title: A Review of Sparse Recovery Algorithms

abstract: Nowadays, a large amount of information has to be transmitted or processed. This implies high-power processing, large memory density, and increased energy consumption. In several applications, such as imaging, radar, speech recognition, and data acquisition, the signals involved can be considered sparse or compressive in some domain. The compressive sensing theory could be a proper candidate to deal with these constraints. It can be used to recover sparse or compressive signals with fewer measurements than the traditional methods. Two problems must be addressed by compressive sensing theory: design of the measurement matrix and development of an efficient sparse recovery algorithm. These algorithms are usually classified into three categories: convex relaxation, non-convex optimization techniques, and greedy algorithms. This paper intends to supply a comprehensive study and a state-of-the-art review of these algorithms to researchers who wish to develop and use them. Moreover, a wide range of compressive sensing theory applications is summarized and some open research challenges are presented.


title: Inverse Multipath Fingerprinting for Millimeter Wave V2I Beam Alignment

abstract: Efficient beam alignment is a crucial component in millimeter wave systems with analog beamforming, especially in fast-changing vehicular settings. This paper proposes to use the vehicle's position (e.g., available via GPS) to query a multipath fingerprint database, which provides prior knowledge of potential pointing directions for reliable beam alignment. The approach is the inverse of fingerprinting localization, where the measured multipath signature is compared to the fingerprint database to retrieve the most likely position. The power loss probability is introduced as a metric to quantify misalignment accuracy and is used for optimizing candidate beam selection. Two candidate beam selection methods are developed, where one is a heuristic while the other minimizes the misalignment probability. The proposed beam alignment is evaluated using realistic channels generated from a commercial ray-tracing simulator. Using the generated channels, an extensive investigation is provided, which includes the required measurement sample size to build an effective fingerprint, the impact of measurement noise, the sensitivity to changes in traffic density, and beam alignment overhead comparison with IEEE 802.11ad as the baseline. Using the concept of beam coherence time, which is the duration between two consecutive beam alignments, and parameters of IEEE 802.11ad, the overhead is compared in the mobility context. The results show that while the proposed approach provides increasing rates with larger antenna arrays, IEEE 802.11ad has decreasing rates due to the higher beam training overhead that eats up a large portion of the beam coherence time, which becomes shorter with increasing mobility.


title: Brain EEG Time Series Selection: A Novel Graph-Based Approach for Classification

abstract: Brain Electroencephalography (EEG) classification is widely applied to analyze cerebral diseases in recent years. Unfortunately, invalid/noisy EEGs degrade the diagnosis performance and most previously developed methods ignore the necessity of EEG selection for classification. To this end, this paper proposes a novel maximum weight clique-based EEG selection approach, named mwcEEGs, to map EEG selection to searching maximum similarity-weighted cliques from an improved Fr\'{e}chet distance-weighted undirected EEG graph simultaneously considering edge weights and vertex weights. Our mwcEEGs improves the classification performance by selecting intra-clique pairwise similar and inter-clique discriminative EEGs with similarity threshold $\delta$. Experimental results demonstrate the algorithm effectiveness compared with the state-of-the-art time series selection algorithms on real-world EEG datasets.


title: Behavior Analysis of NLI Models: Uncovering the Influence of Three Factors on Robustness

abstract: Natural Language Inference is a challenging task that has received substantial attention, and state-of-the-art models now achieve impressive test set performance in the form of accuracy scores. Here, we go beyond this single evaluation metric to examine robustness to semantically-valid alterations to the input data. We identify three factors - insensitivity, polarity and unseen pairs - and compare their impact on three SNLI models under a variety of conditions. Our results demonstrate a number of strengths and weaknesses in the models' ability to generalise to new in-domain instances. In particular, while strong performance is possible on unseen hypernyms, unseen antonyms are more challenging for all the models. More generally, the models suffer from an insensitivity to certain small but semantically significant alterations, and are also often influenced by simple statistical correlations between words and training labels. Overall, we show that evaluations of NLI models can benefit from studying the influence of factors intrinsic to the models or found in the dataset used.


title: Molecular genetic identification of skeletal remains from the Second World War Konfin I mass grave in Slovenia

abstract: This paper describes molecular genetic identification of one third of the skeletal remains of 88 victims of postwar (June 1945) killings found in the Konfin I mass grave in Slovenia. Living relatives were traced for 36 victims. We analyzed 84 right femurs and compared their genetic profiles to the genetic material of living relatives. We cleaned the bones, removed surface contamination, and ground the bones into powder. Prior to DNA isolation using Biorobot EZ1 (Qiagen), the powder was decalcified. The nuclear DNA of the samples was quantified using the real-time polymerase chain reaction method. We extracted 0.8 to 100 ng DNA/g of bone powder from 82 bones. Autosomal genetic profiles and Y-chromosome haplotypes were obtained from 98% of the bones, and mitochondrial DNA (mtDNA) haplotypes from 95% of the bones for the HVI region and from 98% of the bones for the HVII region. Genetic profiles of the nuclear and mtDNA were determined for reference persons. For traceability in the event of contamination, we created an elimination database including genetic profiles of the nuclear and mtDNA of all persons that had been in contact with the skeletal remains. When comparing genetic profiles, we matched 28 of the 84 bones analyzed with living relatives (brothers, sisters, sons, daughters, nephews, or cousins). The statistical analyses showed a high confidence of correct identification for all 28 victims in the Konfin I mass grave (posterior probability ranged from 99.9% to more than 99.999999%).


title: Enforcing forward-edge control-flow integrity in GCC & LLVM

abstract: Constraining dynamic control transfers is a common technique for mitigating software vulnerabilities. This defense has been widely and successfully used to protect return addresses and stack data; hence, current attacks instead typically corrupt vtable and function pointers to subvert a forward edge (an indirect jump or call) in the control-flow graph. Forward edges can be protected using Control-Flow Integrity (CFI) but, to date, CFI implementations have been research prototypes, based on impractical assumptions or ad hoc, heuristic techniques. To be widely adoptable, CFI mechanisms must be integrated into production compilers and be compatible with software-engineering aspects such as incremental compilation and dynamic libraries. ::: ::: This paper presents implementations of fine-grained, forward-edge CFI enforcement and analysis for GCC and LLVM that meet the above requirements. An analysis and evaluation of the security, performance, and resource consumption of these mechanisms applied to the SPEC CPU2006 benchmarks and common benchmarks for the Chromium web browser show the practicality of our approach: these fine-grained CFI mechanisms have significantly lower overhead than recent academic CFI prototypes. Implementing CFI in industrial compiler frameworks has also led to insights into design tradeoffs and practical challenges, such as dynamic loading.


title: Image analysis of broken rice grains of Khao Dawk Mali rice

abstract: In process of rice grain milling, rice grains are sorted by size into many categories for sale at different prices. By observation on “small broken”, which is a low-quality category of sorted result, we found that it composes of significant amounts of more expensive rice grains. In this research, we propose a method to evaluate broken rice grains in order to make higher profit from its higher quality portion by image analysis. Our algorithm is to categorize “small broken” into four types: small broken, broken, big broken and head rice, which are classes described by the Department of Rice, Thailand. Least-Square Support Vector Machine (LS-SVM) with Radius Basis Function (RBF) kernel is used as a classifier in the algorithm. The accuracy of the algorithm is 98.20%.


title: Deep 3D face identification

abstract: We propose a novel 3D face recognition algorithm using a deep convolutional neural network (DCNN) and a 3D augmentation technique. The performance of 2D face recognition algorithms has significantly increased by leveraging the representational power of deep neural networks and the use of large-scale labeled training data. As opposed to 2D face recognition, training discriminative deep features for 3D face recognition is very difficult due to the lack of large-scale 3D face datasets. In this paper, we show that transfer learning from a CNN trained on 2D face images can effectively work for 3D face recognition by fine-tuning the CNN with a relatively small number of 3D facial scans. We also propose a 3D face augmentation technique which synthesizes a number of different facial expressions from a single 3D face scan. Our proposed method shows excellent recognition results on Bosphorus, BU-3DFE, and 3D-TEC datasets, without using hand-crafted features. The 3D identification using our deep features also scales well for large databases.


title: Atlas: on the expression of spatial-keyword group queries using extended relational constructs

abstract: The popularity of GPS-enabled cellular devices introduced numerous applications, e.g., social networks, micro-blogs, and crowd-powered reviews. These applications produce large amounts of geo-tagged textual data that need to be processed and queried. Nowadays, many complex spatio-textual operators and their matching complex indexing structures are being proposed in the literature to process this spatio-textual data. For example, there exist several complex variations of the spatio-textual group queries that retrieve groups of objects that collectively satisfy certain spatial and textual criteria. However, having complex operators is against the spirit of SQL and relational algebra. In contrast to these complex spatio-textual operators, in relational algebra, simple relational operators are offered, e.g., relational selects, projects, order by, and group by, that are composable to form more complex queries. In this paper, we introduce Atlas, an SQL extension to express complex spatial-keyword group queries. Atlas follows the philosophy of SQL and relational algebra in that it uses simple declarative spatial and textual building-block operators and predicates to extend SQL. Not only that Atlas can represent spatio-textual group queries from the literature, but also it can compose other important queries, e.g., retrieve spatio-textual groups from subsets of object datasets where the selected subset satisfies user-defined relational predicates and the groups of close-by objects contain miss-spelled keywords. We demonstrate that Atlas is able to represent a wide range of spatial-keyword queries that existing indexes and algorithms would not be able to address. The building- block paradigm adopted by Atlas creates room for query optimization, where multiple query execution plans can be formed.


