title: In vivo 3-dimensional analysis of scapular kinematics: comparison of dominant and nondominant shoulders

abstract: Background Alterations in scapular motion frequently are seen in association with various shoulder disorders. It is common clinically to compare the pathological shoulder with the contralateral shoulder, in spite of arm dominance, to characterize the disorder. However, there have been few articles that test the underlying assumption that dominant and nondominant shoulders exhibit comparable dynamic kinematics. The purpose of this study was to compare the 3-dimensional (3-D) scapular kinematics of dominant and nondominant shoulders during dynamic scapular plane elevation using 3-D–2-D (2-dimensional) registration techniques. Materials and methods Twelve healthy males with a mean age of 32 years (range, 27–36) were enrolled in this study. Bilateral fluoroscopic images during scapular plane elevation and lowering were taken, and CT-derived 3-D bone models were matched with the silhouette of the bones in the fluoroscopic images using 3-D–2-D registration techniques. Angular values of the scapula and scapulohumeral rhythm were compared between dominant and nondominant shoulders with statistical analysis. Results There was a significant difference in upward rotation angles between paired shoulders ( P Discussion/Conclusion Scapular motion was not the same between dominant and nondominant arms in healthy subjects. The dominant scapula was rotated further downward at rest and reached greater upward rotation with abduction. These differences should be considered in clinical assessment of shoulder pathology.


title: Mechanisms regulating melanogenesis*

abstract: Skin pigmentation is an important human phenotypic trait whose regulation, in spite of recent advances, has not yet been fully understood. The pigment melanin is produced in melanosomes by melanocytes in a complex process called melanogenesis. The melanocyte interacts with endocrine, immune, inflammatory and central nervous systems, and its activity is also regulated by extrinsic factors such as ultraviolet radiation and drugs. We have carried out a review of the current understanding of intrinsic and extrinsic factors regulating skin pigmentation, the melanogenesis stages and related gene defects. We focused on melanocyte-keratinocyte interaction, activation of melanocortin type 1 receptor (MC1-R) by peptides (melanocyte-stimulating hormone and adrenocorticotropic hormone) resulting from proopiomelanocortin (POMC) cleavage, and mechanisms of ultraviolet-induced skin pigmentation. The identification and comprehension of the melanogenesis mechanism facilitate the understanding of the pathogenesis of pigmentation disorders and the development of potential therapeutic options.


title: Hive - A Warehousing Solution Over a Map-Reduce Framework

abstract: The size of data sets being collected and analyzed in the industry for business intelligence is growing rapidly, making traditional warehousing solutions prohibitively expensive. Hadoop [3] is a popular open-source map-reduce implementation which is being used as an alternative to store and process extremely large data sets on commodity hardware. However, the map-reduce programming model is very low level and requires developers to write custom programs which are hard to maintain and reuse.


title: Automatic Speech Recognition: Reliability and Pedagogical Implications for Teaching Pronunciation

abstract: This study examines the reliability of automatic speech recognition (ASR) software used to teach English pronunciation, focusing on one particular piece of software, FluSpeak, as a typical example. Thirty-six Korean English as a Foreign Language (EFL) college students participated in an experiment in which they listened to 15 sentences that appeared in FluSpeak and recorded their voices, repeating sentence by sentence. The ASR software analysis of their production was then compared to pronunciation scores determined by native English speaking (NES) instructors. Although the correlation coefficient for intonation was nearly zero, indicating that ASR technology is still not as accurate as human analysis, the software may be very useful for student practice with aspects of pronunciation. The paper suggests a lesson plan for teaching English pronunciation through ASR software.


A2C: Self Destructing Exploit Executions via Input Perturbation


title: Manifold-Learning-Based Feature Extraction for Classification of Hyperspectral Data: A Review of Advances in Manifold Learning

abstract: Advances in hyperspectral sensing provide new capability for characterizing spectral signatures in a wide range of physical and biological systems, while inspiring new methods for extracting information from these data. HSI data often lie on sparse, nonlinear manifolds whose geometric and topological structures can be exploited via manifold-learning techniques. In this article, we focused on demonstrating the opportunities provided by manifold learning for classification of remotely sensed data. However, limitations and opportunities remain both for research and applications. Although these methods have been demonstrated to mitigate the impact of physical effects that affect electromagnetic energy traversing the atmosphere and reflecting from a target, nonlinearities are not always exhibited in the data, particularly at lower spatial resolutions, so users should always evaluate the inherent nonlinearity in the data. Manifold learning is data driven, and as such, results are strongly dependent on the characteristics of the data, and one method will not consistently provide the best results. Nonlinear manifold-learning methods require parameter tuning, although experimental results are typically stable over a range of values, and have higher computational overhead than linear methods, which is particularly relevant for large-scale remote sensing data sets. Opportunities for advancing manifold learning also exist for analysis of hyperspectral and multisource remotely sensed data. Manifolds are assumed to be inherently smooth, an assumption that some data sets may violate, and data often contain classes whose spectra are distinctly different, resulting in multiple manifolds or submanifolds that cannot be readily integrated with a single manifold representation. Developing appropriate characterizations that exploit the unique characteristics of these submanifolds for a particular data set is an open research problem for which hierarchical manifold structures appear to have merit. To date, most work in manifold learning has focused on feature extraction from single images, assuming stationarity across the scene. Research is also needed in joint exploitation of global and local embedding methods in dynamic, multitemporal environments and integration with semisupervised and active learning.


title: Drum Transcription via Classification of Bar-Level Rhythmic Patterns

abstract: We propose a novel method for automatic drum transcription from audio that achieves the recognition of individual drums by classifying bar-level drum patterns. Automatic drum transcription has to date been tackled by recognising individual drums or drum combinations. In high-level tasks such as audio similarity, statistics of longer rhythmic patterns have been used, reflecting that musical rhythm emerges over time. We combine these two approaches by classifying bar-level drum patterns on sub-beat quantised timbre features using support vector machines. We train the classifier using synthesised audio and carry out a series of experiments to evaluate our approach. Using six different drum kits, we show that the classifier generalises to previously unseen drum kits when trained on the other five (80% accuracy). Measures of precision and recall show that even for incorrectly classified patterns many individual drum events are correctly transcribed. Tests on 14 acoustic performances from the ENST-Drums dataset indicate that the system generalises to real-world recordings. Limited by the set of learned patterns, performance is slightly below that of a comparable method. However, we show that for rock music, the proposed method performs as well as the other method and is substantially more robust to added polyphonic accompaniment.


title: Timing of Examinations Affects School Performance Differently in Early and Late Chronotypes

abstract: Circadian clocks of adolescents typically run late—including sleep times—yet adolescents generally are expected at school early in the morning. Due to this mismatch between internal (circadian) and external (social) times, adolescents suffer from chronic sleep deficiency, which, in turn, affects academic performance negatively. This constellation affects students’ future career prospects. Our study correlates chronotype and examination performance. In total, 4734 grades were collected from 741 Dutch high school students (ages 11-18 years) who had completed the Munich ChronoType Questionnaire to estimate their internal time. Overall, the lowest grades were obtained by students who were very late chronotypes (MSFsc > 5.31 h) or slept very short on schooldays (SDw < 7.03 h). The effect of chronotype on examination performance depended on the time of day that examinations were taken. Opposed to late types, early chronotypes obtained significantly higher grades during the early (0815-0945 h) and late (1000-1215 h) morning. This group difference in grades disappeared in the early afternoon (1245-1500 h). Late types also obtained lower grades than early types when tested at the same internal time (hours after MSFsc), which may reflect general attention and learning disadvantages of late chronotypes during the early morning. Our results support delaying high school starting times as well as scheduling examinations in the early afternoon to avoid discrimination of late chronotypes and to give all high school students equal academic opportunities.


title: A Survey of Traditional and MapReduceBased Spatial Query Processing Approaches

abstract: Various indexing methods of spatial data have come out after rigorous efforts put by many researchers for fast processing of spatial queries. Parallelizing spatial index building and query processing have become very popular for improving efficiency. The MapReduce framework provides a modern way of parallel processing. A MapReduce-based works for spatial queries consider the existing traditional spatial indexing for building spatial indexes in parallel. The majority of the spatial indexes implemented in MapReduce use R-Tree and its variants. Therefore, R-Tree and its variantbased traditional spatial indexes are thoroughly surveyed in the paper. The objective is to search for still less explored spatial indexing approaches, having the potential for parallelism in MapReduce. The review work also provides a detailed survey of MapReduce-based spatial query processing approaches - hierarchical indexed and packed key-value storage based spatial dataset. Both approaches use different data partitioning strategies for distributing data among cluster nodes and managing the partitioned dataset through different indexing. Finally, a number of parameters are selected for comparison and analysis of all the existing approaches in the literature.


title: Low-Level Violence in Schools: Is There an Association Between School Safety Measures and Peer Victimization?

abstract: BACKGROUND ::: Low-level violent behavior, particularly school bullying, remains a critical public health issue that has been associated with negative mental and physical health outcomes. School-based prevention programs, while a valuable line of defense to stave off bullying, have shown inconsistent results in terms of decreasing bullying. This study explored whether school safety measures (eg, security guards, cameras, ID badges) were associated with student reports of different forms of peer victimization related to bullying. ::: ::: ::: METHODS ::: Data came from the 2007 School Crime Supplement of the National Crime Victimization Survey. Chi-square tests of independence were used to examine differences among categorical variables. Logistic regression models were constructed for the peer victimization outcomes. A count variable was constructed among the bullying outcomes (0-7) with which a Poisson regression model was constructed to analyze school safety measures' impacts on degree of victimization. ::: ::: ::: RESULTS ::: Of the various school safety measures, only having adults in hallways resulted in a significant reduction in odds of being physically bullied, having property vandalized, or having rumors spread. In terms of degree of victimization, having adults and/or staff supervising hallways was associated with an approximate 26% decrease in students experiencing an additional form of peer victimization. ::: ::: ::: CONCLUSIONS ::: Results indicated that school safety measures overall were not associated with decreased reports of low-level violent behaviors related to bullying. More research is needed to further explore what best promotes comprehensive safety in schools.


