title: Steganalysis based on steganography pattern discovery

abstract: The goal of steganalysis algorithms is detection of stego images from clean images. Each steganography method based on its embedding mechanism puts a special pattern on the stego images. Finding this pattern in the images leads us to employ a classifier to be constructed specially for detecting stego images which are the results of a special steganography algorithm. In this paper, to have high detection accuracy, we propose an approach for Steganography Pattern Discovery (SPD). Our proposed approach employs an evolutionary method to extract the signature of stego images against clean images via fuzzy if-then rules. Based on the discovered knowledge, suitable trained models for steganalysis can be employed and stego images will be detected with high accuracy. Using SPD, we can predict the type of steganography method from a stego image. Employing SPD can enhance the approaches, which assume that a special steganography method is used. The effect of SPD before applying steganalysis methods has been investigated by some steganography and steganalysis techniques and it has been validated using some image databases. The results indicate that the pattern of a steganography method is extracted well and the type of steganography method used to make a stego image can be predicted with high accuracy.


title: Artificial Intelligence in Sports Prediction

abstract: This paper presents an extension of earlier work in the use of artificial intelligence for prediction of sporting outcomes. An expanded model is described, as well as a broadening of the area of application of the original work. The model used is a form of multi-layer perceptron and it is presented with a number of features which attempt to capture the quality of various sporting teams. The system performs well and compares favourably with human tipsters in several environments. A study of less rigid "World Cup" formats appears, along with extensive live testing results in a major international tipping competition.


A generic difference algorithm for UML models


title: Bootstrapping Trust in Online Dating: Social Verification of Online Dating Profiles

abstract: Online dating is an increasingly thriving business which boasts billion-dollar revenues and attracts users in the tens of millions. Notwithstanding its popularity, online dating is not impervious to worrisome trust and privacy concerns raised by the disclosure of potentially sensitive data as well as the exposure to self-reported (and thus potentially misrepresented) information. Nonetheless, little research has, thus far, focused on how to enhance privacy and trustworthiness. In this paper, we report on a series of semi-structured interviews involving 20 participants, and show that users are significantly concerned with the veracity of online dating profiles. To address some of these concerns, we present the user-centered design of an interface, called Certifeye, which aims to bootstrap trust in online dating profiles using existing social network data. Certifeye verifies that the information users report on their online dating profile (e.g., age, relationship status, and/or photos) matches that displayed on their own Facebook profile. Finally, we present the results of a 161-user Mechanical Turk study assessing whether our veracity-enhancing interface successfully reduced concerns in online dating users and find a statistically significant trust increase.


title: Brain imaging in fibromyalgia syndrome.

abstract: Fibromyalgia (FM) is a chronic musculoskeletal pain syndrome which is characterised by clinical pain as well as widespread hyperalgesia/allodynia to mechanical, thermal, electrical, and chemical stimuli. Lack of consistent tissue abnormalities in FM patients has more and more shifted the focus away from peripheral factors and towards central nervous system abnormalities including central sensitisation as well as aberrant pain facilitation and inhibition. Besides quantitative sensory testing, functional brain imaging has been increasingly utilised to characterise the abnormal pain processing of FM patients. Whereas initial work in FM patients identified abnormally increased pain-related brain activity within the thalamus, insula, anterior cingulate, S1, and prefrontal cortex (so-called 'pain matrix'), more recent research focused on altered 'connectivity' between multiple interconnected brain networks in these patients. Additionally, magnetic resonance spectroscopy studies demonstrated high concentration of the excitatory neurotransmitter glutamate in FM patients in pain-related brain areas which correlated not only with experimental but also with clinical pain ratings. Overall, functional brain imaging studies have provided compelling evidence for abnormal pain processing in FM, including brain activity that correlated with patients' augmented pain sensitivity (hyperalgesia/allodynia), temporal summation of pain, and prolonged pain aftersensations. Future imaging work needs to focus on identifying the neural correlates of FM patients' abnormal endogenous pain modulation which will likely not only shed more light on this important pain regulatory mechanism but may also provide useful information for future treatments of FM symptoms.


title: Silver Lining: Enforcing Secure Information Flow at the Cloud Edge

abstract: SilverLine is a novel, exceptionally modular framework for enforcing mandatory information flow policies for Java computations on commodity, data-processing, Platform-as-a-Service clouds by leveraging Aspect-Oriented Programming (AOP) and In-lined Reference Monitors (IRMs). Unlike traditional system-level approaches, which typically require modifications to the cloud kernel software, OS/hypervisor, VM, or cloud file system, SilverLine automatically in-lines secure information flow tracking code into untrusted Java binaries as they arrive at the cloud. This facilitates efficient enforcement of a large, flexible class of information flow and mandatory access control policies without any customization of the cloud or its underlying infrastructure. The cloud and the enforcement framework can therefore be maintained completely separately and orthogonally (i.e., modularly). To demonstrate the approach's feasibility, a prototype implements and deploys SilverLine on a real-world data processing cloud-Hadoop MapReduce. Evaluation results demonstrate that SilverLine provides inter-process information flow security for Hadoop clouds with easy maintainability (through modularity) and low overhead.


title: Transfer Components Between Subjects for EEG-based Driving Fatigue Detection

abstract: In this paper, we first build up an electroencephalogram (EEG)-based driving fatigue detection system, and then propose a subject transfer framework for this system via component analysis. We apply a subspace projecting approach called transfer component analysis (TCA) for subject transfer. The main idea is to learn a set of transfer components underlying source domain (source subjects) and target domain (target subjects). When projected to this subspace, the difference of feature distributions of both domains can be reduced. Meanwhile, the discriminative information can be preserved. From the experiments, we show that the TCA-based algorithm can achieve a significant improvement on performance with the best mean accuracy of 77.56 %, in comparison of the baseline accuracy of 66.56 %. The improvement shows the feasibility and efficiency of our approach for subject transfer driving fatigue detection from EEG.


title: A sensory feedback system for prosthetic hand based on evoked tactile sensation

abstract: The lack of reliable sensory feedback has been one of the barriers in prosthetic hand development. Restoring sensory function from prosthetic hand to amputee remains a great challenge to neural engineering. In this paper, we present the development of a sensory feedback system based on the phenomenon of evoked tactile sensation (ETS) at the stump skin of residual limb induced by transcutaneous electrical nerve stimulation (TENS). The system could map a dynamic pattern of stimuli to an electrode placed on the corresponding projected finger areas on the stump skin. A pressure transducer placed at the tip of prosthetic fingers was used to sense contact pressure, and a high performance DSP processor sampled pressure signals, and calculated the amplitude of feedback stimulation in real-time. Biphasic and charge-balanced current pulses with amplitude modulation generated by a multi-channel laboratory stimulator were delivered to activate sensory nerves beneath the skin. We tested this sensory feedback system in amputee subjects. Preliminary results showed that the subjects could perceive different levels of pressure at the tip of prosthetic finger through evoked tactile sensation (ETS) with distinct grades and modalities. We demonstrated the feasibility to restore the perceptual sensation from prosthetic fingers to amputee based on the phenomenon of evoked tactile sensation (ETS) with TENS.


title: CacheZoom: How SGX Amplifies The Power of Cache Attacks

abstract: In modern computing environments, hardware resources are commonly shared, and parallel computation is widely used. Parallel tasks can cause privacy and security problems if proper isolation is not enforced. Intel proposed SGX to create a trusted execution environment within the processor. SGX relies on the hardware, and claims runtime protection even if the OS and other software components are malicious. However, SGX disregards side-channel attacks. We introduce a powerful cache side-channel attack that provides system adversaries a high resolution channel. Our attack tool named CacheZoom is able to virtually track all memory accesses of SGX enclaves with high spatial and temporal precision. As proof of concept, we demonstrate AES key recovery attacks on commonly used implementations including those that were believed to be resistant in previous scenarios. Our results show that SGX cannot protect critical data sensitive computations, and efficient AES key recovery is possible in a practical environment. In contrast to previous works which require hundreds of measurements, this is the first cache side-channel attack on a real system that can recover AES keys with a minimal number of measurements. We can successfully recover AES keys from T-Table based implementations with as few as ten measurements.


title: Linear Contextual Bandits with Global Constraints and Objective

abstract: We consider the linear contextual bandit problem with global convex constraints and a concave objective function. In each round, the outcome of pulling an arm is a vector, that depends linearly on the context of that arm. The global constraints require the average of these vectors to lie in a certain convex set. The objective is a concave function of this average vector. This problem turns out to be a common generalization of classic linear contextual bandits (linContextual) [Auer 2003], bandits with concave rewards and convex knapsacks (BwCR) [Agrawal, Devanur 2014], and the online stochastic convex programming (OSCP) problem [Agrawal, Devanur 2015]. We present algorithms with near-optimal regret bounds for this problem. Our bounds compare favorably to results on the unstructured version of the problem [Agrawal et al. 2015, Badanidiyuru et al. 2014] where the relation between the contexts and the outcomes could be arbitrary, but the algorithm only competes against a fixed set of policies.


title: The Cancer Genome Atlas (TCGA): an immeasurable source of knowledge

abstract: The Cancer Genome Atlas (TCGA) is a public funded project that aims to catalogue and discover major cancer-causing genomic alterations to create a comprehensive “atlas” of cancer genomic profiles. So far, TCGA researchers have analysed large cohorts of over 30 human tumours through large-scale genome sequencing and integrated multi-dimensional analyses. Studies of individual cancer types, as well as comprehensive pan-cancer analyses have extended current knowledge of tumorigenesis. A major goal of the project was to provide publicly available datasets to help improve diagnostic methods, treatment standards, and finally to prevent cancer. This review discusses the current status of TCGA Research Network structure, purpose, and achievements.