title: Coupling Interactions and Performance: Predicting Team Performance from Thin Slices of Conflict

abstract: Do teams show stable conflict interaction patterns that predict their performance hours, weeks, or even months in advance? Two studies demonstrate that two of the same patterns of emotional interaction dynamics that distinguish functional from dysfunctional marriages also distinguish high from low-performance design teams in the field, up to 6 months in advance, with up to 91p accuracy, and based on just 15minutes of interaction data: Group Affective Balance, the balance of positive to negative affect during an interaction, and Hostile Affect, the expression of a set of specific negative behaviors were both found as predictors of team performance. The research also contributes a novel method to obtain a representative sample of a team's conflict interaction. Implications for our understanding of design work in teams and for the design of groupware and feedback intervention systems are discussed.


title: Few-shot 3D Multi-modal Medical Image Segmentation using Generative Adversarial Learning

abstract: We address the problem of segmenting 3D multi-modal medical images in scenarios where very few labeled examples are available for training. Leveraging the recent success of adversarial learning for semi-supervised segmentation, we propose a novel method based on Generative Adversarial Networks (GANs) to train a segmentation model with both labeled and unlabeled images. The proposed method prevents over-fitting by learning to discriminate between true and fake patches obtained by a generator network. Our work extends current adversarial learning approaches, which focus on 2D single-modality images, to the more challenging context of 3D volumes of multiple modalities. The proposed method is evaluated on the problem of segmenting brain MRI from the iSEG-2017 and MRBrainS 2013 datasets. Significant performance improvement is reported, compared to state-of-art segmentation networks trained in a fully-supervised manner. In addition, our work presents a comprehensive analysis of different GAN architectures for semi-supervised segmentation, showing recent techniques like feature matching to yield a higher performance than conventional adversarial training approaches. Our code is publicly available at this https URL


title: Autonomic computing: An overview

abstract: The increasing scale complexity, heterogeneity and dynamism of networks, systems and applications have made our computational and information infrastructure brittle, unmanageable and insecure. This has necessitated the investigation of an alternate paradigm for system and application design, which is based on strategies used by biological systems to deal with similar challenges – a vision that has been referred to as autonomic computing. The overarching goal of autonomic computing is to realize computer and software systems and applications that can manage themselves in accordance with high-level guidance from humans. Meeting the grand challenges of autonomic computing requires scientific and technological advances in a wide variety of fields, as well as new software and system architectures that support the effective integration of the constituent technologies. This paper presents an introduction to autonomic computing, its challenges, and opportunities.


title: A survey of sparse matrix-vector multiplication performance on large matrices

abstract: We contribute a third-party survey of sparse matrix-vector (SpMV) product performance on industrial-strength, large matrices using: (1) The SpMV implementations in Intel MKL, the Trilinos project (Tpetra subpackage), the CUSPARSE library, and the CUSP library, each running on modern architectures. (2) NVIDIA GPUs and Intel multi-core CPUs (supported by each software package). (3) The CSR, BSR, COO, HYB, and ELL matrix formats (supported by each software package).


title: Channel Estimation for mmWave Massive MIMO Based Access and Backhaul in Ultra-Dense Network

abstract: Millimeter-wave (mmWave) massive MIMO used for access and backhaul in ultra-dense network (UDN) has been considered as the promising 5G technique. We consider such an heterogeneous network (HetNet) that ultra-dense small base stations (BSs) exploit mmWave massive MIMO for access and backhaul, while macrocell BS provides the control service with low frequency band. However, the channel estimation for mmWave massive MIMO can be challenging, since the pilot overhead to acquire the channels associated with a large number of antennas in mmWave massive MIMO can be prohibitively high. This paper proposes a structured compressive sensing (SCS)-based channel estimation scheme, where the angular sparsity of mmWave channels is exploited to reduce the required pilot overhead. Specifically, since the path loss for non-line-of-sight paths is much larger than that for line-of-sight paths, the mmWave massive channels in the angular domain appear the obvious sparsity. By exploiting such sparsity, the required pilot overhead only depends on the small number of dominated multipath. Moreover, the sparsity within the system bandwidth is almost unchanged, which can be exploited for the further improved performance. Simulation results demonstrate that the proposed scheme outperforms its counterpart, and it can approach the performance bound.


title: The pathophysiology of propofol infusion syndrome: a simple name for a complex syndrome

abstract: Propofol infusion syn- drome (PRIS) is a rare and often fa- tal syndrome described in critically ill children undergoing long-term propofol infusion at high doses. Re- cently several cases have been re- ported in adults, too. The main fea- tures of the syndrome consist of car- diac failure, rhabdomyolysis, severe metabolic acidosis and renal failure. To date 21 paediatric cases and 14 adult cases have been described. These latter were mostly patients with acute neurological illnesses or acute inflammatory diseases compli- cated by severe infections or even sepsis, and receiving catecholamines


Abstract Collaborative Recommendation via Adaptive Association Rule Mining


title: Path Planning and Autonomous Navigation using AMCL and AD*

abstract: Path planning and autonomous navigation are some of the most important challenges in mobile robotics. These are difficult tasks because the robot has to accurately and safely perform autonomous maneuverings. This paper presents a methodology to efficiently plan the trajectory of a robot in dynamic and complex environments, which it should traverse autonomously. A planner based in the AD* algorithm is used to plan a less costly trajectory to the destination. The methodology enables the robot to reach the goal using a local planner, which is applied to determine the speed and steering of the robot. Experimental results show that the planner is reliable for simulated and real tasks, as the robot has reached the goal while safely avoided obstacles.


title: THE VIRTUAL 3D CITY MODEL OF BERLIN- MANAGING, INTEGRATING AND COMMUNICATING COMPLEX URBAN INFORMATION

abstract: This contribution reports on concepts, implementation, and experience of the Berlin virtual 3D city model, a project that has developed a system for integrating, managing, integrating, presenting, and distributing complex urban geoinformation. It has been initiated by the Senate Department of Economics and the Senate Department of Urban Development in order to extend Berlin’s geodata infrastructure by a novel, flexible, and state-of-the-art geoinformation technology. As first applications, the virtual 3D city model forms core part of the investor information system hosted at the Berlin Business Location Center, and it represents the basis for ongoing projects in city planning at the architecture working group of the Senate Department of Urban Development.


title: Unsupervised Bayesian Detection of Independent Motion in Crowds

abstract: While crowds of various subjects may offer applicationspecific cues to detect individuals, we demonstrate that for the general case, motion itself contains more information than previously exploited. This paper describes an unsupervised data driven Bayesian clustering algorithm which has detection of individual entities as its primary goal. We track simple image features and probabilistically group them into clusters representing independently moving entities. The numbers of clusters and the grouping of constituent features are determined without supervised learning or any subject-specific model. The new approach is instead, that space-time proximity and trajectory coherence through image space are used as the only probabilistic criteria for clustering. An important contribution of this work is how these criteria are used to perform a one-shot data association without iterating through combinatorial hypotheses of cluster assignments. Our proposed general detection algorithm can be augmented with subject-specific filtering, but is shown to already be effective at detecting individual entities in crowds of people, insects, and animals. This paper and the associated video examine the implementation and experiments of our motion clustering framework.


title: Timespace in the workplace: dealing with interruptions

abstract: We report tindings from an observational study on the nature of interruptions in the workplace. The results show that in most cases, (64”A), the recipient received some benefit from the interruption. However in just over 40’%. of interruptions the recipient did not resume the work they were doing prior to the interruption. Some implications for time management and communication technology are presented.


title: A 1.2V Self-Referenced Temperature Sensor With a Time-Domain Readout and a Two-Step Improvement on Output Dynamic Range

abstract: A self-referenced temperature sensor with a time-domain readout and a two-step improvement on output dynamic range is presented in this paper. The proposed temperature sensor utilizes BJTs to generate temperature dependent/independent currents, which are converted to digital bits using a ring-oscillator-based time-domain readout scheme. A novel two-step improvement on output dynamic range is proposed to improve the power-efficiency and simplify the hardware design with a high conversion rate. Furthermore, the adopted process compensation strategy and the self-referenced readout scheme make the whole temperature sensor robust to process and supply variations. Fabricated in a standard 0.13- $\mu \text{m}$ CMOS process, the proposed temperature sensor occupies a die area of 0.06 mm2 and consumes 9.92 nJ per conversion at a conversion rate of 75 kSa/s. After one-point calibration at 20 °C, the sensor achieves inaccuracies of −2.88 °C/+2.71 °C and −1.7 °C/1.36 °C from −20 °C to 100 °C, with and without systematic nonlinearity removal, respectively. It shows an average supply sensitivity of 0.0136 °C/mV from 1.05 V to 1.4 V without any external reference clock or voltage regulators.


title: Automated Intracranial Pressure Prediction Using Multiple Features Sources

abstract: Raised intracranial pressure (ICP) causes serious problem on traumatic brain injury patient. Automated and non-intrusive ICP level prediction saves cost and enhances efficiency. An automated ICP level prediction model based on machine learning method is proposed in this paper. Multiple features, including midline shift, intracranial air cavities, ventricle size, texture patterns, and blood amount, are selected, extracted and aggregated using different methods. Some demographic information, such as age and injury severity score, is also considered as candidate features. After the feature aggregation, the most important features are selected by a feature selection scheme applied on 10 fold nested cross validation. The final support vector machine classification result using RapidMiner shows the effectiveness of the proposed method in ICP level prediction.


title: Promoting medical tourism to India: messages, images, and the marketing of international patient travel.