title: Adapting Robot Behavior for Human--Robot Interaction

abstract: Human beings subconsciously adapt their behaviors to a communication partner in order to make interactions run smoothly. In human-robot interactions, not only the human but also the robot is expected to adapt to its partner. Thus, to facilitate human-robot interactions, a robot should be able to read subconscious comfort and discomfort signals from humans and adjust its behavior accordingly, just like a human would. However, most previous research works expected the human to consciously give feedback, which might interfere with the aim of interaction. We propose an adaptation mechanism based on reinforcement learning that reads subconscious body signals from a human partner, and uses this information to adjust interaction distances, gaze meeting, and motion speed and timing in human-robot interactions. The mechanism uses gazing at the robot's face and human movement distance as subconscious body signals that indicate a human's comfort and discomfort. A pilot study with a humanoid robot that has ten interaction behaviors has been conducted. The study result of 12 subjects suggests that the proposed mechanism enables autonomous adaptation to individual preferences. Also, detailed discussion and conclusions are presented.


title: Person Reidentification With Reference Descriptor

abstract: Person identification across nonoverlapping cameras, also known as person reidentification, aims to match people at different times and locations. Reidentifying people is of great importance in crucial applications such as wide-area surveillance and visual tracking. Due to the appearance variations in pose, illumination, and occlusion in different camera views, person reidentification is inherently difficult. To address these challenges, a reference-based method is proposed for person reidentification across different cameras. Instead of directly matching people by their appearance, the matching is conducted in a reference space where the descriptor for a person is translated from the original color or texture descriptors to similarity measures between this person and the exemplars in the reference set. A subspace is first learned in which the correlations of the reference data from different cameras are maximized using regularized canonical correlation analysis (RCCA). For reidentification, the gallery data and the probe data are projected onto this RCCA subspace and the reference descriptors (RDs) of the gallery and probe are generated by computing the similarity between them and the reference data. The identity of a probe is determined by comparing the RD of the probe and the RDs of the gallery. A reranking step is added to further improve the results using a saliency-based matching scheme. Experiments on publicly available datasets show that the proposed method outperforms most of the state-of-the-art approaches.


title: Real-Time Street Parking Availability Estimation

abstract: Real-time parking availability information is important in urban areas, and if available could reduce congestion, pollution, and gas consumption. In this paper, we present a software solution called PhonePark for detecting the availability of on-street parking spaces. The solution uses the GPS and/or accelerometer sensors in a traveler's mobile phone to automatically detect when and where the traveler parked her car, and when she released a parking slot. PhonePark can also utilize the mobile phone's Bluetooth sensor or piggyback on street parking payment transactions for parking activity detection. Thus, the solution considers only mobile phones and does not rely on any external sensors such as cameras, wireless sensors embedded in the pavements, or ultrasonic sensors on vehicles. Further contributions include an algorithm to compute the historical parking availability profile for an arbitrary street block and algorithms to estimate the parking availability in real-time for a given street block. The algorithms are evaluated using real-time and real world street parking data.


Reputation mechanisms


title: Towards Automatic&Personalised Mobile Health Interventions: An Interactive Machine Learning Perspective

abstract: Machine learning (ML) is the fastest growing field in computer science and healthcare, providing future benefits in improved medical diagnoses, disease analyses and prevention. In this paper, we introduce an application of interactive machine learning (iML) in a telemedicine system, to enable automatic and personalised interventions for lifestyle promotion. We first present the high level architecture of the system and the components forming the overall architecture. We then illustrate the interactive machine learning process design. Prediction models are expected to be trained through the participants' profiles, activity performance, and feedback from the caregiver. Finally, we show some preliminary results during the system implementation and discuss future directions. We envisage the proposed system to be digitally implemented, and behaviourally designed to promote healthy lifestyle and activities, and hence prevent users from the risk of chronic diseases.


title: Ensemble: exploring complementary strengths of leaders and crowds in creative collaboration

abstract: In story writing, the diverse perspectives of the crowd could support an author's search for the perfect character, setting, or plot. However, structuring crowd collaboration is challenging. Too little structure leads to unfocused, sprawling narratives, and too much structure stifles creativity. Motivated by the idea that individual creative leaders and the crowd have complementary creative strengths, we present an approach where a leader directs the high-level vision for a story and articulates creative constraints for the crowd. This approach is embodied in Ensemble, a novel collaborative story-writing platform. In a month-long short story competition, over one hundred volunteer users on the web started over fifty short stories using Ensemble. Leaders used the platform to direct collaborator work by establishing creative goals, and collaborators contributed meaningful, high-level ideas to stories through specific suggestions. This work suggests that asymmetric creative contributions may support a broad new class of creative collaborations.


VIBRATION ENERGY HARVESTERS OF LEAD-FREE ( K , Na ) NbO 3 PIEZOELECTRIC THIN FILMS


title: The secure haptic keypad: a tactile password system

abstract: Authentication in public spaces poses significant security risks. Most significantly, passwords can be stolen, potentially leading to fraud. A common method to steal a PIN is through an observation attack, either using a camera or through direct observation (e.g. shoulder-surfing). This paper addresses this problem by presenting the design and implementation of a novel input keypad which uses tactile cues as means to compose a password. In this system, passwords are encoded as a sequence of randomized vibration patterns, making it visually impossible for an observer to detect which items are selected. An evaluation of this system shows it outperforms previous interfaces which have used tactile feedback to obfuscate passwords.


title: VOLAP: A Scalable Distributed Real-Time OLAP System for High-Velocity Data

abstract: This paper presents VelocityOLAP (VOLAP), a distributed real-time OLAP system for high-velocity data. VOLAP makes use of dimension hierarchies, is highly scalable, exploits both multi-core and multi-processor parallelism, and can guarantee serializable execution of insert and query operations. In contrast to other high performance OLAP systems such as SAP HANA or IBM Netezza that rely on vertical scaling or special purpose hardware, VOLAP supports cost-efficient horizontal scaling on commodity hardware or modest cloud instances. Experiments on 20 Amazon EC2 nodes with TPC-DS data show that VOLAP is capable of bulk ingesting data at over 600 thousand items per second, and processing streams of interspersed insertions and aggregate queries at a rate of approximately 50 thousand insertions and 20 thousand aggregate queries per second with a database of 1 billion items. VOLAP is designed to support applications that perform large aggregate queries, and provides similar high performance for aggregations ranging from a few items to nearly the entire database.


title: Integrating an urban green space typology into procedural 3D visualization for collaborative planning

abstract: Urban green spaces offer multiple ecosystem services (ES), which provide a variety of benefits to human well-being. Yet in urban planning they are not taken into account systematically. Recently new tools have been developed integrating ES into procedural modeling and visualization to raise stakeholder awareness for the explicit ES trade-offs that have to be made. These tools yet do not allow fast and comprehensive integration of ES provision in urban environments. In this paper we show how urban green space typologies can be linked to ES provision for facilitating collaboration between stakeholders of different backgrounds. Based on a generic typology green spaces were mapped and linked with information on potentially provided ES and their parameters. Further, pattern designs of the green space types were described with a form-based code. Both the map of green space types and the pattern designs were integrated into the parametric modeling and visualization chain of Esri CityEngine resulting in 3D visualizations of the green space patterns and correlating ES indicators. The green space typology allows for integrating different kinds of knowledge from both science and practice communities. The procedural model enables rapid interactive visualization of urban patterns and calculation of simple indicator values on the provision of ES. The simple approach for mapping green space types with low data requirements and the generic green space design patterns allow for transferability to other places and application to large areas. The developed approach is simple and fast yet comprehensive to communicate the vital importance of all green space types within the urban environment.


title: An Efficient Approach to Correct the Signals and Generate High-Resolution Quadrature Pulses for Magnetic Encoders

abstract: A magnetic encoder (ME) is a kind of sinusoidal encoder using magnetic effects that is currently utilized in many industrial control systems because it has many advantageous characteristics: low cost, simple structure, works in harsh environments, high reliability, and so on. The signals generated by an ME are always disturbed by noises; therefore, these signals are not ideal. The challenge is to achieve the highest resolution and to get the maximum operating speed as well as to use the most cost-effective hardware. To solve this problem, this paper humbly proposes an effective approach, which contains two parts: The main part is “compensating the noisy signals of MEs” by applying a new proposed method called the advanced adaptive digital phase-locked loop (AADPLL), while the second is a pulse interpolator which generates high-resolution quadrature pulses. The AADPLL algorithm provides a robust filtering characteristic to eliminate the noises and improve the accuracy of the ME's input signals. It also takes advantage of tracking high-speed input signals without time lag, unlike the traditional filters. Additionally, the computation burden is significantly reduced in this algorithm to allow it to be easily implemented in a low-cost processor. The pulse interpolator is based on an existing idea that extracts high-order sinusoids from the original ME signals. However, a new scheme is presented to achieve higher resolution per period with smaller noises affecting the output pulses. Both parts are mainly implemented in a unique hardware platform using a low-cost digital signal processor, such as the TMS320F2812, combined with a small-size field-programmable gate array. This method has already been applied to control a linear motor without using an expensive optical linear encoder. Practical results are provided to demonstrate the effectiveness of the proposed method.