title: Ontology-Based Semantic Modeling and Evaluation for Internet of Things Applications

abstract: Internet of Things (IoT) is a major paradigm shift from traditional Internet applications that connects things / objects to the Internet for automatic sensing, information processing, understanding, reasoning, decision and response to virtual and physical world. Due to its highly distributed and heterogeneous characteristics, it requires more semantic interactions between different scenarios. Therefore, interoperability is one of the most fundamental requirements for IoT systems. Currently, semantic modeling for IoT applications has significant deficiency in its reusability and interoperability. In this paper, we propose a semantic information model for IoT applications, named OntoIoT. This model has a two-level architecture including a set of general upper ontology and flexible interfaces for different application domain ontology extensions. We have preliminarily implemented the OntoIoT framework and domain extension in selected scenarios of smart space and healthcare. A data-driven and other ontology evaluation approaches are used to prove the scalability and interoperability of OntoIoT. Result demonstrates that, compared to other test ontologies, Onto IoT is more suitable for the given application scenario.


title: Question routing in community question answering: putting category in its place

abstract: This paper investigates a ground-breaking incorporation of question category to Question Routing (QR) in Community Question Answering (CQA) services. The incorporation of question category was designed to estimate answerer expertise for routing questions to potential answerers. Two category-sensitive Language Models (LMs) were developed with large-scale real world data sets being experimented. Results demonstrated that higher accuracies of routing questions with lower computational costs were achieved, relative to traditional Query Likelihood LM (QLLM), state-of-the-art Cluster-Based LM (CBLM) and the mixture of Latent Dirichlet Allocation and QLLM (LDALM).


Synaesthesia -- A Window Into Perception, Thought and Language


title: Discriminative learned dictionaries for local image analysis

abstract: Sparse signal models have been the focus of much recent research, leading to (or improving upon) state-of-the-art results in signal, image, and video restoration. This article extends this line of research into a novel framework for local image discrimination tasks, proposing an energy formulation with both sparse reconstruction and class discrimination components, jointly optimized during dictionary learning. This approach improves over the state of the art in texture segmentation experiments using the Brodatz database, and it paves the way for a novel scene analysis and recognition framework based on simultaneously learning discriminative and reconstructive dictionaries. Preliminary results in this direction using examples from the Pascal VOC06 and Graz02 datasets are presented as well.


title: A Study of Pilot-Aided Channel Estimation in MIMO-GFDM Systems.

abstract: Generalized frequency division multiplexing (GFDM) is a promising candidate waveform for next generation wireless communications systems. Unlike conventional orthogonal frequency division multiplexing (OFDM) based systems, it is a non-orthogonal waveform subject to inter-carrier and inter-symbol interference. In multiple-input multiple-output (MIMO) systems, the additional inter-antenna interference also takes place. The presence of such three-dimensional interference challenges the receiver design. This paper addresses the MIMOGFDM channel estimation problem with the aid of known reference signals also referred as pilots. Specifically, the received signal is expressed as the joint effect of the pilot part, unknown data part and noise part. On top of this formulation, least squares (LS) and linear minimum mean square error (LMMSE) estimators are presented, while their performance is evaluated for various pilot arrangements.


The Perceptions of Students toward Online Learning at a Midwestern University: What are Students Telling Us and What Are We Doing About It?


title: Analyzing Customer Experience Feedback Using Text Mining

abstract: Complexity surrounding the holistic nature of customer experience has made measuring customer perceptions of interactive service experiences challenging. At the same time, advances in technology and changes in methods for collecting explicit customer feedback are generating increasing volumes of unstructured textual data, making it difficult for managers to analyze and interpret this information. Consequently, text mining, a method enabling automatic extraction of information from textual data, is gaining in popularity. However, this method has performed below expectations in terms of depth of analysis of customer experience feedback and accuracy. In this study, we advance linguistics-based text mining modeling to inform the process of developing an improved framework. The proposed framework incorporates important elements of customer experience, service methodologies, and theories such as cocreation processes, interactions, and context. This more holistic approach for analyzing feedback facilitates a deeper analysis of customer feedback experiences, by encompassing three value creation elements: activities, resources, and context (ARC). Empirical results show that the ARC framework facilitates the development of a text mining model for analysis of customer textual feedback that enables companies to assess the impact of interactive service processes on customer experiences. The proposed text mining model shows high accuracy levels and provides flexibility through training. As such, it can evolve to account for changing contexts over time and be deployed across different (service) business domains; we term it an "open learning" model. The ability to timely assess customer experience feedback represents a prerequisite for successful cocreation processes in a service environment.


title: Struck: Structured Output Tracking with Kernels

abstract: Adaptive tracking-by-detection methods are widely used in computer vision for tracking arbitrary objects. Current approaches treat the tracking problem as a classification task and use online learning techniques to update the object model. However, for these updates to happen one needs to convert the estimated object position into a set of labelled training examples, and it is not clear how best to perform this intermediate step. Furthermore, the objective for the classifier (label prediction) is not explicitly coupled to the objective for the tracker (estimation of object position). In this paper, we present a framework for adaptive visual object tracking based on structured output prediction. By explicitly allowing the output space to express the needs of the tracker, we avoid the need for an intermediate classification step. Our method uses a kernelised structured output support vector machine (SVM), which is learned online to provide adaptive tracking. To allow our tracker to run at high frame rates, we (a) introduce a budgeting mechanism that prevents the unbounded growth in the number of support vectors that would otherwise occur during tracking, and (b) show how to implement tracking on the GPU. Experimentally, we show that our algorithm is able to outperform state-of-the-art trackers on various benchmark videos. Additionally, we show that we can easily incorporate additional features and kernels into our framework, which results in increased tracking performance.


title: ROCK: A Robust Clustering Algorithm for Categorical Attributes

abstract: Abstract Clustering, in data mining, is useful to discover distribution patterns in the underlying data. Clustering algorithms usually employ a distance metric based (e.g., euclidean) similarity measure in order to partition the database such that data points in the same partition are more similar than points in different partitions. In this paper, we study clustering algorithms for data with boolean and categorical attributes. We show that traditional clustering algorithms that use distances between points for clustering are not appropriate for boolean and categorical attributes. Instead, we propose a novel concept of links to measure the similarity/proximity between a pair of data points. We develop a robust hierarchical clustering algorithm ROCK that employs links and not distances when merging clusters. Our methods naturally extend to non-metric similarity measures that are relevant in situations where a domain expert/similarity table is the only source of knowledge. In addition to presenting detailed complexity results for ROCK, we also conduct an experimental study with real-life as well as synthetic data sets to demonstrate the effectiveness of our techniques. For data with categorical attributes, our findings indicate that ROCK not only generates better quality clusters than traditional algorithms, but it also exhibits good scalability properties.


title: The Impact of Perceived Value on Customer Satisfaction, Loyalty, Recommendation and Repurchase. An Empirical Study of Spa Industry in Indonesia

abstract: The authors investigate the impact of five dimensions of perceived value that effect customer satisfaction and customer loyalty in Indonesia. The research focuses on spas located in Jakarta, the capital city of Indonesia. To measure customer perceived value, a multi-dimensional constructs from previous research was employed, we also examine the behavioral loyalty by measuring the willingness of spa customers to recommend and repurchase spa services. We hypothesize and find that there is a positive relationship between five dimensions of perceived value and customer satisfaction. Furthermore, perceived value is also identified as one of indicators of customer loyalty that leads to customer recommendation and repurchase.


title: Team Effectiveness 1997-2007: A Review of Recent Advancements and a Glimpse Into the Future

abstract: The authors review team research that has been conducted over the past 10 years. They discuss the nature of work teams in context and note the substantive differences underlying different types of teams. They then review representative studies that have appeared in the past decade in the context of an enhanced input-process-outcome framework that has evolved into an inputs-mediators-outcome time-sensitive approach. They note what has been learned along the way and identify fruitful directions for future research. They close with a reconsideration of the typical team research investigation and call for scholars to embrace the complexity that surrounds modern team-based organizational designs as we move forward.


title: A Compressed Representation for Ray Tracing Parametric Surfaces

abstract: Parametric surfaces are an essential modeling tool in computer aided design and movie production. Even though their use is well established in industry, generating ray-traced images adds significant cost in time and memory consumption. Ray tracing such surfaces is usually accomplished by subdividing the surfaces on the fly, or by conversion to a polygonal representation. However, on-the-fly subdivision is computationally very expensive, whereas polygonal meshes require large amounts of memory. This is a particular problem for parametric surfaces with displacement, where very fine tessellation is required to faithfully represent the shape. Hence, memory restrictions are the major challenge in production rendering. In this article, we present a novel solution to this problem. We propose a compression scheme for a priori Bounding Volume Hierarchies (BVHs) on parametric patches, that reduces the data required for the hierarchy by a factor of up to 48. We further propose an approximate evaluation method that does not require leaf geometry, yielding an overall reduction of memory consumption by a factor of 60 over regular BVHs on indexed face sets and by a factor of 16 over established state-of-the-art compression schemes. Alternatively, our compression can simply be applied to a standard BVH while keeping the leaf geometry, resulting in a compression rate of up to 2:1 over current methods. Although decompression generates additional costs during traversal, we can manage very complex scenes even on the memory restrictive GPU at competitive render times.