abstract: Abstract The practice of medical tourism depends on successfully informing potential patients about procedure options, treatment facilities, tourism opportunities, travel arrangements, and destination countries. The promotion of medical tourism includes a wide range of marketing materials such as flyers, booklets, and websites. Yet, there is a paucity of knowledge about the dissemination, content, and reception of these promotional materials. Drawing on a thematic content analysis of the promotional print material distributed at the first medical tourism trade show in Canada in 2009, the main purpose of this article is to identify and understand the messages and images that companies use to market India as a global destination. While researchers and news media frequently cite low cost procedures as a key determinant for international patient travel, particularly to developing nations, our analysis reveals few low cost-related images or messages in the promotional materials distributed at the trade show. To help explain this surprising disjuncture, we consider four related issues: (1) promotional materials may be designed to be circulated amongst potential patients’ concerned family and friends who privilege knowing about things such as the use of advanced technologies; (2) developing nations need to portray safe and advanced treatment facilities in order to dispel potential patients’ suspicions that their medical care is inferior; (3) companies may avoid making cost saving claims that cannot be fulfilled for all of their international patients, especially those traveling from developing nations; and (4) messages of low cost may detract from and even undermine messages about quality. We conclude by identifying numerous avenues for future research by social and health scientists, and by considering the implications of our findings for existing knowledge gaps and debates within health geography specifically.


title: Current Studies On Intrusion Detection System, Genetic Algorithm And Fuzzy Logic

abstract: Nowadays Intrusion Detection System (IDS) which is increasingly a key element of system security is used to identify the malicious activities in a computer system or network. There are different approaches being employed in intrusion detection systems, but unluckily each of the technique so far is not entirely ideal. The prediction process may produce false alarms in many anomaly based intrusion detection systems. With the concept of fuzzy logic, the false alarm rate in establishing intrusive activities can be reduced. A set of efficient fuzzy rules can be used to define the normal and abnormal behaviors in a computer network. Therefore some strategy is needed for best promising security to monitor the anomalous behavior in computer network. In this paper I present a few research papers regarding the foundations of intrusion detection systems, the methodologies and good fuzzy classifiers using genetic algorithm which are the focus of current development efforts and the solution of the problem of Intrusion Detection System to offer a realworld view of intrusion detection. Ultimately, a discussion of the upcoming technologies and various methodologies which promise to improve the capability of computer systems to detect intrusions is offered.


title: Application of bee colony algorithm for FANET routing

abstract: FANET are wireless ad hoc networks on unmanned aerial vehicles, and are characterized by high nodes mobility, dynamically changing topology and movement in 3D-space. FANET routing is an extremely complicated problem. The article describes the bee algorithm and the routing process based on the mentioned algorithm in ad hoc networks. The classification of FANET routing methods is given. The overview of the routing protocols based on the bee colony algorithms is provided. Owing to the experimental analysis, bio-inspired algorithms based on the bee colony were proved to show good results, having better efficiency than traditional FANET routing algorithms in most cases.


title: Population anomaly detection through deep gaussianization

abstract: We introduce an algorithmic method for population anomaly detection based on gaussianization through an adversarial autoencoder. This method is applicable to detection of 'soft' anomalies in arbitrarily distributed highly-dimensional data. A soft, or population, anomaly is characterized by a shift in the distribution of the data set, where certain elements appear with higher probability than anticipated. Such anomalies must be detected by considering a sufficiently large sample set rather than a single sample. Applications include, but not limited to, payment fraud trends, data exfiltration, disease clusters and epidemics, and social unrests. We evaluate the method on several domains and obtain both quantitative results and qualitative insights.


title: Exploiting coherence for the simultaneous discovery of latent facets and associated sentiments

abstract: Facet-based sentiment analysis involves discovering the ::: latent facets, sentiments and their associations. Traditional facet-based sentiment analysis algorithms typically perform the various tasks in sequence, and fail to take advantage of the mutual reinforcement of the tasks. Additionally,inferring sentiment levels typically requires domain knowledge or human intervention. In this paper, we propose aseries of probabilistic models that jointly discover latent facets and sentiment topics, and also order the sentiment topics with respect to a multi-point scale, in a language and domain independent manner. This is achieved by simultaneously capturing both short-range syntactic structure and long range semantic dependencies between the sentiment and facet words. The models further incorporate coherence in reviews, where reviewers dwell on one facet or sentiment level before moving on, for more accurate facet and sentiment discovery. For reviews which are supplemented with ratings, our models automatically order the latent sentiment topics, without requiring seed-words or domain-knowledge. To the best of our knowledge, our work is the first attempt to combine the notions of syntactic and semantic dependencies ::: in the domain of review mining. Further, the concept of ::: facet and sentiment coherence has not been explored earlier either. Extensive experimental results on real world review data show that the proposed models outperform various state of the art baselines for facet-based sentiment analysis.


title: A unified approach for subspace simulation of deformable bodies in multiple domains

abstract: Multi-domain subspace simulation can efficiently and conveniently simulate the deformation of a large deformable body, by constraining the deformation of each domain into a different subspace. The key challenge in implementing this method is how to handle the coupling among multiple deformable domains, so that the overall effect is free of gap or locking issues. In this paper, we present a new domain decomposition framework that connects two disjoint domains through coupling elements. Under this framework, we present a unified simulation system that solves subspace deformations and rigid motions of all of the domains by a single linear solve. Since the coupling elements are part of the deformable body, their elastic properties are the same as the rest of the body and our system does not need stiffness parameter tuning. To quickly evaluate the reduced elastic forces and their Jacobian matrices caused by the coupling elements, we further develop two cubature optimization schemes using uniform and non-uniform cubature weights. Our experiment shows that the whole system can efficiently handle large and complex scenes, many of which cannot be easily simulated by previous techniques without limitations.


title: Can Mendeley bookmarks reflect readership? A survey of user motivations

abstract: Although Mendeley bookmarking counts appear to correlate moderately with conventional citation metrics, it is not known whether academic publications are bookmarked in Mendeley in order to be read or not. Without this information, it is not possible to give a confident interpretation of altmetrics derived from Mendeley. In response, a survey of 860 Mendeley users shows that it is reasonable to use Mendeley bookmarking counts as an indication of readership because most 55% users with a Mendeley library had read or intended to read at least half of their bookmarked publications. This was true across all broad areas of scholarship except for the arts and humanities 42%. About 85% of the respondents also declared that they bookmarked articles in Mendeley to cite them in their publications, but some also bookmark articles for use in professional 50%, teaching 25%, and educational activities 13%. Of course, it is likely that most readers do not record articles in Mendeley and so these data do not represent all readers. In conclusion, Mendeley bookmark counts seem to be indicators of readership leading to a combination of scholarly impact and wider professional impact.


title: Deep Generative Learning via Variational Gradient Flow

abstract: We propose a general framework to learn deep generative models via \textbf{V}ariational \textbf{Gr}adient Fl\textbf{ow} (VGrow) on probability spaces. The evolving distribution that asymptotically converges to the target distribution is governed by a vector field, which is the negative gradient of the first variation of the $f$-divergence between them. We prove that the evolving distribution coincides with the pushforward distribution through the infinitesimal time composition of residual maps that are perturbations of the identity map along the vector field. The vector field depends on the density ratio of the pushforward distribution and the target distribution, which can be consistently learned from a binary classification problem. Connections of our proposed VGrow method with other popular methods, such as VAE, GAN and flow-based methods, have been established in this framework, gaining new insights of deep generative learning. We also evaluated several commonly used divergences, including Kullback-Leibler, Jensen-Shannon, Jeffrey divergences as well as our newly discovered `logD' divergence which serves as the objective function of the logD-trick GAN. Experimental results on benchmark datasets demonstrate that VGrow can generate high-fidelity images in a stable and efficient manner, achieving competitive performance with state-of-the-art GANs.


title: Using Relational Topic Models to capture coupling among classes in object-oriented software systems

abstract: Coupling metrics capture the degree of interaction and relationships among source code elements in software systems. A vast majority of existing coupling metrics rely on structural information, which captures interactions such as usage relations between classes and methods or execute after associations. However, these metrics lack the ability to identify conceptual dependencies, which, for instance, specify underlying relationships encoded by developers in identifiers and comments of source code classes. We propose a new coupling metric for object-oriented software systems, namely Relational Topic based Coupling (RTC) of classes, which uses Relational Topic Models (RTM), generative probabilistic model, to capture latent topics in source code classes and relationships among them. A case study on thirteen open source software systems is performed to compare the new measure with existing structural and conceptual coupling metrics. The case study demonstrates that proposed metric not only captures new dimensions of coupling, which are not covered by the existing coupling metrics, but also can be used to effectively support impact analysis.


title: Control of Variable Speed Wind Turbines

abstract: Uncontrolled wind turbine configuration, such as stall-regulation captures, energy relative to the amount of wind speed. This configuration requires constant turbine speed because the generator that is being directly coupled is also connected to a fixed-frequency utility grid. In extremely strong wind conditions, only a fraction of available energy is captured. Plants designed with such a configuration are economically unfeasible to run in these circumstances. Thus, wind turbines operating at variable speed are better alternatives. This paper focuses on a controller design methodology applied to a variable-speed, horizontal axis wind turbine. A simple but rigid wind turbine model was used and linearised to some operating points to meet the desired objectives. By using blade pitch control, the deviation of the actual rotor speed from a reference value is minimised. The performances of PI and PID controllers were compared relative to a step wind disturbance. Results show comparative responses between these two controllers. The paper also concludes that with the present methodology, despite the erratic wind data, the wind turbine still manages to operate most of the time at 88% in the stable region.


