embodied Cognition Research Programme

abstract: Embodied Cognition is an approach to cognition that departs from traditional cognitive science in its reluctance to conceive of cognition as computational and in its emphasis on the significance of an organism’s body in how and what the organism thinks. Three lines of embodied cognition research are described and some thoughts on the future of embodied cognition offered. The embodied cognition research programme, hereafter EC, departs from more traditional cognitive science in the emphasis it places on the role the body plays in an organism’s cognitive processes. Saying more beyond this vague claim is difficult, but this is perhaps not surprising given the diversity of fields ‐ phenomenology, robotics, ecological psychology, artificial life, ethology ‐ from which EC has emerged. Indeed, the point of labelling EC a research programme, rather than a theory, is to indicate that the commitments and subject matters of EC remain fairly nebulous. Yet, much of the flavour of EC becomes evident when considering three prominent directions that researchers in this programme have taken. Before turning to these lines of research, it pays to have in sight the traditional view of cognitive science against which EC positions itself.


title: K-best MIMO detection VLSI architectures achieving up to 424 Mbps

abstract: From an error rate performance perspective, maximum likelihood (ML) detection is the preferred detection method for multiple-input multiple-output (MIMO) communication systems. However, for high transmission rates a straight forward exhaustive search implementation suffers from prohibitive complexity. The K-best algorithm provides close-to-ML bit error rate (BER) performance, while its circuit complexity is reduced compared to an exhaustive search. In this paper, a new VLSI architecture for the implementation of the K-best algorithm is presented. Instead of the mostly sequential processing that has been applied in previous VLSI implementations of the algorithm, the presented solution takes a more parallel approach. Furthermore, the application of a simplified norm is discussed. The implementation in an ASIC achieves up to 424 Mbps throughput with an area that is almost on par with current state-of-the-art implementations.


bunji at SemEval-2016 Task 5: Neural and Syntactic Models of Entity-Attribute Relationship for Aspect-based Sentiment Analysis


title: A Human Identification Technique Using Images of the Iris and Wavelet Transform

abstract: A new approach for recognizing the iris of the human eye is presented. Zero-crossings of the wavelet transform at various resolution levels are calculated over concentric circles on the iris, and the resulting one-dimensional (1-D) signals are compared with model features using different dissimilarity functions.


3 D Model-Based Data Augmentation for Hand Gesture Recognition


title: Learning the unlearnable: the role of missing evidence

abstract: Syntactic knowledge is widely held to be partially innate, rather than learned. In a classic example, it is sometimes argued that children know the proper use of anaphoric one, although that knowledge could not have been learned from experience. Lidz et al. [Lidz, J., Waxman, S., & Freedman, J. (2003). What infants know about syntax but couldn’t have learned: Experimental evidence for syntactic structure at 18 months. Cognition, 89, B65–B73.] pursue this argument, and present corpus and experimental evidence that appears to support it; they conclude that specific aspects of this knowledge must be innate. We demonstrate, contra Lidz et al., that this knowledge may in fact be acquired from the input, through a simple Bayesian learning procedure. The learning procedure succeeds because it is sensitive to the absence of particular input patterns—an aspect of learning that is apparently overlooked by Lidz et al. More generally, we suggest that a prominent form of the “argument from poverty of the stimulus” suffers from the same oversight, and is as a result logically unsound.


title: A novel feature selection approach for biomedical data classification

abstract: This paper presents a novel feature selection approach to deal with issues of high dimensionality in biomedical data classification. Extensive research has been performed in the field of pattern recognition and machine learning. Dozens of feature selection methods have been developed in the literature, which can be classified into three main categories: filter, wrapper and hybrid approaches. Filter methods apply an independent test without involving any learning algorithm, while wrapper methods require a predetermined learning algorithm for feature subset evaluation. Filter and wrapper methods have their, respectively, drawbacks and are complementary to each other in that filter approaches have low computational cost with insufficient reliability in classification while wrapper methods tend to have superior classification accuracy but require great computational power. The approach proposed in this paper integrates filter and wrapper methods into a sequential search procedure with the aim to improve the classification performance of the features selected. The proposed approach is featured by (1) adding a pre-selection step to improve the effectiveness in searching the feature subsets with improved classification performances and (2) using Receiver Operating Characteristics (ROC) curves to characterize the performance of individual features and feature subsets in the classification. Compared with the conventional Sequential Forward Floating Search (SFFS), which has been considered as one of the best feature selection methods in the literature, experimental results demonstrate that (i) the proposed approach is able to select feature subsets with better classification performance than the SFFS method and (ii) the integrated feature pre-selection mechanism, by means of a new selection criterion and filter method, helps to solve the over-fitting problems and reduces the chances of getting a local optimal solution.