title: Dual Encoding for Abstractive Text Summarization

abstract: Recurrent neural network-based sequence-to-sequence attentional models have proven effective in abstractive text summarization. In this paper, we model abstractive text summarization using a dual encoding model. Different from the previous works only using a single encoder, the proposed method employs a dual encoder including the primary and the secondary encoders. Specifically, the primary encoder conducts coarse encoding in a regular way, while the secondary encoder models the importance of words and generates more fine encoding based on the input raw text and the previously generated output text summarization. The two level encodings are combined and fed into the decoder to generate more diverse summary that can decrease repetition phenomenon for long sequence generation. The experimental results on two challenging datasets (i.e., CNN/DailyMail and DUC 2004) demonstrate that our dual encoding model performs against existing methods.


title: Multilingual acoustic models using distributed deep neural networks

abstract: Today's speech recognition technology is mature enough to be useful for many practical applications. In this context, it is of paramount importance to train accurate acoustic models for many languages within given resource constraints such as data, processing power, and time. Multilingual training has the potential to solve the data issue and close the performance gap between resource-rich and resource-scarce languages. Neural networks lend themselves naturally to parameter sharing across languages, and distributed implementations have made it feasible to train large networks. In this paper, we present experimental results for cross- and multi-lingual network training of eleven Romance languages on 10k hours of data in total. The average relative gains over the monolingual baselines are 4%/2% (data-scarce/data-rich languages) for cross- and 7%/2% for multi-lingual training. However, the additional gain from jointly training the languages on all data comes at an increased training time of roughly four weeks, compared to two weeks (monolingual) and one week (crosslingual).


title: Comparing apples to oranges: a scalable solution with heterogeneous hashing

abstract: Although hashing techniques have been popular for the large scale similarity search problem, most of the existing methods for designing optimal hash functions focus on homogeneous similarity assessment, i.e., the data entities to be indexed are of the same type. Realizing that heterogeneous entities and relationships are also ubiquitous in the real world applications, there is an emerging need to retrieve and search similar or relevant data entities from multiple heterogeneous domains, e.g., recommending relevant posts and images to a certain Facebook user. In this paper, we address the problem of ``comparing apples to oranges'' under the large scale setting. Specifically, we propose a novel Relation-aware Heterogeneous Hashing (RaHH), which provides a general framework for generating hash codes of data entities sitting in multiple heterogeneous domains. Unlike some existing hashing methods that map heterogeneous data in a common Hamming space, the RaHH approach constructs a Hamming space for each type of data entities, and learns optimal mappings between them simultaneously. This makes the learned hash codes flexibly cope with the characteristics of different data domains. Moreover, the RaHH framework encodes both homogeneous and heterogeneous relationships between the data entities to design hash functions with improved accuracy. To validate the proposed RaHH method, we conduct extensive evaluations on two large datasets; one is crawled from a popular social media sites, Tencent Weibo, and the other is an open dataset of Flickr(NUS-WIDE). The experimental results clearly demonstrate that the RaHH outperforms several state-of-the-art hashing methods with significant performance gains.


title: Shallow vs. Deep Sum-Product Networks

abstract: We investigate the representational power of sum-product networks (computation networks analogous to neural networks, but whose individual units compute either products or weighted sums), through a theoretical analysis that compares deep (multiple hidden layers) vs. shallow (one hidden layer) architectures. We prove there exist families of functions that can be represented much more efficiently with a deep network than with a shallow one, i.e. with substantially fewer hidden units. Such results were not available until now, and contribute to motivate recent research involving learning of deep sum-product networks, and more generally motivate research in Deep Learning.


title: Support Vector Machines based on a semantic kernel for text categorization

abstract: We propose to solve a text categorization task using a new metric between documents, based on a priori semantic knowledge about words. This metric can be incorporated into the definition of radial basis kernels of Support Vector Machines or directly used in a K-nearest neighbors algorithm. Both SVM and KNN are tested and compared on the 20-newsgroups database. Support Vector Machines provide the best accuracy on test data.


Toolpath Planning for Continuous Extrusion Additive Manufacturing


title: Early Enteral Nutrition and Outcomes of Critically Ill Patients Treated With Vasopressors and Mechanical Ventilation

abstract: OBJECTIVE ::: To determine the effect of early enteral feeding on the outcome of critically ill medical patients whose hemodynamic condition is unstable. ::: ::: ::: METHODS ::: Prospectively collected data in a multi-institutional medical intensive care unit database were analyzed retrospectively. A total of 1174 patients were identified who required mechanical ventilation for more than 2 days and were treated with vasopressor agents to support blood pressure. The patients were divided into 2 groups: those who received enteral nutrition (n = 707) within 48 hours of the start of mechanical ventilation, termed the early enteral nutrition group, and those who did not (n = 467), termed the late enteral nutrition group. The primary end points were overall intensive care unit and hospital mortality. Subgroup analyses were used to evaluate potential confounding variables. The data were also analyzed after adjustments for confounding by matching for propensity score. ::: ::: ::: RESULTS ::: Intensive care unit and hospital mortality were lower in the early enteral nutrition group than in the late enteral group: 22.5% vs 28.3%; P = 03; and 34.0% vs 44.0%; P < .001, respectively. The beneficial effect of early feeding was more evident in the sickest patients, that is, those treated with multiple vasopressors (odds ratio, 0.36; 95% confidence interval, 0.15-0.85) and those without early improvement (odds ratio, 0.59; 95% confidence interval, 0.39-0.90). When adjustments were made for confounding by matching for propensity score, early feeding was associated with decreased hospital mortality. ::: ::: ::: CONCLUSION ::: Early enteral nutrition may be associated with reduced intensive care unit and hospital mortality in patients whose hemodynamic condition is unstable.


title: A Survey on Survey of Migration of Legacy Systems

abstract: Legacy systems are mission critical complex systems which are hard to maintain owing to shortage of skill sets and monolithic code architecture with tightly coupled tiers, all of which are indication of obsoleteness of the system technology. Thus, they have to be migrated to the latest technology(ies). Migration is an offspring research in Software Engineering which is almost three decades old research and numerous publications have emerged in many topics in the migration domain with focus areas of code migration, architecture migration, case study on migration and effort estimation on migration. In addition, various survey works surveying different aspects of migration have also emerged. This paper provides a survey of these survey works in order to provide a consolidation of these survey works. As an outcome of the survey on survey of migration, a road map of migration evolution from the early stages to the recent works on migration comprising of significant milestones in migration research has been presented in this work.


title: The contribution of health literacy to disparities in self-rated health status and preventive health behaviors in older adults.

abstract: PURPOSE ::: Health literacy is associated with a range of poor health-related outcomes. Evidence that health literacy contributes to disparities in health is minimal and based on brief screening instruments that have limited ability to assess health literacy. The purpose of this study was to assess whether health literacy contributes, through mediation, to racial/ethnic and education-related disparities in self-rated health status and preventive health behaviors among older adults. ::: ::: ::: METHODS ::: We undertook a cross-sectional study of a nationally representative sample of 2,668 US adults aged 65 years and older from the 2003 National Assessment of Adult Literacy. Multiple regression analysis was used to assess for evidence of mediation. ::: ::: ::: RESULTS ::: Of older adults in the United States, 29% reported fair or poor health status, and 27% to 39% reported not utilizing 3 recommended preventive health care services in the year preceding the assessment (influenza vaccination 27%, mammography 34%, dental checkup 39%). Health literacy and the 4 health outcomes (self-rated health status and utilization of the 3 preventive health care services) varied by race/ethnicity and educational attainment. Regression analyses indicated that, after controlling for potential confounders, health literacy significantly mediated both racial/ethnic and education-related disparities in self-rated health status and receipt of influenza vaccination, but only education-related disparities in receipt of mammography and dental care. ::: ::: ::: CONCLUSIONS ::: Health literacy contributes to disparities associated with race/ethnicity and educational attainment in self-rated health and some preventive health behaviors among older adults. Interventions addressing low health literacy may reduce these disparities.


title: A novel parametrization of the perspective-three-point problem for a direct computation of absolute camera position and orientation

abstract: The Perspective-Three-Point (P3P) problem aims at determining the position and orientation of the camera in the world reference frame from three 2D-3D point correspondences. This problem is known to provide up to four solutions that can then be disambiguated using a fourth point. All existing solutions attempt to first solve for the position of the points in the camera reference frame, and then compute the position and orientation of the camera in the world frame, which alignes the two point sets. In contrast, in this paper we propose a novel closed-form solution to the P3P problem, which computes the aligning transformation directly in a single stage, without the intermediate derivation of the points in the camera frame. This is made possible by introducing intermediate camera and world reference frames, and expressing their relative position and orientation using only two parameters. The projection of a world point into the parametrized camera pose then leads to two conditions and finally a quartic equation for finding up to four solutions for the parameter pair. A subsequent backsubstitution directly leads to the corresponding camera poses with respect to the world reference frame. We show that the proposed algorithm offers accuracy and precision comparable to a popular, standard, state-of-the-art approach but at much lower computational cost (15 times faster). Furthermore, it provides improved numerical stability and is less affected by degenerate configurations of the selected world points. The superior computational efficiency is particularly suitable for any RANSAC-outlier-rejection step, which is always recommended before applying PnP or non-linear optimization of the final solution.


