title: On the Two Segmented and High Aspect Ratio Rectangular Dielectric Resonator Antennas for Bandwidth Enhancement and Miniaturization

abstract: The goal of this communication is to maintain wide impedance bandwidth while miniaturizing a microstrip-fed dielectric resonator antenna (DRA). The approach of using high aspect ratio structures is proposed, discussed, and compared with the two segmented structures approach. Finite element method parametric study of high permittivity high aspect ratio structures is used to demonstrate how two appropriate resonant modes can be merged to obtain miniaturized antennas with broadband operation, stable radiation patterns, and low cross polarization levels. To show the effectiveness of the approach, a set of microstrip-fed two segmented and high aspect ratio DRAs is fabricated, characterized, and the results are compared with Ansoft HFSS simulations. A high aspect ratio DRA with cross section as small as 0.08lambda by 0.12lambda has better than 11 percent bandwidth and is especially attractive for compact array applications.


title: SEDA: Secure Over-the-Air Code Dissemination Protocol for the Internet of Things

abstract: The capability to securely (re)program embedded devices over-the-air is a fundamental functionality for the emerging Internet of Things (IoT). Current approaches work efficiently by exploiting the homogeneity within sensing devices, where all nodes require the update and participate in the process. Due to the heterogeneity of IoT deployments, where the type of devices and program images vary, existing solutions suffer from severe performance degradation. We address this shortcoming with our system SEDA, which leverages a secure multicast approach . SEDA outperforms existing systems since the program image is securely delivered and processed on targeted nodes only, hence reducing the overhead for not involved nodes. In order to enable the multicast approach, we introduce an asymmetric broadcast encryption primitive, which we have optimized towards constrained nodes to reduce the communication/computation overhead as compared to existing approaches. With an extensive experimental study on a public testbed in several practical settings, we show SEDA's efficient performance compared to state-of-the-art approaches. Finally, our theoretical security analysis shows SEDA's security against identified adversary models.


title: Virtual harm reduction efforts for Internet gambling: effects of deposit limits on actual Internet sports gambling behavior

abstract: BackgroundIn an attempt to reduce harm related to gambling problems, an Internet sports betting service provider, bwin Interactive Entertainment, AG (bwin), imposes limits on the amount of money that users can deposit into their online gambling accounts. We examined the effects of these limits on gambling behavior.MethodsWe compared (1) gambling behavior of those who exceeded deposit limits with those who did not, and (2) gambling behavior before and after exceeding deposit limits. We analyzed 2 years of the actual sports gambling behavior records of 47000 subscribers to bwin.ResultsOnly 160 (0.3%) exceeded deposit limits at least once. Gamblers who exceeded deposit limits evidenced higher average number of bets per active betting day and higher average size of bets than gamblers who did not exceed deposit limits. Comparing the gambling behavior before and after exceeding deposit limits revealed slightly more unfavorable gambling behavior after exceeding deposit limits.ConclusionOur findings indicate that Internet gamblers who exceed deposit limits constitute a group of bettors willing to take high risks; yet, surprisingly, they appear to do this rather successfully because their percentage of losses is lower than others in the sample. However, some of these gamblers exhibit some poor outcomes. Deposit limits might be necessary harm reduction measures to prevent the loss of extremely large amounts of money and cases of bankruptcy. We discuss how these limits might be modified based on our findings.


title: Microbiological, technological and therapeutic properties of kefir: a natural probiotic beverage

abstract: Kefir is a fermented milk beverage produced by the action of bacteria and yeasts that exist in symbiotic association in kefir grains. The artisanal production of the kefir is based on the tradition of the peoples of Caucasus, which has spread to other parts of the world, from the late 19(th) century, and nowadays integrates its nutritional and therapeutic indications to the everyday food choices of several populations. The large number of microorganisms present in kefir and their microbial interactions, the possible bioactive compounds resulting of microbial metabolism, and the benefits associated with the use this beverage confers kefir the status of a natural probiotic, designated as the 21(th) century yoghurt. Several studies have shown that kefir and its constituents have antimicrobial, antitumor, anticarcinogenic and immunomodulatory activity and also improve lactose digestion, among others. This review includes data on the technological aspects, the main beneficial effects on human health of kefir and its microbiological composition. Generally, kefir grains contain a relatively stable and specific microbiota enclosed in a matrix of polysaccharides and proteins. Microbial interactions in kefir are complex due to the composition of kefir grains, which seems to differ among different studies, although some predominant Lactobacillus species are always present. Besides, the specific populations of individual grains seem to contribute to the particular sensory characteristics present in fermented beverages. This review also includes new electron microscopy data on the distribution of microorganisms within different Brazilian kefir grains, which showed a relative change in its distribution according to grain origin.


title: A passive weight compensation mechanism with a non-circular pulley and a spring

abstract: We propose a new weight compensation mechanism with a non-circular pulley and a spring. We show the basic principle and numerical design method to derive the shape of the non-circular pulley. After demonstration of the weight compensation for an inverted/ordinary pendulum system, we extend the same mechanism to a parallel five-bar linkage system, analyzing the required torques using transposed Jacobian matrices. Finally, we develop a three degree of freedom manipulator with relatively small output actuators and verified that the weight compensation mechanism significantly contributes to decrease static torques to keep the same posture within manipulator's work space.


title: Robust Markov Decision Processes

abstract: Markov decision processes MDPs are powerful tools for decision making in uncertain dynamic environments. However, the solutions of MDPs are of limited practical use because of their sensitivity to distributional model parameters, which are typically unknown and have to be estimated by the decision maker. To counter the detrimental effects of estimation errors, we consider robust MDPs that offer probabilistic guarantees in view of the unknown parameters. To this end, we assume that an observation history of the MDP is available. Based on this history, we derive a confidence region that contains the unknown parameters with a prespecified probability 1-β. Afterward, we determine a policy that attains the highest worst-case performance over this confidence region. By construction, this policy achieves or exceeds its worst-case performance with a confidence of at least 1-β. Our method involves the solution of tractable conic programs of moderate size.


title: Matching theory—a sampler: from Dénes König to the present

abstract: Abstract : It was Koenig who gave the next strong impetus to the study of graph factorization after Petersen's ground breaking work, and it is Koenig with whom we are charged to begin out brief summary of the history of matching theory. Fortunately, matching theory serves well as an historical thread extending from the time of Koenig (and before) up to the present, wending its way through graph theory and intersecting many of the most important new ideas which have sprung forth in our discipline. One sees in particular that after the close of World War II this intertwining of matching theory with the study of graphs as a whole became ever more inextricable, even as the study of graph theory as a discipline unto itself literally exploded upon the mathematical scene. Although it is jumping the gun somewhat with respect to the organization of this paper, we can mention three major areas which have joined with graph theory to give rise to many new and deep results. These are: (1) linear programming and polyhydral combinatorics; (2) the linking of graphs and probability theory in the area of random graphs and finally (3) the theory of algorithms and computational complexity.


title: Single-view hair modeling using a hairstyle database

abstract: Human hair presents highly convoluted structures and spans an extraordinarily wide range of hairstyles, which is essential for the digitization of compelling virtual avatars but also one of the most challenging to create. Cutting-edge hair modeling techniques typically rely on expensive capture devices and significant manual labor. We introduce a novel data-driven framework that can digitize complete and highly complex 3D hairstyles from a single-view photograph. We first construct a large database of manually crafted hair models from several online repositories. Given a reference photo of the target hairstyle and a few user strokes as guidance, we automatically search for multiple best matching examples from the database and combine them consistently into a single hairstyle to form the large-scale structure of the hair model. We then synthesize the final hair strands by jointly optimizing for the projected 2D similarity to the reference photo, the physical plausibility of each strand, as well as the local orientation coherency between neighboring strands. We demonstrate the effectiveness and robustness of our method on a variety of hairstyles and challenging images, and compare our system with state-of-the-art hair modeling algorithms.


title: Multimodal Classification of Remote Sensing Images: A Review and Future Directions

abstract: Earth observation through remote sensing images allows the accurate characterization and identification of mate- rials onthe surfacefromspace and airborne platforms. Multiple andheterogeneous image sourcescanbeavailableforthesame geographical region: multispectral, hyperspectral, radar, multi- temporal, and multiangular images can today be acquired over a given scene. These sources can be combined/fused to im- prove classification of the materials on the surface. Even if this type of systems is generally accurate, the field is about to face new challenges: the upcoming constellations of satellite sen- sors will acquire large amounts of images of different spatial, spectral, angular, and temporal resolutions. In this scenario, multimodal image fusion stands out as the appropriate frame- work to address these problems. In this paper, we provide a taxonomical view of the field and review the current method- ologies for multimodal classification of remote sensing images. We also highlight the most recent advances, which exploit sy- nergies with machine learning and signal processing: sparse methods, kernel-based fusion, Markov modeling, and manifold alignment.Then,weillustrate the differentapproaches inseven challenging remote sensing applications: 1) multiresolution fusion for multispectral image classification; 2) image down- scaling as a form of multitemporal image fusion and multidi- mensional interpolation among sensors of different spatial, spectral, and temporal resolutions; 3) multiangular image clas, sification; 4) multisensor image fusion exploiting physically- based feature extractions; 5) multitemporal image classifica- tion of land covers in incomplete, inconsistent, and vague image sources; 6) spatiospectral multisensor fusion of optical and radar images for change detection; and 7) cross-sensor adaptation of classifiers. The adoption of these techniques in operational settings will help to monitor our planet from space in the very near future.