title: An Improved RD Imaging Algorithm for FMCW SAR

abstract: As a kind of high resolution radar, Synthetic Aperture Radar (SAR) is very important in many military applications. Frequency modulated continuous wave (FMCW) synthetic aperture radar (SAR), combining FMCW technique with synthetic aperture imaging methods, is a newly proposed imaging radar system, and has the advantages such as small cubage, light weight, cost-effective, high-resolution, etc. However, for FMCW SAR, the platform continuously moves while transmitting and receiving signal. The duration of the transmitted pulse increases. The stop-and-go approximation is invalid. So the proper algorithms have to be used to achieve the expected results. For the peculiarity of FMCW SAR, the signal processing is deduced in detail when stop-and-go approximation is invalid, and a modification to RD is presented in order to compensate Doppler frequency shift. The simulation results are finally presented.


title: The Irony of Satire Political Ideology and the Motivation to See What You Want to See in The Colbert Report

abstract: This study investigated biased message processing of political satire in The Colbert Report and the influence of political ideology on perceptions of Stephen Colbert. Results indicate that political ideology influences biased processing of ambiguous political messages and source in late-night comedy. Using data from an experiment (N = 332), we found that individual-level political ideology significantly predicted perceptions of Colbert's political ideology. Additionally, there was no significant difference between the groups in thinking Colbert was funny, but conservatives were more likely to report that Colbert only pretends to be joking and genuinely meant what he said while liberals were more likely to report that Colbert used satire and was not serious when offering political statements. Conservatism also significantly predicted perceptions that Colbert disliked liberalism. Finally, a post hoc analysis revealed that perceptions of Colbert's political opinions fully mediated the relationship between po...


title: A neural network model for finding a near-maximum clique

abstract: Abstract A parallel algorithm based on the neural network model for finding a near-maximum clique is presented in this paper. A maximum clique of a graph G is a maximum complete subgraph of G where any two vertices are adjacent. The problem of finding a maximum clique is NP-complete. The parallel algorithm requires n processing elements for an n-vertex graph problem. The algorithm is verified by solving 230 different graph problems. The simulation results show that our computation time on a Macintosh IIfx is shorter than that of two better known algorithms on a Cray 2 and an IBM 3090 while the solution quality is similar. The algorithm solves a near-maximum clique problem in nearly constant time on a parallel machine with n processors.


title: Agricultural Plant Leaf Disease Detection and Diagnosis Using Image Processing Based on Morphological Feature Extraction

abstract: Leaf spots can be indicative of crop diseases, where leaf batches (spots) are usually examined and subjected to expert opinion. In our proposed system, we are going to develop an integrated image processing system to help automated inspection of these leaf batches and helps identify the disease type. Conventional Expert systems mainly those which used to diagnose the disease in agriculture domain depends only on textual input. Usually abnormalities for a given crop are manifested as symptoms on various plant parts. To enable an expert system to produce correct results, end user must be capable of mapping what they see in a form of abnormal symptoms to answer to questions asked by that expert system. This mapping may be inconsistent if a full understanding of the abnormalities does not exist. The proposed system consists of four stages; the first is the enhancement, which includes HIS transformation, histogram analysis, and intensity adjustment. The second stage is segmentation, which includes adaptation of fuzzy c-means algorithm. Feature extraction is the third stage, which deals with three features, namely color size and shape of spot. The fourth stage is classification, which comprises back propagation based neural networks.


title: Provably Secure Password Authenticated Key Exchange Based on RLWE for the Post-Quantum World

abstract: Authenticated Key Exchange (AKE) is a cryptographic scheme with the aim to establish a high-entropy and secret session key over a insecure communications network. Password-Authenticated Key Exchange (PAKE) assumes that the parties in play share a simple password, which is cheap and human-memorable and is used to achieve the authentication. PAKEs are practically relevant as these features are extremely appealing in an age where most people access sensitive personal data remotely from more-and-more pervasive hand-held devices. Theoretically, PAKEs allow the secure computation and authentication of a high-entropy piece of data using a low-entropy string as a starting point. In this paper, we apply the recently proposed technique introduced in [19] to construct two lattice-based PAKE protocols enjoying a very simple and elegant design that is an parallel extension of the class of Random Oracle Model (ROM)-based protocols \(\mathsf {PAK}\) and \(\mathsf {PPK}\) [13, 41], but in the lattice-based setting. The new protocol resembling \(\mathsf {PAK}\) is three-pass, and provides mutual explicit authentication, while the protocol following the structure of \(\mathsf {PPK}\) is two-pass, and provides implicit authentication. Our protocols rely on the Ring-Learning-with-Errors (RLWE) assumption, and exploit the additive structure of the underlying ring. They have a comparable level of efficiency to \(\mathsf {PAK}\) and \(\mathsf {PPK}\), which makes them highly attractive. We present a preliminary implementation of our protocols to demonstrate that they are both efficient and practical. We believe they are suitable quantum safe replacements for \(\mathsf {PAK}\) and \(\mathsf {PPK}\).


title: Inferring binary trust relationships in Web-based social networks

abstract: The growth of Web-based social networking and the properties of those networks have created great potential for producing intelligent software that integrates a user's social network and preferences. Our research looks particularly at assigning trust in Web-based social networks and investigates how trust information can be mined and integrated into applications. This article introduces a definition of trust suitable for use in Web-based social networks with a discussion of the properties that will influence its use in computation. We then present two algorithms for inferring trust relationships between individuals that are not directly connected in the network. Both algorithms are shown theoretically and through simulation to produce calculated trust values that are highly accurate.. We then present TrustMail, a prototype email client that uses variations on these algorithms to score email messages in the user's inbox based on the user's participation and ratings in a trust network.


title: Simultaneous acquisition of spatial harmonics (SMASH): Fast imaging with radiofrequency coil arrays,” Magn

abstract: SiMultaneous Acquisition of Spatial Harmonics (SMASH) is a new fast-imaging technique that increases MR image acquisition speed by an integer factor over existing fast-imaging methods, without significant sacrifices in spatial resolution or signal-to-noise ratio. Image acquisition time is reduced by exploiting spatial information inherent in the geometry of a surface coil array to substitute for some of the phase encoding usually produced by magnetic field gradients. This allows for partially parallel image acquisitions using many of the existing fast-imaging sequences. Unlike the data combination algorithms of prior proposals for parallel imaging, SMASH reconstruction involves a small set of MR signal combinations prior to Fourier transformation, which can be advantageous for artifact handling and practical implementation. A twofold savings in image acquisition time is demonstrated here using commercial phased array coils on two different MR-imaging systems. Larger time savings factors can be expected for appropriate coil designs.


title: Towards an implementation framework for business intelligence in healthcare

abstract: As healthcare organizations continue to be asked to do more with less, access to information is essential for sound evidence-based decision making. Business intelligence (BI) systems are designed to deliver decision-support information and have been repeatedly shown to provide value to organizations. Many healthcare organizations have yet to implement BI systems and no existing research provides a healthcare-specific framework to guide implementation. To address this research gap, we employ a case study in a Canadian Health Authority in order to address three questions: (1) what are the most significant adverse impacts to the organization's decision processes and outcomes attributable to a lack of decision-support capabilities? (2) what are the root causes of these impacts, and what workarounds do they necessitate? and (3) in light of the issues identified, what are the key considerations for healthcare organizations in the early stages of BI implementation? Using the concept of co-agency as a guide we identified significant decision-related adverse impacts and their root causes. We found strong management support, the right skill sets and an information-oriented culture to be key implementation considerations. Our major contribution is a framework for defining and prioritizing decision-support information needs in the context of healthcare-specific processes.


title: Visualizing multi-dimensional decision boundaries in 2D

abstract: In many applications experts need to make decisions based on the analysis of multi-dimensional data. Various classification models can support the decision making process. To obtain an intuitive understanding of the classification model, interactive visualizations are essential. We argue that this is best done by a series of interactive 2D scatterplots. In this paper, we define a set of characteristics of the multi-dimensional classification model that have to be visually represented in those scatterplots. Our proposed method presents those characteristics in a uniform manner for both linear and non-linear classification methods. We combine a visualization of a Voronoi based representation of multi-dimensional decision boundaries with visualization of the distances of the data elements to these boundaries. To allow the developer of the model to refine the threshold of the classification model and instantly observe the results, we use interactive decision point selection on a performance curve. Finally, we show how the combination of those techniques allows exploration of multi-dimensional decision boundaries in 2D.


title: Analysis of data using machine learning approaches in social networks

abstract: The amount of data circulating on the Internet is increasing day by day. With the increasing use of social media in particular, the importance of analyzing these data is increasing. The use of machine learning approaches to analyze large amounts of data is still popular today. Today, the social network Facebook is the most popular social networking sites. In this study, some data taken on Facebook were analyzed by machine learning approaches and compared with performance metrics. Logistic Regression (LR), Random Forest (RF) and Adaboost (AB) were used for machine learning approaches. Performance metrics used for comparison are precision, recall and F1 score. Confusion matrix values and Receiver Operating Characteristic (ROC) curves for the results are also presented. It was observed that the results of the RF and LR studies were close to each other and gave better results than the study done with the AB.


title: Meta-Learning and Universality: Deep Representations and Gradient Descent can Approximate any Learning Algorithm

abstract: Learning to learn is a powerful paradigm for enabling models to learn from data more effectively and efficiently. A popular approach to meta-learning is to train a recurrent model to read in a training dataset as input and output the parameters of a learned model, or output predictions for new test inputs. Alternatively, a more recent approach to meta-learning aims to acquire deep representations that can be effectively fine-tuned, via standard gradient descent, to new tasks. In this paper, we consider the meta-learning problem from the perspective of universality, formalizing the notion of learning algorithm approximation and comparing the expressive power of the aforementioned recurrent models to the more recent approaches that embed gradient descent into the meta-learner. In particular, we seek to answer the following question: does deep representation combined with standard gradient descent have sufficient capacity to approximate any learning algorithm? We find that this is indeed true, and further find, in our experiments, that gradient-based meta-learning consistently leads to learning strategies that generalize more widely compared to those represented by recurrent models.