Detecting Offensive Statements towards Foreigners in Social Media


Mental Artery Occlusion From Poly-L-Lactic Acid Injection at the Lateral Chin


title: Design and Synthesis of Ruthenium Oligothienylacetylide Complexes. New Materials for Acoustically Induced Nonlinear Optics

abstract: A series of new donor−π-acceptor ruthenium acetylide systems built around thiophene-based π-conjugating spacers of different lengths have been developed. Comparison of the linear and third-order nonlinear optical properties of these donor−π-acceptor chromophores shows that the elongation of the oligothiophene-based spacer or further introduction of a double bond leading to a dithienylethylene spacer produces a considerable bathochromic shift of the absorption maximum together with a dramatic enhancement of the molecular cubic hyperpolarizability. Furthermore acoustically induced second-harmonic generation (AISHG) has been observed for film composites of these complexes incorporated in PMMA matrixes and reaches values among the highest reported so far.


title: The fully informed particle swarm: simpler, maybe better

abstract: The canonical particle swarm algorithm is a new approach to optimization, drawing inspiration from group behavior and the establishment of social norms. It is gaining popularity, especially because of the speed of convergence and the fact that it is easy to use. However, we feel that each individual is not simply influenced by the best performer among his neighbors. We, thus, decided to make the individuals "fully informed." The results are very promising, as informed individuals seem to find better solutions in all the benchmark functions.


title: Speech-to-text and speech-to-speech summarization of spontaneous speech

abstract: This paper presents techniques for speech-to-text and speech-to-speech automatic summarization based on speech unit extraction and concatenation. For the former case, a two-stage summarization method consisting of important sentence extraction and word-based sentence compaction is investigated. Sentence and word units which maximize the weighted sum of linguistic likelihood, amount of information, confidence measure, and grammatical likelihood of concatenated units are extracted from the speech recognition results and concatenated for producing summaries. For the latter case, sentences, words, and between-filler units are investigated as units to be extracted from original speech. These methods are applied to the summarization of unrestricted-domain spontaneous presentations and evaluated by objective and subjective measures. It was confirmed that proposed methods are effective in spontaneous speech summarization.


title: Detecting Pornographic Images by Localizing Skin ROIs

abstract: AbstractIn this study 1 , a novel algorithm for recognizing pornographic images based on the analysis of skin color regionsis presented. The skin color information essentially provides Regions of Interest (ROIs). It is demonstrated that theconvex hull of these ROIs provides semantically useful information for pornographic image detection. Based onthese convex hulls, the authors extract a small set of low-level visual features that are empirically proven to possessdiscriminative power for pornographic image classiﬁcation. In this study, we consider multi-class pornographicimage classiﬁcation, where the ”nude” and ”benign” image classes are further split into two specialized sub-classes, namely ”bikini” / ”porn” and ”skin” / ”non-skin”, respectively. The extracted feature vectors are fed to anensemble of random forest classiﬁers for image classiﬁcation. Each classiﬁer is trained on a partition of the trainingset and solves a binary classiﬁcation problem. In this sense, the model allows for seamless coarse-to-ﬁne-grainedclassiﬁcation by means of a tree-structured topology of a small number of intervening binary classiﬁers. The overalltechnique is evaluated on the AIIA-PID challenge of 9;000 samples of pornographic and benign images collectedfrom the Web. The technique is shown to exhibit state-of-the-art performance against publicly available integratedpornographic image classiﬁers.Index Termsconvex hull calculation, multi-class classiﬁcation, porn detection, random forests, skin ROI localization


title: What I see when I think it's about me: people low in rejection-sensitivity downplay cues of rejection in self-relevant interpersonal situations.

abstract: Gauging one’s impression on a potential mate is challenging. There is a need to make reasonably accurate inferences from subtle, dynamic facial expressions and to maintain motivation to connect despite the risk of rejection. The interpersonal optimism of people low in rejection sensitivity (RS), people who confidently expect acceptance rather than anxiously expect rejection, as do their high RS counterparts, suggests that they may strategically underestimate social threat cues when inferring the impression they have made on others. To test this hypothesis, participants viewed the videotaped reactions of individuals said to have read the participant’s own or someone else’s biographical sketch in an online dating context, and then estimated the emotions of the targets. Estimates of negativity were unrelated to RS when participants believed the videos captured the reactions to someone else’s biographical sketch. However, to the extent that participants were low in RS, they made lower estimates of negativity when they believed the videos showed reactions to their biographical sketch compared to when they believed the videos captured the reactions to someone else’s biographical sketch. The tracking accuracy of participants estimating negativity was unrelated to RS under either condition, but increased with trait empathy. RS was unrelated to estimates of positivity. Supporting functional perspectives on interpersonal perception, results show that interpersonal optimism shapes impressions of others’ reactions to the self in ways that can foster relationship initiation.


title: Large-Scale Data Analysis Using Heuristic Methods

abstract: Estimation and modelling problems as they arise in many data analysis areas often turn out to be unstable and/or intractable by standard numerical methods. Such problems frequently occur in fitting of large data sets to a certain model and in predictive learning. Heuristics are general recommendations based on practical statistical evidence, in contrast to a fixed set of rules that cannot vary, although guarantee to give the correct answer. Although the use of these methods became more standard in several fields of sciences, their use for estimation and modelling in statistics appears to be still limited. This paper surveys a set of problem-solving strategies, guided by heuristic information, that are expected to be used more frequently. The use of recent advances in different fields of large-scale data analysis is promoted focusing on applications in medicine, biology and technology.


title: Exploiting temporal information for 3D pose estimation

abstract: In this work, we address the problem of 3D human pose estimation from a sequence of 2D human poses. Although the recent success of deep networks has led many state-of-the-art methods for 3D pose estimation to train deep networks end-to-end to predict from images directly, the top-performing approaches have shown the effectiveness of dividing the task of 3D pose estimation into two steps: using a state-of-the-art 2D pose estimator to estimate the 2D pose from images and then mapping them into 3D space. They also showed that a low-dimensional representation like 2D locations of a set of joints can be discriminative enough to estimate 3D pose with high accuracy. However, estimation of 3D pose for individual frames leads to temporally incoherent estimates due to independent error in each frame causing jitter. Therefore, in this work we utilize the temporal information across a sequence of 2D joint locations to estimate a sequence of 3D poses. We designed a sequence-to-sequence network composed of layer-normalized LSTM units with shortcut connections connecting the input to the output on the decoder side and imposed temporal smoothness constraint during training. We found that the knowledge of temporal consistency improves the best reported result on Human3.6M dataset by approximately \(12.2\%\) and helps our network to recover temporally consistent 3D poses over a sequence of images even when the 2D pose detector fails.


title: Proposing Enhanced Feature Engineering and a Selection Model for Machine Learning Processes

abstract: Machine Learning (ML) requires a certain number of features (i.e., attributes) to train the model. One of the main challenges is to determine the right number and the type of such features out of the given dataset’s attributes. It is not uncommon for the ML process to use dataset of available features without computing the predictive value of each. Such an approach makes the process vulnerable to overfit, predictive errors, bias, and poor generalization. Each feature in the dataset has either a unique predictive value, redundant, or irrelevant value. However, the key to better accuracy and fitting for ML is to identify the optimum set (i.e., grouping) of the right feature set with the finest matching of the feature’s value. This paper proposes a novel approach to enhance the Feature Engineering and Selection (eFES) Optimization process in ML. eFES is built using a unique scheme to regulate error bounds and parallelize the addition and removal of a feature during training. eFES also invents local gain (LG) and global gain (GG) functions using 3D visualizing techniques to assist the feature grouping function (FGF). FGF scores and optimizes the participating feature, so the ML process can evolve into deciding which features to accept or reject for improved generalization of the model. To support the proposed model, this paper presents mathematical models, illustrations, algorithms, and experimental results. Miscellaneous datasets are used to validate the model building process in Python, C#, and R languages. Results show the promising state of eFES as compared to the traditional feature selection process.


title: Music Generation by Deep Learning - Challenges and Directions

abstract: In addition to traditional tasks such as prediction, classification and translation, deep learning is receiving growing attention as an approach for music generation, as witnessed by recent research groups such as Magenta at Google and CTRL (Creator Technology Research Lab) at Spotify. The motivation is in using the capacity of deep learning architectures and training techniques to automatically learn musical styles from arbitrary musical corpora and then to generate samples from the estimated distribution. However, a direct application of deep learning to generate content rapidly reaches limits as the generated content tends to mimic the training set without exhibiting true creativity. Moreover, deep learning architectures do not offer direct ways for controlling generation (e.g., imposing some tonality or other arbitrary constraints). Furthermore, deep learning architectures alone are autistic automata which generate music autonomously without human user interaction, far from the objective of interactively assisting musicians to compose and refine music. Issues such as control, structure, creativity and interactivity are the focus of our analysis. In this paper, we select some limitations of a direct application of deep learning to music generation and analyze why the issues are not fulfilled and how to address them by possible approaches. Various examples of recent systems are cited as examples of promising directions.


title: Primipaternity and duration of exposure to sperm antigens as risk factors for pre-eclampsia

abstract: Abstract Objective: To establish whether primipaternity and duration of unprotected sexual cohabitation is associated with an increased risk of pre-eclampsia. Method: At a tertiary referral center, the study had a case and control group of 60 multigravid women each, as well as a case and control group of 50 primigravid women each. Information was compiled by means of a confidential questionnaire. Result: After multiple logistic regression analysis using age, smoking, hypertension in previous pregnancies, change of paternity and duration of unprotected sexual cohabitation as predictors, the regression coefficients for change of paternity and sexual cohabitation of longer than 6 months in multigravid women were −0.4 ( P =0.15) and −1.4 ( P =0.03), respectively. Conclusion: Multigravid women with a period of unprotected sexual cohabitation of longer than 6 months had a decreased risk of pre-eclampsia. Primipaternity was not a significant risk factor for pre-eclampsia.