title: Probabilistic Logic Programming

abstract: Abstract Of all scientific investigations into reasoning with uncertainty and chance, probability theory is perhaps the best understood paradigm. Nevertheless, all studies conducted thus far into the semantics of quantitative logic programming have restricted themselves to non-probabilistic semantic characterizations. In this paper, we take a few steps towards rectifying this situation. We define a logic programming language that is syntactically similar to the annotated logics of Blair et al., 1987 , Blair and Subrahmanian, 1988 , 45–73) but in which the truth values are interpreted probabilistically. A probabilistic model theory and fixpoint theory is developed for such programs. This probabilistic model theory satisfies the requirements proposed by Fenstad (in “Studies in Inductive Logic and Probabilities” (R. C. Jeffrey, Ed.), Vol. 2, pp. 251–262, Univ. of California Press, Berkeley, 1980) for a function to be called probabilistic. The logical treatment of probabilities is complicated by two facts: first, that the connectives cannot be interpreted truth-functionally when truth values are regarded as probabilities; second, that negation-free definite-clause-like sentences can be inconsistent when interpreted probabilistically. We address these issues here and propose a formalism for probabilistic reasoning in logic programming. To our knowledge, this is the first probabilistic characterization of logic programming semantics.


title: Connectionist nonparametric regression: multilayer feedforward networks can learn arbitrary mappings

abstract: Abstract It has been recently shown (e.g., Hornik, Stinchcombe & White, 1989, 1990) that sufficiently complex multilayer feedforward networks are capable of representing arbitrarily accurate approximations to arbitrary mappings. We show here that these approximations are learnable by proving the consistency of a class of connectionist nonparametric regression estimators for arbitrary (square integrable) regression functions. The consistency property ensures that as network “experience” accumulates (as indexed by the size of the training set), the probability of network approximation error exceeding any specified level tends to zero. A key feature of the demonstration of consistency is the proper control of the growth of network complexity as a function of network experience. We give specific growth rates for network complexity compatible with consistency. We also consider automatic and semi-automatic data-driven methods for determining network complexity in applications, based on minimization of a cross-validated average squared error measure of network performance. We recommend cross-validated average squared error as a generally applicable criterion for comparing relative performance of differing network architectures and configurations.


title: Watch-n-patch: Unsupervised understanding of actions and relations

abstract: We focus on modeling human activities comprising multiple actions in a completely unsupervised setting. Our model learns the high-level action co-occurrence and temporal relations between the actions in the activity video. We consider the video as a sequence of short-term action clips, called action-words, and an activity is about a set of action-topics indicating which actions are present in the video. Then we propose a new probabilistic model relating the action-words and the action-topics. It allows us to model long-range action relations that commonly exist in the complex activity, which is challenging to capture in the previous works. We apply our model to unsupervised action segmentation and recognition, and also to a novel application that detects forgotten actions, which we call action patching. For evaluation, we also contribute a new challenging RGB-D activity video dataset recorded by the new Kinect v2, which contains several human daily activities as compositions of multiple actions interacted with different objects. The extensive experiments show the effectiveness of our model.


title: Vertical Si-Nanowire $n$-Type Tunneling FETs With Low Subthreshold Swing ($\leq \hbox{50}\ \hbox{mV/decade}$ ) at Room Temperature

abstract: This letter presents a Si nanowire based tunneling field-effect transistor (TFET) using a CMOS-compatible vertical gate-all-around structure. By minimizing the thermal budget with low-temperature dopant-segregated silicidation for the source-side dopant activation, excellent TFET characteristics were obtained. We have demonstrated for the first time the lowest ever reported subthreshold swing (SS) of 30 mV/decade at room temperature. In addition, we reported a very convincing SS of 50 mV/decade for close to three decades of drain current. Moreover, our TFET device exhibits excellent characteristics without ambipolar behavior and with high Ion/Ioff ratio (105), as well as low Drain-Induced Barrier Lowering of 70 mV/V.


title: Is a diamond forever? Feminist transformations of sport.

abstract: Abstract This paper focuses on how individuals and groups overcome the hegemonic grasp of alienating ideologies and institute social practices which have authentic meaning in their lives. Specifically, the paper investigates how women who define themselves as feminists actively construct and maintain alternatives to the male preserve of sport. The analysis is based on data drawn from intensive interviews and observations collected over a four year period of a women's summer recreational softball league. The sample was a purposive one: women who define themselves as feminists were purposely sought out and interviewed about their feminist attitudes, their attitudes toward sport and softball, and the relationship they saw between a feminist consciousness and the practice of sport. Several themes emerged as central issues in their complaints: the overemphasis on winning; the hierarchy of authority; the elitism of skill; the exclusivity represented by sexism, classism, racism, ageism, sizism, and heterosexism; the disparagement of opponents; and an ethic of endangerment. These criticisms serve as a blueprint for the changes they were putting into practice: a form of softball which is process oriented, collective, inclusive, supportive, and infused with an ethic of care. The analysis draws upon feminist theory, interactionist principles, and cultural studies approaches to explore the ways in which individuals can actively resist inimical social practices and create, maintain, and inhabit social worlds that fit their own needs.


title: Honda humanoid robots development

abstract: Honda has been doing research on robotics since 1986 with a focus upon bipedal walking technology. The research started with straight and static walking of the first prototype two-legged robot. Now, the continuous transition from walking in a straight line to making a turn has been achieved with the latest humanoid robot ASIMO. ASIMO is the most advanced robot of Honda so far in the mechanism and the control system. ASIMO9s configuration allows it to operate freely in the human living space. It could be of practical help to humans with its ability of five-finger arms as well as its walking function. The target of further development of ASIMO is to develop a robot to improve life in human society. Much development work will be continued both mechanically and electronically, staying true to Honda9s ‘challenging spirit’.


title: Deep Generative Dual Memory Network for Continual Learning

abstract: Despite advances in deep learning, artificial neural networks do not learn the same way as humans do. Today, neural networks can learn multiple tasks when trained on them jointly, but cannot maintain performance on learnt tasks when tasks are presented one at a time -- this phenomenon called catastrophic forgetting is a fundamental challenge to overcome before neural networks can learn continually from incoming data. In this work, we derive inspiration from human memory to develop an architecture capable of learning continuously from sequentially incoming tasks, while averting catastrophic forgetting. Specifically, our model consists of a dual memory architecture to emulate the complementary learning systems (hippocampus and the neocortex) in the human brain and maintains a consolidated long-term memory via generative replay of past experiences. We (i) substantiate our claim that replay should be generative, (ii) show the benefits of generative replay and dual memory via experiments, and (iii) demonstrate improved performance retention even for small models with low capacity. Our architecture displays many important characteristics of the human memory and provides insights on the connection between sleep and learning in humans.


title: Benefits of quantile regression for the analysis of customer lifetime value in a contractual setting: An application in financial services

abstract: The move towards a customer-centred approach to marketing, coupled with the increasing availability of customer transaction data, has led to an interest in understanding and estimating customer lifetime value (CLV). Several authors point out that, when evaluating customer profitability, profitable customers are rare compared to the unprofitable ones. In spite of this, most authors fail to recognize the implications of these skewed distributions on the performance of models they use. In this study, we propose analyzing CLV by means of quantile regression. In a financial services application, we show that this technique provides management more in-depth insights into the effects of the covariates that are missed with linear regression. Moreover, we show that in the common situation where interest is in a top-customer segment, quantile regression outperforms linear regression. The method also has the ability of constructing prediction intervals. Combining the CLV point estimate with the prediction intervals leads to a new segmentation scheme that is the first to account for uncertainty in the predictions. This segmentation is ideally suited for managing the portfolio of customers.


title: KAML: A Flexible, High-Performance Key-Value SSD

abstract: Modern solid state drives (SSDs) unnecessarily confine host programs to the conventional block I/O interface, leading to suboptimal performance and resource under-utilization. Recent attempts to replace or extend this interface with a key-value-oriented interface and/or built-in support for transactions offer some improvements, but the details of their implementations make them a poor match for many applications. This paper presents the key-addressable, multi-log SSD (KAML), an SSD with a key-value interface that uses a novel multi-log architecture and stores data as variable-sized records rather than fixed-sized sectors. Exposing a key-value interface allows applications to remove a layer of indirection between application-level keys (e.g., database record IDs or file inode numbers) and data stored in the SSD. KAML also provides native transaction support tuned to support fine-grained locking, achieving improved performance compared to previous designs that require page-level locking. Finally, KAML includes a caching layer analogous to a conventional page cache that leverages host DRAM to improve performance and provides additional transactional features. We have implemented a prototype of KAML on a commercial SSD prototyping platform, and our results show that compared with existing key-value stores, KAML improves the performance of online transaction processing (OLTP) workloads by 1.1X – 4.0X, and NoSQL key-value store applications by 1.1X – 3.0X.


title: BERT and PALs: Projected Attention Layers for Efficient Adaptation in Multi-Task Learning

abstract: Multi-task learning allows the sharing of useful information between multiple related tasks. In natural language processing several recent approaches have successfully leveraged unsupervised pre-training on large amounts of data to perform well on various tasks, such as those in the GLUE benchmark. These results are based on fine-tuning on each task separately. We explore the multi-task learning setting for the recent BERT model on the GLUE benchmark, and how to best add task-specific parameters to a pre-trained BERT network, with a high degree of parameter sharing between tasks. We introduce new adaptation modules, PALs or `projected attention layers', which use a low-dimensional multi-head attention mechanism, based on the idea that it is important to include layers with inductive biases useful for the input domain. By using PALs in parallel with BERT layers, we match the performance of fine-tuned BERT on the GLUE benchmark with roughly 7 times fewer parameters, and obtain state-of-the-art results on the Recognizing Textual Entailment dataset.