Emotional intelligence: Five pieces to the puzzle.


title: Hole filling method using depth based in-painting for view synthesis in free viewpoint television and 3-D video

abstract: Depth image-based rendering (DIBR) is generally used to synthesize virtual view images in free viewpoint television (FTV) and three-dimensional (3-D) video. One of the main problems in DIBR is how to fill the holes caused by disocclusion regions and inaccurate depth values. In this paper, we propose a new hole filling method using a depth based in-painting technique. Experimental results show that the proposed hole filling method provides improved rendering quality both objectively and subjectively.


title: Design of Gate-Ground-NMOS-Based ESD Protection Circuits with Low Trigger Voltage, Low Leakage Current, and Fast Turn-On

abstract: In this paper, electrostatic discharge (ESD) protection circuits with an advanced substrate-triggered NMOS and a gate-substrate-triggered NMOS are proposed to provide low trigger voltage, low leakage current, and fast turn-on speed. The proposed ESD protection devices are designed using 0.13 µm CMOS technology. The experimental results show that the proposed substrate-triggered NMOS using a bipolar transistor has a low trigger voltage of 5.98 V and a fast turn-on time of 37 ns. The proposed gatesubstrate-triggered NMOS has a lower trigger voltage of 5.35 V and low leakage current of 80 pA.


title: Energy-efficient motion planning for mobile robots

abstract: This paper presents a new approach to find energy-efficient motion plans for mobile robots. Motion planning has two goals: finding the routes and determining the velocities. We model the relationship of motors' speed and their power consumption with polynomials. The velocity of the robot is related to its wheels' velocities by performing a linear transformation. We compare the energy consumption of different routes at different velocities and consider the energy consumed for acceleration and turns. We use experiment-validated simulation to demonstrate up to 51% energy savings for searching an open area.


title: Anonymity, unobservability, and pseudeonymity — a proposal for terminology

abstract: Based on the nomenclature of the early papers in the field, we propose a set of terminology which is both expressive and precise. More particularly, we define anonymity, unlinkability, unobservability, and pseudonymity (pseudonyms and digital pseudonyms, and their attributes). ::: ::: We hope that the adoption of this terminology might help to achieve better progress in the field by avoiding that each researcher invents a language of his/her own from scratch. Of course, each paper will need additional vocabulary, which might be added consistently to the terms defined here.


title: Are improvements in shame and self-compassion early in eating disorders treatment associated with better patient outcomes?

abstract: Compassion-focused therapy (CFT; Gilbert, 2005, 2009) is a transdiagnostic treatment approach focused on building self-compassion and reducing shame. It is based on the theory that feelings of shame contribute to the maintenance of psychopathology, whereas self-compassion contributes to the alleviation of shame and psychopathology. We sought to test this theory in a transdiagnostic sample of eating disorder patients by examining whether larger improvements in shame and self-compassion early in treatment would facilitate faster eating disorder symptom remission over 12 weeks. Participants were 97 patients with an eating disorder admitted to specialized day hospital or inpatient treatment. They completed the Eating Disorder Examination-Questionnaire, Experiences of Shame Scale, and Self-Compassion Scale at intake, and again after weeks 3, 6, 9, and 12. Multilevel modeling revealed that patients who experienced greater decreases in their level of shame in the first 4 weeks of treatment had faster decreases in their eating disorder symptoms over 12 weeks of treatment. In addition, patients who had greater increases in their level of self-compassion early in treatment had faster decreases in their feelings of shame over 12 weeks, even when controlling for their early change in eating disorder symptoms. These results suggest that CFT theory may help to explain the maintenance of eating disorders. Clinically, findings suggest that intervening with shame early in treatment, perhaps by building patients' self-compassion, may promote better eating disorders treatment response. (Int J Eat Disord 2014; 47:54–64)


SoK : On the Soundness and Precision of Dynamic Taint Analysis


title: Miniaturized Rat Race Coupler With Suppression of Spurious Passband

abstract: A new hybrid ring coupler design is devised for circuit miniaturization based on the periodic stepped-impedance ring resonator structure. The circuit has an area of 21.5% of a conventional rat race coupler and no passband up to the sixth harmonic of the design frequency. The designs are validated by comparing measured four-port parameters with the simulated results


title: DeepMon: Mobile GPU-based Deep Learning Framework for Continuous Vision Applications

abstract: The rapid emergence of head-mounted devices such as the Microsoft Holo-lens enables a wide variety of continuous vision applications. Such applications often adopt deep-learning algorithms such as CNN and RNN to extract rich contextual information from the first-person-view video streams. Despite the high accuracy, use of deep learning algorithms in mobile devices raises critical challenges, i.e., high processing latency and power consumption. In this paper, we propose DeepMon, a mobile deep learning inference system to run a variety of deep learning inferences purely on a mobile device in a fast and energy-efficient manner. For this, we designed a suite of optimization techniques to efficiently offload convolutional layers to mobile GPUs and accelerate the processing; note that the convolutional layers are the common performance bottleneck of many deep learning models. Our experimental results show that DeepMon can classify an image over the VGG-VeryDeep-16 deep learning model in 644ms on Samsung Galaxy S7, taking an important step towards continuous vision without imposing any privacy concerns nor networking cost.


title: Zero knowledge in the random oracle model, revisited

abstract: We revisit previous formulations of zero knowledge in the random oracle model due to Bellare and Rogaway (CCS '93) and Pass (Crypto '03), and present a hierarchy for zero knowledge that includes both of these formulations. The hierarchy relates to the programmability of the random oracle, previously studied by Nielsen (Crypto '02). ::: ::: ::: We establish a subtle separation between the Bellare-Rogaway formulation and a weaker formulation, which yields a finer distinction than the separation in Nielsen's work. ::: We show that zero-knowledge according to each of these formulations is not preserved under sequential composition. We introduce stronger definitions wherein the adversary may receive auxiliary input that depends on the random oracle (as in Unruh (Crypto '07)) and establish closure under sequential composition for these definitions. We also present round-optimal protocols for NP satisfying the stronger requirements. ::: Motivated by our study of zero knowledge, we introduce a new definition of proof of knowledge in the random oracle model that accounts for oracle-dependent auxiliary input. We show that two rounds of interaction are necessary and sufficient to achieve zero-knowledge proofs of knowledge according to this new definition, whereas one round of interaction is sufficient in previous definitions. ::: Extending our work on zero knowledge, we present a hierarchy for circuit obfuscation in the random oracle model, the weakest being that achieved in the work of Lynn, Prabhakaran and Sahai (Eurocrypt '04). We show that the stronger notions capture precisely the class of circuits that is efficiently and exactly learnable under membership queries.


title: Anomalies -- Risk Aversion

abstract: Economists ubiquitously employ a simple and elegant explanation for risk aversion: It derives from the concavity of the utility-of-wealth function within the expected-utility framework. We show that this explanation is not plausible in most applications, since anything more than economically negligible risk aversion over moderate stakes requires a utility-of-wealth function that is so concave that it predicts absurdly severe risk aversion over very large stakes. We present examples of how the expected-utility framework has misled economists, and why we believe a better explanation for risk aversion must incorporate loss aversion and mental accounting.


title: ActiveCrowd: A Framework for Optimized Multitask Allocation in Mobile Crowdsensing Systems

abstract: Worker selection is a key issue in mobile crowd sensing (MCS). While the previous worker selection approaches mainly focus on selecting a proper subset of workers for a single MCS task, a multitask-oriented worker selection is essential and useful for the efficiency of large-scale MCS platforms. This paper proposes ActiveCrowd, a worker selection framework for multitask MCS environments. We study the problem of multitask worker selection under two situations: worker selection based on workers’ intentional movement for time-sensitive tasks and unintentional movement for delay-tolerant tasks. For time-sensitive tasks, workers are required to move to the task venue intentionally and the goal is to minimize the total distance moved. For delay-tolerant tasks, we select workers whose route is predicted to pass by the task venues and the goal is to minimize the total number of workers. Two greedy-enhanced genetic algorithms are proposed to solve them. Experiments verify that the proposed algorithms outperform baseline methods under different experiment settings (scale of task sets, available workers, varied task distributions, etc.).


title: A Review of Wireless Sensor Technologies and Applications in Agriculture and Food Industry: State of the Art and Current Trends

abstract: The aim of the present paper is to review the technical and scientific state of the art of wireless sensor technologies and standards for wireless communications in the Agri-Food sector. These technologies are very promising in several fields such as environmental monitoring, precision agriculture, cold chain control or traceability. The paper focuses on WSN (Wireless Sensor Networks) and RFID (Radio Frequency Identification), presenting the different systems available, recent developments and examples of applications, including ZigBee based WSN and passive, semi-passive and active RFID. Future trends of wireless communications in agriculture and food industry are also discussed.