title: OVERLAPPING WHITE BLOOD CELL SEGMENTATION AND COUNTING ON MICROSCOPIC BLOOD CELL IMAGES

abstract: Overlapping white blood cell identification on microscopic blood cell images is proposed for increasing the accuracy of white blood cell segmentation and counting. The accurate identification of overlapping cells can increase the accuracy of cell counting system for diagnosing diseases. The overlapping cells have different characteristic such as area and shape with a single cell of microscopic cell images therefore the overlapping cell identification based on geometric feature is preferred. As a result, the proposed method identifies and counts the number of overlapping cells similar with manual white blood cell counting. In addition, the proposed method segment nucleus and cytoplasm of white blood cell with average of accuracy 85.22% and 70.27% from the manual segmented respectively. For future work, the results can be extended to separate the identified overlapping cell therefore it can applied for differential white blood cell counting for diagnosing diseases.


title: Application of Vegetation Indices for Agricultural Crop Yield Prediction Using Neural Network Techniques

abstract: Spatial variability in a crop field creates a need for precision agriculture. Economical and rapid means of identifying spatial variability is obtained through the use of geotechnology (remotely sensed images of the crop field, image processing, GIS modeling approach, and GPS usage) and data mining techniques for model development. Higher-end image processing techniques are followed to establish more precision. The goal of this paper was to investigate the strength of key spectral vegetation indices for agricultural crop yield prediction using neural network techniques. Four widely used spectral indices were investigated in a study of irrigated corn crop yields in the Oakes Irrigation Test Area research site of North Dakota, USA. These indices were: (a) red and near-infrared (NIR) based normalized difference vegetation index (NDVI), (b) green and NIR based green vegetation index (GVI), (c) red and NIR based soil adjusted vegetation index (SAVI), and (d) red and NIR based perpendicular vegetation index (PVI). These four indices were investigated for corn yield during 3 years (1998, 1999, and 2001) and for the pooled data of these 3 years. Initially, Back-propagation Neural Network (BPNN) models were developed, including 16 models (4 indices * 4 years including the data from the pooled years) to test for the efficiency determination of those four vegetation indices in corn crop yield prediction. The corn yield was best predicted using BPNN models that used the means and standard deviations of PVI grid images. In all three years, it provided higher prediction accuracies, coefficient of determination (r2), and lower standard error of prediction than the models involving GVI, NDVI, and SAVI image information. The GVI, NDVI, and SAVI models for all three years provided average testing prediction accuracies of 24.26% to 94.85%, 19.36% to 95.04%, and 19.24% to 95.04%, respectively while the PVI models for all three years provided average testing prediction accuracies of 83.50% to 96.04%. The PVI pool model provided better average testing prediction accuracy of 94% with respect to other vegetation models, for which it ranged from 89–93%. Similarly, the PVI pool model provided coefficient of determination (r2) value of 0.45 as compared to 0.31–0.37 for other index models. Log10 data transformation technique was used to enhance the prediction ability of the PVI models of years 1998, 1999, and 2001 as it was chosen as the preferred index. Another model (Transformed PVI (Pool)) was developed using the log10 transformed PVI image information to show its global application. The transformed PVI models provided average corn yield prediction accuracies of 90%, 97%, and 98% for years 1998, 1999, and 2001, respectively. The pool PVI transformed model provided as average testing accuracy of 93% along with r2 value of 0.72 and standard error of prediction of 0.05 t/ha.