title: Effect of passivation annealing on the electromigration properties of hybrid bonding stack

abstract: This paper presents electromigration results on a hybrid bonding-based test vehicle to study the impact of bonding and passivation annealings on backend of line robustness. Black's parameters extraction leads to typical values of Cu-based interconnects. Electromigration lifetime remains the same whatever the bonding annealing conditions but a significant influence of passivation annealing is observed. Chemical analyses evidence the effect of the annealing atmosphere. A discussion is lead on the chemical species concentration at different locations of the stack and the reduction of the Time to Failure with passivation final annealing.


title: Smart TV forensics : Digital traces on televisions

abstract: The Smart TV is becoming increasingly popular amongst consumers. Many consumers use a Smart TV to gain quick access to the Internet including video on demand, social networking and instant messaging. Most Smart TVs also provide capabilities to connect with external devices such as a USB flash drive, a mobile phone etc. All of these features make a Smart TV a potentially rich source of information for forensic purposes. With increasing utilisation, it is also easier for malicious users to abuse a Smart TV. Therefore a digital forensics study on the field of Smart TV is imperative. This paper proposes new procedures for acquiring, analysing and investigating a Smart TV.


title: 3D Scene Mesh from CNN Depth Predictions and Sparse Monocular SLAM

abstract: In this paper, we propose a novel framework for integrating geometrical measurements of monocular visual simultaneous localization and mapping (SLAM) and depth prediction using a convolutional neural network (CNN). In our framework, SLAM-measured sparse features and CNN-predicted dense depth maps are fused to obtain a more accurate dense 3D reconstruction including scale. We continuously update an initial 3D mesh by integrating accurately tracked sparse features points. Compared to prior work on integrating SLAM and CNN estimates [26], there are two main differences: Using a 3D mesh representation allows as-rigid-as-possible update transformations. We further propose a system architecture suitable for mobile devices, where feature tracking and CNN-based depth prediction modules are separated, and only the former is run on the device. We evaluate the framework by comparing the 3D reconstruction result with 3D measurements obtained using an RGBD sensor, showing a reduction in the mean residual error of 38% compared to CNN-based depth map prediction alone.


title: Preventing the Complications Associated with the Use of Dermal Fillers in Facial Aesthetic Procedures: An Expert Group Consensus Report

abstract: Background ::: The use of dermal fillers in minimally invasive facial aesthetic procedures has become increasingly popular of late, yet as the indications and the number of procedures performed increase, the number of complications is also likely to increase. Paying special attention to specific patient characteristics and to the technique used can do much to avoid these complications. Indeed, a well-trained physician can also minimize the impact of such problems when they do occur.


title: Broadband Fixed Phase Shifters

abstract: A method to design planar and compact phase shifters with broadband characteristics is presented. It utilizes broadside-coupled microstrip-coplanar waveguide, and thus the proposed devices can be fabricated using the simple and cheap double-side printed circuit boards. The method is used to design 60° and 90° phase shifters. The simulated and measured results show that the developed phase shifters achieve 3 to 11 GHz bandwidth with low phase instability (±2°), very low insertion loss (0.4 dB), high return loss (15 dB), and a compact size (1.5 cm × 2 cm).


title: A Context-aware Time Model for Web Search

abstract: In web search, information about times between user actions has been shown to be a good indicator of users' satisfaction with the search results. Existing work uses the mean values of the observed times, or fits probability distributions to the observed times. This implies a context-independence assumption that the time elapsed between a pair of user actions does not depend on the context, in which the first action takes place. We validate this assumption using logs of a commercial web search engine and discover that it does not always hold. For between 37% to 80% of query-result pairs, depending on the number of observations, the distributions of click dwell times have statistically significant differences in query sessions for which a given result (i) is the first item to be clicked and (ii) is not the first. To account for this context bias effect, we propose a context-aware time model (CATM). The CATM allows us (i) to predict times between user actions in contexts, in which these actions were not observed, and (ii) to compute context-independent estimates of the times by predicting them in predefined contexts. Our experimental results show that the CATM provides better means than existing methods to predict and interpret times between user actions.


title: Graph-Based Fraud Detection in the Face of Camouflage

abstract: Given a bipartite graph of users and the products that they review, or followers and followees, how can we detect fake reviews or follows? Existing fraud detection methods (spectral, etc.) try to identify dense subgraphs of nodes that are sparsely connected to the remaining graph. Fraudsters can evade these methods using camouflage, by adding reviews or follows with honest targets so that they look “normal.” Even worse, some fraudsters use hijacked accounts from honest users, and then the camouflage is indeed organic. Our focus is to spot fraudsters in the presence of camouflage or hijacked accounts. We propose FRAUDAR, an algorithm that (a) is camouflage resistant, (b) provides upper bounds on the effectiveness of fraudsters, and (c) is effective in real-world data. Experimental results under various attacks show that FRAUDAR outperforms the top competitor in accuracy of detecting both camouflaged and non-camouflaged fraud. Additionally, in real-world experiments with a Twitter follower--followee graph of 1.47 billion edges, FRAUDAR successfully detected a subgraph of more than 4, 000 detected accounts, of which a majority had tweets showing that they used follower-buying services.


title: New Design Method for a Dual band Waveguide Iris Polarizer

abstract: In this paper, we present new design method for a square iris polarizer to be used in dual-band (Ka-band; 20.8 ~ 21.2 GHz, 30.6 ~31 GHz) antenna feeds. To act principles for a square iris polarizer, design concept, design method are proposed and Ka-band polarizer is designed using the commercial software HFSSreg The designed polarizer has less than 30 dB return loss and less than 0.3 dB axial ratio in all operation band.


title: Automatic UAV Image Geo-Registration by Matching UAV Images to Georeferenced Image Data

abstract: Recent years have witnessed the fast development of UAVs (unmanned aerial vehicles). As an alternative to traditional image acquisition methods, UAVs bridge the gap between terrestrial and airborne photogrammetry and enable flexible acquisition of high resolution images. However, the georeferencing accuracy of UAVs is still limited by the low-performance on-board GNSS and INS. This paper investigates automatic geo-registration of an individual UAV image or UAV image blocks by matching the UAV image(s) with a previously taken georeferenced image, such as an individual aerial or satellite image with a height map attached or an aerial orthophoto with a DSM (digital surface model) attached. As the biggest challenge for matching UAV and aerial images is in the large differences in scale and rotation, we propose a novel feature matching method for nadir or slightly tilted images. The method is comprised of a dense feature detection scheme, a one-to-many matching strategy and a global geometric verification scheme. The proposed method is able to find thousands of valid matches in cases where SIFT and ASIFT fail. Those matches can be used to geo-register the whole UAV image block towards the reference image data. When the reference images offer high georeferencing accuracy, the UAV images can also be geolocalized in a global coordinate system. A series of experiments involving different scenarios was conducted to validate the proposed method. The results demonstrate that our approach achieves not only decimeter-level registration accuracy, but also comparable global accuracy as the reference images.


title: Direct Instruction of Metacognition Benefits Adolescent Science Learning, Transfer, and Motivation: An In Vivo Study.

abstract: Prior studies have not tested whether an instructional intervention aimed at improving metacognitive skills results in changes to student metacognition, motivation, learning, and future learning in the classroom. We examined whether a 6-hr intervention designed to teach the declarative and procedural components of planning, monitoring, and evaluation could increase students’ metacognition, motivation, learning, and preparation for future learning for middle school science. Forty-six eighth-grade students were randomly assigned to either a control group, which received extensive problem-solving practice, or an experimental group, which received more limited problem-solving practice along with metacognitive instruction and training. Results revealed that those who received the metacognitive instruction and training were less biased when making metacognitive judgments, p = .03, d = 0.65, endorsed higher levels of motivation after instruction (e.g., there was a large effect on task value, p = .006, d = 0.87), performed better on a conceptual physics test, p = .03, d = 0.64, and performed better on a novel self-guided learning activity, p = .007, d = 0.87. This study demonstrates that metacognitive instruction can lead to better self-regulated learning outcomes during adolescence, a period in which students’ academic achievement and motivation often decline. (PsycINFO Database Record (c) 2016 APA, all rights reserved)


DERMOSCOPY OF INFLAMMATORY CONDITIONS : THE JOURNEY SO FAR *


title: Conditional Random Field Autoencoders for Unsupervised Structured Prediction

abstract: We introduce a framework for unsupervised learning of structured predictors with overlapping, global features. Each input's latent representation is predicted conditional on the observed data using a feature-rich conditional random field (CRF). Then a reconstruction of the input is (re)generated, conditional on the latent structure, using a generative model which factorizes similarly to the CRF. The autoencoder formulation enables efficient exact inference without resorting to unrealistic independence assumptions or restricting the kinds of features that can be used. We illustrate connections to traditional autoencoders, posterior regularization, and multi-view learning. We then show competitive results with instantiations of the framework for two canonical tasks in natural language processing: part-of-speech induction and bitext word alignment, and show that training the proposed model can be substantially more efficient than a comparable feature-rich baseline.