title: Energy Efficient Cooperative Computing in Mobile Wireless Sensor Networks

abstract: Advances in future computing to support emerging sensor applications are becoming more important as the need to better utilize computation and communication resources and make them energy efficient. As a result, it is predicted that intelligent devices and networks, including mobile wireless sensor networks (MWSN), will become the new interfaces to support future applications. In this paper, we propose a novel approach to minimize energy consumption of processing an application in MWSN while satisfying a certain completion time requirement. Specifically, by introducing the concept of cooperation, the logics and related computation tasks can be optimally partitioned, offloaded and executed with the help of peer sensor nodes, thus the proposed solution can be treated as a joint optimization of computing and networking resources. Moreover, for a network with multiple mobile wireless sensor nodes, we propose energy efficient cooperation node selection strategies to offer a tradeoff between fairness and energy consumption. Our performance analysis is supplemented by simulation results to show the significant energy saving of the proposed solution.


title: Modeling Spatio-Temporal Human Track Structure for Action Localization

abstract: This paper addresses spatio-temporal localization of human actions in video. In order to localize actions in time, we propose a recurrent localization network (RecLNet) designed to model the temporal structure of actions on the level of person tracks. Our model is trained to simultaneously recognize and localize action classes in time and is based on two layer gated recurrent units (GRU) applied separately to two streams, i.e. appearance and optical flow streams. When used together with state-of-the-art person detection and tracking, our model is shown to improve substantially spatio-temporal action localization in videos. The gain is shown to be mainly due to improved temporal localization. We evaluate our method on two recent datasets for spatio-temporal action localization, UCF101-24 and DALY, demonstrating a significant improvement of the state of the art.


title: FSelector: a Ruby gem for feature selection

abstract: SUMMARY ::: The FSelector package contains a comprehensive list of feature selection algorithms for supporting bioinformatics and machine learning research. FSelector primarily collects and implements the filter type of feature selection techniques, which are computationally efficient for mining large datasets. In particular, FSelector allows ensemble feature selection that takes advantage of multiple feature selection algorithms to yield more robust results. FSelector also provides many useful auxiliary tools, including normalization, discretization and missing data imputation. ::: ::: ::: AVAILABILITY ::: FSelector, written in the Ruby programming language, is free and open-source software that runs on all Ruby supporting platforms, including Windows, Linux and Mac OS X. FSelector is available from https://rubygems.org/gems/fselector and can be installed like a breeze via the command gem install fselector. The source code is available (https://github.com/need47/fselector) and is fully documented (http://rubydoc.info/gems/fselector/frames).


Rumor Identification and Belief Investigation on Twitter


title: Part-of-speech Taggers for Low-resource Languages using CCA Features

abstract: In this paper, we address the challenge of creating accurate and robust partof-speech taggers for low-resource languages. We propose a method that leverages existing parallel data between the target language and a large set of resourcerich languages without ancillary resources such as tag dictionaries. Crucially, we use CCA to induce latent word representations that incorporate cross-genre distributional cues, as well as projected tags from a full array of resource-rich languages. We develop a probability-based confidence model to identify words with highly likely tag projections and use these words to train a multi-class SVM using the CCA features. Our method yields average performance of 85% accuracy for languages with almost no resources, outperforming a state-of-the-art partiallyobserved CRF model.


title: Efficient Data Augmentation for Fitting Stochastic Epidemic Models to Prevalence Data

abstract: ABSTRACTStochastic epidemic models describe the dynamics of an epidemic as a disease spreads through a population. Typically, only a fraction of cases are observed at a set of discrete times. The absence of complete information about the time evolution of an epidemic gives rise to a complicated latent variable problem in which the state space size of the epidemic grows large as the population size increases. This makes analytically integrating over the missing data infeasible for populations of even moderate size. We present a data augmentation Markov chain Monte Carlo (MCMC) framework for Bayesian estimation of stochastic epidemic model parameters, in which measurements are augmented with subject-level disease histories. In our MCMC algorithm, we propose each new subject-level path, conditional on the data, using a time-inhomogenous continuous-time Markov process with rates determined by the infection histories of other individuals. The method is general, and may be applied to a broad class of epidemic m...


title: Representational Power of Restricted Boltzmann Machines and Deep Belief Networks

abstract: Deep belief networks (DBN) are generative neural network models with many layers of hidden explanatory factors, recently introduced by Hinton, Osindero, and Teh (2006) along with a greedy layer-wise unsupervised learning algorithm. The building block of a DBN is a probabilistic model called a restricted Boltzmann machine (RBM), used to represent one layer of the model. Restricted Boltzmann machines are interesting because inference is easy in them and because they have been successfully used as building blocks for training deeper models. We first prove that adding hidden units yields strictly improved modeling power, while a second theorem shows that RBMs are universal approximators of discrete distributions. We then study the question of whether DBNs with more layers are strictly more powerful in terms of representational power. This suggests a new and less greedy criterion for training RBMs within DBNs.


title: 3D real-time indoor localization via broadband nonlinear backscatter in passive devices with centimeter precision

abstract: We propose and demonstrate accurate 3D real-time indoor localization via broadband nonlinear backscatter in passive devices. The proposed method does not need any relative motion between a reader and a tag or the use of reference anchor nodes. In the conventional radio frequency identification (RFID) system, a passive tag responds to a reader by switching its antenna "on" and "off". The operation of such conventional backscatter is essentially "linear", since the reader-to-tag (downlink) and tag-to-reader (uplink) signals overlap on the same carrier frequency. Although linear backscatter is straightforward, the self-jamming problem caused by strong leakage signals from the transmitter to the receiver is notorious and poses many constraints on the received signal quality, operation bandwidth, modulation flexibility and system complexity. To enable high-accuracy real-time 3D indoor localization for passive devices, we show that nonlinear backscatter is more effective than linear backscatter. Nonlinear backscatter exploits nonlinear elements in passive devices to generate second or higher-order harmonics as the uplink response. Separation of downlink and uplink on different carriers allows immediate self-jamming cancellation and direct un-modulated carrier phase decoding, hence resulting in better received signal quality and broad bandwidth of operation, both of which are critical for the localization system. The broad bandwidth allows the design of an efficient phase-based ranging algorithm - heuristic multi-frequency continuous wave (HMFCW) ranging which resolves ambiguous phase cycles with heuristically optimized sparse carrier frequencies. HMFCW ranging can correctly pin down the phase cycle integer with 100% reliability as long as the phase error falls within ±90° × BW% (percentage bandwidth). In our present implementation, we achieved a median ranging error below 1 cm under phase error bounds of ±50°. We realized real-time 3D localization from differential ranging by nonlinear conjugate gradient (CG) search for hyperboloids intersection in a multi-static transceiving system with 1 Tx antenna and 4 Rx antennas. The measured 3D localization median error was 3.5 cm in the indoor environment. Presently, the measurement latency was less than 0.155 seconds. We will present system design, algorithms and a prototype with experimental evaluation.


title: Characteristics and Effectiveness of Fall Prevention Programs in Nursing Homes: A Systematic Review and Meta-Analysis of Randomized Controlled Trials

abstract: OBJECTIVES ::: To determine characteristics and effectiveness of prevention programs on fall-related outcomes in a defined setting. ::: ::: ::: DESIGN ::: Systematic review and meta-analysis. ::: ::: ::: SETTING ::: A clearly described subgroup of nursing homes defined as residential facilities that provide 24-hour-a-day surveillance, personal care, and limited clinical care for persons who are typically elderly and infirm. ::: ::: ::: PARTICIPANTS ::: Nursing home residents (N = 22,915). ::: ::: ::: MEASUREMENTS ::: The primary outcomes were number of falls, fallers, and recurrent fallers. ::: ::: ::: RESULTS ::: Thirteen studies met the inclusion criteria. Six fall prevention programs were single (one intervention component provided to the residents), one was multiple (two or more intervention components not customized to individual fall risk), and six were multifactorial (two or more intervention components customized to each resident's fall risk). Meta-analysis found significantly fewer recurrent fallers in the intervention groups (4 studies, relative risk (RR) = 0.79, 95% confidence interval (CI) = 0.65-0.97) but no significant effect of the intervention on fallers (6 studies, RR = 0.97, 95% CI = 0.84-1.11) or falls (10 studies, RR = 0.93, 95% CI = 0.76-1.13). Multifactorial interventions significantly reduced falls (4 studies, RR = 0.67, 95% CI = 0.55-0.82) and the number of recurrent fallers (4 studies, RR = 0.79, CI = 0.65-0.97), whereas single or multiple interventions did not. Training and education showed a significant harmful effect in the intervention groups on the number of falls (2 studies, RR = 1.29, 95% CI = 1.23-1.36). ::: ::: ::: CONCLUSION ::: This meta-analysis failed to reveal a significant effect of fall prevention interventions on falls or fallers but, for the first time, showed that fall prevention interventions significantly reduced the number of recurrent fallers by 21%.


title: Attribute-Based Encryption with Multi-keyword Search

abstract: Public key encryption with keyword search applies only to the certain circumstances that keyword ciphertext can only be retrieved by a specific user and only supports single-keyword matching. In the existing searchable encryption schemes, either the communication mode is one-to-one, or only single-keyword search is supported. This paper proposes a searchable encryption that is based on attributes and supports multi-keyword search. The proposed scheme allows keyword ciphertext to be inquired correctly by multiple users if and only if the users defined by a series of attributes satisfy the access structure. This can realize the multi-user information query. In particular, the keyword of the user query does not need to match the keyword ciphertext exactly. The keywords in the ciphertext simply determine whether the keyword belongs to the set of keywords which are used for encryption. Extensive analysis indicates that the proposed scheme is secure and efficient.