title: A Comparative Analysis of Network-Based Similarity Measures for Scientific Paper Recommendations

abstract: In this paper three similarity measures for scientific papers are compared: bibliographic coupling, co-citation coupling and cosine similarity. All three measures are based on the connections of papers in citation networks. The comparison is conducted both on a mathematical as well as an empirical level. The latter is performed on a real citation network as well as artificially generated networks. The mathematical comparison shows that some measures are structurally very similar, yet if node pairs are ordered according to their similarity, the two measures do not always produce the same rankings. The empirical evaluation shows that bibliographic coupling and one variant of cosine similarity tend to produce the same rankings. The same holds for co-citation coupling and another variant of cosine similarity. Therefore, if only rankings are considered, these measures are interchangeable. The rankings produced by co-citation coupling and bibliographic coupling on the other hand are very different. This also applies to the two cosine similarity variants. Therefore, these measures are not interchangeable.


title: Research on Aerial Vehicle for Robust Navigation System Under Natural Disaster

abstract: In the developing countries, aging of construction sites, such as tunnel or bridge, becomes one of dangerous problems in society. Though this is a critical topic for researchers and inventors, very few studies address the problem and meet conditions, for example low-cost, feasible, effective and not require expert to operate. In response, this paper introduces a new technique to navigate the local coordinate of an unmanned aerial vehicles (UAVs) which is strained by the light tether from a known address on the ground. By constructing and analyzing the mechanical structure, main frame of system is able to work in the outdoor environment. Later, there are several sensor levels that feed-back information to CPU. Hence, mainboard robustly adjusts the speed of motor depend on working condition. Especially, the overall system is done in practical experiments to verify the proposed design.


title: Learning Deep Binary Descriptor with Multi-quantization

abstract: In this paper, we propose an unsupervised feature learning method called deep binary descriptor with multi-quantization (DBD-MQ) for visual matching. Existing learning-based binary descriptors such as compact binary face descriptor (CBFD) and DeepBit utilize the rigid sign function for binarization despite of data distributions, thereby suffering from severe quantization loss. In order to address the limitation, our DBD-MQ considers the binarization as a multi-quantization task. Specifically, we apply a K-AutoEncoders (KAEs) network to jointly learn the parameters and the binarization functions under a deep learning framework, so that discriminative binary descriptors can be obtained with a fine-grained multi-quantization. Extensive experimental results on different visual analysis including patch retrieval, image matching and image retrieval show that our DBD-MQ outperforms most existing binary feature descriptors.


title: Human–Agent Teaming for Multirobot Control: A Review of Human Factors Issues

abstract: The human factors literature on intelligent systems was reviewed in relation to the following: efficient human supervision of multiple robots, appropriate human trust in the automated systems, maintenance of human operator's situation awareness, individual differences in human-agent (H-A) interaction, and retention of human decision authority. A number of approaches-from flexible automation to autonomous agents-were reviewed, and their advantages and disadvantages were discussed. In addition, two key human performance issues (trust and situation awareness) related to H-A teaming for multirobot control and some promising user interface design solutions to address these issues were discussed. Some major individual differences factors (operator spatial ability, attentional control ability, and gaming experience) were identified that may impact H-A teaming in the context of robotics control.


title: An Evaluation of the MOOD set of Object-Oriented Software Metrics

abstract: This paper describes the results of an investigation into a set of metrics for object-oriented design, called the MOOD metrics. The merits of each of the six MOOD metrics is discussed from a measurement theory viewpoint, taking into account the recognized object-oriented features which they were intended to measure: encapsulation, inheritance, coupling, and polymorphism. Empirical data, collected from three different application domains, is then analyzed using the MOOD metrics, to support this theoretical validation. Results show that (with appropriate changes to remove existing problematic discontinuities) the metrics could be used to provide an overall assessment of a software system, which may be helpful to managers of software development projects. However, further empirical studies are needed before these results can be generalized.