title: Multi-block ADMM for big data optimization in smart grid

abstract: In this paper, we review the parallel and distributed optimization algorithms based on alternating direction method of multipliers (ADMM) for solving "big data" optimization problem in smart grid communication networks. We first introduce the canonical formulation of the large-scale optimization problem. Next, we describe the general form of ADMM and then focus on several direct extensions and sophisticated modifications of ADMM from $2$-block to $N$-block settings to deal with the optimization problem. The iterative schemes and convergence properties of each extension/modification are given, and the implementation on large-scale computing facilities is also illustrated. Finally, we numerate several applications in power system for distributed robust state estimation, network energy management and security constrained optimal power flow problem.


title: Stress- and treatment-induced elevations of cortisol levels associated with impaired declarative memory in healthy adults.

abstract: Two studies investigated the association between cortisol levels and memory performance in healthy adults. In a first study, 13 subjects were exposed to a brief psychosocial laboratory stress ("Trier Social Stress Test") with a subsequent test of declarative memory performance. Results indicated a significant negative relationship between stress-induced cortisol levels and performance in the memory task, i.e. subjects with high cortisol response to the stressor showed poorer memory performance. In a second experiment it was investigated if cortisol, alone, i.e. independent of psychological stress, would also impair memory function. In this study, 40 healthy subjects received either 10 mg cortisol or placebo orally. One hour later they were tested for procedural and declarative memory and spatial thinking. Subjects who received cortisol showed impaired performance in the declarative memory and spatial thinking tasks but not in the procedural memory task. From these results we conclude that in healthy adults elevated free cortisol levels are associated with impaired memory function.


title: Implementation of Automatic Meter Reading (AMR) using radio frequency (RF) module

abstract: Automatic Meter Reading (AMR) aims to move away from the traditional method of manual reading of electricity meters in which a meter reader who visit every meter location periodically and read the meter value manually. AMR by using radio frequency (RF) is the remote collection of consumption data from customers' utility meters using radio frequency technologies. This system provides electric utility service company (TNB) the opportunity to increase operational efficiency, improve customer services, reduce data-collection costs and quickly gather critical information that provides insight to company decision-makers. The existing digital electric in the marketing is upgraded by adding RF module to provide remote communication capabilities. At the same time, the receiver module unit interfaced directly to a computer/laptop to display all necessary details.


Investigating the most common problems after delivery of removable partial dentures Running title : complication on removable partial denture


title: BPR: Bayesian Personalized Ranking from Implicit Feedback

abstract: Item recommendation is the task of predicting a personalized ranking on a set of items (e.g. websites, movies, products). In this paper, we investigate the most common scenario with implicit feedback (e.g. clicks, purchases). There are many methods for item recommendation from implicit feedback like matrix factorization (MF) or adaptive k-nearest-neighbor (kNN). Even though these methods are designed for the item prediction task of personalized ranking, none of them is directly optimized for ranking. In this paper we present a generic optimization criterion BPR-Opt for personalized ranking that is the maximum posterior estimator derived from a Bayesian analysis of the problem. We also provide a generic learning algorithm for optimizing models with respect to BPR-Opt. The learning method is based on stochastic gradient descent with bootstrap sampling. We show how to apply our method to two state-of-the-art recommender models: matrix factorization and adaptive kNN. Our experiments indicate that for the task of personalized ranking our optimization method outperforms the standard learning techniques for MF and kNN. The results show the importance of optimizing models for the right criterion.


title: Paired Learners for Concept Drift

abstract: To cope with concept drift, we paired a stable online learner with a reactive one. A stable learner predicts based on all of its experience, whereas are active learner predicts based on its experience over a short, recent window of time. The method of paired learning uses differences in accuracy between the two learners over this window to determine when to replace the current stable learner, since the stable learner performs worse than does there active learner when the target concept changes. While the method uses the reactive learner as an indicator of drift, it uses the stable learner to predict, since the stable learner performs better than does the reactive learner when acquiring target concept. Experimental results support these assertions. We evaluated the method by making direct comparisons to dynamic weighted majority, accuracy weighted ensemble, and streaming ensemble algorithm (SEA) using two synthetic problems, the Stagger concepts and the SEA concepts, and three real-world data sets: meeting scheduling, electricity prediction, and malware detection. Results suggest that, on these problems, paired learners outperformed or performed comparably to methods more costly in time and space.


title: Are Effective Teachers Like Good Parents? Teaching Styles and Student Adjustment in Early Adolescence

abstract: This study examined the utility of parent socialization models for understanding teachers’ influence on student adjustment in middle school. Teachers were assessed with respect to their modeling of motivation and to Baumrind’s parenting dimensions of control, maturity demands, democratic communication, and nurturance. Student adjustment was defined in terms of their social and academic goals and interest in class, classroom behavior, and academic performance. Based on information from 452 sixth graders from two suburban middle schools, results of multiple regressions indicated that the five teaching dimensions explained significant amounts of variance in student motivation, social behavior, and achievement. High expectations (maturity demands) was a consistent positive predictor of students’ goals and interests, and negative feedback (lack of nurturance) was the most consistent negative predictor of academic performance and social behavior. The role of motivation in mediating relations between teaching dimensions and social behavior and academic achievement also was examined; evidence for mediation was not found. Relations of teaching dimensions to student outcomes were the same for African American and European American students, and for boys and girls. The implications of parent socialization models for understanding effective teaching are discussed.


title: Deep End-to-End Time-of-Flight Imaging

abstract: We present an end-to-end image processing framework for time-of-flight (ToF) cameras. Existing ToF image processing pipelines consist of a sequence of operations including modulated exposures, denoising, phase unwrapping and multipath interference correction. While this cascaded modular design offers several benefits, such as closed-form solutions and power-efficient processing, it also suffers from error accumulation and information loss as each module can only observe the output from its direct predecessor, resulting in erroneous depth estimates. We depart from a conventional pipeline model and propose a deep convolutional neural network architecture that recovers scene depth directly from dual-frequency, raw ToF correlation measurements. To train this network, we simulate ToF images for a variety of scenes using a time-resolved renderer, devise depth-specific losses, and apply normalization and augmentation strategies to generalize this model to real captures. We demonstrate that the proposed network can efficiently exploit the spatio-temporal structures of ToF frequency measurements, and validate the performance of the joint multipath removal, denoising and phase unwrapping method on a wide range of challenging scenes.


title: A Review on Bilevel Optimization: From Classical to Evolutionary Approaches and Applications

abstract: Bilevel optimization is defined as a mathematical program, where an optimization problem contains another optimization problem as a constraint. These problems have received significant attention from the mathematical programming community. Only limited work exists on bilevel problems using evolutionary computation techniques; however, recently there has been an increasing interest due to the proliferation of practical applications and the potential of evolutionary algorithms in tackling these problems. This paper provides a comprehensive review on bilevel optimization from the basic principles to solution strategies; both classical and evolutionary. A number of potential application problems are also discussed. To offer the readers insights on the prominent developments in the field of bilevel optimization, we have performed an automated text-analysis of an extended list of papers published on bilevel optimization to date. This paper should motivate evolutionary computation researchers to pay more attention to this practical yet challenging area.


Large vocabulary speech recognition using deep tensor neural networks


title: Mobile Forensic Images and Videos Signature Pattern Matching using M-Aho-Corasick

abstract: Mobile forensics is an exciting new field of research. An increasing number of Open source and commercial digital forensics tools are focusing on less time during digital forensic examination. There is a major issue affecting some mobile forensic tools that allow the tools to spend much time during the forensic examination. It is caused by implementation of poor file searching algorithms by some forensic tool developers. This research is focusing on reducing the time taken to search for a file by proposing a novel, multi-pattern signature matching algorithm called M-Aho-Corasick which is adapted from the original Aho-Corasick algorithm. Experiments are conducted on five different datasets which one of the data sets is obtained from Digital Forensic Research Workshop (DFRWS 2010). Comparisons are made between M-Aho-Corasick using M_Triage with Dec0de, Lifter, XRY, and Xaver. The result shows that M-Aho-Corasick using M_Triage has reduced the searching time by 75% as compared to Dec0de, 36% as compared to Lifter, 28% as compared to XRY, and 71% as compared to Xaver. Thus, M-Aho-Corasick using M_Triage tool is more efficient than Dec0de, Lifter, XRY, and Xaver in avoiding the extraction of high number of false positive results.


title: Clinical and forensic indicators of "suicide by cop".

abstract: This paper reviews the literature pertaining to the phenomenon of "suicide by cop"--any incident in which a suicidal individual attempts to get law enforcement to kill him. This article defines the term "suicide by cop," discusses the various motivations of individuals who engage in this type of behavior, presents the risk factors and indicators for suicide and violence, and describes specific indicators for suicide by cop. Proper recognition of these events, prior and subsequent to their occurrence, has important implications for prevention, officer safety, equivocal death analysis and psychological autopsy, civil litigation, criminal justice proceedings, and community stability. This paper presents seven case studies which demonstrate the clinical and forensic indicators of this phenomenon.