title: Multi-Instance Multi-Label Learning with Application to Scene Classification

abstract: In this paper, we formalize multi-instance multi-label learning, where each training example is associated with not only multiple instances but also multiple class labels. Such a problem can occur in many real-world tasks, e.g. an image usually contains multiple patches each of which can be described by a feature vector, and the image can belong to multiple categories since its semantics can be recognized in different ways. We analyze the relationship between multi-instance multi-label learning and the learning frameworks of traditional supervised learning, multi-instance learning and multi-label learning. Then, we propose the MIMLBOOST and MIMLSVM algorithms which achieve good performance in an application to scene classification.


title: Structural health management for future aerospace vehicles

abstract: Structural Health Management (SHM) will be of critical importance to provide the safety, reliability and affordability necessary for the future long duration space missions described in America's Vision for Space Exploration. Long duration missions to the Moon, Mars and beyond cannot be accomplished with the current paradigm of periodic, ground based structural integrity inspections. As evidenced by the Columbia tragedy, this approach is also inadequate for the current Shuttle fleet, thus leading to its initial implementation of on-board SHM sensing for impact detection as part of the return to flight effort. However, future space systems, to include both vehicles as well as structures such as habitation modules, will require an integrated array of onboard in-situ sensing systems. In addition, advanced data systems architectures will be necessary to communicate, store and process massive amounts of SHM data from large numbers of diverse sensors. Further, improved structural analysis and design algorithms will be necessary to incorporate SHM sensing into the design and construction of aerospace structures, as well as to fully utilize these sensing systems to provide both diagnosis and prognosis of structural integrity. Ultimately, structural integrity information will feed into an Integrated Vehicle Health Management (IVHM) system that will provide real-time knowledge of structural, propulsion, thermal protection and other critical systems for optimal vehicle management and mission control. This paper will provide an overview of NASA research and development in the area of SHM as well as to highlight areas of technology improvement necessary to meet these future mission requirements.


title: Text Encoder and Annotator: an all-in-one Editor for Transcribing and Annotating Manuscripts with RDF

abstract: In the context of the digitization of manuscripts, transcription and annotation are often distinct, sequential steps. This could lead to difficulties in improving the transcribed text when annotations have already been defined. In order to avoid this, we devised an approach which merges the two steps into the same process. Text Encoder and Annotator (TEA) is a prototype application embracing this concept. TEA is based on a lightweight language syntax which annotates text using Semantic Web technologies. Our approach is currently being developed within the Clavius on the Web project, devoted to studying the manuscripts of Christophorus Clavius, an influential 16th century mathematician and astronomer.


title: Anterior temporal face patches: a meta-analysis and empirical study

abstract: Studies of nonhuman primates have reported face sensitive patches in the ventral anterior temporal lobes (ATL). In humans, ATL resection or damage causes an associative prosopagnosia in which face perception is intact but face memory is compromised. Some fMRI studies have extended these findings using famous and familiar faces. However, it is unclear whether these regions in the human ATL are in locations comparable to those reported in non-human primates, typically using unfamiliar faces. We present the results of two studies of person memory: a meta-analysis of existing fMRI studies and an empirical fMRI study using optimized imaging parameters. Both studies showed left-lateralized ATL activations to familiar individuals while novel faces activated the right ATL. Activations to famous faces were quite ventral, similar to what has been reported in monkeys. These findings suggest that face memory-sensitive patches in the human ATL are in the ventral/polar ATL.


title: A Structured Self-attentive Sentence Embedding

abstract: This paper proposes a new model for extracting an interpretable sentence embedding by introducing self-attention. Instead of using a vector, we use a 2-D matrix to represent the embedding, with each row of the matrix attending on a different part of the sentence. We also propose a self-attention mechanism and a special regularization term for the model. As a side effect, the embedding comes with an easy way of visualizing what specific parts of the sentence are encoded into the embedding. We evaluate our model on 3 different tasks: author profiling, sentiment classification, and textual entailment. Results show that our model yields a significant performance gain compared to other sentence embedding methods in all of the 3 tasks.


title: Classification of clustered microcalcifications using a Shape Cognitron neural network

abstract: A new shape recognition-based neural network built with universal feature planes, called Shape Cognitron (S-Cognitron) is introduced to classify clustered microcalcifications. The architecture of S-Cognitron consists of two modules and an extra layer, called 3D figure layer lies in between. The first module contains a shape orientation layer, built with 20 cell planes of low level universal shape features to convert first-order shape orientations into numeric values, and a complex layer, to extract second-order shape features. The 3D figure layer is a feature extract-display layer that extracts the shape curvatures of an input pattern and displays them as a 3D figure. It is then followed by a second module made up of a feature formation layer and a probabilistic neural network-based classification layer. The system is evaluated by using Nijmegen mammogram database and experimental results show that sensitivity and specificity can reach 86.1 and 74.1%, respectively.


HTML 5 / WebGL vs Flash in 3 D Visualisation


title: Extending Deep Meta-Modelling for Practical Model-Driven Engineering

abstract: Meta-modelling is one of the pillars of Model-Driven Engineering (MDE), where it is used for language engineering and domain modelling. Even though the current trend is the use of two-level meta-modelling frameworks, several researchers have pointed out limitations of this scheme for some scenarios and suggested a metamodelling approach with an arbitrary number of meta-levels in order to obtain more flexible and simpler system descriptions. Unfortunately, such multi-level meta-modelling systems are still in their infancy, lacking for example, integration with model manipulation languages, a characterization of different possibilities for instantiation and inheritance, and primitives for interconnecting multi-level languages in a flexible way. In this paper, we propose a number of extensions to multi-level (also called deep) meta-modelling, based on the needs raised by its use for practical MDE. In particular, we discuss on the issues related to code generation from deep languages, the benefits of allowing inheritance at every meta-level, and patterns and techniques for a fine-grain control of the meta-level of elements. Finally, we provide primitives to control the impedance mismatch when connecting models at different meta-levels.


Word Level Handwritten and Printed Text Separation Based on Shape Features


title: Cooperative Cuts: Graph Cuts with Submodular Edge Weights

abstract: We introduce a problem we call Cooperative cut, where the goal is to find a minimum-cost graph cut but where a submodular function is used to define the cost of a subsets of edges. That means, the cost of an edge that is added to the current cut set C depends on the edges in C. This generalization of the cost in the standard min-cut problem to a submodular cost function immediately makes the problem harder. Not only do we prove NP hardness even for nonnegative submodular costs, but also show a lower bound of Ω(|V |) on the approximation factor for the problem. On the positive side, we propose and compare four approximation algorithms with an overall approximation factor of min { |V |/2, |C∗|, O( √ |E| log |V |), |Pmax| } , where C∗ is the optimal solution, and Pmax is the longest s, t path across the cut between given s, t. We also introduce additional heuristics for the problem which have attractive properties from the perspective of practical applications and implementations in that existing fast min-cut libraries may be used as subroutines. Both our approximation algorithms, and our heuristics, appear to do well in practice.


title: Learning and inferencing in user ontology for personalized Semantic Web search

abstract: User modeling is aimed at capturing the users' interests in a working domain, which forms the basis of providing personalized information services. In this paper, we present an ontology based user model, called user ontology, for providing personalized information service in the Semantic Web. Different from the existing approaches that only use concepts and taxonomic relations for user modeling, the proposed user ontology model utilizes concepts, taxonomic relations, and non-taxonomic relations in a given domain ontology to capture the users' interests. As a customized view of the domain ontology, a user ontology provides a richer and more precise representation of the user's interests in the target domain. Specifically, we present a set of statistical methods to learn a user ontology from a given domain ontology and a spreading activation procedure for inferencing in the user ontology. The proposed user ontology model with the spreading activation based inferencing procedure has been incorporated into a semantic search engine, called OntoSearch, to provide personalized document retrieval services. The experimental results, based on the ACM digital library and the Google Directory, support the efficacy of the user ontology approach to providing personalized information services.


title: Cross-Domain Traffic Scene Understanding: A Dense Correspondence-Based Transfer Learning Approach

abstract: Understanding traffic scene images taken from vehicle mounted cameras is important for high-level tasks, such as advanced driver assistance systems and autonomous driving. It is a challenging problem due to large variations under different weather or illumination conditions. In this paper, we tackle the problem of traffic scene understanding from a cross-domain perspective. We attempt to understand the traffic scene from images taken from the same location but under different weather or illumination conditions (e.g., understanding the same traffic scene from images on a rainy night with the help of images taken on a sunny day). To this end, we propose a dense correspondence-based transfer learning (DCTL) approach, which consists of three main steps: 1) extracting deep representations of traffic scene images via a fine-tuned convolutional neural network; 2) constructing compact and effective representations via cross-domain metric learning and subspace alignment for cross-domain retrieval; and 3) transferring the annotations from the retrieved best matching image to the test image based on cross-domain dense correspondences and a probabilistic Markov random field. To verify the effectiveness of our DCTL approach, we conduct extensive experiments on a challenging data set, which contains 1828 images from six weather or illumination conditions.


title: Fast Domain Decomposition for Global Image Smoothing

abstract: Edge-preserving smoothing (EPS) can be formulated as minimizing an objective function that consists of data and regularization terms. At the price of high-computational cost, this global EPS approach is more robust and versatile than a local one that typically has a form of weighted averaging. In this paper, we introduce an efficient decomposition-based method for global EPS that minimizes the objective function of ${L_{2}}$ data and (possibly non-smooth and non-convex) regularization terms in linear time. Different from previous decomposition-based methods, which require solving a large linear system, our approach solves an equivalent constrained optimization problem, resulting in a sequence of 1-D sub-problems. This enables applying fast linear time solver for weighted-least squares and - $L_{1}$ smoothing problems. An alternating direction method of multipliers algorithm is adopted to guarantee fast convergence. Our method is fully parallelizable, and its runtime is even comparable to the state-of-the-art local EPS approaches. We also propose a family of fast majorization–minimization algorithms that minimize an objective with non-convex regularization terms. Experimental results demonstrate the effectiveness and flexibility of our approach in a range of image processing and computational photography applications.