title: Ethnobotany of the Chinantec Indians, Oaxaca, Mexico

abstract: This report represents the greater part of data collected over a six-month period in the Chinantec-speaking municipio of San Pedro Sochiapam, Oaxaca. The main subject of the research was whether observed and discussed cultural patterns could be checked and recorded against an ethnobotanical and taxonomical system, arriving at an adequate statement of the actual relations within an ecological system from the ethnobotanical data. There are more than 2,000 vascular species of plants in the area. Repeated interviews with native informants suggest that more than 1,000 are known by Chinantec names. For the purposes of this report, I have taken a sample of about 40% of the data collected by using 100 Chinantec name specifics with high cultural significance. To determine how important plants are to the Chinantec and to determine to what extent their knowledge of plants has been influenced by their primitive sedentary life, a corpus of over 300 plants, 100 of high cultural significance, was recorded, along with plant significance data. Plant significance data comprises: Chinantec name specific; etymology of the name; the plants function and usage; part utilized and the exact manner the flora is treated, identified, and grouped taxonomically by members of the community. Also the relative importance of each plant was part of the questionnaire framework. Although the geographic area investigated was relatively small in comparison with the total Chinantec-speaking area, a greater degree of accuracy was attained by using informants from three neighboring communities: San Pedro, San Juan Zautla and Nueva Rancho. Thus, the data could be checked and corrected by comparing the three sources of information. This was also a con-


title: Supply Chain Finance in China: Business Innovation and Theory Development

abstract: Supply chain finance (SCF) is concerned with the capital flows within a supply chain, an area often neglected in past decades, while SCF does have an impact on a firm’s capability to adopt sustainable supply chain management (SCM) practices. The aim of this study is to explore new insight from a growing body of research which is investigating SCF issues in China, an evolving transition economy. A content analysis on a review of 151 Chinese-written SCF papers from 2004–2014, based on a sample of 45 leading Chinese journals (Chinese Social Science Citation Index, CSSCI) was conducted from three perspectives: topical coverage, theoretical application and methodological inquiry. The study reveals that the research stream of SCF in China has emerged and evolved to a considerable extent. However, the SCF phenomenon in China is not exactly the same as “SCF” as it is perceived in the mature economy, which is articulated in mainstream SCM English literature. The Chinese business context in which SCF has been implemented has played a dominant role in initiating, affecting and even shaping SCF. This study represents the first endeavor in the field of SCM. It diffuses the Chinese-written SCF research in mainstream SCM English literature.


A NEW OPTIMIZED LOW-PASS IIR DIGITAL DIFFERENTIATOR


title: The Navigation and Control technology inside the AR.Drone micro UAV

abstract: Abstract This paper exposes the Navigation and Control technology embedded in a recently commercialized micro Unmanned Aerial Vehicle (UAV), the AR.Drone, which cost and performance are unprecedented among any commercial product for mass markets. The system relies on state-of-the-art indoor navigation systems combining low-cost inertial sensors, computer vision techniques, sonar, and accounting for aerodynamics models.


Transfer Learning for Entity Recognition of Novel Classes


title: Distilling at the Edge: A Local Differential Privacy Obfuscation Framework for IoT Data Analytics

abstract: Edge computing has emerged as a promising paradigm for delay-sensitive and context-aware IoT data analytics, through migrating data processing from the cloud to the edge of the network. However, traditional solutions adopting homomorphic encryption to achieve data protection and aggregation at edge servers are infeasible because of their heavy computational overhead. How to preserve data privacy while guaranteeing data utility in edge computing becomes an extremely important problem for IoT data analytics. In this article, we propose a local differential privacy obfuscation (LDPO) framework for IoT data analytics to aggregate and distill the IoT data at the edge without disclosing users' sensitive data. We first introduce the architecture and benefits of the LDPO framework, followed by some technical challenges in guaranteeing its performance. Then we present a preliminary implementation of the LDPO framework, and validate its performance in terms of privacy preservation level and data utility using real-world apps and datasets. Some future directions are finally envisioned for further research.


title: Consensus, Clines, and Edges in Celtic Cultures

abstract: Using data from four localities in peripheral areas of the British Isles, this article explores a conception of culture that emphasizes continuous variation, or clines, rather than boundaries. These localities are sites for the performance of cultural practices, many of which may be shared across socially constructed boundaries such as those of nation, ethnic group, and class. Consensus analysis provides a tool for exploring areas of greater or lesser sharing of cultural models indicated by responses to 21 brief narratives of everyday cultural practices. Informants were asked to judge on a 5-point scale whether the practices were characteristic of their location. With a high consensus in each site about typical practices, it was possible to compare the culturally correct profiles of responses between each of the sites, revealing incremental changes, or clines, from site to site. Edges, or the conjunction of clines, might be mapped using consensus analysis.