title: An Almost-Linear-Time Algorithm for Approximate Max Flow in Undirected Graphs, and its Multicommodity Generalizations

abstract: In this paper, we introduce a new framework for approximately solving flow problems in capacitated, undirected graphs and apply it to provide asymptotically faster algorithms for the maximum $s$-$t$ flow and maximum concurrent multicommodity flow problems. For graphs with $n$ vertices and $m$ edges, it allows us to find an $\epsilon$-approximate maximum $s$-$t$ flow in time $O(m^{1+o(1)}\epsilon^{-2})$, improving on the previous best bound of $\tilde{O}(mn^{1/3} poly(1/\epsilon))$. Applying the same framework in the multicommodity setting solves a maximum concurrent multicommodity flow problem with $k$ commodities in $O(m^{1+o(1)}\epsilon^{-2}k^2)$ time, improving on the existing bound of $\tilde{O}(m^{4/3} poly(k,\epsilon^{-1})$. ::: Our algorithms utilize several new technical tools that we believe may be of independent interest: ::: - We give a non-Euclidean generalization of gradient descent and provide bounds on its performance. Using this, we show how to reduce approximate maximum flow and maximum concurrent flow to the efficient construction of oblivious routings with a low competitive ratio. ::: - We define and provide an efficient construction of a new type of flow sparsifier. In addition to providing the standard properties of a cut sparsifier our construction allows for flows in the sparse graph to be routed (very efficiently) in the original graph with low congestion. ::: - We give the first almost-linear-time construction of an $O(m^{o(1)})$-competitive oblivious routing scheme. No previous such algorithm ran in time better than $\tilde{{\Omega}}(mn)$. ::: We also note that independently Jonah Sherman produced an almost linear time algorithm for maximum flow and we thank him for coordinating submissions.


title: Levitation forces of bulk RE–Ba–Cu–O in high magnetic fields

abstract: Abstract The levitation force of bulk RE–Ba–Cu–O (RE: rare earth elements) superconductors can be drastically increased by increasing the strength of the external field. We have studied the effects of the temperature and magnetic field on the levitation force and its time relaxation in the field range of 0–2 T by using a superconducting magnet as a magnetic field source. The samples we used were bulk Y–Ba–Cu–O and Sm–Ba–Cu–O superconductors 46 mm in diameter and 15 mm in thickness, which were fabricated with a top-seeded melt-growth process. The levitation forces were measured by applying the magnetic fields of 0–2 T at 10–80 K. It was found that the levitation force increased with decreasing temperature in both Y–Ba–Cu–O and Sm–Ba–Cu–O. The time relaxation of the levitation force was also reduced with lowering temperature. Sm–Ba–Cu–O exhibited better performance in the levitation force and its time relaxation only in a high temperature and high field region.


title: Energy Harvesting Sensor Nodes: Survey and Implications

abstract: Sensor networks with battery-powered nodes can seldom simultaneously meet the design goals of lifetime, cost, sensing reliability and sensing and transmission coverage. Energy-harvesting, converting ambient energy to electrical energy, has emerged as an alternative to power sensor nodes. By exploiting recharge opportunities and tuning performance parameters based on current and expected energy levels, energy harvesting sensor nodes have the potential to address the conflicting design goals of lifetime and performance. This paper surveys various aspects of energy harvesting sensor systems- architecture, energy sources and storage technologies and examples of harvesting-based nodes and applications. The study also discusses the implications of recharge opportunities on sensor node operation and design of sensor network solutions.


title: High-Capacity Free-Space Optical Communications Between a Ground Transmitter and a Ground Receiver via a UAV Using Multiplexing of Multiple Orbital-Angular-Momentum Beams

abstract: We explore the use of orbital-angular-momentum (OAM)-multiplexing to increase the capacity of free-space data transmission to moving platforms, with an added potential benefit of decreasing the probability of data intercept. Specifically, we experimentally demonstrate and characterize the performance of an OAM-multiplexed, free-space optical (FSO) communications link between a ground transmitter and a ground receiver via a moving unmanned-aerial-vehicle (UAV). We achieve a total capacity of 80 Gbit/s up to 100-m-roundtrip link by multiplexing 2 OAM beams, each carrying a 40-Gbit/s quadrature-phase-shift-keying (QPSK) signal. Moreover, we investigate for static, hovering, and moving conditions the effects of channel impairments, including: misalignments, propeller-induced airflows, power loss, intermodal crosstalk, and system bit error rate (BER). We find the following: (a) when the UAV hovers in the air, the power on the desired mode fluctuates by 2.1 dB, while the crosstalk to the other mode is −19 dB below the power on the desired mode; and (b) when the UAV moves in the air, the power fluctuation on the desired mode increases to 4.3 dB and the crosstalk to the other mode increases to −10 dB. Furthermore, the channel crosstalk decreases with an increase in OAM mode spacing.


title: Centiman: elastic, high performance optimistic concurrency control by watermarking

abstract: We present Centiman, a system for high performance, elastic transaction processing in the cloud. Centiman provides serializability on top of a key-value store with a lightweight protocol based on optimistic concurrency control (OCC). Centiman is designed for the cloud setting, with an architecture that is loosely coupled and avoids synchronization wherever possible. Centiman supports sharded transaction validation; validators can be added or removed on-the-fly in an elastic manner. Processors and validators scale independently of each other and recover from failure transparently to each other. Centiman's loosely coupled design creates some challenges: it can cause spurious aborts and it makes it difficult to implement common performance optimizations for read-only transactions. To deal with these issues, Centiman uses a watermark abstraction to asynchronously propagate information about transaction commits through the system. In an extensive evaluation we show that Centiman provides fast elastic scaling, low-overhead serializability for read-heavy workloads, and scales to millions of operations per second.


title: A Multi-GPU Spectrometer System for Real-Time Wide Bandwidth Radio Signal Analysis

abstract: This paper describes the implementation of a large bandwidth multi-GPU signal processing system for radio astronomy observation. This system performs very large Fast Fourier Transform (FFT) and spectrum analysis to achieve real-time analysis of a large bandwidth spectrum. This is accomplished by implementing a four-step FFT algorithm in Compute Unified Device Architecture (CUDA). The key feature of this implementation is that the data size transferred between CPU and GPU is reduced using redundant calculation. We also apply pipeline execution to our system to minimize idle processor time, even with multiple GPUs on a shared bus. Using a single GPU, this system can analyze 1 GB of signal data (128 MHz bandwidth at 1 Hz resolution in single precision floating-point complex format) in 0.44 seconds. With the multi-GPU setup, using four GPUs enables 4 GB of signal data to be processed in 0.82 seconds. This is equivalent to a processing speed of around 60 GFLOPS. In particular, we focus on using this system in the Search for Extraterrestrial Radio Emissions from Nearby Developed Intelligent Populations (SERENDIP) project. By using multiple GPUs we can get enough practical performance for high bandwidth radio astronomy projects such as SERENDIP.


title: Timelines with temporal uncertainty

abstract: Timelines are a formalism to model planning domains where the temporal aspects are predominant, and have been used in many real-world applications. Despite their practical success, a major limitation is the inability to model temporal uncertainty, i.e. the fact that the plan executor cannot decide the actual duration of some activities. ::: ::: In this paper we make two key contributions. First, we propose a comprehensive, semantically well founded framework that (conservatively) extends with temporal uncertainty the state of the art timeline approach. ::: ::: Second, we focus on the problem of producing time-triggered plans that are robust with respect to temporal uncertainty, under a bounded horizon. In this setting, we present the first complete algorithm, and we show how it can be made practical by leveraging the power of Satisfiability Modulo Theories.


title: A modified Chevrel technique for ventral hernia repair: long-term results of a single centre cohort

abstract: PURPOSE ::: To evaluate the short- and long-term results after a modified Chevrel technique for midline incisional hernia repair, regarding surgical technique, hospital stay, wound complications, recurrence rate, and postoperative quality of life. These results will be compared to the literature derived reference values regarding the original and modified Chevrel techniques. ::: ::: ::: METHODS ::: In this large retrospective, single surgeon, single centre cohort all modified Chevrel hernia repairs between 2000 and 2012 were identified. Results were obtained by reviewing patients' medical charts. Postoperative quality of life was measured using the Carolina Comfort Scale. A multi-database literature search was conducted to compare the results of our series to the literature based reference values. ::: ::: ::: RESULTS ::: One hundred and fifty-five patients (84 male, 71 female) were included. Eighty patients (52%) had a large incisional hernia (width ≥ 10 cm) according the definition of the European Hernia Society. Fourteen patients (9%) underwent a concomitant procedure. Median length-of-stay was 5 days. Within 30 days postoperative 36 patients (23.2%) had 39 postoperative complications of which 30 were mild (CDC I-II), and nine severe (CDC III-IV). Thirty-one surgical site occurrences were observed in thirty patients (19.4%) of which the majority were seroma (16 patients 10.3%). There was no hernia-related mortality during follow-up. Recurrence rate was 1.8% after a median follow-up of 52 months (12-128 months). Postoperative quality of life was rated excellent. ::: ::: ::: CONCLUSIONS ::: The modified Chevrel technique for midline ventral hernias results in a moderate complication rate, low recurrence rate and high rated postoperative quality of life.


title: Use of Non-Orthogonal Multiple Access in Dual-hop relaying

abstract: To improve the sum-rate (SR) of the dual-hop relay system, a novel two-stage power allocation scheme with non-orthogonal multiple access (NOMA) is proposed. In this scheme, after the reception of the superposition coded symbol with a power allocation from the source, the relay node forwards a new superposition coded symbol with another power allocation to the destination. By employing the maximum ratio combination (MRC), the destination jointly decodes the information symbols from the source and the relay. Assuming Rayleigh fading channels, closed-form solution of the ergodic SR at high signal-to-noise ratio (SNR) is derived and a practical power allocation is also designed for the proposed NOMA scheme. Through numerical results, it is shown that the performance of the proposed scheme is significantly improved compared with the existing work.