title: Social Networking Sites, Depression, and Anxiety: A Systematic Review

abstract: Background: Social networking sites (SNSs) have become a pervasive part of modern culture, which may also affect mental health. Objective: The aim of this systematic review was to identify and summarize research examining depression and anxiety in the context of SNSs. It also aimed to identify studies that complement the assessment of mental illness with measures of well-being and examine moderators and mediators that add to the complexity of this environment. Methods: A multidatabase search was performed. Papers published between January 2005 and June 2016 relevant to mental illness (depression and anxiety only) were extracted and reviewed. Results: Positive interactions, social support, and social connectedness on SNSs were consistently related to lower levels of depression and anxiety, whereas negative interaction and social comparisons on SNSs were related to higher levels of depression and anxiety. SNS use related to less loneliness and greater self-esteem and life satisfaction. Findings were mixed for frequency of SNS use and number of SNS friends. Different patterns in the way individuals with depression and individuals with social anxiety engage with SNSs are beginning to emerge. Conclusions: The systematic review revealed many mixed findings between depression, anxiety, and SNS use. Methodology has predominantly focused on self-report cross-sectional approaches; future research will benefit from leveraging real-time SNS data over time. The evidence suggests that SNS use correlates with mental illness and well-being; however, whether this effect is beneficial or detrimental depends at least partly on the quality of social factors in the SNS environment. Understanding these relationships will lead to better utilization of SNSs in their potential to positively influence mental health. [JMIR Ment Health 2016;3(4):e50]


title: Guidelines for adopting frontend architectures and patterns in microservices-based systems

abstract: Microservice-based systems enable the independent development, deployment, and scalability for separate system components of enterprise applications. A significant aspect during development is the microservice integration in frontends of web, mobile, and desktop applications. One challenge here is the selection of an adequate frontend architecture as well as suitable patterns that satisfy the application requirements. This paper analyses available strategies for organizing and implementing microservices frontends. These approaches are then evaluated based on a quality model and various prototypes of the same application implemented using the distinct approaches. The results of this analysis are generalized to a guideline that supports the selection of a suitable architecture.


title: Automatic object detection for AUV navigation using imaging sonar within confined environments

abstract: Navigation is a crucial issue for the operation of autonomous underwater vehicle (AUV). Range sensors mounted on an AUV can provide real-time information about the surroundings. The use of an active sonar sensor in an enclosed shallow water environment results in received echoes that may reflect several times before attenuation. Furthermore, echo intensities from boundaries are much higher than those from targets, which impedes their detection. This paper presents an innovative method for the detection of underwater objects (in this case, spherical shapes) in such environments for AUV navigation. A mechanically scanning sonar sensor was used in laboratory based trials. Experiments have been conducted in a cylindrical water-tank with an elliptical cross section. Results indicate that the presented method leads efficient and stable detection of both quiescent and moving objects. Potential applications include the detection of targets inside shipwrecks and/or hostile environments, e.g. discovering suspicious objects in the shallow water near harbor areas or water channels connected to the sea.


title: F-SVM: Combination of Feature Transformation and SVM Learning via Convex Relaxation

abstract: The generalization error bound of the support vector machine (SVM) depends on the ratio of the radius and margin. However, conventional SVM only considers the maximization of the margin but ignores the minimization of the radius, which restricts its performance when applied to joint learning of feature transformation and the SVM classifier. Although several approaches have been proposed to integrate the radius and margin information, most of them either require the form of the transformation matrix to be diagonal, or are nonconvex and computationally expensive. In this paper, we suggest a novel approximation for the radius of the minimum enclosing ball in feature space, and then propose a convex radius-margin-based SVM model for joint learning of feature transformation and the SVM classifier, i.e., F-SVM. A generalized block coordinate descent method is adopted to solve the F-SVM model, where the feature transformation is updated via the gradient descent and the classifier is updated by employing the existing SVM solver. By incorporating with kernel principal component analysis, F-SVM is further extended for joint learning of nonlinear transformation and the classifier. F-SVM can also be incorporated with deep convolutional networks to improve image classification performance. Experiments on the UCI, LFW, MNIST, CIFAR-10, CIFAR-100, and Caltech101 data sets demonstrate the effectiveness of F-SVM.


title: Repeated low-dose rituximab treatment based on the assessment of circulating B cells in patients with refractory myasthenia gravis

abstract: Background: The objective of this study was to evaluate the efficacy and safety of repeated low-dose rituximab treatment guided by monitoring circulating CD19+ B cells in patients with refractory myasthenia gravis (MG). Methods: Patients with refractory MG who had received rituximab treatment at two teaching hospitals between September 2013 and January 2017 were reviewed retrospectively. The treatment protocol consisted of an induction treatment with low-dose rituximab (375 mg/m2 twice with a 2-week interval), followed by retreatment (375 mg/m2 once). Retreatment was based on either circulating CD19+ B-cell repopulation or clinical relapse. Outcome measures included the MG Foundation of America (MGFA) clinical classification and postintervention status, prednisolone dose, CD19+ B-cell counts, clinical relapse, and adverse effects. Results: Of 17 patients, 11 (65%) achieved the primary endpoint, defined as the minimal manifestation or better status with prednisolone ⩽5 mg/day, after median 7.6 months (range, 2–17 months) following rituximab treatment. Over a median follow up of 24 months (range, 7–49 months), a total of 30 retreatments were undertaken due to clinical relapse without B-cell repopulation (n = 6), on the basis of B-cell repopulation alone (n = 16) and both (n = 8). B-cell recovery appeared to be in parallel with clinical relapse on the group level, although the individual-level association appeared to be modest, with B-cell repopulation observed only at 57% (8/14) of clinical relapses. Conclusions: The repeated low-dose rituximab treatment based on the assessment of circulating B-cell depletion could be a cost-effective therapeutic option for refractory MG. Further studies are needed to verify the potentially better cost-effectiveness of low-dose rituximab, and to identify biomarkers that help optimize treatment in MG patients.


title: Natural Language Processing for Dialectical Arabic: A Survey

abstract: This paper presents a wide literature review of natural language processing for dialectical Arabic. Four main research areas were identified and the dialect coverage in research work was outlined. The paper can be used as a quick reference to identify relevant contributions that address a specific NLP aspect for a specific dialect.


title: Impossible Spaces: Maximizing Natural Walking in Virtual Environments with Self-Overlapping Architecture

abstract: Walking is only possible within immersive virtual environments that fit inside the boundaries of the user's physical workspace. To reduce the severity of the restrictions imposed by limited physical area, we introduce "impossible spaces," a new design mechanic for virtual environments that wish to maximize the size of the virtual environment that can be explored with natural locomotion. Such environments make use of self-overlapping architectural layouts, effectively compressing comparatively large interior environments into smaller physical areas. We conducted two formal user studies to explore the perception and experience of impossible spaces. In the first experiment, we showed that reasonably small virtual rooms may overlap by as much as 56% before users begin to detect that they are in an impossible space, and that the larger virtual rooms that expanded to maximally fill our available 9.14m × 9.14m workspace may overlap by up to 31%. Our results also demonstrate that users perceive distances to objects in adjacent overlapping rooms as if the overall space was uncompressed, even at overlap levels that were overtly noticeable. In our second experiment, we combined several well-known redirection techniques to string together a chain of impossible spaces in an expansive outdoor scene. We then conducted an exploratory analysis of users' verbal feedback during exploration, which indicated that impossible spaces provide an even more powerful illusion when users are naive to the manipulation.


title: A self-learning evolutionary chess program

abstract: A central challenge of artificial intelligence is to create machines that can learn from their own experience and perform at the level of human experts. Using an evolutionary algorithm, a computer program has learned to play chess by playing games against itself. The program learned to evaluate chessboard configurations by using the positions of pieces, material and positional values, and neural networks to assess specific sections of the chessboard. During evolution, the program improved its play by almost 400 rating points. Testing under simulated tournament conditions against Pocket Fritz 2.0 indicated that the evolved program performs above the master level.


title: Real-Time Nonlinear Shape Interpolation

abstract: We introduce a scheme for real-time nonlinear interpolation of a set of shapes. The scheme exploits the structure of the shape interpolation problem, in particular the fact that the set of all possible interpolated shapes is a low-dimensional object in a high-dimensional shape space. The interpolated shapes are defined as the minimizers of a nonlinear objective functional on the shape space. Our approach is to construct a reduced optimization problem that approximates its unreduced counterpart and can be solved in milliseconds. To achieve this, we restrict the optimization to a low-dimensional subspace that is specifically designed for the shape interpolation problem. The construction of the subspace is based on two components: a formula for the calculation of derivatives of the interpolated shapes and a Krylov-type sequence that combines the derivatives and the Hessian of the objective functional. To make the computational cost for solving the reduced optimization problem independent of the resolution of the example shapes, we combine the dimensional reduction with schemes for the efficient approximation of the reduced nonlinear objective functional and its gradient. In our experiments, we obtain rates of 20--100 interpolated shapes per second, even for the largest examples which have 500k vertices per example shape.