Ray Reordering Techniques for GPU Ray-Cast Ambient Occlusion


title: Countermeasure Techniques for Deceptive Phishing Attack

abstract: Phishing is a form of online identity theft. Phishers use social engineering to steal victims' personal identity data and financial account credentials. Social engineering schemes use spoofed e-mails to lure unsuspecting victims into counterfeit websites designed to trick recipients into divulging financial data such as credit card numbers, account usernames, passwords and social security numbers. This is called a deceptive phishing attack. In this paper, a thorough overview of a deceptive phishing attack and its countermeasure techniques, which is called anti-phishing, is presented. Firstly, technologies used by phishers and the definition, classification and future works of deceptive phishing attacks are discussed. Following with the existing anti-phishing techniques in literatures and research-stage technologies are shown, and a thorough analysis which includes the advantages and shortcomings of countermeasures is given. At last, we show the research of why people fall for phishing attack.


title: Deep Learning with Topological Signatures

abstract: Inferring topological and geometrical information from data can offer an alternative perspective on machine learning problems. Methods from topological data analysis, e.g., persistent homology, enable us to obtain such information, typically in the form of summary representations of topological features. However, such topological signatures often come with an unusual structure (e.g., multisets of intervals) that is highly impractical for most machine learning techniques. While many strategies have been proposed to map these topological signatures into machine learning compatible representations, they suffer from being agnostic to the target learning task. In contrast, we propose a technique that enables us to input topological signatures to deep neural networks and learn a task-optimal representation during training. Our approach is realized as a novel input layer with favorable theoretical properties. Classification experiments on 2D object shapes and social network graphs demonstrate the versatility of the approach and, in case of the latter, we even outperform the state-of-the-art by a large margin.


title: A graph-based global registration for 2D mosaics

abstract: We describe a graph-based global registration method for creating 2D mosaic images. When multi-frames overlap in space, global registration is necessary to minimize the accumulated registration errors. We use a graph to represent the temporal and spatial connectivity and show that global registration can be obtained through the search for an optimal path in the constructed graph. The definition of an adequate objective function characterizing the global registration provides a direct manipulation of the graph. The framework presented here allows the automatic construction of the graph, and the construction of a consistent mosaic from a collection of frames using projective transformations.


title: Tear Trough Deformity: Review of Anatomy and Treatment Options

abstract: The lower eyelid can be a challenging area in facial rejuvenation. While lower eyelid bags are commonly the reason that patients present for lower eyelid rejuvenation, a separate entity known as a tear trough deformity may occur in conjunction with lower eyelid bags or alone. In this article, the authors outline the current understanding of the tear trough anatomy; describe multiple classification systems, which provide an objective means of evaluating the deformity and aid the surgeon in choosing appropriate treatment options; and review surgical and nonsurgical techniques for correcting the tear trough deformity. Treatment options include hyaluronic acid filler, fat grafting, skeletal implants, and fat transposition. Each procedure is associated with advantages and disadvantages, and each should be considered more complex than traditional lower blepharoplasty alone. While lower blepharoplasty removes excess fat and may tighten the anterior lamella, tear trough procedures require the addition of volume to the underlying depression. These procedures requiring release of the ligamentous structures and orbicularis (of which the tear trough is composed), as well as fat transposition or fat grafting, are associated with additional complications, which are also reviewed.


title: Recent Development and Applications of SUMO - Simulation of Urban MObility

abstract: SUMO is an open source traffic simulation package including the simulation application itself as well as supporting tools, mainly for network import and demand modeling. SUMO helps to investigate a large variety of research topics, mainly in the context of traffic management and vehicular communications. We describe the current state of the package, its major applications, both by research topic and by example, as well as future developments and extensions. Keywords-microscopic traffic simulation; traffic management; open source; software


title: Using dynamic and contextual features to predict issue lifetime in GitHub projects

abstract: Methods for predicting issue lifetime can help software project managers to prioritize issues and allocate resources accordingly. Previous studies on issue lifetime prediction have focused on models built from static features, meaning features calculated at one snapshot of the issue's lifetime based on data associated to the issue itself. However, during its lifetime, an issue typically receives comments from various stakeholders, which may carry valuable insights into its perceived priority and difficulty and may thus be exploited to update lifetime predictions. Moreover, the lifetime of an issue depends not only on characteristics of the issue itself, but also on the state of the project as a whole. Hence, issue lifetime prediction may benefit from taking into account features capturing the issue's context (contextual features). In this work, we analyze issues from more than 4000 GitHub projects and build models to predict, at different points in an issue's lifetime, whether or not the issue will close within a given calendric period, by combining static, dynamic and contextual features. The results show that dynamic and contextual features complement the predictive power of static ones, particularly for long-term predictions.


title: The Traveling Salesman Problem for Cubic Graphs

abstract: The chassis of an information recording disk player, carrying various mechanisms is made of a noncontractive synthetic resin. The chassis comprises a flat body and peripheral walls integrally formed with the flat body so that the flat body is reinforced providing sufficient rigidity comparable to a conventional diecasting-made chassis. A casing which encloses the chassis comprises front, rear, top and bottom plates. The front and rear plates function as stands of an intermediate product for holding horizontally the chassis so that assembly and adjustment are readily performed. The casing is made of a conductive material to prevent spurious irradiation of electromagnetic waves from the pickup assembly of the player.


title: A deep learning architecture for sentiment analysis

abstract: The fabulous results of Deep Convolution Neural Networks in computer vision and image analysis have recently attracted considerable attention from researchers of other application domains as well. In this paper we present NgramCNN, a neural network architecture we designed for sentiment analysis of long text documents. It uses pretrained word embeddings for dense feature representation and a very simple single-layer classifier. The complexity is encapsulated in feature extraction and selection parts that benefit from the effectiveness of convolution and pooling layers. For evaluation we utilized different kinds of emotional text datasets and achieved an accuracy of 91.2 % accuracy on the popular IMDB movie reviews. NgramCNN is more accurate than similar shallow convolution networks or deeper recurrent networks that were used as baselines. In the future, we intent to generalize the architecture for state of the art results in sentiment analysis of variable-length texts.


title: Boosting Cross-Language Retrieval by Learning Bilingual Phrase Associations from Relevance Rankings

abstract: We present an approach to learning bilingual n-gram correspondences from relevance rankings of English documents for Japanese queries. We show that directly optimizing cross-lingual rankings rivals and complements machine translation-based cross-language information retrieval (CLIR). We propose an efficient boosting algorithm that deals with very large cross-product spaces of word correspondences. We show in an experimental evaluation on patent prior art search that our approach, and in particular a consensus-based combination of boosting and translation-based approaches, yields substantial improvements in CLIR performance. Our training and test data are made publicly available.


title: Ontology-Based Prediction and Prioritization of Gene Functional Annotations

abstract: Genes and their protein products are essential molecular units of a living organism. The knowledge of their functions is key for the understanding of physiological and pathological biological processes, as well as in the development of new drugs and therapies. The association of a gene or protein with its functions, described by controlled terms of biomolecular terminologies or ontologies, is named gene functional annotation . Very many and valuable gene annotations expressed through terminologies and ontologies are available. Nevertheless, they might include some erroneous information, since only a subset of annotations are reviewed by curators. Furthermore, they are incomplete by definition, given the rapidly evolving pace of biomolecular knowledge. In this scenario, computational methods that are able to quicken the annotation curation process and reliably suggest new annotations are very important. Here, we first propose a computational pipeline that uses different semantic and machine learning methods to predict novel ontology-based gene functional annotations; then, we introduce a new semantic prioritization rule to categorize the predicted annotations by their likelihood of being correct. Our tests and validations proved the effectiveness of our pipeline and prioritization of predicted annotations, by selecting as most likely manifold predicted annotations that were later confirmed.


LDA-based Topic Modelling in Text Sentiment Classification: An Empirical Analysis


title: Subspace Clustering by Exploiting a Low-Rank Representation with a Symmetric Constraint

abstract: In this paper, we propose a low-rank representation with symmetric constraint (LRRSC) method for robust subspace clustering. Given a collection of data points approximately drawn from multiple subspaces, the proposed technique can simultaneously recover the dimension and members of each subspace. LRRSC extends the original low-rank representation algorithm by integrating a symmetric constraint into the low-rankness property of high-dimensional data representation. The symmetric low-rank representation, which preserves the subspace structures of high-dimensional data, guarantees weight consistency for each pair of data points so that highly correlated data points of subspaces are represented together. Moreover, it can be efficiently calculated by solving a convex optimization problem. We provide a rigorous proof for minimizing the nuclear-norm regularized least square problem with a symmetric constraint. The affinity matrix for spectral clustering can be obtained by further exploiting the angular information of the principal directions of the symmetric low-rank representation. This is a critical step towards evaluating the memberships between data points. Experimental results on benchmark databases demonstrate the effectiveness and robustness of LRRSC compared with several state-of-the-art subspace clustering algorithms.