title: The NHGRI GWAS Catalog, a curated resource of SNP-trait associations

abstract: The National Human Genome Research Institute (NHGRI) Catalog of Published Genome-Wide Association Studies (GWAS) Catalog provides a publicly available manually curated collection of published GWAS assaying at least 100,000 single-nucleotide polymorphisms (SNPs) and all SNP-trait associations with P <1 × 10(-5). The Catalog includes 1751 curated publications of 11 912 SNPs. In addition to the SNP-trait association data, the Catalog also publishes a quarterly diagram of all SNP-trait associations mapped to the SNPs' chromosomal locations. The Catalog can be accessed via a tabular web interface, via a dynamic visualization on the human karyotype, as a downloadable tab-delimited file and as an OWL knowledge base. This article presents a number of recent improvements to the Catalog, including novel ways for users to interact with the Catalog and changes to the curation infrastructure.


title: Delving Deeper into Convolutional Networks for Learning Video Representations

abstract: We propose an approach to learn spatio-temporal features in videos from intermediate visual representations we call "percepts" using Gated-Recurrent-Unit Recurrent Networks (GRUs).Our method relies on percepts that are extracted from all level of a deep convolutional network trained on the large ImageNet dataset. While high-level percepts contain highly discriminative information, they tend to have a low-spatial resolution. Low-level percepts, on the other hand, preserve a higher spatial resolution from which we can model finer motion patterns. Using low-level percepts can leads to high-dimensionality video representations. To mitigate this effect and control the model number of parameters, we introduce a variant of the GRU model that leverages the convolution operations to enforce sparse connectivity of the model units and share parameters across the input spatial locations. ::: We empirically validate our approach on both Human Action Recognition and Video Captioning tasks. In particular, we achieve results equivalent to state-of-art on the YouTube2Text dataset using a simpler text-decoder model and without extra 3D CNN features.


title: Overview and Future Opportunities of Sentiment Analysis Approaches for Big Data

abstract: The ability to exploit public sentiment in social media is increasingly considered as an important tool for market understanding, customer segmentation and stock price prediction for strategic marketing planning and manoeuvring. This evolution of technology adoption is energised by the healthy growth in big data framework, which caused applications based on Sentiment Analysis (SA) in big data to become common for businesses. However, scarce works have studied the gaps of SA application in big data. The contribution of this paper is two-fold: (i) this study reviews the state of the art of SA approaches. including sentiment polarity detection, SA features (explicit and implicit), sentiment classification techniques and applications of SA and (ii) this study reviews the suitability of SA approaches for application in the big data frameworks, as well as highlights the gaps and suggests future works that should be explored. SA studies are predicted to be expanded into approaches that utilise scalability, possess high adaptability for source variation, velocity and veracity to maximise value mining for the benefit of the users.


title: Procedural Modeling of a Building from a Single Image

abstract: Creating a virtual city is demanded for computer games, movies, and urban planning, but it takes a lot of time to create numerous 3D building models. Procedural modeling has become popular in recent years to overcome this issue, but creating a grammar to get a desired output is difficult and time consuming even for expert users. In this paper, we present an interactive tool that allows users to automatically generate such a grammar from a single image of a building. The user selects a photograph and highlights the silhouette of the target building as input to our method. Our pipeline automatically generates the building components, from large-scale building mass to fine-scale windows and doors geometry. Each stage of our pipeline combines convolutional neural networks (CNNs) and optimization to select and parameterize procedural grammars that reproduce the building elements of the picture. In the first stage, our method jointly estimates camera parameters and building mass shape. Once known, the building mass enables the rectification of the facades, which are given as input to the second stage that recovers the facade layout. This layout allows us to extract individual windows and doors that are subsequently fed to the last stage of the pipeline that selects procedural grammars for windows and doors. Finally, the grammars are combined to generate a complete procedural building as output. We devise a common methodology to make each stage of this pipeline tractable. This methodology consists in simplifying the input image to match the visual appearance of synthetic training data, and in using optimization to refine the parameters estimated by CNNs. We used our method to generate a variety of procedural models of buildings from existing photographs.


title: A Storage-Computation-Communication Tradeoff for Distributed Computing

abstract: This paper investigates distributed computing systems where computations are split into"Map"and"Reduce"functions. A new coded scheme, called distributed computing and coded communication (D3C), is proposed, and its communication load is analyzed as a function of the available storage space and the number of intermediate values (IVA) to be computed. D3C achieves the smallest possible communication load for a given storage space, while a smaller number of IVAs need to be computed compared to Li et al.'s coded distributed computing (CDC) scheme. More generally, our scheme can flexibly trade between storage space and the number of IVAs to be computed. Communication load is then analyzed for any given tradeoff.


title: Detecting Influence Campaigns in Social Networks Using the Ising Model

abstract: We consider the problem of identifying coordinated influence campaigns conducted by automated agents or bots in a social network. We study several different Twitter datasets which contain such campaigns and find that the bots exhibit heterophily - they interact more with humans than with each other. We use this observation to develop a probability model for the network structure and bot labels based on the Ising model from statistical physics. We present a method to find the maximum likelihood assignment of bot labels by solving a minimum cut problem. Our algorithm allows for the simultaneous detection of multiple bots that are potentially engaging in a coordinated influence campaign, in contrast to other methods that identify bots one at a time. We find that our algorithm is able to more accurately find bots than existing methods when compared to a human labeled ground truth. We also look at the content posted by the bots we identify and find that they seem to have a coordinated agenda.


title: Visual Relationship Detection Based on Guided Proposals and Semantic Knowledge Distillation

abstract: A thorough comprehension of image content demands a complex grasp of the interactions that may occur in the natural world. One of the key issues is to describe the visual relationships between objects. When dealing with real world data, capturing these very diverse interactions is a difficult problem. It can be alleviated by incorporating common sense in a network. For this, we propose a framework that makes use of semantic knowledge and estimates the relevance of object pairs during both training and test phases. Extracted from precomputed models and training annotations, this information is distilled into the neural network dedicated to this task. Using this approach, we observe a significant improvement on all classes of Visual Genome, a challenging visual relationship dataset. A 68.5% relative gain on the recall at 100 is directly related to the relevance estimate and a 32.7% gain to the knowledge distillation.


title: Anisotropic diffusion map based spectral embedding for 3D CAD model retrieval

abstract: In the product life cycle, design reuse can save cost and improve existing products conveniently in most new product development. To retrieve similar models from big database, most search algorithms convert CAD model into a shape descriptor and compute the similarity two models according to a descriptor metric. This paper proposes a new 3D shape matching approach by matching the coordinates directly. It is based on diffusion maps which integrate the rand walk and graph spectral analysis to extract shape features embedded in low dimensional spaces and then they are used to form coordinations for non-linear alignment of different models. These coordinates could capture multi-scale properties of the 3D geometric features and has shown good robustness to noise. The results also have shown better performance compared to the celebrated Eigenmap approach in the 3D model retrieval.


title: Electrospinning of Polymeric Nanofibers for Tissue Engineering Applications: A Review

abstract: Interest in electrospinning has recently escalated due to the ability to produce materials with nanoscale properties. Electrospun fibers have been investigated as promising tissue engineering scaffolds since they mimic the nanoscale properties of native extracellular matrix. In this review, we examine electrospinning by providing a brief description of the theory behind the process, examining the effect of changing the process parameters on fiber morphology, and discussing the potential applications and impacts of electrospinning on the field of tissue engineering.


title: Link prediction in social networks: the state-of-the-art

abstract: In social networks, link prediction predicts missing links in current networks and new or dissolution links in future networks, is important for mining and analyzing the evolution of social networks. In the past decade, many works have been done about the link prediction in social networks. The goal of this paper is to comprehensively review, analyze and discuss the state-of-the-art of the link prediction in social networks. A systematical category for link prediction techniques and problems is presented. Then link prediction techniques and problems are analyzed and discussed. Typical applications of link prediction are also addressed. Achievements and roadmaps of some active research groups are introduced. Finally, some future challenges of the link prediction in social networks are discussed.


title: Why Go Green: The Influence of Green Practices Coordination on Firm Performance

abstract: The aims of the research are to present a model of environmental performance by measuring the effects of a firm’s green practice in conjunction with inter-functional coordination mechanisms, and in turn, measure how environmental performance influences firms’ economic performance. Moreover, this study synthesizes both ecological modernization theory (EMT) and complementarity theory (CT) as a theoretical lens to develop a comprehensive model. The results of this study showed that green practice-manufacturing coordination and green practice-supply chain coordination have positive effect on environmental performance. However, the relationship between green practice-marketing coordination and environmental performance and the relationship between green practice-IS coordination and environmental performance were insignificant. Moreover, environmental performance is confirmed as an important antecedent of economic performance. Discussion and implications are provided.


title: Synchronous reluctance machine flux barrier design based on the flux line patterns in a solid rotor

abstract: The paper focuses on the synchronous reluctance machine rotor design. The rotor's shape in this machine should maximize the flux passing in the d axis while minimizing the flux in its q axis. This can be achieved by designing the rotor to respect the natural flow of the flux in the d axis in order to reduce the magnetic resistance while increasing the insulation in the q axis. The proposed design method in this paper is based on determining the analytical equation of the flux lines through a solid rotor. The analytical equation will allow the definition of the optimal curvature of the flux barriers. In a second phase, the width of the flux barriers is determined on the basis of the flux preservation theory in every flux segment. A comparison study using a finite elements simulation will allow us to validate the proposed approach.


title: Undefined By Data: A Survey of Big Data Definitions

abstract: The term big data has become ubiquitous. Owing to a shared origin between academia, industry and the media there is no single unified definition, and various stakeholders provide diverse and often contradictory definitions. The lack of a consistent definition introduces ambiguity and hampers discourse relating to big data. This short paper attempts to collate the various definitions which have gained some degree of traction and to furnish a clear and concise definition of an otherwise ambiguous term.