title: Real-world charged board model (CBM) failures

abstract: ICs that are robust to ESD at the component-level may be damaged by ESD at the board-level. Two case studies show that real-world Charged Board Model (CBM) ESD damage is typically more severe than HBM or CDM damage. Consequently, CBM damage can be easily mistaken for EOS damage. A high-capacitance yet compact PCB evaluation board facilitates qualitative CBM testing using conventional CDM test systems. Based on the case studies and CBM test results, guidelines are provided on how to minimize the likelihood of real-world CBM failures.


title: An Adaptive User Interface Based on Personalized Learning

abstract: This adaptive user interface provides individualized, just-in-time assistance to users by recording user interface events and frequencies, organizing them into episodes, and automatically deriving patterns. It also builds, maintains, and makes suggestions based on user profiles.


title: Low-temperature-processed ZnO–SnO 2 nanocomposite for efficient planar perovskite solar cells

abstract: Abstract Electron collection layer (ECL) is one of the most important fundamentals to determine the power conversion efficiency (PCE) in organometal halide-based perovskite solar cells (PSCs). Herein, we prepared ZnO–SnO 2 nanocomposites with different Zn/Sn ratios at low temperature as ECLs for CH 3 NH 3 PbI 3 -based planar-structured PSCs. ZnO–SnO 2 nanocomposite with the optimal ~89 mol% of the ZnO content gives higher PCE than the ZnO for the best fabricated PSC. The photoluminescence spectroscopies measured in both steady and transient states and the electrochemical impedance spectroscopy were carried out to characterize the interface of CH 3 NH 3 PbI 3 and different ECLs, namely ZnO, ZnO–SnO 2 composite, and SnO 2 . The high PCE of PSCs based on the ZnO–SnO 2 nanocomposite ECL was thus attributed to joint contributions of the high charge extraction efficiency and large charge recombination resistance both on the CH 3 NH 3 PbI 3 /ECL interface. The thermal stability of CH 3 NH 3 PbI 3 absorber and the device stability of the corresponding PSC are both dependent on the ECLs in the order: SnO 2 >ZnO–SnO 2 >ZnO, suggesting that the hydroxyl-induced degradation of CH 3 NH 3 PbI 3 may be predominant in the ambient air environment in the initial ~700 h. The PCE of the optimized device was further improved to 15.2% by introducing the low-temperature processable Al 2 O 3 as a capping layer to the ZnO–SnO 2 composite.


title: The Generalized Reparameterization Gradient

abstract: The reparameterization gradient has become a widely used method to obtain Monte Carlo gradients to optimize the variational objective. However, this technique does not easily apply to commonly used distributions such as beta or gamma without further approximations, and most practical applications of the reparameterization gradient fit Gaussian distributions. In this paper, we introduce the generalized reparameterization gradient, a method that extends the reparameterization gradient to a wider class of variational distributions. Generalized reparameterizations use invertible transformations of the latent variables which lead to transformed distributions that weakly depend on the variational parameters. This results in new Monte Carlo gradients that combine reparameterization gradients and score function gradients. We demonstrate our approach on variational inference for two complex probabilistic models. The generalized reparameterization is effective: even a single sample from the variational distribution is enough to obtain a low-variance gradient.


title: Incremental dynamic analysis for estimating seismic performance sensitivity and uncertainty

abstract: SUMMARY Incremental Dynamic Analysis (IDA) is presented as a powerful tool to evaluate the variability in the seismic demand and capacity of non-deterministic structural models, building upon existing methodologies of Monte Carlo simulation and approximate moment-estimation. A nine-story steel moment-resisting frame is used as a testbed, employing parameterized moment-rotation relationships with non-deterministic quadrilinear backbones for the beam plastic-hinges. The uncertain properties of the backbones include the yield moment, the post-yield hardening ratio, the end-of-hardening rotation, the slope of the descending branch, the residual moment capacity and the ultimate rotation reached. IDA is employed to accurately assess the seismic performance of the model for any combination of the parameters by performing multiple nonlinear timehistory analyses for a suite of ground motion records. Sensitivity analyses on both the IDA and the static pushover level reveal the yield moment and the two rotational-ductility parameters to be the most in∞uential for the frame behavior. To propagate the parametric uncertainty to the actual seismic performance we employ a) Monte Carlo simulation with latin hypercube sampling, b) point-estimate and c) flrst-order second-moment techniques, thus ofiering competing methods that represent difierent compromises between speed and accuracy. The flnal results provide flrm ground for challenging current assumptions in seismic guidelines on using a median-parameter model to estimate the median seismic performance and employing the well-known square-root-sum-of-squares rule to combine aleatory randomness and epistemic uncertainty. Copyright c ∞ 2009 John Wiley & Sons, Ltd.


title: Behavioral decisions made under the risk of predation: a review and prospectus

abstract: Predation has long been implicated as a major selective force in the evolution of several morphological and behavioral characteristics of animals. The importance of predation during evolutionary time is clear, but growing evidence suggests that animals also have the ability to assess and behaviorally influence their risk of being preyed upon in ecological time (i.e., during their lifetime). We develop an abstraction of the predation process in which several components of predation risk are identified. A review of the literature indicates that an animal's ability to assess and behaviorally control one or more of these components strongly influences decision making in feeding animals, as well as in animals deciding when and how to escape predators, when and how to be social, or even, for fishes, when and how to breathe air. This review also reveals that such decision making reflects apparent trade-offs between the risk of predation and the benefits to be gained from engaging in a given activity. Despite thi...


title: Highlight sound effects detection in audio stream

abstract: This paper addresses the problem of highlight sound effects detection in audio stream, which is very useful in fields of video summarization and highlight extraction. Unlike researches on audio segmentation and classification, in this domain, it just locates those highlight sound effects in audio stream. An extensible framework is proposed and in current system three sound effects are considered: laughter, applause and cheer, which are tied up with highlight events in entertainments, sports, meetings and home videos. HMMs are used to model these sound effects and a log-likelihood scores based method is used to make final decision. A sound effect attention model is also proposed to extend general audio attention model for highlight extraction and video summarization. Evaluations on a 2-hours audio database showed very encouraging results.


title: Serious games as edX MOOC activities

abstract: Massive Online Open Courses are in the process of radically changing the use of technology in education, as measured both by the number of enrolled students and the level of academic excellence of the institutions that are driving their adoption. Typically, online learning management systems have relied on simple questionnaires for feedback and evaluation. On the other hand, MOOC platforms such as EDX have, from the onset, provided a great flexibility in this regard, providing peer grading and several examples of highly interactive activities such as hands-on simulations. This paper explores the integration of serious games as a new type of MOOC activity, providing increased engagement and a valuable source of learning analytics. The inclusion of serious games has implications for both courses and games. Due to the diversity of existing serious game and MOOC platforms, we focus on the specific case of integrating EADVENTURE games into EDX.


title: Efficient SIMD code generation for runtime alignment and length conversion

abstract: When generating codes for today's multimedia extensions, one of the major challenges is to deal with memory alignment issues. While hand programming still yields best performing SIMD codes, it is both time consuming and error prone. Compiler technology has greatly improved, including techniques that simdize loops with misaligned accesses by automatically rearranging mis-aligned memory streams in registers. Current techniques are applicable to runtime alignments, but they aggressively reduce the alignment overhead only when all alignments are known at compile time. This paper presents two major enhancements to the state of the art, improving both performance and coverage. First, we propose a novel technique to simdize loops with runtime alignment nearly as efficiently as those with compile-time misalignment. Runtime alignment is pervasive in real applications because it is either part of the algorithms, or it is an artifact of the compiler's inability to extract accurate alignment information from complex applications. Second, we incorporate length conversion operations, e.g., conversions between data of different sizes, into the alignment handling framework. Length conversions are pervasive in multimedia applications where mixed integer types are often used. Supporting length conversion can greatly improve the coverage of simdizable loops. Experimental results indicate that our runtime alignment technique achieves a 19% to 32% speedup increase over prior art for a benchmark stressing the impact of misaligned data. We also demonstrate speedup factors of up to 8.11 for real benchmarks over sequential execution.


title: Litz wire in the MHz range: Modeling and improved designs

abstract: Although conventional litz-wire design and modeling indicates that it has little benefit for frequencies much beyond where skin depth equals the strand diameter, we investigate this region of operation more closely, including the effect of the effective complex permeability of the winding region with different winding and inductor geometries, and the effect of the configuration of strands within a litz bundle. We show that the benefit of litz wire extends to higher frequencies than was indicated by previous models with some field geometries and show that a “hollow” litz wire structure in which strands are confined to the outer perimeter of a dielectric core is superior to conventional litz in many high-frequency designs.