title: Convolutional Features for Correlation Filter Based Visual Tracking

abstract: Visual object tracking is a challenging computer vision problem with numerous real-world applications. This paper investigates the impact of convolutional features for the visual tracking problem. We propose to use activations from the convolutional layer of a CNN in discriminative correlation filter based tracking frameworks. These activations have several advantages compared to the standard deep features (fully connected layers). Firstly, they miti-gate the need of task specific fine-tuning. Secondly, they contain structural information crucial for the tracking problem. Lastly, these activations have low dimensionality. We perform comprehensive experiments on three benchmark datasets: OTB, ALOV300++ and the recently introduced VOT2015. Surprisingly, different to image classification, our results suggest that activations from the first layer provide superior tracking performance compared to the deeper layers. Our results further show that the convolutional features provide improved results compared to standard hand-crafted features. Finally, results comparable to state-of-the-art trackers are obtained on all three benchmark datasets.


title: Contextual Phrase-Level Polarity Analysis Using Lexical Affect Scoring and Syntactic N-Grams

abstract: We present a classifier to predict contextual polarity of subjective phrases in a sentence. Our approach features lexical scoring derived from the Dictionary of Affect in Language (DAL) and extended through WordNet, allowing us to automatically score the vast majority of words in our input avoiding the need for manual labeling. We augment lexical scoring with n-gram analysis to capture the effect of context. We combine DAL scores with syntactic constituents and then extract n-grams of constituents from all sentences. We also use the polarity of all syntactic constituents within the sentence as features. Our results show significant improvement over a majority class baseline as well as a more difficult baseline consisting of lexical n-grams.


title: Factors influencing the alignment of accounting information systems in small and medium sized Malaysian manufacturing firms

abstract: The concept of alignment or fit between information technology (IT) and business strategy has been discussed for many years, and strategic alignment is deemed crucial in increasing firm performance. Yet few attempts have been made to investigate the factors that influence alignment, especially in the context of small and medium sized firms (SMEs). This issue is important because results from previous studies suggest that many firms struggle to achieve alignment. Therefore, this study sought to identify different levels of alignment and then investigated the factors that influence alignment. In particular, it focused on the alignment between the requirements for accounting information (AIS requirements) and the capacity of accounting systems (AIS capacity) to generate the information, in the specific context of manufacturing SMEs in Malaysia. Using a mail questionnaire, data from 214 firms was collected on nineteen accounting information characteristics for both requirements and capacity. The fit between these two sets was explored using the moderation approach and evidence was gained that AIS alignment in some firms was high. Cluster analysis was used to find two sets of groups which could be considered more aligned and less aligned. The study then investigated some factors that might be associated with a small firm’s level of AIS alignment. Findings from the study suggest that AIS alignment was related to the firm’s: level of IT maturity; level of owner/manager’s accounting and IT knowledge; use of expertise from government agencies and accounting firms; and existence of internal IT staff.


title: Microstrip Magnetic Dipole Yagi Array Antenna With Endfire Radiation and Vertical Polarization

abstract: A new microstrip Yagi array antenna with endfire radiation and vertical polarization is proposed. The Yagi antenna has a low profile, a wide bandwidth and a high gain. Each element of the Yagi array is based on a new microstrip antenna that has one edge opened and the other three edges shorted, working as a “magnetic dipole antenna”. As opposed to previous microstrip Yagi array antennas, the proposed Yagi antenna could produce a beam radiating at exactly endfire for infinite ground plane, with vertical polarization in the horizontal plane. A coupling microstrip line is introduced between the driven element and the first director element to strengthen the coupling between them, and therefore the front-to-back ratio and bandwidth of the array can be improved. The endfire gain can be enhanced as the number of the director elements increases, in either case where the array has an infinite or a finite ground plane.


LOSSLESS IMAGE COMPRESSION METHOD USING REVERSIBLE LOW CONTRAST MAPPING (RLCM)


Reconstructing the house from the ad: Structured prediction on real estate classifieds