title: Predicting the Sentiment Polarity and Rating of Yelp Reviews

abstract: Online reviews of businesses have become increasingly important in recent years, as customers and even competitors use them to judge the quality of a business. Yelp is one of the most popular websites for users to write such reviews, and it would be useful for them to be able to predict the sentiment or even the star rating of a review. In this paper, we develop two classifiers to perform positive/negative classification and 5-star classification. We use Naive Bayes, Support Vector Machines, and Logistic Regression as models, and achieved the best accuracy with Logistic Regression: 92.90% for positive/negative classification, and 63.92% for 5-star classification. These results demonstrate the quality of the Logistic Regression model using only the text of the review, yet there is a promising opportunity for improvement with more data, more features, and perhaps different models.


title: Closed Shortest Path in the Original Coordinates with an Application to Breast Cancer

abstract: Breast cancer is one of the most mediated malignant diseases, because of its high incidence and prevalence, but principally due to its physical and psychological invasiveness. The study of this disease using computer science tools resorts often to the image segmentation operation. Image segmentation, although having been extensively studied, is still an open problem. Shortest path algorithms are extensively used to tackle this problem. There are, however, applications where the starting and ending positions of the shortest path need to be constrained, defining a closed contour enclosing a previously detected seed. Mass and calcification segmentation in mammograms and areola segmentation in digital images are two particular examples of interest within the field of breast cancer research. Usually the closed contour computation is addressed by transforming the image into polar coordinates, where the closed contour is transformed into an open contour between two opposite margins. In this work, after illustrating some of the limitations of this approach, we show how to compute the closed contour in the original coordinate space. After defining a directed acyclic graph appropriate for this task, we address the main difficulty in operating in the original coordinate space. Since small paths collapsing in the seed point are naturally favored, we modulate the cost of the edges to counterbalance this bias. A thorough evaluation is conducted with datasets from the breast cancer field. The algorithm is shown to be fast and reliable and suffers no loss in resolution.


title: Towards more accurate retrieval of duplicate bug reports

abstract: In a bug tracking system, different testers or users may submit multiple reports on the same bugs, referred to as duplicates, which may cost extra maintenance efforts in triaging and fixing bugs. In order to identify such duplicates accurately, in this paper we propose a retrieval function (REP) to measure the similarity between two bug reports. It fully utilizes the information available in a bug report including not only the similarity of textual content in summary and description fields, but also similarity of non-textual fields such as product, component, version, etc. For more accurate measurement of textual similarity, we extend BM25F - an effective similarity formula in information retrieval community, specially for duplicate report retrieval. Lastly we use a two-round stochastic gradient descent to automatically optimize REP for specific bug repositories in a supervised learning manner. We have validated our technique on three large software bug repositories from Mozilla, Eclipse and OpenOffice. The experiments show 10 -- 27% relative improvement in recall rate@k and 17 -- 23% relative improvement in mean average precision over our previous model. We also applied our technique to a very large dataset consisting of 209,058 reports from Eclipse, resulting in a recall rate@k of 37 -- 71% and mean average precision of 47%.


title: A lived informatics model of personal informatics

abstract: Current models of how people use personal informatics systems are largely based in behavior change goals. They do not adequately characterize the integration of self-tracking into everyday life by people with varying goals. We build upon prior work by embracing the perspective of lived informatics to propose a new model of personal informatics. We examine how lived informatics manifests in the habits of self-trackers across a variety of domains, first by surveying 105, 99, and 83 past and present trackers of physical activity, finances, and location and then by interviewing 22 trackers regarding their lived informatics experiences. We develop a model characterizing tracker processes of deciding to track and selecting a tool, elaborate on tool usage during collection, integration, and reflection as components of tracking and acting, and discuss the lapsing and potential resuming of tracking. We use our model to surface underexplored challenges in lived informatics, thus identifying future directions for personal informatics design and research.


title: Design of hybrid nonlinear spline adaptive filters for active noise control

abstract: In this paper, we focus on the problem of removing noise in the acoustic domain. To this end, we introduce a class of hybrid nonlinear spline filters, which are designed as a cascade of an adaptive spline function and a single layer adaptive nonlinear network. The adaptive nonlinear networks employed in this work are the functional link network and the even mirror Fourier nonlinear network. Suitable update rules, which not only update the adaptive weights of the nonlinear networks, but also introduce adaptability in the developed spline function are derived. The proposed nonlinear filters have been successfully applied to nonlinear system identification as well as nonlinear active noise control. The new filters have been shown to outperform other popular nonlinear filters.


title: Vehicular Ad Hoc Networks (VANETs): Challenges and Perspectives

abstract: Vehicular Ad hoc Network (VANET), a subclass of mobile Ad Hoc networks (MANETs), is a promising approach for future intelligent transportation system (ITS). These networks have no fixed infrastructure and instead rely on the vehicles themselves to provide network functionality. However, due to mobility constraints, driver behavior, and high mobility, VANETs exhibit characteristics that are dramatically different from many generic MANETs. This article provides a comprehensive study of challenges in these networks, which we concentrate on the problems and proposed solutions. Then we outline current state of the research and future perspectives. With this article, readers can have a more thorough understanding of vehicle ad hoc networking and the research trends in this area.


title: Reconstruction of the Maxilla and Mandible With Particulate Bone Graft and Titanium Mesh for Implant Placement

abstract: Purpose The purpose of the study was to evaluate the magnitude of ridge augmentation with titanium mesh, overall graft success, anatomic location of ridge defects and their relationship to mesh exposure. Materials and Methods This retrospective study evaluated 44 patients who received mandibular or maxillary reconstruction with autogenous particulate bone graft and titanium mesh for the purpose of implant placement. Autogenous bone graft was harvested from the iliac crest, tibia, and mandibular symphysis. A total of 45 sites were included in the study. Average augmentation bone heights were measured and compared. Statistical analysis was done with ANOVA and Student’s t test. Histomorphometric analysis was performed on the soft tissue specimen found between the mesh and the bone graft. Results Twenty-nine sites underwent mandibular reconstruction and 16 underwent maxillary reconstruction. The mean augmentation in partial maxillary defects was 11.33 ± 1.56 mm, and in complete maxillary augmentation, the height achieved was 14.3 ± 1.39 mm. In the mandible, mean increase in height for partial defects was 14 ± 1.42 mm and for complete augmentation it was 13.71 ± 1.14 mm. The mean augmentation for all sites was 13.7 mm (12.8 mm in the maxilla and 13.9 mm in the mandible). A total of 82 implants were placed in the maxilla and 92 implants were placed in the mandible. In the maxillary group, 7 sites had exposure of the titanium mesh and 16 sites were exposed in the mandible. The success of the bone grafting procedure was 97.72%. Conclusions Porous titanium mesh is a reliable containment system used for reconstruction of the maxilla and the mandible. This material tolerates exposure very well and gives predictable results.


Broad Learning based Social Community Detection


DESIGN OF A LOW COST EMG AMPLIFIER WITH DISCREET OP-AMPS FOR MACHINE CONTROL


GENERATING JAVA CLASS SKELETON USING A NATURAL LANGUAGE INTERFACE


title: A Theoretical Analysis of Two-Stage Recommendation for Cold-Start Collaborative Filtering

abstract: In this paper, we present a theoretical framework for tackling the cold-start collaborative filtering problem, where unknown targets (items or users) keep coming to the system, and there is a limited number of resources (users or items) that can be allocated and related to them. The solution requires a trade-off between exploitation and exploration since with the limited recommendation opportunities, we need to, on one hand, allocate the most relevant resources right away, but, on the other hand, it is also necessary to allocate resources that are useful for learning the target's properties in order to recommend more relevant ones in the future. In this paper, we study a simple two-stage recommendation combining a sequential and a batch solution together. We first model the problem with the partially observable Markov decision process (POMDP) and provide its exact solution. Then, through an in-depth analysis over the POMDP value iteration solution, we identify that an exact solution can be abstracted as selecting resources that are not only highly relevant to the target according to the initial-stage information, but also highly correlated, either positively or negatively, with other potential resources for the next stage. With this finding, we propose an approximate solution to ease the intractability of the exact solution. Our initial results on synthetic data and the MovieLens 100K dataset confirm our theoretical development and analysis.


title: New acquisition method based on firmware update protocols for Android smartphones

abstract: Android remains the dominant OS in the smartphone market even though the iOS share of the market increased during the iPhone 6 release period. As various types of Android smartphones are being launched in the market, forensic studies are being conducted to test data acquisition and analysis. However, since the application of new Android security technologies, it has become more difficult to acquire data using existing forensic methods. In order to address this problem, we propose a new acquisition method based on analyzing the firmware update protocols of Android smartphones. A physical acquisition of Android smartphones can be achieved using the flash memory read command by reverse engineering the firmware update protocol in the bootloader. Our experimental results demonstrate that the proposed method is superior to existing forensic methods in terms of the integrity guarantee, acquisition speed, and physical dump with screen-locked smartphones (USB debugging disabled).


title: Battery modelling methods for electric vehicles - A review

abstract: Electric vehicles and hybrid electric vehicles are rapidly gaining popularity as an important means of decarbonizing the transport sector in tackling sustainable energy supply and environment pollution problems. To build a proper battery model is essential in predicting battery behaviour under various operating conditions for avoiding unsafe battery operations and in developing proper controlling algorithms and maintenance strategies. This paper presents a comprehensive review of battery modelling methods. In particular, the mechanism and characteristics of Li-ion batteries are presented, and different modelling methods are discussed. Considering that equivalent electric circuit models are most widely used in the battery management system, a detailed analysis of the modelling procedure is presented.