The Isotope Effect in Band Spectra, II: The Spectrum of Boron Monoxide


title: Design and Implementation of a TEM Stripline for EMC Testing

abstract: In this paper, a stripline for radiated field immunity testing of audio/video products is designed, constructed, calibrated, and verified. Since the canonical one is only suitable for testing equipments with height less than 70 cm, there is a need of a new device which is also in compliance with EN 55020 standard for testing new large sets, like 47'' thin-film transistor liquid crystal display (TFT LCD) television sets, in the frequency range of 150 kHz-150 MHz. Increasing the height and width of testing area causes important problems in terms of field uniformity regarding the higher order modes, characteristic impedance, and reflections in addition to the room resonances and field interference sources like corners at the edges of stripline. Comprehensive numerical study is performed to overcome these problems and obtain the optimum design before the construction. Measured data show that the new stripline is in very good agreement with the specifications determined by the standards and therefore it can be used for the electromagnetic compatibility (EMC) testing.


title: Orchestration by approximation: mapping stream programs onto multicore architectures

abstract: We present a novel 2-approximation algorithm for deploying stream graphs on multicore computers and a stream graph transformation that eliminates bottlenecks. The key technical insight is a data rate transfer model that enables the computation of a "closed form", i.e., the data rate transfer function of an actor depending on the arrival rate of the stream program. A combinatorial optimization problem uses the closed form to maximize the throughput of the stream program. Although the problem is inherently NP-hard, we present an efficient and effective 2-approximation algorithm that provides a lower bound on the quality of the solution. We introduce a transformation that uses the closed form to identify and eliminate bottlenecks. We show experimentally that state-of-the art integer linear programming approaches for orchestrating stream graphs are (1) intractable or at least impractical for larger stream graphs and larger number of processors and (2)our 2-approximation algorithm is highly efficient and its results are close to the optimal solution for a standard set of StreamIt benchmark programs.


title: Automatic Text-to-Scene Conversion in the Traffic Accident Domain

abstract: In this paper, we describe a system that automatically converts narratives into 3D scenes. The texts, written in Swedish, describe road accidents. One of the program's key features is that it animates the generated scene using temporal relations between the events. We believe that this system is the first text-to-scene converter that is not restricted to invented narratives. ::: ::: The system consists of three modules: natural language interpretation based on information extraction (IE) methods, a planning module that produces a geometric description of the accident, and finally a visualization module that renders the geometric description as animated graphics. ::: ::: An evaluation of the system was carried out in two steps: First, we used standard IE scoring methods to evaluate the language interpretation. The results are on the same level as for similar systems tested previously. Secondly, we performed a small user study to evaluate the quality of the visualization. The results validate our choice of methods, and since this is the first evaluation of a text-to-scene conversion system, they also provide a baseline for further studies.


NoSQL Database Performance Tuning for IoT Data - Cassandra Case Study


title: IoT-based smart city development using big data analytical approach

abstract: To meet the needs of urban public and the city development smartly, the use of IoT devices, such as sensors, actuators, and smartphones, etc., and the smart system is the very fast and valuable source. However, interconnecting thousands of IoT devices while communicating with each other over the Internet to establish a smart system, results in the generation of huge amount of data, termed as Big Data. To integrate IoT services in order to get real-time city data and then processing such big amount of data in an efficient way aimed at establishing smart city is a challenging task. Therefore, in this paper, we proposed and developed a smart city system based on IoT using Big Data Analytics. We use sensors deployment including smart home sensors, vehicular networking, weather and water sensors, smart parking sensors, surveillance objects, etc. The complete architecture and implementation model is proposed, which is implemented using Hadoop ecosystem in a real environment. The system implementation consists of various steps that start from data generation and collecting, aggregating, filtration, classification, preprocessing, computing and finished at decision making. The efficiency in Big Data processing is achieved using spark over Hadoop. The system is practically implemented by taken smart systems as city data source to develop smart city. The evaluation results show that the proposed system is scalable and efficient.


title: Improving Collaborative Recommendation via User-Item Subgroups

abstract: Collaborative filtering (CF) is out of question the most widely adopted and successful recommendation approach. A typical CF-based recommender system associates a user with a group of like-minded users based on their individual preferences over all the items, either explicit or implicit, and then recommends to the user some unobserved items enjoyed by the group. However, we find that two users with similar tastes on one item subset may have totally different tastes on another set. In other words, there exist many user-item subgroups each consisting of a subset of items and a group of like-minded users on these items. It is more reasonable to predict preferences through one user's correlated subgroups, but not the entire user-item matrix. In this paper, to find meaningful subgroups, we formulate a new Multiclass Co-Clustering (MCoC) model, which captures relations of user-to-item, user-to-user, and item-to-item simultaneously. Then, we combine traditional CF algorithms with subgroups for improving their top- $N$ recommendation performance. Our approach can be seen as a new extension of traditional clustering CF models. Systematic experiments on several real data sets have demonstrated the effectiveness of our proposed approach.


title: Text and image based spam email classification using KNN, Naïve Bayes and Reverse DBSCAN algorithm

abstract: Internet has changed the way of communication, which has become more and more concentrated on emails. Emails, text messages and online messenger chatting have become part and parcel of our lives. Out of all these communications, emails are more prone to exploitation. Thus, various email providers employ algorithms to filter emails based on spam and ham. In this research paper, our prime aim is to detect text as well as image based spam emails. To achieve the objective we applied three algorithms namely: KNN algorithm, Naive Bayes algorithm and reverse DBSCAN algorithm. Pre-processing of email text before executing the algorithms is used to make them predict better. This paper uses Enron corpus's dataset of spam and ham emails. In this research paper, we provide comparison performance of all three algorithms based on four measuring factors namely: precision, sensitivity, specificity and accuracy. We are able to attain good accuracy by all the three algorithms. The results have shown comparison of all three algorithms applied on same data set.


title: A Semidigital Dual Delay-Locked Loop

abstract: This paper describes a dual delay-locked loop architecture which achieves low jitter, unlimited (modulo 2/spl pi/) phase shift, and large operating range. The architecture employs a core loop to generate coarsely spaced clocks, which are then used by a peripheral loop to generate the main system clock through phase interpolation. The design of an experimental prototype in a 0.8-/spl mu/m CMOS technology is described. The prototype achieves an operating range of 80 kHz-400 MHz. At 250 MHz, its peak-to-peak jitter with quiescent supply is 68 ps, and its jitter supply sensitivity is 0.4 ps/mV.


title: Design, Implementation, and Performance Evaluation of a Flexible Low-Latency Nanowatt Wake-Up Radio Receiver

abstract: Wireless sensor networks (WSNs) have received significant attention in recent years and have found a wide range of applications, including structural and environmental monitoring, mobile health, home automation, Internet of Things, and others. As these systems are generally battery operated, major research efforts focus on reducing power consumption, especially for communication, as the radio transceiver is one of the most power-hungry components of a WSN. Moreover, with the advent of energy-neutral systems, the emphasis has shifted toward research in microwatt (or even nanowatt) communication protocols or systems. A significant number of wake-up radio receiver (WUR) architectures have been proposed to reduce the communication power of WSN nodes. In this work, we present an optimized ultra-low power (nanowatt) wake-up receiver for use in WSNs, designed with low-cost off-the-shelf components. The wake-up receiver achieves power consumption of 152 nW (with $-32\,{\text {dBm}}$ sensitivity), sensitivity up to $-55\,{\text {dBm}}$ (with maximum power of $1,2 \upmu{\text {W}}$ ), latency from $8\;\upmu\text{s}$ , tunable frequency, and short commands communication. In addition, a low power solution, which includes addressing capability directly in the wake-up receiver, is proposed. Experimental results and simulations demonstrate low power consumption, functionality, and benefits of the design optimization compared with other solutions, as well as the benefits of addressing false positive (FP) outcomes reduction.


title: Malware Behavior Ontology for Digital Evidence

abstract: Not only in computer security, dealing with malware also presents one of vital challenges in computer forensics. Attempting to get away from their criminal activities, a suspect usually uses malware defense where they can claim that they did nothing wrong but malware did. Therefore, the burden of proof for the prosecution in pinning down the criminals is often extremely heavy due to the complex behavior demonstrated by malware. The goal of this project is to apply the theory and technique of ontology in analyzing the malware behavior and then to use the result of this study in classifying malware into the forensic-aware categories. Assisted by these proposed categories, prosecutors will have the upper hand to defeat suspect's alibi in a more efficient way. Moreover, the result of this project will help computer forensics professionals to present their expert opinions against malware defense in court with the uniformity and clarity.


title: GPstuff: Bayesian modeling with Gaussian processes

abstract: The GPstuff toolbox is a versatile collection of Gaussian process models and computational tools required for Bayesian inference. The tools include, among others, various inference methods, sparse approximations and model assessment methods.


Novel Cellular Active Array Antenna System at Base Station for Beyond 4G


title: A new control method for series resonant inverter with inherently phase-locked coil current with induction cookware applications

abstract: In this paper, a new control method for induction cookware applications with series resonant inverter has been proposed. Generally, the output power of series resonant inverter is controlled by pulse frequency modulation (PFM). However, PFM has a large turn off current and a high switching frequency as the output power decreases, resulting in a large switching turn off loss and poor efficiency. To beat these limitations, pulse width modulation (PWM), PWM-PFM mixed control, and pulse density modulation (PDM) methods have been investigated. However, they have still limitations such as control complexity or loosing zero voltage switching (ZVS) characteristics. The most powerful advantage of the proposed method is the simplicity, since the only on-time is used as the control variable. Also, it has inherent soft switching characteristics with ZVS turn-on and zero current switching (ZCS) turnoff of low side switches and zero voltage and ZVZCS turn-on of two high side switches. The effectiveness of the proposed method has been verified by 200V input voltage and 2.8kW output prototype.


abstract: The recent development of CNN-based image dehazing has revealed the effectiveness of end-to-end modeling. However, extending the idea to end-to-end video dehazing has not been explored yet. In this paper, we propose an End-to-End Video Dehazing Network (EVD-Net), to exploit the temporal consistency between consecutive video frames. A thorough study has been conducted over a number of structure options, to identify the best temporal fusion strategy. Furthermore, we build an End-to-End United Video Dehazing and Detection Network(EVDD-Net), which concatenates and jointly trains EVD-Net with a video object detection model. The resulting augmented end-to-end pipeline has demonstrated much more stable and accurate detection results in hazy video.