title: Bayesian inference for a covariance matrix

abstract: Covariance matrix estimation arises in multivariate problems including multivariate normal sampling models and regression models where random effects are jointly modeled, e.g. random-intercept, random-slope models. A Bayesian analysis of these problems requires a prior on the covariance matrix. Here we assess, through a simulation study and a real data set, the impact this prior choice has on posterior inference of the covariance matrix. ::: Inverse Wishart distribution is the natural choice for a covariance matrix prior because its conjugacy on normal model and simplicity, is usually available in Bayesian statistical software. However inverse Wishart distribution presents some undesirable properties from a modeling point of view. It can be too restrictive because assume the same amount of prior information about every variance parameters and, more important, it shows a prior relationship between the variances and correlations. ::: Some alternatives distributions has been proposed. The scaled inverse Wishart distribution, which give more flexibility on the variance priors conserving the conjugacy property but does not eliminate the prior relationship between variances and correlations. Secondly, it is possible to fit separate priors for individual correlations and standard deviations. This strategy eliminates any prior relationship within the covariance matrix parameters, but it is not conjugate and therefore computationally slow.


title: Critical analysis of vendor lock-in and its impact on cloud computing migration: a business perspective

abstract: Vendor lock-in is a major barrier to the adoption of cloud computing, due to the lack of standardization. Current solutions and efforts tackling the vendor lock-in problem are predominantly technology-oriented. Limited studies exist to analyse and highlight the complexity of vendor lock-in problem in the cloud environment. Consequently, most customers are unaware of proprietary standards which inhibit interoperability and portability of applications when taking services from vendors. This paper provides a critical analysis of the vendor lock-in problem, from a business perspective. A survey based on qualitative and quantitative approaches conducted in this study has identified the main risk factors that give rise to lock-in situations. The analysis of our survey of 114 participants shows that, as computing resources migrate from on-premise to the cloud, the vendor lock-in problem is exacerbated. Furthermore, the findings exemplify the importance of interoperability, portability and standards in cloud computing. A number of strategies are proposed on how to avoid and mitigate lock-in risks when migrating to cloud computing. The strategies relate to contracts, selection of vendors that support standardised formats and protocols regarding standard data structures and APIs, developing awareness of commonalities and dependencies among cloud-based solutions. We strongly believe that the implementation of these strategies has a great potential to reduce the risks of vendor lock-in.


title: Bayesian Nonparametric Hidden Semi-Markov Models

abstract: There is much interest in the Hierarchical Dirichlet Process Hidden Markov Model (HDP-HMM) as a natural Bayesian nonparametric extension of the ubiquitous Hidden Markov Model for learning from sequential and time-series data. However, in many settings the HDP-HMM's strict Markovian constraints are undesirable, particularly if we wish to learn or encode non-geometric state durations. We can extend the HDP-HMM to capture such structure by drawing upon explicit-duration semi-Markovianity, which has been developed mainly in the parametric frequentist setting, to allow construction of highly interpretable models that admit natural prior information on state durations. In this paper we introduce the explicit-duration Hierarchical Dirichlet Process Hidden semi-Markov Model (HDP-HSMM) and develop sampling algorithms for efficient posterior inference. The methods we introduce also provide new methods for sampling inference in the finite Bayesian HSMM. Our modular Gibbs sampling methods can be embedded in samplers for larger hierarchical Bayesian models, adding semi-Markov chain modeling as another tool in the Bayesian inference toolbox. We demonstrate the utility of the HDP-HSMM and our inference methods on both synthetic and real experiments.


Visualization of Features in 3 D Terrain


Pedestrian Dead Reckoning : A Basis for Personal Positioning


title: Medical Monitoring Applications for Wearable Computing

abstract: Medical monitors have benefited from technological advances in the field of wireless communication, processing and power sources. These advances have made possible miniaturization and prolonged operating times of medical monitors, as well as their global integration into telemedical systems. This allows patients to have real-time feedback about medical conditions while going about their normal daily activities. System designers are facing specific issues related to monitor acceptability, application requirements, power consumption and system connectivity. In this paper we discuss system design issues, present a survey of existing systems and sensors, and introduce two taxonomies of medical monitoring applications for wearable computing.


title: SDF: software-defined flash for web-scale internet storage systems

abstract: In the last several years hundreds of thousands of SSDs have been deployed in the data centers of Baidu, China's largest Internet search company. Currently only 40\% or less of the raw bandwidth of the flash memory in the SSDs is delivered by the storage system to the applications. Moreover, because of space over-provisioning in the SSD to accommodate non-sequential or random writes, and additionally, parity coding across flash channels, typically only 50-70\% of the raw capacity of a commodity SSD can be used for user data. Given the large scale of Baidu's data center, making the most effective use of its SSDs is of great importance. Specifically, we seek to maximize both bandwidth and usable capacity. To achieve this goal we propose {\em software-defined flash} (SDF), a hardware/software co-designed storage system to maximally exploit the performance characteristics of flash memory in the context of our workloads. SDF exposes individual flash channels to the host software and eliminates space over-provisioning. The host software, given direct access to the raw flash channels of the SSD, can effectively organize its data and schedule its data access to better realize the SSD's raw performance potential. Currently more than 3000 SDFs have been deployed in Baidu's storage system that supports its web page and image repository services. Our measurements show that SDF can deliver approximately 95% of the raw flash bandwidth and provide 99% of the flash capacity for user data. SDF increases I/O bandwidth by 300\% and reduces per-GB hardware cost by 50% on average compared with the commodity-SSD-based system used at Baidu.


title: Semi-Supervised Learning via New Deep Network Inversion

abstract: We exploit a recently derived inversion scheme for arbitrary deep neural networks to develop a new semi-supervised learning framework that applies to a wide range of systems and problems. The approach reaches current state-of-the-art methods on MNIST and provides reasonable performances on SVHN and CIFAR10. Through the introduced method, residual networks are for the first time applied to semi-supervised tasks. Experiments with one-dimensional signals highlight the generality of the method. Importantly, our approach is simple, efficient, and requires no change in the deep network architecture.


title: A broadband compact low-loss 4×4 Butler Matrix in CMOS with stacked transformer based quadrature couplers

abstract: This paper presents an ultra-broadband ultracompact Butler Matrix design scheme. The design employs stacked transformer based couplers and lumped LC π-network phase shifters for substantial size reduction. As a proof-of-concept design, a 4×4 Butler Matrix is implemented in a standard 130nm bulk CMOS process at a center frequency of 2.0 GHz. Compared with reported fully integrated 2.0 GHz 4×4 Butler Matrix designs in CMOS, the proposed design achieves the lowest insertion loss of 1.10dB, the smallest amplitude mismatch of 0.3 dB, the largest fractional bandwidth of 34.6%, and the smallest chip core area of 0.635×1.122 mm2. Based on the measured S-parameters, the four concurrent electrical array patterns of the Butler Matrix achieve array peak-to-null ratio (PNR) of 29.5 dB at 2.0 GHz and better than 15.0 dB between 1.55 GHz and 2.50 GHz.


title: Supporting the Screening of Corporate Acquisition Targets

abstract: This paper discusses the screening of potential corporate acquisition targets from the acquiring company perspective. A structured process to screen acquisition targets using both, financial and non-financial data and a developed prototype DSS to support the screening process are presented.


title: Autopoiesis: a review and a reappraisal

abstract: The aim of the paper is to review critically the notion of autopoiesis as presented by Maturana and Varela. In particular, recognizing that there are difficulties in obtaining a complete and clear picture from the primary literature, an effort is made to present a coherent view-also based on many years of personal contact with Francisco Varela. The paper begins with a few historical notes to highlight the cultural background from which the notion of autopoiesis arose. The basic principles of autopoiesis as a theory of cellular life are then described, emphasizing also what autopoiesis is not: not an abstract theory, not a concept of artificial life, not a theory about the origin of life-but rather a pragmatic blueprint of life based on cellular life. It shown how this view leads to a conceptually clear definition of minimal life and to a logical link with related notions, such as self-organization, emergence, biological autonomy, auto-referentiality, and interactions with the environment. The perturbations brought about by the environment are seen as changes selected and triggered by the inner organization of the living. These selective coupling interactions impart meaning to the minimal life and are thus defined by Maturana and Varela with the arguable term of "cognition". This particular view on the mutual interactions between living organism and environment leads these authors to the notion of "enaction", and to the surprising view that autopoiesis and cognition are two complementary, and in a way equivalent, aspects of life. It is then shown how cognition, so defined, permits us to build a bridge between biology and cognitive science. Autopoiesis also allows one to conceive chemical models of minimal cellular life that can be implemented experimentally. The corresponding work on "chemical autopoiesis" is then reviewed. The surprising impact of autopoiesis in the social sciences ("social autopoiesis") is also briefly discussed. This review also comments on why the theory of autopoiesis had, and still has, a difficult time being accepted into the mainstream of life-science research. Finally, it is pointed out that the new interest in system biology and complexity theories may lead to a reappraisal of autopoiesis and related notions, as outlined also by other authors, such as Tibor Ganti and Stuart Kauffmann.


title: Natural Language Processing by Reasoning and Learning

abstract: This paper reports the preliminary experiments in a general-purpose reasoning system to carry out natural language comprehension and production using a reasoning-learning mechanism designed to realize general intelligence.


title: A Framework for Personal Mobile Commerce Pattern Mining and Prediction

abstract: Due to a wide range of potential applications, research on mobile commerce has received a lot of interests from both of the industry and academia. Among them, one of the active topic areas is the mining and prediction of users' mobile commerce behaviors such as their movements and purchase transactions. In this paper, we propose a novel framework, called Mobile Commerce Explorer (MCE), for mining and prediction of mobile users' movements and purchase transactions under the context of mobile commerce. The MCE framework consists of three major components: 1) Similarity Inference Model (SIM) for measuring the similarities among stores and items, which are two basic mobile commerce entities considered in this paper; 2) Personal Mobile Commerce Pattern Mine (PMCP-Mine) algorithm for efficient discovery of mobile users' Personal Mobile Commerce Patterns (PMCPs); and 3) Mobile Commerce Behavior Predictor (MCBP) for prediction of possible mobile user behaviors. To our best knowledge, this is the first work that facilitates mining and prediction of mobile users' commerce behaviors in order to recommend stores and items previously unknown to a user. We perform an extensive experimental evaluation by simulation and show that our proposals produce excellent results.


title: Enhancing stance phase propulsion during level walking by combining fes with a powered exoskeleton for persons with paraplegia

abstract: This paper describes the design and implementation of a cooperative controller that combines functional electrical stimulation (FES) with a powered lower limb exoskeleton to provide enhanced hip extension during the stance phase of walking in persons with paraplegia. The controller utilizes two sources of actuation: the electric motors of the powered exoskeleton and the user's machine (FSM), a set of FES. It consists of a finite-state machine (FSM), a set of proportional-derivative (PD) controllers for the exoskeleton and a cycle-to-cycle adaptive controller for muscle stimulation. Level ground walking is conducted on a single subject with complete T10 paraplegia. Results show a 34% reduction in electrical power requirements at the hip joints during the stance phase of the gait cycle with the cooperative controller compared to using electric motors alone.