title: Persistence-Based Clustering in Riemannian Manifolds

abstract: We present a clustering scheme that combines a mode-seeking phase with a cluster merging phase in the corresponding density map. While mode detection is done by a standard graph-based hill-climbing scheme, the novelty of our approach resides in its use of topological persistence to guide the merging of clusters. Our algorithm provides additional feedback in the form of a set of points in the plane, called a persistence diagram (PD), which provably reflects the prominences of the modes of the density. In practice, this feedback enables the user to choose relevant parameter values, so that under mild sampling conditions the algorithm will output the correct number of clusters, a notion that can be made formally sound within persistence theory. In addition, the output clusters have the property that their spatial locations are bound to the ones of the basins of attraction of the peaks of the density. The algorithm only requires rough estimates of the density at the data points, and knowledge of (approximate) pairwise distances between them. It is therefore applicable in any metric space. Meanwhile, its complexity remains practical: although the size of the input distance matrix may be up to quadratic in the number of data points, a careful implementation only uses a linear amount of memory and takes barely more time to run than to read through the input.


title: Compositional Attention Networks for Interpretability in Natural Language Question Answering

abstract: MAC Net is a compositional attention network designed for Visual Question Answering. We propose a modified MAC net architecture for Natural Language Question Answering. Question Answering typically requires Language Understanding and multi-step Reasoning. MAC net's unique architecture - the separation between memory and control, facilitates data-driven iterative reasoning. This makes it an ideal candidate for solving tasks that involve logical reasoning. Our experiments with 20 bAbI tasks demonstrate the value of MAC net as a data-efficient and interpretable architecture for Natural Language Question Answering. The transparent nature of MAC net provides a highly granular view of the reasoning steps taken by the network in answering a query.


title: The highD Dataset: A Drone Dataset of Naturalistic Vehicle Trajectories on German Highways for Validation of Highly Automated Driving Systems

abstract: Scenario-based testing for the safety validation of highly automated vehicles is a promising approach that is being examined in research and industry. This approach heavily relies on data from real-world scenarios to derive the necessary scenario information for testing. Measurement data should be collected at a reasonable effort, contain naturalistic behavior of road users and include all data relevant for a description of the identified scenarios in sufficient quality. However, the current measurement methods fail to meet at least one of the requirements. Thus, we propose a novel method to measure data from an aerial perspective for scenario-based validation fulfilling the mentioned requirements. Furthermore, we provide a large-scale naturalistic vehicle trajectory dataset from German highways called highD. We evaluate the data in terms of quantity, variety and contained scenarios. Our dataset consists of 16.5 hours of measurements from six locations with 110 000 vehicles, a total driven distance of 45 000 km and 5600 recorded complete lane changes. The highD dataset is available online at: http://www.highD-dataset.com


title: CNN-Based Preprocessing to Optimize Watershed-Based Cell Segmentation in 3D Confocal Microscopy Images

abstract: The quantitative analysis of cellular membranes helps understanding developmental processes at the cellular level. Particularly 3D microscopic image data offers valuable insights into cell dynamics, but error-free automatic segmentation remains challenging due to the huge amount of data generated and strong variations in image intensities. In this paper, we propose a new 3D segmentation approach that combines the discriminative power of convolutional neural networks (CNNs) for preprocessing and investigates the performance of three watershed-based postprocessing strategies (WS), which are well suited to segment object shapes, even when supplied with vague seed and boundary constraints. To leverage the full potential of the watershed algorithm, the multi-instance segmentation problem is initially interpreted as three-class semantic segmentation problem, which in turn is well-suited for the application of CNNs. Using manually annotated 3D confocal microscopy images of Arabidopsis thaliana, we show the superior performance of the proposed method compared to the state of the art.


Collaborative Systems for Smart Networked Environments


title: Predicting Multiple Metrics for Queries: Better Decisions Enabled by Machine Learning

abstract: One of the most challenging aspects of managing a very large data warehouse is identifying how queries will behave before they start executing. Yet knowing their performance characteristics --- their runtimes and resource usage --- can solve two important problems. First, every database vendor struggles with managing unexpectedly long-running queries. When these long-running queries can be identified before they start, they can be rejected or scheduled when they will not cause extreme resource contention for the other queries in the system. Second, deciding whether a system can complete a given workload in a given time period (or a bigger system is necessary) depends on knowing the resource requirements of the queries in that workload. We have developed a system that uses machine learning to accurately predict the performance metrics of database queries whose execution times range from milliseconds to hours. For training and testing our system, we used both real customer queries and queries generated from an extended set of TPC-DS templates. The extensions mimic queries that caused customer problems. We used these queries to compare how accurately different techniques predict metrics such as elapsed time, records used, disk I/Os, and message bytes. The most promising technique was not only the most accurate, but also predicted these metrics simultaneously and using only information available prior to query execution. We validated the accuracy of this machine learning technique on a number of HP Neoview configurations. We were able to predict individual query elapsed time within 20% of its actual time for 85% of the test queries. Most importantly, we were able to correctly identify both the short and long-running (up to two hour) queries to inform workload management and capacity planning.


Trade Policy and Economic Growth: A Skeptic's Guide to the Cross-National Evidence


title: A skew detection and correction technique for Arabic script text-line based on subwords bounding

abstract: Text-line skew detection and correction is the first step in Arabic document recognition and analysis. It is a crucial pre-processing stage of Arabic Character Recognition (ACR). It has a direct effect on the dependability and efficiency of other system stages such as baseline detection, segmentation and feature extraction stages. In this paper an efficient skew detection and correction method for Arabic handwritten text-line based on sub-words bounding is presented. It is constructed from three stages including: pre-processing, skew detection and skew correction stages. The proposed method estimates a text-line baseline based on calculating the middle point for its sub-words bounding. Then align the text-line components on the estimated baseline. The proposed method is implemented on 3960 text-line handwritten images, which were written by 40 writers. It is discussed with the horizontal projection method in terms of effectiveness. The proposed method obtained an accuracy ratio of 96.15%, and takes 6.7 seconds as average time. It can also automatically detect text baselines of documents with any orientation.


Outlier Detection and Motion Segmentation


title: Freirean Cultural Lenses for Promoting Future Teacher Literacy Knowledge: Dominant Literacy Discourses and Majority Interns in a Minority High School

abstract: How is it possible for us to work in a community without feeling the spirit of the culture that has been there for many years, without trying to understand the soul of the culture?" --Paulo Freire cited in Gadotti, 1994 Introduction Upon reflection about my teaching practice, I began my first phase of qualitative research in literacy policy research, moving from practice to theory. The result of this part of the project was a history of literacy discourses 1970-1998 (Agnello, 2001). In that history, I traced the development of what Bernstein called official pedagogical discourse, utilizing discourse analytical procedures to frame a critical study of prescriptions for literacy development in landmark educational policies (Bernstein, 1980). Citing official literacy statements as data, I constructed a history of four discourses of literacy including human capital, cultural, critical, and feminist literacies. The time range spans from the publication of Paulo Freire's Pedagogy of the Oppressed (1970) to an effort to unite welfare and literacy in the Workforce Investment Act (1998). Most recently, I have added an analysis of No Child Left Behind in an effort to bring the dominant discourses of literacy study up to date. In sum, discourse analysis of landmark policy texts chronicled archaeological discursive relics that contributed to the development and regulation of a literate citizenry (Freire, 1970; Foucault, 1972; Agnello, 2001). Regulation results from discourses of human capital and cultural literacy and the power exercised in many locations to perpetuate the knowledge arrangements such discourses afford the political economy (Freire, 1970). For those on the bottom of the social hierarchy, oppression often results from implemented literacy policies (Freire, 1970; McNeil, 2000; Valenzuela, 2005). In the second stage, I moved from theory to practice. The history of literacy discourses became a backdrop against which I approached critical applications of teaching literacy in a language arts methods class. The objective of such teaching was to promote reflective and critical teaching practice. Cochran-Smith (1991, 2001), Darling-Hammond (1990), and Darling-Hammond and Wise (1991) have discussed the possibilities for such critical practice in the turbulent seas of educational restructuring resulting from policy reform. Followers of Freire promote reflection through teacher as researcher and teacher as public intellectual endeavors. In new multicultural times, with new literacy awareness, and upon new epistemological foundations of literacy research, I shared new teaching methods and postformal practices with language arts methods students in order to transform schooling and the world (Kincheloe, 1991, 1993; Giroux, 1988; Kincheloe & Steinberg, 1993; Agnello, 2006). Responding to Duke and Mallette, who express a paucity of lenses utilized to explore the many facets of national literacy research, I pursued archaeological, genealogical research, employing synthetic and interpretive discourse analysis of both student interns' influences on their literacy development and formal literacy policy articulations. This research is phenomenological and hermeneutical making interpretive connections among future teachers' experiences, national literacy policy, and the educational possibilities afforded a literate citizenry (Agnello, 2001). Such multi-epistemological and multi-methodological endeavors promote change in the way we view knowledge connoted by the "truths" of literacy and the practices implemented to achieve such literacy at large. Revolutions and struggles evidenced in research methodologies, in education, and in literacy studies stem from epistemological questioning of the "inscription of political and social doctrines of progress into science" (Popkewitz, 1997, p. 18; Lankshear, 2001). This revolutionary phenomenon has occurred particularly in the production of knowledge, social sciences, and education that "question[s] the presuppositions of progress and power underlying intellectual work" (Popkewitz, 1997, p. …


title: Assessing breast cancer risk in a primary care setting.