Challenging Issues of Automatic Summarization: Relevance Detection and Quality-based Evaluation


title: WGBSSuite: simulating whole-genome bisulphite sequencing data and benchmarking differential DNA methylation analysis tools

abstract: Motivation: As the number of studies looking at differences between DNA methylation increases, there is a growing demand to develop and benchmark statistical methods to analyse these data. To date no objective approach for the comparison of these methods has been developed and as such it remains difficult to assess which analysis tool is most appropriate for a given experiment. As a result, there is an unmet need for a DNA methylation data simulator that can accurately reproduce a wide range of experimental setups, and can be routinely used to compare the performance of different statistical models. ::: ::: Results: We have developed WGBSSuite, a flexible stochastic simulation tool that generates single-base resolution DNA methylation data genome-wide. Several simulator parameters can be derived directly from real datasets provided by the user in order to mimic real case scenarios. Thus, it is possible to choose the most appropriate statistical analysis tool for a given simulated design. To show the usefulness of our simulator, we also report a benchmark of commonly used methods for differential methylation analysis. ::: ::: Availability and implementation: WGBS code and documentation are available under GNU licence at http://www.wgbssuite.org.uk/ ::: ::: Contact: ku.ca.lairepmi@mahkcar.newo or ku.ca.lairepmi@olottob.l ::: ::: Supplementary information: Supplementary data are available at Bioinformatics online.


Multicluster interleaving on linear paths and cycles


title: Efficient Object Category Recognition Using

abstract: We introduce a new descriptor for images which allows the construction of efficient and compact classifiers with good accuracy on object category recognition. The descriptor is the output of a large number of weakly trained object category classifiers on the image. The trained categories are selected from an ontology of visual concepts, but the intention is not to encode an explicit decomposition of the scene. Rather, we accept that existing object category classifiers often encode not the category per se but ancillary image characteristics; and that these ancillary characteristics can combine to represent visual classes unrelated to the constituent categories' semantic meanings. ::: ::: The advantage of this descriptor is that it allows object-category queries to be made against image databases using efficient classifiers (efficient at test time) such as linear support vector machines, and allows these queries to be for novel categories. Even when the representation is reduced to 200 bytes per image, classification accuracy on object category recognition is comparable with the state of the art (36% versus 42%), but at orders of magnitude lower computational cost.


title: Functional decomposition of Unmanned Aircraft Systems (UAS) for CNS capabilities in NAS integration

abstract: This paper presents the approach developed for the partial MASPS level document DO-344 “Operational and Functional Requirements and Safety Objectives” for the UAS standards. Previous RTCA1 work led to the production of an Operational Services Environment Description document, from which operational requirements were extracted and refined. Following the principles described in the Department of Defense Architecture Framework, the overall UAS architecture and major interfaces were defined. Interacting elements included the unmanned aircraft (airborne component), the ground control station (ground component), the Air Traffic Control (ATC), the Air Traffic Service besides ATC, other traffic in the NAS, and the UAS ground support. Furthering the level of details, a functional decomposition was produced prior to the allocation onto the UAS architecture. These functions cover domains including communication, control, navigation, surveillance, and health monitoring. The communication function addressed all elements in the UAS connected with external interfaces: the airborne component, the ground component, the ATC, the other traffic and the ground support. The control function addressed the interface between the ground control station and the unmanned aircraft for the purpose of flying in the NAS. The navigation function covered the capability to determine and fly a trajectory using conventional and satellite based navigation means. The surveillance function addressed the capability to detect and avoid collisions with hazards, including other traffic, terrain and obstacles, and weather. Finally, the health monitoring function addressed the capability to oversee UAS systems, probe for their status and feedback issues related to degradation or loss of performance. An additional function denoted ‘manage’ was added to the functional decomposition to complement the heath monitoring coverage and included manual modes for the operation of the UAS.


title: Omnidirectional mobile robots with powered caster wheels: design guidelines from kinematic isotropy analysis

abstract: Kinematic isotropy of omnidirectional mobile robots with powered caster wheels is discussed and utilized in optimizing the design parameters of the robots. The analysis is done to cover the most general cases, by observing the mathematical expression of the equations of motion rather than by numerical evaluation of specific parameters or actuation schemes. Optimal design guidelines are provided. Singularity analysis is given to support the proposed design guidelines. Simulation results are presented to verify the conclusions drawn.


title: Speech Recognition by Machine, A Review

abstract: This paper presents a brief survey on Automatic Speech Recognition and discusses the major themes and advances made in the past 60 years of research, so as to provide a technological perspective and an appreciation of the fundamental progress that has been accomplished in this important area of speech communication. After years of research and development the accuracy of automatic speech recognition remains one of the important research challenges (e.g., variations of the context, speakers, and environment).The design of Speech Recognition system requires careful attentions to the following issues: Definition of various types of speech classes, speech representation, feature extraction techniques, speech classifiers, database and performance evaluation. The problems that are existing in ASR and the various techniques to solve these problems constructed by various research workers have been presented in a chronological order. Hence authors hope that this work shall be a contribution in the area of speech recognition. The objective of this review paper is to summarize and compare some of the well known methods used in various stages of speech recognition system and identify research topic and applications which are at the forefront of this exciting and challenging field.


title: Haiku generator that reads blogs and illustrates them with sounds and images

abstract: In this paper we introduce our haiku generator, which, in contrast to other systems, is not restricted to limited classic vocabulary sets and preserves a classic style without becoming too random and abstract because it performs a semantic integrity check using the Internet. Moreover, it is able to analyze blog entry input and, by using nouns and adjectives for web-mining, to stay on topic and still preserve kigo, traditional seasonal words used in Japanese poetry. The haiku generator utilizes grammar templates automatically generated from poems written by Japanese poets and a lexicon of 2,473 kigo words from an online haiku repository. In addition to generating haiku poems, it can output them vocally together with related sound effects and images retrieved from the WWW. Our experiments demonstrate that the proposed system generates high-quality haikus and that using content-related input and multimedia-rich output is effective for increasing users' satisfaction. We have performed impression evaluation experiments and confirmed that our method is especially useful for generating haikus with higher depth and sound-sharpness, which are two very important categories in professional evaluation of Japanese poetry. Next, haikus generated using the proposed method were evaluated by blog authors and blog readers and again, the proposed method outperformed the baseline. We also measured how the presence or absence of multimedia output influenced the evaluation. While using both vocal output and an image achieved higher scores than text alone, there were cases in which some combinations of effects were evaluated higher than all the effects used together. With our original approach to generating poetry, we wish to show the importance of new media and possibilities that are arising from the utilization of the "wisdom of (web-)crowds" in order to achieve higher standards for AI-generated art.


title: Support Vectors Machine-based identification of heart valve diseases using heart sounds

abstract: Taking into account that heart auscultation remains the dominant method for heart examination in the small health centers of the rural areas and generally in primary healthcare set-ups, the enhancement of this technique would aid significantly in the diagnosis of heart diseases. In this context, the present paper initially surveys the research that has been conducted concerning the exploitation of heart sound signals for automated and semi-automated detection of pathological heart conditions. Then it proposes an automated diagnosis system for the identification of heart valve diseases based on the Support Vector Machines (SVM) classification of heart sounds. This system performs a highly difficult diagnostic task (even for experienced physicians), much more difficult than the basic diagnosis of the existence or not of a heart valve disease (i.e. the classification of a heart sound as 'healthy' or 'having a heart valve disease'): it identifies the particular heart valve disease. The system was applied in a representative global dataset of 198 heart sound signals, which come both from healthy medical cases and from cases suffering from the four most usual heart valve diseases: aortic stenosis (AS), aortic regurgitation (AR), mitral stenosis (MS) and mitral regurgitation (MR). Initially the heart sounds were successfully categorized using a SVM classifier as normal or disease-related and then the corresponding murmurs in the unhealthy cases were classified as systolic or diastolic. For the heart sounds diagnosed as having systolic murmur we used a SVM classifier for performing a more detailed classification of them as having aortic stenosis or mitral regurgitation. Similarly for the heart sounds diagnosed as having diastolic murmur we used a SVM classifier for classifying them as having aortic regurgitation or mitral stenosis. Alternative classifiers have been applied to the same data for comparison (i.e. back-propagation neural networks, k-nearest-neighbour and naïve Bayes classifiers), however their performance for the same diagnostic problems was lower than the SVM classifiers proposed in this work.


title: Modifying Cleaning Method in Big Data Analytics Process using Random Forest Classifier

abstract: Accurate data is a key success factor influencing the performance of data analytics results, especially for the detection and prediction purpose. Nowadays, Big Data analytics (BDA) is used to analyze the sheer volume of data available in an organization. These data quality must be maintained in order to obtain correct alert and valuable insights from the rapidly changing data of high volume, velocity, variety, veracity, and value. This paper aim is to modify existing framework of big data analytics by improving an important step in pre-processing (i.e. Data Cleaning). Initially, feature selection based on Random Forest is used to extract effective features. Then, two classifier algorithms (i.e. Random Forest classifier and Linear SVM classifier) are applied to train using the dataset to classify data quality and to develop an intelligent model. In evaluation, our experimental results show a consistent accuracy of Random Forest and Linear Regression around 90%. Using this approach, we expect to provide a set of cleaned data for further processing. Besides, analysts can benefit from this system in data analytical process in cleaning stage and conclude that the data is cleaned. Finally, a comparison is presented between available functions which are used to handle missing values with the developed system


title: Big Scholarly Data: A Survey

abstract: With the rapid growth of digital publishing, harvesting, managing, and analyzing scholarly information have become increasingly challenging. The term Big Scholarly Data is coined for the rapidly growing scholarly data, which contains information including millions of authors, papers, citations, figures, tables, as well as scholarly networks and digital libraries. Nowadays, various scholarly data can be easily accessed and powerful data analysis technologies are being developed, which enable us to look into science itself with a new perspective. In this paper, we examine the background and state of the art of big scholarly data. We first introduce the background of scholarly data management and relevant technologies. Second, we review data analysis methods, such as statistical analysis, social network analysis, and content analysis for dealing with big scholarly data. Finally, we look into representative research issues in this area, including scientific impact evaluation, academic recommendation, and expert finding. For each issue, the background, main challenges, and latest research are covered. These discussions aim to provide a comprehensive review of this emerging area. This survey paper concludes with a discussion of open issues and promising future directions.