Deep Reinforcement Learning for 2048


Spectral Opportunity Modelling in the Terrestrial Broadcast Frequency Spectrum


title: Dose and image quality for a cone‐beam C‐arm CT system

abstract: We assess dose and image quality of a state-of-the-art angiographic C-arm system (Axiom Artis dTA, Siemens Medical Solutions, Forchheim, Germany) for three-dimensional neuro-imaging at various dose levels and tube voltages and an associated measurement method. Unlike conventional CT, the beam length covers the entire phantom, hence, the concept of computed tomography dose index(CTDI) is not the metric of choice, and one can revert to conventional dosimetry methods by directly measuring the dose at various points using a small ion chamber. This method allows us to define and compute a new dose metric that is appropriate for a direct comparison with the familiar CTDI W of conventional CT. A perception study involving the CATPHAN 600 indicates that one can expect to see at least the 9 mm inset with 0.5% nominal contrast at the recommended head-scan dose ( 60 mGy ) when using tube voltages ranging from 70 kVp to 125 kVp . When analyzing the impact of tube voltage on image quality at a fixed dose, we found that lower tube voltages gave improved low contrastdetectability for small-diameter objects. The relationships between kVp, imagenoise, dose, and contrast perception are discussed.


title: Improvement of Intrusion Detection System in Data Mining using Neural Network

abstract: Many researchers have argued that Artificial Neural Networks (ANNs) can improve the performance of intrusion detection systems (IDS). One of the central areas in network intrusion detection is how to build effective systems that are able to distinguish normal from intrusive traffic. In this paper four different algorithms are used namely as Multilayer Perception, Radial Base Function, Logistic Regression and Voted Perception. All these neural based algorithms are implemented in WEKA data mining tool to evaluate the performance. For experimental work, NSL KDD dataset is used. Each neural based algorithm is tested with conducted dataset. It is shown that the Multilayer Perception neural network algorithm is providing more accurate results than other algorithms.


Task Scheduling in Cloud Computing : Review


title: Debiasing the Mind Through Meditation Mindfulness and the Sunk-Cost Bias

abstract: In the research reported here, we investigated the debiasing effect of mindfulness meditation on the sunk-cost bias. We conducted four studies (one correlational and three experimental); the results suggest that increased mindfulness reduces the tendency to allow unrecoverable prior costs to influence current decisions. Study 1 served as an initial correlational demonstration of the positive relationship between trait mindfulness and resistance to the sunk-cost bias. Studies 2a and 2b were laboratory experiments examining the effect of a mindfulness-meditation induction on increased resistance to the sunk-cost bias. In Study 3, we examined the mediating mechanisms of temporal focus and negative affect, and we found that the sunk-cost bias was attenuated by drawing one's temporal focus away from the future and past and by reducing state negative affect, both of which were accomplished through mindfulness meditation.


title: Main Attributes and Features Effecting Juvenile Delinquents: Evidence from Karachi, Pakistan

abstract: The purpose of the study is focused on the delinquents and their main features and attributes and causes while committing the crime. The universe of this research was the delinquents of Karachi Youthful Offender’s Industrial School, Karachi. Random sampling methods were used for data collection to find the answers of these following questions, degree of crime (Major/ Minor), age at the time of execution of crime, family income at the time of execution of crime, education level at the time of execution of crime and peer involvement/ motivation in the execution of crime. This study facilitate to find the main attributes/ features of juvenile delinquent(s) and their overall hallow of personality at the time of crime, which can assist parents, teachers, peers and organizations to assist their relative children and take them away from potential life spoil. The main factor which affects the adolescents to commit crime is the companion or friend’s stimulus with low education and income of family in age of 11-14.


title: AODV routing protocol implementation design

abstract: To date, the majority of ad hoc routing protocol research has been done using simulation only. One of the most motivating reasons to use simulation is the difficulty of creating a real implementation. In a simulator, the code is contained within a single logical component, which is clearly defined and accessible. On the other hand, creating an implementation requires use of a system with many components, including many that have little or no documentation. The implementation developer must understand not only the routing protocol, but all the system components and their complex interactions. Further, since ad hoc routing protocols are significantly different from traditional routing protocols, a new set of features must be introduced to support the routing protocol. In this paper we describe the event triggers required for AODV operation, the design possibilities and the decisions for our ad hoc on-demand distance vector (AODV) routing protocol implementation, AODV-UCSB. This paper is meant to aid researchers in developing their own on-demand ad hoc routing protocols and assist users in determining the implementation design that best fits their needs.


title: Reducing Controversy by Connecting Opposing Views

abstract: Society is often polarized by controversial issues that split the population into groups with opposing views. When such issues emerge on social media, we often observe the creation of `echo chambers', i.e., situations where like-minded people reinforce each other's opinion, but do not get exposed to the views of the opposing side. In this paper we study algorithmic techniques for bridging these chambers, and thus reduce controversy. Specifically, we represent the discussion on a controversial issue with an endorsement graph, and cast our problem as an edge-recommendation problem on this graph. The goal of the recommendation is to reduce the controversy score of the graph, which is measured by a recently-developed metric based on random walks. At the same time, we take into account the acceptance probability of the recommended edge, which represents how likely the edge is to materialize in the endorsement graph. We propose a simple model based on a recently-developed user-level controversy score, that is competitive with state-of-the-art link-prediction algorithms. Our goal then becomes finding the edges that produce the largest reduction in the controversy score, in expectation. To solve this problem, we propose an efficient algorithm that considers only a fraction of all the possible combinations of edges. Experimental results show that our algorithm is more efficient than a simple greedy heuristic, while producing comparable score reduction. Finally, a comparison with other state-of-the-art edge-addition algorithms shows that this problem is fundamentally different from what has been studied in the literature.


title: The Role of Apolipoprotein E in Alzheimer's Disease

abstract: The epsilon4 allele of apolipoprotein E (APOE) is the major genetic risk factor for Alzheimer's disease (AD). Although there have been numerous studies attempting to elucidate the underlying mechanism for this increased risk, how apoE4 influences AD onset and progression has yet to be proven. However, prevailing evidence suggests that the differential effects of apoE isoforms on Abeta aggregation and clearance play the major role in AD pathogenesis. Other potential mechanisms, such as the differential modulation of neurotoxicity and tau phosphorylation by apoE isoforms as well as its role in synaptic plasticity and neuroinflammation, have not been ruled out. Inconsistent results among studies have made it difficult to define whether the APOE epsilon4 allele represents a gain of toxic function, a loss of neuroprotective function, or both. Therapeutic strategies based on apoE propose to reduce the toxic effects of apoE4 or to restore the physiological, protective functions of apoE.


title: A Group Theoretic Perspective on Unsupervised Deep Learning

abstract: Why does Deep Learning work? What representations does it capture? How do higher-order representations emerge? We study these questions from the perspective of group theory, thereby opening a new approach towards a theory of Deep learning. ::: One factor behind the recent resurgence of the subject is a key algorithmic step called {\em pretraining}: first search for a good generative model for the input samples, and repeat the process one layer at a time. We show deeper implications of this simple principle, by establishing a connection with the interplay of orbits and stabilizers of group actions. Although the neural networks themselves may not form groups, we show the existence of {\em shadow} groups whose elements serve as close approximations. ::: Over the shadow groups, the pre-training step, originally introduced as a mechanism to better initialize a network, becomes equivalent to a search for features with minimal orbits. Intuitively, these features are in a way the {\em simplest}. Which explains why a deep learning network learns simple features first. Next, we show how the same principle, when repeated in the deeper layers, can capture higher order representations, and why representation complexity increases as the layers get deeper.


title: Discourse Complements Lexical Semantics for Non-factoid Answer Reranking

abstract: We propose a robust answer reranking model for non-factoid questions that integrates lexical semantics with discourse information, driven by two representations of discourse: a shallow representation centered around discourse markers, and a deep one based on Rhetorical Structure Theory. We evaluate the proposed model on two corpora from different genres and domains: one from Yahoo! Answers and one from the biology domain, and two types of non-factoid questions: manner and reason. We experimentally demonstrate that the discourse structure of nonfactoid answers provides information that is complementary to lexical semantic similarity between question and answer, improving performance up to 24% (relative) over a state-of-the-art model that exploits lexical semantic similarity alone. We further demonstrate excellent domain transfer of discourse information, suggesting these discourse features have general utility to non-factoid question answering.