abstract: Individuals who are given a preventive exam by a primary care provider are more likely to agree to cancer screening. The provider recommendation has been identified as the strongest factor associated with screening utilization. This article provides a framework for breast cancer risk assessment for an advanced practice registered nurse working in primary care practice.


title: Online Object Tracking Based on CNN with Metropolis-Hasting Re-Sampling

abstract: Tracking-by-learning strategies have been effective in solving many challenging problems in visual tracking, in which the learning sample generation and labeling play important roles for final performance. Since the concern of deep learning based approaches has shown an impressive performance in different vision tasks, how to properly apply the learning model, such as CNN, to an online tracking framework is still challenging. In this paper, to overcome the overfitting problem caused by straight-forward incorporation, we propose an online tracking framework by constructing a CNN based adaptive appearance model to generate more reliable training data over time. With a reformative Metropolis-Hastings re-sampling scheme to reshape particles for a better state posterior representation during online learning, the proposed tracking outperforms most of the state-of-art trackers on challenging benchmark video sequences.


title: Faster isosurface ray tracing using implicit KD-trees

abstract: The visualization of high-quality isosurfaces at interactive rates is an important tool in many simulation and visualization applications. Today, isosurfaces are most often visualized by extracting a polygonal approximation that is then rendered via graphics hardware or by using a special variant of preintegrated volume rendering. However, these approaches have a number of limitations in terms of the quality of the isosurface, lack of performance for complex data sets, or supported shading models. An alternative isosurface rendering method that does not suffer from these limitations is to directly ray trace the isosurface. However, this approach has been much too slow for interactive applications unless massively parallel shared-memory supercomputers have been used. In this paper, we implement interactive isosurface ray tracing on commodity desktop PCs by building on recent advances in real-time ray tracing of polygonal scenes and using those to improve isosurface ray tracing performance as well. The high performance and scalability of our approach will be demonstrated with several practical examples, including the visualization of highly complex isosurface data sets, the interactive rendering of hybrid polygonal/isosurface scenes, including high-quality ray traced shading effects, and even interactive global illumination on isosurfaces.


EXTRACTING ARABIC COLLOCATIONS BASED ON JAPE RULES


title: The Critical Success Factors in Customer Relationship Management (CRM) (ERP) Implementation

abstract: C ustomer Relationship Management (CRM) is fast emerging as a top priority initiative for of businesses According to Gartner, “Worldwide customer relationship management (CRM) software revenue totaled $8.1 billion in 2007, a 23.1 percent increase from 2006 revenue of $6.6 billion...” (gartner.com). In its 2007 press release, Gartner ranked vendors in worldwide CRM software revenue accounting for 25.4 percent of the market;


title: Financial forecasting based on artificial neural networks: Promising directions for modeling

abstract: Financial forecasting plays a critical role in present economic context where neural networks have become a good alternative technique over traditional methods. Vast ranges of neural models are developed to achieve better accuracy in forecasting. In addition, the ways to find out a good neural architecture is being explored by the research community. In the literature, main problems are figured out within the area of data preparing and neural network design. In this paper, the reasons that affect the performance of the models are discussed based on empirical and mathematical evidence. Finally, this paper presents the directions towards a more suitable neural model for financial forecasting by combining data preprocessing techniques, clustering techniques and support vector machine.


ELDEN: Improved Entity Linking Using Densified Knowledge Graphs


title: Sharing Heterogeneous Spatial Knowledge: Map Fusion Between Asynchronous Monocular Vision and Lidar or Other Prior Inputs

abstract: To enable low-cost mobile devices and robots equipped with monocular cameras to obtain accurate position information in GPS-denied environments, we propose to use pre-collected lidar or other prior data to rectify imprecise visual simultaneous localization and mapping (SLAM) results. This leads to a novel and nontrivial problem that fuses vision and prior/lidar data acquired at different perspectives and time. In fact, the lidar inputs can be replaced by other prior mapping inputs as long as we can extract vertical planes from these inputs. Hence, they are referred as prior/lidar data in general. We exploit the planar structure extracted from both vision and prior/lidar data and use it as the anchoring information to fuse the heterogeneous maps. We formulate a constrained global bundle adjustment using coplanarity constraints and solve it using a penalty-barrier approach. By error analysis we prove that the coplanarity constraints help reduce the estimation uncertainties. We have implemented the system and tested it with real data. The initial results show that our algorithm significantly reduces the absolute trajectory error of visual SLAM by as much as 68.3%.


title: QuantMiner: A Genetic Algorithm for Mining Quantitative Association Rules

abstract: In this paper, we propose QUANTMINER, a mining quantitative association rules system. This system is based on a genetic algorithm that dynamically discovers "good" intervals in association rules by optimizing both the support and the confidence. The experiments on real and artificial databases have shown the usefulness of QUANTMINER as an interactive data mining tool.


title: An equivalent current source model and Laplacian weighted minimum norm current estimates of brain electrical activity

abstract: We have developed a method for estimating the three-dimensional distribution of equivalent current sources inside the brain from scalp potentials. Laplacian weighted minimum norm algorithm has been used in the present study to estimate the inverse solutions. A three-concentric-sphere inhomogeneous head model was used to represent the head volume conductor. A closed-form solution of the electrical potential over the scalp and inside the brain due to a point current source was developed for the three-concentric-sphere inhomogeneous head model. Computer simulation studies were conducted to validate the proposed equivalent current source imaging. Assuming source configurations as either multiple dipoles or point current sources/sinks, in computer simulations we used our method to reconstruct these sources, and compared with the equivalent dipole source imaging. Human experimental studies were also conducted and the equivalent current source imaging was performed on the visual evoked potential data. These results highlight the advantages of the equivalent current source imaging and suggest that it may become an alternative approach to imaging spatially distributed current sources-sinks in the brain and other organ systems.


title: Information System Use - Related Activity: An Expanded Behavioral Conceptualization of Individual-Level Information System Use

abstract: Despite calls for improving current approaches to conceptualizing and measuring the construct of information system use, theoretical advances in this regard are still insufficient. The present paper proposes to expand the focus of existing conceptualizations that exclusively focus on technology interaction behaviors via the construct of IS use-related activity. Based on task-technology fit and activity theory, IS use-related activity is conceptualized as a second-order aggregate construct that comprises both technology interaction behaviors, as well as activities users undertake to adapt the task-technology-individual system. A multiple-indicators and multiple-causes analysis of data collected from 190 users in 21 organizations is found to support the proposed conceptualization.


title: Generative Adversarial Networks with Decoder-Encoder Output Noise

abstract: In recent years, research on image generation methods has been developing fast. The auto-encoding variational Bayes method (VAEs) was proposed in 2013, which uses variational inference to learn a latent space from the image database and then generates images using the decoder. The generative adversarial networks (GANs) came out as a promising framework, which uses adversarial training to improve the generative ability of the generator. However, the generated pictures by GANs are generally blurry. The deep convolutional generative adversarial networks (DCGANs) were then proposed to leverage the quality of generated images. Since the input noise vectors are randomly sampled from a Gaussian distribution, the generator has to map from a whole normal distribution to the images. This makes DCGANs unable to reflect the inherent structure of the training data. In this paper, we propose a novel deep model, called generative adversarial networks with decoder-encoder output noise (DE-GANs), which takes advantage of both the adversarial training and the variational Bayesain inference to improve the performance of image generation. DE-GANs use a pre-trained decoder-encoder architecture to map the random Gaussian noise vectors to informative ones and pass them to the generator of the adversarial networks. Since the decoder-encoder architecture is trained by the same images as the generators, the output vectors could carry the intrinsic distribution information of the original images. Moreover, the loss function of DE-GANs is different from GANs and DCGANs. A hidden-space loss function is added to the adversarial loss function to enhance the robustness of the model. Extensive empirical results show that DE-GANs can accelerate the convergence of the adversarial training process and improve the quality of the generated images.


title: Octree Rasterization: Accelerating High-Quality Out-of-Core GPU Volume Rendering

abstract: We present a novel approach for GPU-based high-quality volume rendering of large out-of-core volume data. By focusing on the locations and costs of ray traversal, we are able to significantly reduce the rendering time over traditional algorithms. We store a volume in an octree (of bricks); in addition, every brick is further split into regular macrocells. Our solutions move the branch-intensive accelerating structure traversal out of the GPU raycasting loop and introduce an efficient empty-space culling method by rasterizing the proxy geometry of a view-dependent cut of the octree nodes. This rasterization pass can capture all of the bricks that the ray penetrates in a per-pixel list. Since the per-pixel list is captured in a front-to-back order, our raycasting pass needs only to cast rays inside the tighter ray segments. As a result, we achieve two levels of empty space skipping: the brick level and the macrocell level. During evaluation and testing, this technique achieved 2 to 4 times faster rendering speed than a current state-of-the-art algorithm across a variety of data sets.


title: Modeling and performance evaluation of IEEE 802.22 physical layer

abstract: IEEE 802.22, also called Wireless Regional Area Network (WRAN), is the newest wireless standard being developed for remote and rural areas. In this paper an overview of the standard, and more specifically its PHY layer is introduced. In order to evaluate the performance of the system, we model the PHY layer in MATLAB/SIMULINK and extract the Bit Error Rate (BER) of the system for different code rates and modulation schemes with noisy channel.