title: Mapping computational concepts to GPUs

abstract: Recently, graphics processors have emerged as a powerful computational platform. A variety of encouraging results, mostly from researchers using GPUs to accelerate scientific computing and visualization applications, have shown that significant speedups can be achieved by applying GPUs to data-parallel computational problems. However, attaining these speedups requires knowledge of GPU programming and architecture.The preceding chapters have described the architecture of modern GPUs and the trends that govern their performance and design. Continuing from the concepts introduced in those chapters, in this chapter we present intuitive mappings of standard computational concepts onto the special-purpose features of GPUs. After presenting the basics, we introduce a simple GPU programming framework and demonstrate the use of the framework in a short sample program.


title: A Feasibility Study of Tissue Characterization Using LC Sensors

abstract: Bio-implantable sensors using radio-frequency telemetry links that enable the continuous monitoring and recording of physiological data are receiving a great deal of attention. The objective of this paper is to study the feasibility of an implantable sensor for tissue characterization. This has been done by querying an LC sensor surrounded by dispersive tissues by an external antenna. The resonant frequency of the sensor is monitored by measuring the input impedance of the antenna, and correlated to the desired quantities. Using an equivalent circuit model of the sensor that accounts for the properties of the encapsulating tissue, analytical expressions have been developed for the extraction of the tissue permittivity and conductivity. Finally, experimental validation has been performed with a telemetry link that consists of a loop antenna and a fabricated LC sensor immersed in single and multiple dispersive phantom materials.


title: PassiveVLC: Enabling Practical Visible Light Backscatter Communication for Battery-free IoT Applications

abstract: This paper investigates the feasibility of practical backscatter communication using visible light for battery-free IoT applications. Based on the idea of modulating the light retroreflection with a commercial LCD shutter, we effectively synthesize these off-the-shelf optical components into a sub- mW low power visible light passive transmitter along with a retroreflecting uplink design dedicated for power constrained mobile/IoT devices. On top of that, we design, implement and evaluate PassiveVLC, a novel visible light backscatter communication system. PassiveVLC system enables a battery-free tag device to perform passive communication with the illuminating LEDs over the same light carrier and thus offers several favorable features including battery-free, sniff-proof, and biologically friendly for human-centric use cases. Experimental results from our prototyped system show that PassiveVLC is flexible with tag orientation, robust to ambient lighting conditions, and can achieve up to 1 kbps uplink speed. Link budget analysis and two proof-of-concept applications are developed to demonstrate PassiveVLC's efficacy and practicality.


title: Dust Removal with Boundary and Spatial Constraint for Videos Captured in Car

abstract: Videos captured in car often suffer from duston the wind screen glass. The dust particles on wind screendecrease the video quality and make them blur. Removingdust and restoring high quality dust-free video is a challengingtask in the field of video stream processing. In this paper, we propose an improved iterative optimization pipeline toremove dust from the videos. Our method employs boundaryconstraint to keep transmission map in a reasonable rangeand use spatial constraint on the transmission map to avoidintroduction of significant halo artifacts into the resultantvideo. With optimized transmission map as an initial condition, our method can separate dust layer and background layer frominput video frames and keeps the background frames beingcolor faithful with fine details. Test results demonstrate thatour proposed method can recover dust-free videos from thedust contaminated input videos and keep the resultant video color faithful.


title: Fear and Trembling: Terrorism in Three Religious Traditions

abstract: As the first comparative study of religious terror groups, the article provides detailed analyses of the different doctrines and methods of the three best-known groups: the Thugs, Assassins, and Zealots-Sicarii. Despite a primitive technology, each developed much more durable and destructive organizations than has any modern secular group. The differences among the groups reflect the distinguishing characteristics of their respective originating religious communities: Hinduism, Islam, and Judaism. The distinctive characteristics of religious terror are discussed, and relationships between religious and secular forms of terror are suggested.


title: Big Data Driven Supply Chain Management and Business Administration

abstract: Big Data helps improve visibility throughout the supply chain, provides an integrated view of operational performance and customer interaction and gives businesses real-time insights that help make critical decisions. Big Data also has a potential to yield new management principles. This paper introduces the Big Data concept, its characteristics and some major issues of Big Data in supply chain management and business administration. These issues include supply chain and business data, Big Data benefits and its applications and opportunities. Methods and technology progress about Big Data are presented in this study. General challenges of Big Data and Big Data challenges in supply chain management and business administration are also discussed.


title: Global Calibration of Multiple Cameras Based on Sphere Targets

abstract: Global calibration methods for multi-camera system are critical to the accuracy of vision measurement. Proposed in this paper is such a method based on several groups of sphere targets and a precision auxiliary camera. Each camera to be calibrated observes a group of spheres (at least three), while the auxiliary camera observes all the spheres. The global calibration can be achieved after each camera reconstructs the sphere centers in its field of view. In the process of reconstructing a sphere center, a parameter equation is used to describe the sphere projection model. Theoretical analysis and computer simulation are carried out to analyze the factors that affect the calibration accuracy. Simulation results show that the parameter equation can largely improve the reconstruction accuracy. In the experiments, a two-camera system calibrated by our method is used to measure a distance about 578 mm, and the root mean squared error is within 0.14 mm. Furthermore, the experiments indicate that the method has simple operation and good flexibility, especially for the onsite multiple cameras without common field of view.


title: PurTreeClust: A Clustering Algorithm for Customer Segmentation from Massive Customer Transaction Data

abstract: Clustering of customer transaction data is an important procedure to analyze customer behaviors in retail and e-commerce companies. Note that products from companies are often organized as a product tree, in which the leaf nodes are goods to sell, and the internal nodes (except root node) could be multiple product categories. Based on this tree, we propose the “personalized product tree”, named purchase tree, to represent a customer’s transaction records. So the customers’ transaction data set can be compressed into a set of purchase trees. We propose a partitional clustering algorithm, named PurTreeClust, for fast clustering of purchase trees. A new distance metric is proposed to effectively compute the distance between two purchase trees. To cluster the purchase tree data, we first rank the purchase trees as candidate representative trees with a novel separate density, and then select the top $k$ customers as the representatives of $k$ customer groups. Finally, the clustering results are obtained by assigning each customer to the nearest representative. We also propose a gap statistic based method to evaluate the number of clusters. A series of experiments were conducted on ten real-life transaction data sets, and experimental results show the superior performance of the proposed method.


title: Regenerative braking-driving control system

abstract: Currently, electric vehicle technology is becoming more and more mature. Although the anti-lock braking system (ABS) has been commonly applied, most electric vehicles (EVs) still use traditional hydraulic-based disc braking, which has the drawbacks that vehicle wheels are is easy to skid in the rainy day, and easy to be abraded during emergency brake. As a novel method of braking, regenerative braking has the advantages of compact structure, sensitive response, reliability and controllable braking distance. In this research task, a regenerative driving and braking control system for EVs with satisfactory braking performance is proposed. When braking, a motor is converted into a generator-the acquired energy can be used to generate reverse magnetic braking torque with fast response. On this basis, an anti-lock braking controller is realize. A PID controller is also designed to drive the motor and a fuzzy slip ratio controller is designed and used to obtain the optimal slip ratio. Finally, real-world experiments are conducted to verify the proposed method.


title: All You Need is Love: Current Strategies of Mediating Intimate Relationships through Technology

abstract: A wealth of evidence suggests that love, closeness, and intimacy---in short relatedness---are important for people’s psychological well-being. Nowadays, however, couples are often forced to live apart. Accordingly, there has been a growing and flourishing interest in designing technologies that mediate (and create) a feeling of relatedness when being separated, beyond the explicit verbal communication and simple emoticons available technologies offer. This article provides a review of 143 published artifacts (i.e., design concepts, technologies). Based on this, we present six strategies used by designers/researchers to create a relatedness experience: Awareness, expressivity, physicalness, gift giving, joint action, and memories. We understand those strategies as starting points for the experience-oriented design of technology.


title: Measuring Player Immersion in the Computer Game Narrative

abstract: In order to measure player immersion in the computer game narrative, this study explores and analyzes factors of the computer game narrative that influence players who are immersed in the game story world. Originally a questionnaire consisting of six dimensions—Curiosity, Concentration, Challenge, Control, Comprehension, and Empathy—was proposed. To evaluate the questionnaire, two surveys were conducted on the Internet, and data were collected from 734 respondents. After exploratory factor analysis and confirmatory factor analysis, the dimensions were modified to Curiosity, Concentration, Challenge and Skills, Control, Comprehension, Empathy, and Familiarity.