title: PROTEIN STRUCTURE PREDICTION BY MEANS OF SEQUENTIAL PATTERN MINING

abstract: Mining frequent pattern is a NP-hard problem and has become a hot topic in recent researches. Moreover, protein dataset contains distinct Pattern that can be used in many areas such as drug discovery, disease prediction, etc. In early decades, pattern discovery and protein fold recognition was determined by biophysics and biochemistry approach; and X-ray and NMR have been used for protein structure prediction which are very expensive and time consuming while, a mathematical approach can reduce the cost of such laboratory experiments. Many computer based tests have been applied for the protein fold detection such as graph based algorithms and data mining viewpoints like classification or clustering, and all have their advantages and drawbacks. Pattern matching in protein sequential dataset for fold recognition plays a meaningful role in the field of bioinformatics since it evolved prediction of unknown protein function. There are lots of pattern recognition algorithms but in this work we used PrefixSpan. The reason of selecting this algorithm will be discussed below in section 2. For evaluating the result of experiments we used SCOPE dataset which is a classified protein dataset and ASTRAL, a discriminative sequential dataset of SCOPE.


title: Large-Scale Image Geo-Localization Using Dominant Sets

abstract: This paper presents a new approach for the challenging problem of geo-localization using image matching in a structured database of city-wide reference images with known GPS coordinates. We cast the geo-localization as a clustering problem of local image features. Akin to existing approaches to the problem, our framework builds on low-level features which allow local matching between images. For each local feature in the query image, we find its approximate nearest neighbors in the reference set. Next, we cluster the features from reference images using Dominant Set clustering, which affords several advantages over existing approaches. First, it permits variable number of nodes in the cluster, which we use to dynamically select the number of nearest neighbors for each query feature based on its discrimination value. Second, this approach is several orders of magnitude faster than existing approaches. Thus, we obtain multiple clusters (different local maximizers) and obtain a robust final solution to the problem using multiple weak solutions through constrained Dominant Set clustering on global image features, where we enforce the constraint that the query image must be included in the cluster. This second level of clustering also bypasses heuristic approaches to voting and selecting the reference image that matches to the query. We evaluate the proposed framework on an existing dataset of 102k street view images as well as a new larger dataset of 300k images, and show that it outperforms the state-of-the-art by 20 and 7 percent, respectively, on the two datasets.


title: End-to-end Convolutional Network for Saliency Prediction

abstract: The prediction of saliency areas in images has been traditionally addressed with hand crafted features based on neuroscience principles. This paper however addresses the problem with a completely data-driven approach by training a convolutional network. The learning process is formulated as a minimization of a loss function that measures the Euclidean distance of the predicted saliency map with the provided ground truth. The recent publication of large datasets of saliency prediction has provided enough data to train a not very deep architecture which is both fast and accurate. The convolutional network in this paper, named JuntingNet, won the LSUN 2015 challenge on saliency prediction with a superior performance in all considered metrics.


title: Costing of Cloud Computing Services: A Total Cost of Ownership Approach

abstract: The use of Cloud Computing Services appears to offer significant cost advantages. Particularly start-up companies benefit from these advantages, since frequently they do not operate an internal IT infrastructure. But are costs associated with Cloud Computing Services really that low? We found that particular cost types and factors are frequently underestimated by practitioners. In this paper we present a Total Cost of Ownership (TCO) approach for Cloud Computing Services. We applied a multi-method approach (systematic literature review, analysis of real Cloud Computing Services, expert interview, case study) for the development and evaluation of the formal mathematical model. We found that our model fits the practical requirements and supports decision-making in Cloud Computing.


title: ARRIVE: Algorithm for robust routing in volatile environments

abstract: Wireless sensor networks for environmental monitoring and distributed control will be deployed on a large scale in the near future. Due to the low per-node cost, these networks are expected to be both large and dense. However, because of the limited computation, storage, and power available to each node, conventional ad-hoc routing techniques are not feasible in this domain, and more novel routing algorithms are required. Despite the need for a simpler approach, routing in sensor networks still needs to be both robust to failures and secure against compromised and malicious nodes. We propose ARRIVE, a probabilistic algorithm that leverages the high node density and the inherent broadcast medium found in sensor networks to achieve routing robust to both link failures and patterned node failures without resorting to periodic flooding of the network. Our algorithm is based on a tree-like topology rooted at the sink of the network, and nodes use localized observed behavior of the surrounding nodes to make probabilistic decisions for forwarding packets. We have found that ARRIVE adapts to large patterned failures within a relatively short period of time at the cost of only moderate increases in overall power consumption and source-to-sink latency.


title: Multilayer busbar design for a Si IGBT and SiC MOSFET hybrid switch based 100 kW three-level T-type PEBB

abstract: In this paper, the design of a multi-layer laminated busbar for a Si and SiC hybrid switch based 100 kW three-level T-type, single-phase, power electronics building block is presented. Due to the absence of three-level T-type topology in WBG based power modules at this power level, the PEBB design along with the busbar design is novel. This busbar facilitates the interconnection between the DC-link capacitor bank and the semiconductor devices while introducing ultra-low stray inductance compared to state-of-the-art designs in the current commutation loop (CCL) and also ensure symmetrical CCLs. This ensures low voltage stress operation of the SiC devices and enables high switching frequency operation of the inverter. The design was verified using 3-D finite element analysis (FEA) and switching experiments.


title: The Admissibility of Expert Witness Testimony

abstract: This chapter describes the most important rules and principles that govern the admissibility of expert testimony. It also demonstrates ways in which trial consultants can maximize the likelihood that their own research and opinions will be admitted into evidence and suggests strategies for mounting effective attacks against the expert evidence submitted by opponents. The chapter begins with an overview of the discovery rules governing expert testimony in civil and criminal cases. It then proceeds to describe the evidentiary rules and principles that most directly affect expert witnesses.


title: Tourists responses to mobile augmented reality travel guides

abstract: This research presents a mobile augmented reality (MAR) travel guide, named CorfuAR, which supports personalized recommendations. We report the development process and devise a theoretical model that explores the adoption of MAR applications through their emotional impact. A field study on Corfu visitors ( n = 105 ) shows that the functional properties of CorfuAR evoke feelings of pleasure and arousal, which, in turn, influence the behavioral intention of using it. This is the first study that empirically validates the relation between functional system properties, user emotions, and adoption behavior. The paper discusses also the theoretical and managerial implications of our study.


The Lore of Large Numbers


title: A Comprehensive Performance Evaluation of 3D Local Feature Descriptors

abstract: A number of 3D local feature descriptors have been proposed in the literature. It is however, unclear which descriptors are more appropriate for a particular application. A good descriptor should be descriptive, compact, and robust to a set of nuisances. This paper compares ten popular local feature descriptors in the contexts of 3D object recognition, 3D shape retrieval, and 3D modeling. We first evaluate the descriptiveness of these descriptors on eight popular datasets which were acquired using different techniques. We then analyze their compactness using the recall of feature matching per each float value in the descriptor. We also test the robustness of the selected descriptors with respect to support radius variations, Gaussian noise, shot noise, varying mesh resolution, distance to the mesh boundary, keypoint localization error, occlusion, clutter, and dataset size. Moreover, we present the performance results of these descriptors when combined with different 3D keypoint detection methods. We finally analyze the computational efficiency for generating each descriptor.


title: Object matching in tweets with spatial models

abstract: Despite their 140-character limitation, tweets embody a lot of valuable information, especially temporal and spatial. In this paper we study the geographic aspects of tweets, for a given object domain. We propose a user-level model for spatial encoding in tweets that goes beyond the explicit geo-coding or place name mentions; this model can be used to match objects to tweets. We illustrate our model and methodology using restaurants as the objects, and show a significant improvement in performance over using standard language models. En route, we obtain a method to geolocate users who tweet about geolocated objects; this may be of independent interest.


title: Part of Speech Tagging of Marathi Text Using Trigram Method

abstract: In this paper we present a Marathi part of speech tagger. It is a morphologically rich language. It is spoken by the native people of Maharashtra. The general approach used for development of tagger is statistical using trigram Method. The main concept of trigram is to explore the most likely POS for a token based on given information of previous two tags by calculating probabilities to determine which is the best sequence of a tag. In this paper we show the development of the tagger. Moreover we have also shown the evaluation done.


title: Reduced Basis Decomposition: a Certified and Fast Lossy Data Compression Algorithm

abstract: Dimension reduction is often needed in the area of data mining. The goal of these methods is to map the given high-dimensional data into a low-dimensional space preserving certain properties of the initial data. There are two kinds of techniques for this purpose. The first, projective methods, builds an explicit linear projection from the high-dimensional space to the low-dimensional one. On the other hand, the nonlinear methods utilizes nonlinear and implicit mapping between the two spaces. In both cases, the methods considered in literature have usually relied on computationally very intensive matrix factorizations, frequently the Singular Value Decomposition (SVD). The computational burden of SVD quickly renders these dimension reduction methods infeasible thanks to the ever-increasing sizes of the practical datasets.In this paper, we present a new decomposition strategy, Reduced Basis Decomposition (RBD), which is inspired by the Reduced Basis Method (RBM). Given X the high-dimensional data, the method approximates it by Y T ( ? X ) with Y being the low-dimensional surrogate and T the transformation matrix. Y is obtained through a greedy algorithm thus extremely efficient. In fact, it is significantly faster than SVD with comparable accuracy. T can be computed on the fly. Moreover, unlike many compression algorithms, it easily finds the mapping for an arbitrary "out-of-sample" vector and it comes with an "error indicator" certifying the accuracy of the compression. Numerical results are shown validating these claims.


title: An enhanced framework for aspect-based sentiment analysis of Hotels' reviews: Arabic reviews case study

abstract: This research proposes a framework for aspect-based sentiment analysis (ABSA) of Hotels' reviews. The proposed framework consists of: (a) a reference human annotated Arabic dataset to support ABSA tasks such as aspect category identification, opinion target expression extraction, and opinion sentiment polarity. The dataset was annotated on both sentence-level and text-level, (b) baseline approach where a Support Vector Machine (SVM) was trained as part of the ABSA tasks, (c) baseline experiments and results, and (d) a common evaluation technique to provide a unified evaluation of future research working on the same dataset and ABSA tasks.


title: People Sensemaking and Relationship Building on an Enterprise Social Network Site

abstract: This paper describes a social network site designed to support employees within an enterprise in connecting and learning about each other through personal and professional sharing. We introduce the design concepts and provide a detailed account of the first three months of usage, involving nearly 300 users. Our findings suggest that employees find the site particularly useful as a way to perform people sensemaking of individuals and to connect and maintain relationships with others on the site.


title: 100 nT/√Hz, 0.5 mm2 monolithic, multi-loop low-power 3-axis MEMS magnetometer

abstract: The work proposes the first monolithic, 3-axis, multi-loop MEMS Lorentz-force magnetometer. The sensor is studied to be compatible for 6-degrees-of-freedom (DOF), all-MEMS combos (with a 3-axis accelerometer), and more convenient in terms of cost/area than hybrid solutions. The structure exploits an innovative geometry, which enables — though using 10 current-recirculating loops as a Lorentz-force multiplicative factor — to combine in a single device the sensitivity along all three sensing axes. This in turn generates an 8-fold active area reduction in the magnetic sensor, while halving noise density down to 100 nT/√Hz, with respect to previous solutions based on multi-loop architecture.


title: DSP-Based Real-Time Implementation of a Hybrid $H \infty$ Adaptive Fuzzy Tracking Controller for Servo-Motor Drives

abstract: An embedded hybrid Hinfin adaptive fuzzy control structure is implemented for trajectory tracking control of a brushless servo drive system. The control structure employs a fuzzy logic controller incorporating an Hinfin tracking controller via an acceleration feedback signal. The fuzzy logic controller is equipped with an adaptive-law-based Lyapunov synthesis approach to compensate for system uncertainty and random changes in the external load acting on the drive system. The proposed control structure is experimentally verified on a state-of-the-art dSPACE DS1104 digital signal processor (DSP)-based data acquisition and control system in a laboratory 1-hp brushless drive system. The controllers are first designed in Simulink. Then, the Real-Time Workshop is used to automatically generate optimized C code for real-time applications. Afterward, the interface between MATLAB/Simulink and the dSPACE DS 1104 allows the control algorithm to run on the hardware processor of the DSP. The result is a powerful testbed for the rapid design and implementation of the hybrid tracking controllers for a wide variety of operating conditions. Experimental results are provided to verify the effectiveness of the proposed controller. Considerable improvement in the performance generated by the hybrid controller is compared with the traditional Hinfin controller


title: A Prospective for New Mid-Infrared Medical Endoscopy Using Chalcogenide Glasses

abstract: That chalcogenide glasses potentially provide a solution for mid-infrared medical endoscopy is discussed in detail for the first time. It is shown that chalcogenide glass fiber optics could underpin new mid-infrared medical endoscopic systems for real-time molecular sensing, imaging, and analysis of tissue and for fiber laser surgery at new mid-infrared wavelengths. Moreover, chalcogenide glass fiber optic and waveguide devices and systems could provide the key to new mid-infrared communications for molecular sensing to inform decision-taking in other sectors as diverse as manufacturing, energy, the environment, and security. The development and deployment of chalcogenide glasses for mid-infrared photonics over the next decade or so could mirror the complexity and versatility of silica fiber optics developed in the 20th century for near-infrared photonics. These ideas are developed in this article and the current status of chalcogenide glass photonics is briefly surveyed.


AM An Ontology-Based Forensic Analysis Tool


title: The Surface Exchange Of Nitric Oxide

abstract: Among the different components of the surface exchange of nitric oxide anthropogenic as well as biogenic emissions are overwhelming. While inventories for anthropogenic NO% sources are presently among the most thorough compilations of pollutants' emissions, our knowledge on biogenic NO emissions is comparatively poor. A brief overview is given about (a) the importance of NO% emissions for tropospheric chemistry, (b) the present estimate of global NO emissions, (c) techniques suitable for the mesurement of NO surface exchange, (d) microbial and abiotic processes regulating the soil-biogenic NO emission and its main influencing factors, and (e) the present state of modeling (global) soil-biogenic NO inventories.


title: Features Based IUGR Diagnosis Using Variational Level Set Method and Classification Using Artificial Neural Networks

abstract: Intrauterine growth restriction (IUGR) is the failure of the fetus to achieve his/her intrinsic growth potential. IUGR results in significant perinatal and long-term complications, including the development of insulin resistance/metabolic syndrome in adulthood [5]. Accurate and effective monitoring of fetal growth is one of the key component of prenatal care [3]. Ultrasound evaluation is considered the cornerstone of diagnosis and surveillance of the growth-restricted fetus [2]. Ultrasound measurements play a significant role in obstetrics as an accurate means for the estimation of the fetal age. Several parameters are used as aging parameters, the most important of which are the biparietal diameter (BPD), head circumference (HC), abdominal circumference (AC) and femur length (FL). Serial measurement of these parameters over time is used to determine the fetal condition. Hence, consistency and reproducibility of measurements is an important issue. Consequently the automatic segmentation of anatomical structures in ultrasound imagery is a real challenge due to acoustic interferences (speckle noise) and artifacts which are inherent in these images. In this paper, a novel method is proposed for developing a Computer Aided Diagnosis (CAD) system for diagnosis and classification of IUGR foetuses. Diagnosis is performed by segmenting and extracting the required foetus features from an ultrasound image, using the Re-initialization free level set with Reaction Diffusion (RD) technique. An artificial neural network (ANN) classifier is developed, the features extracted are provided to the designed ANN model. The ANN then classifies normal and abnormal fetuses based on features provided.


title: Virtual machine migration planning in software-defined networks

abstract: Live migration is a key technique for virtual machine (VM) management in data center networks, which enables flexibility in resource optimization, fault tolerance, and load balancing. Despite its usefulness, the live migration still introduces performance degradations during the migration process. Thus, there has been continuous efforts in reducing the migration time in order to minimize the impact. From the network's perspective, the migration time is determined by the amount of data to be migrated and the available bandwidth used for such transfer. In this paper, we examine the problem of how to schedule the migrations and how to allocate network resources for migration when multiple VMs need to be migrated at the same time. We consider the problem in the Software-defined Network (SDN) context since it provides flexible control on routing. More specifically, we propose a method that computes the optimal migration sequence and network bandwidth used for each migration. We formulate this problem as a mixed integer programming, which is NP-hard. To make it computationally feasible for large scale data centers, we propose an approximation scheme via linear approximation plus fully polynomial time approximation, and obtain its theoretical performance bound. Through extensive simulations, we demonstrate that our fully polynomial time approximation (FPTA) algorithm has a good performance compared with the optimal solution and two state-of-the-art algorithms. That is, our proposed FPTA algorithm approaches to the optimal solution with less than 10% variation and much less computation time. Meanwhile, it reduces the total migration time and the service downtime by up to 40% and 20% compared with the state-of-the-art algorithms, respectively.


title: Algorithms for Inverse Reinforcement Learning

abstract: Objective—To evaluate the pharmacokinetics of a novel commercial formulation of ivermectin after administration to goats. Animals—6 healthy adult goats. Procedure—Ivermectin (200 μg/kg) was initially administered IV to each goat, and plasma samples were obtained for 36 days. After a washout period of 3 weeks, each goat received a novel commercial formulation of ivermectin (200 μg/kg) by SC injection. Plasma samples were then obtained for 42 days. Drug concentrations were quantified by use of high-performance liquid chromatography with fluorescence detection. Results—Pharmacokinetics of ivermectin after IV administration were best described by a 2-compartment open model; values for main compartmental variables included volume of distribution at a steady state (9.94 L/kg), clearance (1.54 L/kg/d), and area under the plasma concentration-time curve (AUC; 143 [ng•d]/mL). Values for the noncompartmental variables included mean residence time (7.37 days), AUC (153 [ng•d]/mL), and clearance (1.43 L/kg/d). After ...


title: Temporal recommendation on graphs via long- and short-term preference fusion

abstract: Accurately capturing user preferences over time is a great practical challenge in recommender systems. Simple correlation over time is typically not meaningful, since users change their preferences due to different external events. User behavior can often be determined by individual's long-term and short-term preferences. How to represent users' long-term and short-term preferences? How to leverage them for temporal recommendation? To address these challenges, we propose Session-based Temporal Graph (STG) which simultaneously models users' long-term and short-term preferences over time. Based on the STG model framework, we propose a novel recommendation algorithm Injected Preference Fusion (IPF) and extend the personalized Random Walk for temporal recommendation. Finally, we evaluate the effectiveness of our method using two real datasets on citations and social bookmarking, in which our proposed method IPF gives 15%-34% improvement over the previous state-of-the-art.


title: Interactive Image Segmentation with Latent Diversity

abstract: Interactive image segmentation is characterized by multimodality. When the user clicks on a door, do they intend to select the door or the whole house? We present an end-to-end learning approach to interactive image segmentation that tackles this ambiguity. Our architecture couples two convolutional networks. The first is trained to synthesize a diverse set of plausible segmentations that conform to the user's input. The second is trained to select among these. By selecting a single solution, our approach retains compatibility with existing interactive segmentation interfaces. By synthesizing multiple diverse solutions before selecting one, the architecture is given the representational power to explore the multimodal solution space. We show that the proposed approach outperforms existing methods for interactive image segmentation, including prior work that applied convolutional networks to this problem, while being much faster.